Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
1406.1061	A Methodology for Empirical Analysis of LOD Datasets	CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative?, Is dataset X better than Y fo...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	33,594
1808.02280	ODSQA: Open-domain Spoken Question Answering Dataset	Reading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem. In this paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with more than three thousand questions. To the best of our knowledge, this is the largest real SQA ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	104,744
2303.00506	Fast and Interpretable Dynamics for Fisher Markets via Block-Coordinate Updates	We consider the problem of large-scale Fisher market equilibrium computation through scalable first-order optimization methods. It is well-known that market equilibria can be captured using structured convex programs such as the Eisenberg-Gale and Shmyrev convex programs. Highly performant deterministic full-gradient f...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	348,620
2208.07711	Local Low-light Image Enhancement via Region-Aware Normalization	In the realm of Low-Light Image Enhancement (LLIE), existing research primarily focuses on enhancing images globally. However, many applications require local LLIE, where users are allowed to illuminate specific regions using an input mask, such as creating a protagonist stage or spotlight effect. However, this task ha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,129
2109.05838	IceNet for Interactive Contrast Enhancement	A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this work, which enables a user to adjust image contrast easily according to his or her preference. Specifically, a user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,971
2502.12799	Towards Text-Image Interleaved Retrieval	Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved content. In this work, we introduce the text-image interleaved retrieval (TIIR) task, where the query and document are interleaved text-image se...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	535,055
2403.15523	Towards auditory attention decoding with noise-tagging: A pilot study	Auditory attention decoding (AAD) aims to extract from brain activity the attended speaker amidst candidate speakers, offering promising applications for neuro-steered hearing devices and brain-computer interfacing. This pilot study makes a first step towards AAD using the noise-tagging stimulus protocol, which evokes ...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	440,625
2411.15422	Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration	Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. This paper explores the use of reinforcement learning (RL) for operating grid-scale batteries co-located with solar power. Our results show RL ...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	510,604
2407.09023	Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge	Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP (O2C and P2P) and CRM. However, modeling such complex information requires novel process mining techniques and might result in complex sets of constrain...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	472,417
2502.03317	Contact-Aware Motion Planning Among Movable Objects	Most existing methods for motion planning of mobile robots involve generating collision-free trajectories. However, these methods focusing solely on contact avoidance may limit the robots' locomotion and can not be applied to tasks where contact is inevitable or intentional. To address these issues, we propose a novel ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	530,667
2206.09606	Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development	An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development. InterOpt consists of three parts: a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing proces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,639
2308.05362	FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis	Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically discover risk behaviors. However, due to the lack of transparency, the behavioral semantics cannot be conveyed to downstream security expert...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	384,764
1911.03914	Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles	Text style transfer is usually performed using attributes that can take a handful of discrete values (e.g., positive to negative reviews). In this work, we introduce an architecture that can leverage pre-trained consistent continuous distributed style representations and use them to transfer to an attribute unseen duri...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,827
1212.3925	Elaboration of global quality standards for natural and low energy cooling in French tropical island buildings	Electric load profiles of tropical islands in developed countries are characterised by morning, midday and evening peaks arising from all year round high power demand in the commercial and residential sectors, due mostly to air conditioning appliances and bad thermal conception of the building. The work presented in th...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,444
1908.03475	A Simple Recommender Engine for Matching Final-Year Project Student with Supervisor	This paper discusses a simple recommender engine, which can match final year project student based on their interests with potential supervisors. The recommender engine is constructed based on Euclidean distance algorithm. The initial input data for the recommender system is obtained by distributing questionnaire to fi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	141,247
2402.18650	The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials	Advancing robotic grasping and manipulation requires the ability to test algorithms and/or train learning models on large numbers of grasps. Towards the goal of more advanced grasping, we present the Grasp Reset Mechanism (GRM), a fully automated apparatus for conducting large-scale grasping trials. The GRM automates t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	433,505
2111.03300	Threat Scenarios and Monitoring Requirements for Cyber-Physical Systems of Flexibility Markets	The ongoing integration of renewable generation and distributed energy resources introduces new challenges to distribution network operation. Due to the increasing volatility and uncertainty, distribution system operators (DSOs) are seeking concepts to enable more active management and control. Flexibility markets (FMs...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	265,124
2311.04262	ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations	Electronic theses and dissertations (ETDs) have been proposed, advocated, and generated for more than 25 years. Although ETDs are hosted by commercial or institutional digital library repositories, they are still an understudied type of scholarly big data, partially because they are usually longer than conference proce...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	406,170
1710.04008	A Dynamic Edge Exchangeable Model for Sparse Temporal Networks	We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The model achieved superior link prediction accuracy on multiple data sets when compared to a dynamic variant of the blockmodel, and is able to ext...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	82,406
2311.17921	Do text-free diffusion models learn discriminative visual representations?	While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We identify diffusion models, a state-of-the-art method for generative tasks, as a pri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,467
2107.00993	Optical Braille Recognition using Circular Hough Transform	Braille has empowered visually challenged community to read and write. But at the same time, it has created a gap due to widespread inability of non-Braille users to understand Braille scripts. This gap has fuelled researchers to propose Optical Braille Recognition techniques to convert Braille documents to natural lan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,344
1312.2237	Clustering online social network communities using genetic algorithms	To analyze the activities in an Online Social network (OSN), we introduce the concept of "Node of Attraction" (NoA) which represents the most active node in a network community. This NoA is identified as the origin/initiator of a post/communication which attracted other nodes and formed a cluster at any point in time. ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	28,944
2304.00815	Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design	Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks. Here, we propose to analyze another source of bias: task design bias, which has a particularly strong impact on crowdsourced linguistic annotations where natural l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	355,825
2312.03037	Analysis and mining of low-carbon and energy-saving tourism data characteristics based on machine learning algorithm	In order to study the formation mechanism of residents' low-carbon awareness and provide an important basis for traffic managers to guide urban residents to choose low-carbon travel mode, this paper proposes a low-carbon energy-saving travel data feature analysis and mining based on machine learning algorithm. This pap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,111
1402.0108	Markov Blanket Ranking using Kernel-based Conditional Dependence Measures	Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,534
2307.07367	Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow	Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-gen...	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false	false	379,381
0911.5667	End-to-End Algebraic Network Coding for Wireless TCP/IP Networks	The Transmission Control Protocol (TCP) was designed to provide reliable transport services in wired networks. In such networks, packet losses mainly occur due to congestion. Hence, TCP was designed to apply congestion avoidance techniques to cope with packet losses. Nowadays, TCP is also utilized in wireless networks ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	5,052
2412.11561	RADARSAT Constellation Mission Compact Polarisation SAR Data for Burned Area Mapping with Deep Learning	Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,477
2307.10460	A data science axiology: the nature, value, and risks of data science	Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in ev...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	380,552
2209.12618	Survey on Applications of Neurosymbolic Artificial Intelligence	In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning and reasoning. The new emerging Neuros...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	319,594
1808.09607	Nonlinear regression based on a hybrid quantum computer	Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,228
2201.08451	Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency	The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'p...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	276,340
2009.13650	Towards a Measure of Individual Fairness for Deep Learning	Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions. We propose a novel measure of individual fairness, called prediction sensitivity, that approximates the extent to which a particular...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	197,794
1905.09148	LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning	Gradient-based distributed learning in Parameter Server (PS) computing architectures is subject to random delays due to straggling worker nodes, as well as to possible communication bottlenecks between PS and workers. Solutions have been recently proposed to separately address these impairments based on the ideas of gr...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	131,657
1408.3931	Arithmetic Distribution Matching	In this work, arithmetic distribution matching (ADM) is presented. ADM invertibly transforms a discrete memoryless source (DMS) into a target DMS. ADM can be used for probabilistic shaping and for rate adaption. Opposed to existing algorithms for distribution matching, ADM works online and can transform arbitrarily lon...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,426
2003.04998	Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions	This work shows how to improve and interpret the commonly used dual encoder model for response suggestion in dialogue. We present an attentive dual encoder model that includes an attention mechanism on top of the extracted word-level features from two encoders, one for context and one for label respectively. To improve...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	167,734
2403.16125	A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters	Joint consideration of scheduling and adaptive parallelism offers great opportunities for improving the training efficiency of large models on heterogeneous GPU clusters. However, integrating adaptive parallelism into a cluster scheduler expands the cluster scheduling space. The new space is the product of the original...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	440,881
2310.15707	User Clustering for Coexistence between Near-field and Far-field Communications	This letter investigates the coexistence between near-field (NF) and far-field (FF) communications, where multiple FF users are clustered to be served on the beams of legacy NF users, via non-orthogonal multiple access (NOMA). Three different successive interference cancellation (SIC) decoding strategies are proposed a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	402,419
1512.04808	Causal and anti-causal learning in pattern recognition for neuroimaging	Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,162
2303.14382	Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm	Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the unsupervised pretraining and supervised finetuning in this paradigm, while little attention is paid to exploiting the annotation budget for...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	354,064
1303.0381	Spectral Efficient Optimization in OFDM Systems with Wireless Information and Power Transfer	This paper considers an orthogonal frequency division multiplexing (OFDM) point-to-point wireless communication system with simultaneous wireless information and power transfer. We study a receiver which is able to harvest energy from the desired signal, noise, and interference. In particular, we consider a power split...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,558
2409.11041	Towards No-Code Programming of Cobots: Experiments with Code Synthesis by Large Code Models for Conversational Programming	While there has been a lot of research recently on robots in household environments, at the present time, most robots in existence can be found on shop floors, and most interactions between humans and robots happen there. ``Collaborative robots'' (cobots) designed to work alongside humans on assembly lines traditionall...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	488,985
2405.12676	Experimental investigation of trans-scale displacement responses of wrinkle defects in fiber reinforced composite laminates	Wrinkle defects were found widely exist in the field of industrial products, i.e. wind turbine blades and filament-wound composite pressure vessels. The magnitude of wrinkle wavelength varies from several millimeters to over one hundred millimeters. Locating the wrinkle defects and measuring their responses are very im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	455,613
2110.02871	ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods	Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the pote...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	259,280
2406.04606	Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions	The increasing complexity of foundational models underscores the necessity for explainability, particularly for fine-tuning, the most widely used training method for adapting models to downstream tasks. Instance attribution, one type of explanation, attributes the model prediction to each training example by an instanc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,753
2306.03189	Easy-to-Read in Germany: A Survey on its Current State and Available Resources	Easy-to-Read Language (E2R) is a controlled language variant that makes any written text more accessible through the use of clear, direct and simple language. It is mainly aimed at people with cognitive or intellectual disabilities, among other target users. Plain Language (PL), on the other hand, is a variant of a giv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,221
2207.06120	SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning	Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,776
2211.11581	Modeling 100% Electrified Transportation in NYC	Envisioning a future 100% electrified transportation sector, this paper uses socio-economic, demographic, and geographic data to assess electric energy demand from commuter traffic. We explore the individual mode choices, which allows to create mode-mix scenarios for the entire population, and quantify the electric ene...	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	331,790
2410.05591	TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation	Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains a challenging task. To address this, we present TweedieMix, a novel method for composing customized diffusion models during the infere...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,809
2112.11188	Diagnostic Assessment Generation via Combinatorial Search	Initial assessment tests are crucial in capturing learner knowledge states in a consistent manner. Aside from crafting questions itself, putting together relevant problems to form a question sheet is also a time-consuming process. In this work, we present a generic formulation of question assembly and a genetic algorit...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	272,629
2002.04019	Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization	Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of such variables can degrade the performance of deep-learning models. We study three datasets where there is a strong influence of known extraneo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,480
2210.02729	Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer	Developing neural architectures that are capable of logical reasoning has become increasingly important for a wide range of applications (e.g., natural language processing). Towards this grand objective, we propose a symbolic reasoning architecture that chains many join operators together to model output logical expres...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	321,762
2101.09192	Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning	We introduce Gravity, another algorithm for gradient-based optimization. In this paper, we explain how our novel idea change parameters to reduce the deep learning model's loss. It has three intuitive hyper-parameters that the best values for them are proposed. Also, we propose an alternative to moving average. To comp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	216,529
1003.2138	Need-based Communication for Smart Grid: When to Inquire Power Price?	In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,895
2104.02972	Self-supervised Learning of Depth Inference for Multi-view Stereo	Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during training. However, collecting a large amount of multi-view depth data is very challenging. Here, we propose a self-supervised learning framework f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,924
2409.08382	Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems	Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning algorithms applied to stabilization tasks tend to drive the system close to the equilibrium point. However, these approaches often fall short...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	487,881
2107.01185	Artificial Neural Network for Cybersecurity: A Comprehensive Review	Cybersecurity is a very emerging field that protects systems, networks, and data from digital attacks. With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks. This paper provides a s...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	244,399
2205.09891	Interpolating Compressed Parameter Subspaces	Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a set of trained parameters mapped to a set of train-time distri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,461
2403.03599	Learning Invariant Representations of Graph Neural Networks via Cluster Generalization	Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure information. However, it is widely acknowledged that the test graph structure may differ from the training graph structure, resulting in a str...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,264
2204.09715	Scaling Language Model Size in Cross-Device Federated Learning	Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applicati...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	292,530
1304.7278	On Adaptive Control with Closed-loop Reference Models: Transients, Oscillations, and Peaking	One of the main features of adaptive systems is an oscillatory convergence that exacerbates with the speed of adaptation. Recently it has been shown that Closed-loop Reference Models (CRMs) can result in improved transient performance over their open-loop counterparts in model reference adaptive control. In this paper,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	24,241
2410.03892	Towards Cost Sensitive Decision Making	Many real-world situations allow for the acquisition of additional relevant information when making decisions with limited or uncertain data. However, traditional RL approaches either require all features to be acquired beforehand (e.g. in a MDP) or regard part of them as missing data that cannot be acquired (e.g. in a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	495,027
2306.04781	Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach	Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in t...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	371,893
2409.19381	INC-Math: Integrating Natural Language and Code for Enhanced Mathematical Reasoning in Large Language Models	Large Language Models (LLMs) are commonly used to generate solutions for mathematical reasoning problems in the following formats: natural language, code, or a combination of both. In this paper, we explore fundamental questions related to solving mathematical reasoning problems using natural language and code with sta...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,651
2305.07130	Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots	Beam alignment is an important task for millimeter-wave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is crucial in terms of compensating the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial tas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	363,779
2204.00790	SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images	Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	289,384
2403.15585	MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis	Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR). This paper introduces MedPromptX, the first clinical decision support system that integrates mu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	440,644
2304.02832	Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing	In the traditional vehicular network, computing tasks generated by the vehicles are usually uploaded to the cloud for processing. However, since task offloading toward the cloud will cause a large delay, vehicular edge computing (VEC) is introduced to avoid such a problem and improve the whole system performance, where...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	356,566
2302.13438	P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups	Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss). Although such a strategy provides better privacy guarantees than the traditional centralized approach,...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	347,949
2201.09077	LTC-GIF: Attracting More Clicks on Feature-length Sports Videos	This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	276,547
2405.17368	Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics	Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A robust method to fuse these modalities and obtain biomechanically accura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,872
1904.00597	Learning Combinatorial Embedding Networks for Deep Graph Matching	Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objectiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,909
2112.13166	Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks	As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks. False data injection attacks (FDIAs), specifically, represent a major class of cyber threats to smart grids by targeting the measurement data's integrity. Although various solutions have been proposed to detect tho...	false	false	false	false	true	false	true	false	false	false	true	false	true	false	false	false	false	false	273,155
2412.05876	MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training	3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-empowered models. Radiology reports are easily accessible and can serve as weakly-supervised signals. However, large-scale vision-language pr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	515,010
2211.03035	Synthetic Data for Feature Selection	Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,803
2301.01205	Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions	This paper presents an online-capable controller for the energy management system of a parallel hybrid electric vehicle based on model predictive control. Its task is to minimize the vehicle's fuel consumption along a predicted driving mission by calculating the distribution of the driver's power request between the el...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	339,165
1909.10670	Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss	Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-GAN)---were recently proposed, and their effectiveness in improving GAN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,598
2502.05916	Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local Detection and Static-to-Dynamic Planning	Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of grasping increases with the presence of more uncertainties, where commonly used learning-based approaches struggle to perform consistently ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	531,835
2104.13801	The analytic dually flat space of the mixture family of two prescribed distinct Cauchy distributions	A smooth and strictly convex function on an open convex domain induces both (1) a Hessian manifold with respect to the standard flat Euclidean connection, and (2) a dually flat space of information geometry. We first review these constructions and illustrate how to instantiate them for (a) full regular exponential fami...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	232,618
2302.14362	One-Shot Video Inpainting	Recently, removing objects from videos and filling in the erased regions using deep video inpainting (VI) algorithms has attracted considerable attention. Usually, a video sequence and object segmentation masks for all frames are required as the input for this task. However, in real-world applications, providing segmen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,265
2205.01290	DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries	This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	294,530
1902.00948	Outage Performance in Secure Cooperative NOMA	Enabling cooperation in a NOMA system is a promising approach to improve its performance. In this paper, we study the cooperation in a secure NOMA system, where the legitimate users are distributed uniformly in the network and the eavesdroppers are distributed according to a homogeneous Poisson point process. We consid...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,551
2401.09712	SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model	Large language models (LLMs) have recently been extended to the vision-language realm, obtaining impressive general multi-modal capabilities. However, the exploration of multi-modal large language models (MLLMs) for remote sensing (RS) data is still in its infancy, and the performance is not satisfactory. In this work,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,354
2311.08271	Mobility-Induced Graph Learning for WiFi Positioning	A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This pap...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	407,655
1803.10994	Hierarchical Sparse Channel Estimation for Massive MIMO	The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approach is pursued. Unfortunately, due to the Kronecker-type sensing (measurement) matrix corresponding to this setup, application of standard CS...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	93,800
2308.01602	Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks	Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution of parametrized time-dependent nonlinear partial differential equations (PDEs). In this context, full order models (FOMs), such as those relying on the finite elem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	383,305
2204.03105	AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis	In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	290,190
2309.06131	Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection	Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning PLM-based rankers requires a great amount of annotated training data. Annotating data involves a large manual effort and thus is expensive, e...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	391,326
2501.07256	EdgeTAM: On-Device Track Anything Model	On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compared with previous methods, making it a foundation model for video segmentation task. In this paper, we aim at making SAM 2 much more efficient...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,331
2404.16275	Spectrum Sharing Policy in the Asia-Pacific Region	In this chapter, we investigate the spectrum measurement results in Asia-Pacific region. Then the spectrum sharing policy in the Asia-Pacific region is reviewed in details, where the national projects and strategies on spectrum refarming and spectrum sharing in China, Japan, Singapore, India, Korea and Australia are in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	449,418
2312.02219	Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models	Large Vision and Language Models have enabled significant advances in fully supervised and zero-shot visual tasks. These large architectures serve as the baseline to what is currently known as Instruction Tuning Large Vision and Language models (IT-LVLMs). IT-LVLMs are general-purpose multi-modal assistants whose respo...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	412,770
2207.06813	Network bypasses sustain complexity	Real-world networks are neither regular nor random, a fact elegantly explained by mechanisms such as the Watts-Strogatz or the Barabasi-Albert models, among others. Both mechanisms naturally create shortcuts and hubs, which while enhancing network's connectivity, also might yield several undesired navigational effects:...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	308,000
2501.15740	Propositional Interpretability in Artificial Intelligence	Mechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to date. I argue for the importance of propositional interpretability, which involves i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	527,689
2104.04952	Fine-Grained Attention for Weakly Supervised Object Localization	Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts. In this paper, we propose a novel residual fine-grained attention (RFGA) module that au...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	229,551
1602.08571	Towards Neural Knowledge DNA	In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicate to representing information about the world in a form that computer systems can utilize...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	52,662
2408.04131	Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment	Traffic assignment and traffic flow prediction provide critical insights for urban planning, traffic management, and the development of intelligent transportation systems. An efficient model for calculating traffic flows over the entire transportation network could provide a more detailed and realistic understanding of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,258
2310.09119	A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check	In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion. In this paper, we propose to decompose the CSC workflow into detection, reasoning, and searching subtasks so that...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	399,653
2212.13344	DiffFace: Diffusion-based Face Swapping with Facial Guidance	In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,276
2410.16057	Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations	The success of medical image segmentation usually requires a large number of high-quality labels. But since the labeling process is usually affected by the raters' varying skill levels and characteristics, the estimated masks provided by different raters usually suffer from high inter-rater variability. In this paper, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	500,856
2004.14969	Learning to Ask Screening Questions for Job Postings	At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each applicant, we develop a new product where recruiters can ask screening question...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	175,068

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