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
1110.2728	An Approach to Temporal Planning and Scheduling in Domains with Predictable Exogenous Events	The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain act...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,606
1807.11583	Testing the Efficient Network TRaining (ENTR) Hypothesis: initially reducing training image size makes Convolutional Neural Network training for image recognition tasks more efficient	Convolutional Neural Networks (CNN) for image recognition tasks are seeing rapid advances in the available architectures and how networks are trained based on large computational infrastructure and standard datasets with millions of images. In contrast, performance and time constraints for example, of small devices and...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	104,201
2204.02483	Considerations for Multilingual Wikipedia Research	English Wikipedia has long been an important data source for much research and natural language machine learning modeling. The growth of non-English language editions of Wikipedia, greater computational resources, and calls for equity in the performance of language and multimodal models have led to the inclusion of man...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	289,959
2306.14672	PWSHAP: A Path-Wise Explanation Model for Targeted Variables	Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. However, existing XAI methods are not tailored towards models in sensitive d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,769
2204.05520	OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming	This paper introduces OptimizedDP, a high-performance software library that solves time-dependent Hamilton-Jacobi partial differential equation (PDE), computes backward reachable sets with application in robotics, and contains value iterations algorithm implementation for continuous action-state space Markov Decision P...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	291,056
2205.02202	Two-step Planning of Dynamic UAV Trajectories using Iterative $\delta$-Spaces	UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal, however. On the other hand, direct planning in higher-dimensional state spaces generates globally optimal solutions but is time-consuming an...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	294,870
2502.09622	Theoretical Benefit and Limitation of Diffusion Language Model	Diffusion language models have emerged as a promising approach for text generation. One would naturally expect this method to be an efficient replacement for autoregressive models since multiple tokens can be sampled in parallel during each diffusion step. However, its efficiency-accuracy trade-off is not yet well unde...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	533,519
1711.00502	User Scheduling for Millimeter Wave MIMO Communications with Low-Resolution ADCs	In millimeter wave (mmWave) systems, we investigate uplink user scheduling when a basestation employs low-resolution analog-to-digital converters (ADCs) with a large number of antennas. To reduce power consumption in the receiver, low-resolution ADCs can be a potential solution for mmWave systems in which many antennas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,729
2312.00703	PointBeV: A Sparse Approach to BeV Predictions	Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various downstream tasks. However, conventional models use grids with fixed resolution and range and face computational inefficiencies due to the unifor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,144
2108.12757	Calibrating Class Activation Maps for Long-Tailed Visual Recognition	Real-world visual recognition problems often exhibit long-tailed distributions, where the amount of data for learning in different categories shows significant imbalance. Standard classification models learned on such data distribution often make biased predictions towards the head classes while generalizing poorly to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,601
2202.10594	Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey	A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and emergency management actions. Recent advances in Machine Learning, Natural Language Processing, voice recognition, and speech processing techn...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	281,585
1910.10457	Circularly Pulse Shaped Orthogonal Time Frequency Space Modulation	Orthogonal time-frequency space (OTFS) modulation is a recently proposed waveform for efficient data transfer in high-speed vehicular scenarios. The use of rectangular pulse shape in OTFS results in high out of band (OoB) radiation, which is undesirable for multi-user scenarios. In this work, we present a circular puls...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	150,493
2502.12555	Warm Starting of CMA-ES for Contextual Optimization Problems	Several practical applications of evolutionary computation possess objective functions that receive the design variables and externally given parameters. Such problems are termed contextual optimization problems. These problems require finding the optimal solutions corresponding to the given context vectors. Existing c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	534,934
2107.07863	Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks	Magnetic resonance velocimetry (MRV) is a non-invasive experimental technique widely used in medicine and engineering to measure the velocity field of a fluid. These measurements are dense but have a low signal-to-noise ratio (SNR). The measurements can be de-noised by imposing physical constraints on the flow, which a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,551
2501.04437	Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions	Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, w...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	523,224
2303.08581	Model Extraction Attacks on Split Federated Learning	Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically collects and distributes the model parameters, a free-rider can download t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	351,698
1112.0210	Mesoscopic approach to minority games in herd regime	We study minority games in efficient regime. By incorporating the utility function and aggregating agents with similar strategies we develop an effective mesoscale notion of state of the game. Using this approach, the game can be represented as a Markov process with substantially reduced number of states with explicitl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	13,279
2106.08770	TSSuBERT: Tweet Stream Summarization Using BERT	The development of deep neural networks and the emergence of pre-trained language models such as BERT allow to increase performance on many NLP tasks. However, these models do not meet the same popularity for tweet summarization, which can probably be explained by the lack of existing collections for training and evalu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	241,413
1910.08512	Learning the piece-wise constant graph structure of a varying Ising model	This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes occur in the Ising model, as well as the underlying graph structures. For this purpose, we propose to estimate the neighbor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,886
2411.03271	A Traffic Prediction-Based Individualized Driver Warning System to Reduce Red Light Violations	Red light violation is a major cause of traffic collisions and resulting injuries and fatalities. Despite extensive prior work to reduce red light violations, they continue to be a major problem in practice, partly because existing systems suffer from the flaw of providing the same guidance to all drivers. As a result,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	505,834
1710.10057	Multiwinner Voting with Fairness Constraints	Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity (when selecting a committee), or specified types such as political leaning (when selecting a su...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	83,306
2405.00808	ReeSPOT: Reeb Graph Models Semantic Patterns of Normalcy in Human Trajectories	This paper introduces ReeSPOT, a novel Reeb graph-based method to model patterns of life in human trajectories (akin to a fingerprint). Human behavior typically follows a pattern of normalcy in day-to-day activities. This is marked by recurring activities within specific time periods. In this paper, we model this behav...	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	451,087
1704.04081	Learning to Estimate Pose by Watching Videos	In this paper we propose a technique for obtaining coarse pose estimation of humans in an image that does not require any manual supervision. While a general unsupervised technique would fail to estimate human pose, we suggest that sufficient information about coarse pose can be obtained by observing human motion in mu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,744
2203.04711	On a linear fused Gromov-Wasserstein distance for graph structured data	We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem. Then we propose a novel distance between two graphs, named linearFGW, defined as the Euclidean distance between their embeddings. The advanta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,572
2501.06585	Boundary-enhanced time series data imputation with long-term dependency diffusion models	Data imputation is crucial for addressing challenges posed by missing values in multivariate time series data across various fields, such as healthcare, traffic, and economics, and has garnered significant attention. Among various methods, diffusion model-based approaches show notable performance improvements. However,...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,044
1806.02775	Stein Variational Gradient Descent Without Gradient	Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions. However, the standard SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable. In this work, we develop a gradient-free variant ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,845
2404.02893	ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline	Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving. While many strategies and datasets to enhance LLMs' mathematics are developed, it remains a challenge to simultaneously maintain and improve both langua...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,036
2408.04568	Learning Fine-Grained Grounded Citations for Attributed Large Language Models	Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, have shown potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from s...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	479,428
2210.13917	Connective Reconstruction-based Novelty Detection	Detection of out-of-distribution samples is one of the critical tasks for real-world applications of computer vision. The advancement of deep learning has enabled us to analyze real-world data which contain unexplained samples, accentuating the need to detect out-of-distribution instances more than before. GAN-based ap...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	326,359
2402.11725	How Susceptible are Large Language Models to Ideological Manipulation?	Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models can be easily manipulated. In this work, we investigate how effectively LLMs ca...	false	false	false	false	false	false	false	false	true	false	false	false	true	true	false	false	false	false	430,531
2312.09480	TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks	In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like Imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,744
1210.3420	Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption	The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for "closene...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,078
1912.09288	Online Path Generation and Navigation for Swarms of UAVs	With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the U...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	158,039
2305.13961	Metrics Matter in Surgical Phase Recognition	Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,784
2405.00665	Optimizing Profitability in Timely Gossip Networks	We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in real-time. The users wish to maintain their expected version ages below a threshold, and can either rely on gossip from their neighbors or di...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	451,009
2208.04769	A Temperature Independent Readout Circuit for ISFET-Based Sensor Applications	The ion-sensitive field-effect transistor (ISFET) is an emerging technology that has received much attention in numerous research areas, including biochemistry, medicine, and security applications. However, compared to other types of sensors, the complexity of ISFETs make it more challenging to achieve a sensitive, fas...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	312,222
1506.03599	Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots	Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrai...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	44,070
2007.08087	Starting with data: advancing spatial data science by building and sharing high-quality datasets	Spatial data science has emerged in recent years as an interdisciplinary field. This position paper discusses the importance of building and sharing high-quality datasets for spatial data science.	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	187,518
1707.05411	Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction	This paper introduces and evaluates a hybrid technique that fuses efficiently the eye-tracking principles of photosensor oculography (PSOG) and video oculography (VOG). The main concept of this novel approach is to use a few fast and power-economic photosensors as the core mechanism for performing high speed eye-tracki...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,220
2111.01953	Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation	Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	264,715
2408.10136	Robust spectral clustering with rank statistics	This paper analyzes the statistical performance of a robust spectral clustering method for latent structure recovery in noisy data matrices. We consider eigenvector-based clustering applied to a matrix of nonparametric rank statistics that is derived entrywise from the raw, original data matrix. This approach is robust...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,732
2310.12150	Understanding Retrieval Augmentation for Long-Form Question Answering	We present a study of retrieval-augmented language models (LMs) on long-form question answering. We analyze how retrieval augmentation impacts different LMs, by comparing answers generated from models while using the same evidence documents, and how differing quality of retrieval document set impacts the answers genera...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	400,928
2012.07763	Policy Gradient RL Algorithms as Directed Acyclic Graphs	Meta Reinforcement Learning (RL) methods focus on automating the design of RL algorithms that generalize to a wide range of environments. The framework introduced in (Anonymous, 2020) addresses the problem by representing different RL algorithms as Directed Acyclic Graphs (DAGs), and using an evolutionary meta learner ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,571
0801.3526	Quantized Multimode Precoding in Spatially Correlated Multi-Antenna Channels	Multimode precoding, where the number of independent data-streams is adapted optimally, can be used to maximize the achievable throughput in multi-antenna communication systems. Motivated by standardization efforts embraced by the industry, the focus of this work is on systematic precoder design with realistic assumpti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,192
1401.1152	Hygro-thermo-mechanical analysis of spalling in concrete walls at high temperatures as a moving boundary problem	A mathematical model allowing coupled hygro-thermo-mechanical analysis of spalling in concrete walls at high temperatures by means of the moving boundary problem is presented. A simplified mechanical approach to account for effects of thermal stresses and pore pressure build-up on spalling is incorporated into the mode...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	29,629
2408.15239	Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation	We present a method for generating video sequences with coherent motion between a pair of input key frames. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from a single input image) for key frame interpolation, i.e., to produce a video in b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,850
2501.00432	OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models	Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recognition systems are limited by fixed vocabularies, predefined labels, and rigid interaction categories that often rely on choreographed videos ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	521,664
2106.02522	Price graphs: Utilizing the structural information of financial time series for stock prediction	Great research efforts have been devoted to exploiting deep neural networks in stock prediction. While long-range dependencies and chaotic property are still two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends. In this study, we propose a novel framewo...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	238,902
2005.12710	An improved estimate of the inverse binary entropy function	Two estimates for the inverse binary entropy function are derived using the property of information entropy to estimate combinatorics of sequences as well as related formulas from population genetics for the effective number of alleles. The second estimate shows close correspondence to the actual value of the inverse b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	178,806
2008.07871	Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects	We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architectur...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	192,245
2402.12198	Zero shot VLMs for hate meme detection: Are we there yet?	Multimedia content on social media is rapidly evolving, with memes gaining prominence as a distinctive form. Unfortunately, some malicious users exploit memes to target individuals or vulnerable communities, making it imperative to identify and address such instances of hateful memes. Extensive research has been conduc...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	430,750
2301.04506	A Distinct Unsupervised Reference Model From The Environment Helps Continual Learning	The existing continual learning methods are mainly focused on fully-supervised scenarios and are still not able to take advantage of unlabeled data available in the environment. Some recent works tried to investigate semi-supervised continual learning (SSCL) settings in which the unlabeled data are available, but it is...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	340,088
2212.10098	A Communication Optimal Transport Approach to the Computation of Rate Distortion Functions	In this paper, we propose a new framework named Communication Optimal Transport (CommOT) for computing the rate distortion (RD) function. This work is motivated by observing the fact that the transition law and the relative entropy in communication theory can be viewed as the transport plan and the regularized objectiv...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	337,341
2407.00664	SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images	Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture the intricate interaction features among instances within the local area of WSI. Moreover, existing methods for cancer survival prediction ba...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	468,959
2111.08480	A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals	Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous Blood Pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,708
1807.08696	PS-FCN: A Flexible Learning Framework for Photometric Stereo	This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,596
2210.04393	LAPFormer: A Light and Accurate Polyp Segmentation Transformer	Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities. This prevents deep learning model to generalize well on unseen data. However, Transformer-based approach recently has achieved some remarkable results on performance with the ability of e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,439
2112.04779	Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment	Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-tailed noises. Understanding modal regression's theoretical behavior can be fundamental in learning theory. Despite significant progress in c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,644
1503.04337	Communication-efficient sparse regression: a one-shot approach	We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average "debiased" or "desparsified" lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	41,148
2411.03343	What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks	While `jailbreaks' have been central to research on the safety and reliability of LLMs (large language models), the underlying mechanisms behind these attacks are not well understood. Some prior works have used linear methods to analyze jailbreak prompts or model refusal. Here, however, we compare linear and nonlinear ...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	505,867
cond-mat/0604267	Survey propagation for the cascading Sourlas code	We investigate how insights from statistical physics, namely survey propagation, can improve decoding of a particular class of sparse error correcting codes. We show that a recently proposed algorithm, time averaged belief propagation, is in fact intimately linked to a specific survey propagation for which Parisi's rep...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	536,977
2410.22354	Solve Mismatch Problem in Compressed Sensing	This article proposes a novel algorithm for solving mismatch problem in compressed sensing. Its core is to transform mismatch problem into matched by constructing a new measurement matrix to match measurement value under unknown measurement matrix. Therefore, we propose mismatch equation and establish two types of algo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	503,599
1404.1614	A Denoising Autoencoder that Guides Stochastic Search	An algorithm is described that adaptively learns a non-linear mutation distribution. It works by training a denoising autoencoder (DA) online at each generation of a genetic algorithm to reconstruct a slowly decaying memory of the best genotypes so far. A compressed hidden layer forces the autoencoder to learn hidden f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	32,134
1503.05782	Learning Hypergraph-regularized Attribute Predictors	We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	41,282
2204.09924	Progressive Training of A Two-Stage Framework for Video Restoration	As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts. Among video restorations, compressed video quality enhancement and video super-resolution are two of the main tacks with significant values in prac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	292,612
2105.03650	How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data	We present a Bayesian approach to machine learning with probabilistic programs. In our approach, training on available data is implemented as inference on a hierarchical model. The posterior distribution of model parameters is then used to \textit{stochastically condition} a complementary model, such that inference on ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,215
1912.01522	Convolutional STN for Weakly Supervised Object Localization	Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the architecture of a standard CNN by using the activation maps of the last layer for localizing the object. While this approach is simple and w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,110
2109.03443	ADER:Adapting between Exploration and Robustness for Actor-Critic Methods	Combining off-policy reinforcement learning methods with function approximators such as neural networks has been found to lead to overestimation of the value function and sub-optimal solutions. Improvement such as TD3 has been proposed to address this issue. However, we surprisingly find that its performance lags behin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,069
1909.08423	Deep neural network solution of the electronic Schr\"odinger equation	[New and updated results were published in Nature Chemistry, doi:10.1038/s41557-020-0544-y.] The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is expone...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,976
2107.02221	An Empirical Investigation of Worker Communities in TopCoder	Software crowdsourcing platforms employ extrinsic rewards such as rating or ranking systems to motivate workers. Such rating systems are noisy and provide limited knowledge about workers' preferences and performance. To develop better understanding of worker reliability and trustworthiness in software crowdsourcing, th...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	244,730
2004.07351	Communication Efficient Federated Learning with Energy Awareness over Wireless Networks	In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training parameters over wireless links. With such consideration, we adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Moreov...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	172,753
1801.04871	Building a Conversational Agent Overnight with Dialogue Self-Play	We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	88,352
1610.08668	Real-time optimal control via Deep Neural Networks: study on landing problems	Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	62,956
2501.15973	Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data	Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional Machine Learning (ML) models excel at predicting outcomes, such as identifying high risk patients, they are limited in addressing what-if questions about interventions. This...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,779
1803.00283	Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks	In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and will gather correlated data, and, thus, clustering MTDs ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	91,644
2004.10643	Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection	Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	173,692
2212.03967	Few-shot Medical Image Segmentation with Cycle-resemblance Attention	Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most exi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,277
2308.12736	FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI	The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scala...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,656
1605.08527	Stochastic Optimization for Large-scale Optimal Transport	Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale problems routinely enc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	56,455
2206.07278	Nebula Graph: An open source distributed graph database	This paper introduces the recent work of Nebula Graph, an open-source, distributed, scalable, and native graph database. We present a system design trade-off and a comprehensive overview of Nebula Graph internals, including graph data models, partitioning strategies, secondary indexes, optimizer rules, storage-side tra...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	302,677
2106.01006	SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues	Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we fo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	238,346
1903.08558	Single Image Deraining: A Comprehensive Benchmark Analysis	We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images.This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,854
1907.08854	Incremental Transformer with Deliberation Decoder for Document Grounded Conversations	Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	139,200
2109.11041	Security Analysis of Capsule Network Inference using Horizontal Collaboration	The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can encode and preserve the spatial orientation of input images. Similar to t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	256,819
2406.12018	CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling	Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within the key-value caches of Transformer models can be discarded (also termed evicted) without affecting the perplexity performance in generating l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,179
2109.03946	Quantitative form of Ball's Cube slicing in $\mathbb{R}^n$ and equality cases in the min-entropy power inequality	We prove a quantitative form of the celebrated Ball's theorem on cube slicing in $\mathbb{R}^n$ and obtain, as a consequence, equality cases in the min-entropy power inequality. Independently, we also give a quantitative form of Khintchine's inequality in the special case $p=1$.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	254,225
2311.07039	Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States (Extended Version)	Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrarily given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved. To enhance further comprehension of the problem, this paper establishes a novel notation system and t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	407,170
2409.05771	Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models	Research has repeatedly demonstrated that intermediate hidden states extracted from large language models are able to predict measured brain response to natural language stimuli. Yet, very little is known about the representation properties that enable this high prediction performance. Why is it the intermediate layers...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	486,887
2105.13387	Type III solar radio burst detection and classification: A deep learning approach	Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterisation, a challenge in solar radio physics is the automatic detection and classification of such r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,283
2404.00320	Advancing Multimodal Data Fusion in Pain Recognition: A Strategy Leveraging Statistical Correlation and Human-Centered Perspectives	This research presents a novel multimodal data fusion methodology for pain behavior recognition, integrating statistical correlation analysis with human-centered insights. Our approach introduces two key innovations: 1) integrating data-driven statistical relevance weights into the fusion strategy to effectively utiliz...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	442,865
2411.04548	Convergence and Robustness of Value and Policy Iteration for the Linear Quadratic Regulator	This paper revisits and extends the convergence and robustness properties of value and policy iteration algorithms for discrete-time linear quadratic regulator problems. In the model-based case, we extend current results concerning the region of exponential convergence of both algorithms. In the case where there is unc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	506,311
1702.05119	Evolutionary prisoner's dilemma games coevolving on adaptive networks	We study a model for switching strategies in the Prisoner's Dilemma game on adaptive networks of player pairings that coevolve as players attempt to maximize their return. We use a node-based strategy model wherein each player follows one strategy at a time (cooperate or defect) across all of its neighbors, changing th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	68,354
2304.09349	LLM as A Robotic Brain: Unifying Egocentric Memory and Control	Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their environment. Memory and control are the two essential parts of an embodied system and usually require separate frameworks to model each of t...	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	359,019
2404.10939	More Room for Language: Investigating the Effect of Retrieval on Language Models	Retrieval-augmented language models pose a promising alternative to standard language modeling. During pretraining, these models search in a corpus of documents for contextually relevant information that could aid the language modeling objective. We introduce an 'ideal retrieval' methodology to study these models in a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	447,313
2012.12089	Prediction of Chronic Kidney Disease Using Deep Neural Network	Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing thr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,819
1905.06679	Optimal Status Updating with a Finite-Battery Energy Harvesting Source	We consider an energy harvesting source equipped with a finite battery, which needs to send timely status updates to a remote destination. The timeliness of status updates is measured by a non-decreasing penalty function of the Age of Information (AoI). The problem is to find a policy for generating updates that achiev...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	131,056
2302.14338	Turning a CLIP Model into a Scene Text Detector	The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,253
2406.00001	PhyPlan: Generalizable and Rapid Physical Task Planning with Physics Informed Skill Networks for Robot Manipulators	Given the task of positioning a ball-like object to a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. However, enabling robots to reason similarly is non-trivial. Existing methods for physical reasoning are data-hungry and struggle with complexity ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	459,640
cs/0501006	Formal Languages and Algorithms for Similarity based Retrieval from Sequence Databases	The paper considers various formalisms based on Automata, Temporal Logic and Regular Expressions for specifying queries over sequences. Unlike traditional binary semantics, the paper presents a similarity based semantics for thse formalisms. More specifically, a distance measure in the range [0,1] is associated with a ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	538,478
1908.02786	Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval	In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical o...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	141,087

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