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
2308.02599	Branched Latent Neural Maps	We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable fro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	383,705
2405.19845	Assessing the impact of weather-induced uncertainties in large-scale electricity systems	The future energy system will largely depend on volatile renewable energy sources and temperature-dependent loads, which makes the weather a central influencing factor. This article presents a novel approach for simulating weather scenarios for robust large-scale power system analysis. By applying different signal anal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	459,093
2207.05888	A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation	Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	307,705
2003.05148	Kernel Quantization for Efficient Network Compression	This paper presents a novel network compression framework Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version without significant performance loss. Unlike existing methods struggling with weight bit-length, KQ ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,789
2208.05187	Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation	To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models. Despite improvements made in model robustness, these VUDA methods require access to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,336
1902.03334	Photorealistic Image Synthesis for Object Instance Detection	We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images. The proposed approach has three key ingredients: (1) 3D object models are rendered in 3D models of complete scenes with realistic material...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	121,072
2303.06350	Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets	This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path to (re-)visit all the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	350,810
2406.14760	An LLM Feature-based Framework for Dialogue Constructiveness Assessment	Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases. These object...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	466,450
2004.00184	A theory of independent mechanisms for extrapolation in generative models	Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating proc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	170,543
2003.07029	Joint Power Allocation and Precoding for Network Coding based Cooperative Multicast Systems	In this letter, we propose two power allocation schemes based on the statistical channel state information (CSI) and instantaneous s->r CSI at transmitters respectively for a 2-N-2 cooperative multicast system with non-regenerative network coding.Then the isolated precoder and the distributed precoder are respectively ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	168,311
2110.10054	Generating Symbolic Reasoning Problems with Transformer GANs	We study the capabilities of GANs and Wasserstein GANs equipped with Transformer encoders to generate sensible and challenging training data for symbolic reasoning domains. We conduct experiments on two problem domains where Transformers have been successfully applied recently: symbolic mathematics and temporal specifi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,023
2205.12086	Information-Directed Selection for Top-Two Algorithms	We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially allocating measurement effort. We characterize the necessary and sufficient conditions for the optimal allocation using dual variables. Remark...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,398
1408.5710	Complexity Analysis of Joint Subcarrier and Power Allocation for the Cellular Downlink OFDMA System	Consider the cellular downlink Orthogonal Frequency Division Multiple Access (OFDMA) system where a single transmitter transmits signals to multiple receivers on multiple discrete subcarriers. To adapt fast channel fluctuations, the transmitter should be able to dynamically allocate subcarrier and power resources. Assu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,575
2305.12606	Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages	Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each. However, there are thousands of spoken languages worldwide, and adapting to new languages is an important problem. In this work, we aim to understand which m...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,083
2405.10659	Realistic Evaluation of Toxicity in Large Language Models	Large language models (LLMs) have become integral to our professional workflows and daily lives. Nevertheless, these machine companions of ours have a critical flaw: the huge amount of data which endows them with vast and diverse knowledge, also exposes them to the inevitable toxicity and bias. While most LLMs incorpor...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	454,848
2408.09111	Measuring Agreeableness Bias in Multimodal Models	This paper examines a phenomenon in multimodal language models where pre-marked options in question images can significantly influence model responses. Our study employs a systematic methodology to investigate this effect: we present models with images of multiple-choice questions, which they initially answer correctly...	true	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	481,281
1904.11439	META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation	Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well as algorithms which learn how to improve policies. TD-learning with eligibility traces provides a way to boost sample efficiency by temporal...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	128,858
2301.07566	Distributed Video Coding Based on Polar Codes	In this letter we present an improved distributed video coding (DVC) scheme based on polar coding techniques. Firstly, we adapt log-likelihood ratios (LLRs) for DVC with integer implementation of a discrete cosine transform (DCT). We propose a computationally efficient and numerically stable modification of these LLRs ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	340,959
2410.16100	ExDBN: Exact learning of Dynamic Bayesian Networks	Causal learning from data has received much attention in recent years. One way of capturing causal relationships is by utilizing Bayesian networks. There, one recovers a weighted directed acyclic graph, in which random variables are represented by vertices, and the weights associated with each edge represent the streng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,871
2103.02813	PET Image Reconstruction with Multiple Kernels and Multiple Kernel Space Regularizers	Kernelized maximum-likelihood (ML) expectation maximization (EM) methods have recently gained prominence in PET image reconstruction, outperforming many previous state-of-the-art methods. But they are not immune to the problems of non-kernelized MLEM methods in potentially large reconstruction error and high sensitivit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,071
0909.2074	Sum Capacity of MIMO Interference Channels in the Low Interference Regime	Using Gaussian inputs and treating interference as noise at the receivers has recently been shown to be sum capacity achieving for the two-user single-input single-output (SISO) Gaussian interference channel in a low interference regime, where the interference levels are below certain thresholds. In this paper, such a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,473
2410.02590	Generalization emerges from local optimization in a self-organized learning network	We design and analyze a new paradigm for building supervised learning networks, driven only by local optimization rules without relying on a global error function. Traditional neural networks with a fixed topology are made up of identical nodes and derive their expressiveness from an appropriate adjustment of connectio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,344
1911.08361	Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches	Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,171
2311.02310	Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles	Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning. However, even though zero-shot translations are relatively good, there remains a discernible gap comparing their performance w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	405,384
2410.14957	Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations	Offline-to-online reinforcement learning (O2O RL) aims to obtain a continually improving policy as it interacts with the environment, while ensuring the initial policy behaviour is satisficing. This satisficing behaviour is necessary for robotic manipulation where random exploration can be costly due to catastrophic fa...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	500,282
2307.03210	Sparse Graphical Linear Dynamical Systems	Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis. Extensive research exists on state-space models (SSMs), which are powerful mathematical tools that allow for probabilistic and interpretable...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	377,961
2108.05457	Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning	Recently, needs for unmanned aerial vehicles (UAVs) that are attachable to the wall have been highlighted. As one of the ways to address the need, researches on various tilting multirotors that can increase maneuverability has been employed. Unfortunately, existing studies on the tilting multirotors require considerabl...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	250,305
1806.05833	On the exact minimization of saturated loss functions for robust regression and subspace estimation	This paper deals with robust regression and subspace estimation and more precisely with the problem of minimizing a saturated loss function. In particular, we focus on computational complexity issues and show that an exact algorithm with polynomial time-complexity with respect to the number of data can be devised for r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,570
2301.06358	Post-Train Adaptive U-Net for Image Segmentation	Typical neural network architectures used for image segmentation cannot be changed without further training. This is quite limiting as the network might not only be executed on a powerful server, but also on a mobile or edge device. Adaptive neural networks offer a solution to the problem by allowing certain adaptivity...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	340,625
1802.03358	Deep Learning for Malicious Flow Detection	Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	89,952
2412.05724	A Tiered GAN Approach for Monet-Style Image Generation	Generative Adversarial Networks (GANs) have proven to be a powerful tool in generating artistic images, capable of mimicking the styles of renowned painters, such as Claude Monet. This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process, enhancing the generated images...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	514,946
2103.15888	The Complexity of Nonconvex-Strongly-Concave Minimax Optimization	This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finite-sum settings. We establish nontrivial lower complexity bounds of $\Omega(\sqrt{\kappa}\Delta L\epsilon^{-2})$ and $\Omega(n+\sqrt{n\kappa}...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	227,359
1906.04580	Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks	Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	134,762
2202.10612	A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning	We propose a model enabling decentralized multiple agents to share their perception of environment in a fair and adaptive way. In our model, both the current message and historical observation are taken into account, and they are handled in the same recurrent model but in different forms. We present a dual-level recurr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	281,593
2501.10186	Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education	Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applyi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	525,429
2012.07369	Learning for MPC with Stability & Safety Guarantees	The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature. The hope of this combination is to reduce the reliance of MPC schemes on accurate models, and to tap into the fast developing machine learning and reinforcement learning tool...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	211,433
2005.08448	Deep Convolutional Sparse Coding Networks for Image Fusion	Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents three deep convolutional sparse coding (CSC) networks for three kinds of image f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	177,622
2209.10974	Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning	While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in general, various reward functions can lead to the same optimal policy, and henc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,036
1808.09769	Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification	This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels and tensors of both domains are constructed to include neighboring samples from single superpixel. Then we consider the subsp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,260
2212.01176	Physical layer insecurity	In the classic wiretap model, Alice wishes to reliably communicate to Bob without being overheard by Eve who is eavesdropping over a degraded channel. Systems for achieving that physical layer security often rely on an error correction code whose rate is below the Shannon capacity of Alice and Bob's channel, so Bob can...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	334,333
1105.6314	Activity-Based Search for Black-Box Contraint-Programming Solvers	Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search, the idea of using the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the WDEG...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	10,611
1907.12001	Towards Understanding and Modeling Empathy for Use in Motivational Design Thinking	Design Thinking workshops are used by companies to help generate new ideas for technologies and products by engaging subjects in exercises to understand their users' wants and become more empathetic towards their needs. The "aha moment" experienced during these thought-provoking, step outside the yourself activities oc...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	140,006
2307.03247	Stiffness Change for Reconfiguration of Inflated Beam Robots	Active control of the shape of soft robots is challenging. Despite having an infinite number of passive degrees of freedom (DOFs), soft robots typically only have a few actively controllable DOFs, limited by the number of degrees of actuation (DOAs). The complexity of actuators restricts the number of DOAs that can be ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	377,970
2205.13444	Principled Knowledge Extrapolation with GANs	Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs) into architectures of generator networks. This methodology, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	298,936
2210.16176	A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems	This paper addresses the problem of fault diagnosis in multistation assembly systems. Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements. For such problems, the challenge is solving an underdetermined system caused by a common pheno...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,247
2206.14483	Data augmentation for learning predictive models on EEG: a systematic comparison	Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	305,302
2005.01497	Towards A Sign Language Gloss Representation Of Modern Standard Arabic	Over 5% of the world's population (466 million people) has disabling hearing loss. 4 million are children. They can be hard of hearing or deaf. Deaf people mostly have profound hearing loss. Which implies very little or no hearing. Over the world, deaf people often communicate using a sign language with gestures of bot...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,589
1705.00581	Query-adaptive Video Summarization via Quality-aware Relevance Estimation	Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied. We address this problem by posing query-relevant summarization as a video frame subset selection problem...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	72,710
1901.07129	An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation	In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via sentiment labels. Our proposed model is based on the paradigm of conditional ad...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	119,153
2101.03249	Bayesian U-Net for Segmenting Glaciers in SAR Imagery	Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks h...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	214,864
2012.12540	Evolving Neural Architecture Using One Shot Model	Neural Architecture Search (NAS) is emerging as a new research direction which has the potential to replace the hand-crafted neural architectures designed for specific tasks. Previous evolution based architecture search requires high computational resources resulting in high search time. In this work, we propose a nove...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	212,973
2008.00110	Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification	In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relational teacher student learning (RTSL). Taking into account the structural relationships between acoustic scene classes, our proposed framewo...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	189,903
2412.03177	PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation	Finetuning-free personalized image generation can synthesize customized images without test-time finetuning, attracting wide research interest owing to its high efficiency. Current finetuning-free methods simply adopt a single training stage with a simple image reconstruction task, and they typically generate low-quali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,853
1906.09744	Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel	This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. Isolation kernel outperforms Gaussian kernel in two aspects. First, the use of Isolation kernel in t-SNE overcomes the drawback of misrepresenting some structu...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	136,256
math/0205303	On Asymmetric Coverings and Covering Numbers	An asymmetric covering D(n,R) is a collection of special subsets S of an n-set such that every subset T of the n-set is contained in at least one special S with \|S\| - \|T\| <= R. In this paper we compute the smallest size of any D(n,1) for n <= 8. We also investigate ``continuous'' and ``banded'' versions of the problem....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,622
2203.12232	Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based Approach	Contour tracking plays a crucial role in multi-axis motion control systems, and it requires both multi-axial contouring as well as standard servo performance in each axis. Among the existing contouring control methods, the cross coupled control (CCC) lacks of an asymptotical tracking performance for general contours, a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	287,191
2309.02340	Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture Synthesis	Texture models based on Generative Adversarial Networks (GANs) use zero-padding to implicitly encode positional information of the image features. However, when extending the spatial input to generate images at large sizes, zero-padding can often lead to degradation in image quality due to the incorrect positional info...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,015
2306.02704	Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents	In this paper, we introduce a generalization of the standard Stackelberg Games (SGs) framework: Calibrated Stackelberg Games (CSGs). In CSGs, a principal repeatedly interacts with an agent who (contrary to standard SGs) does not have direct access to the principal's action but instead best-responds to calibrated foreca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	371,019
1807.10221	Unified Perceptual Parsing for Scene Understanding	Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the mach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,898
2203.04308	Breast cancer detection using artificial intelligence techniques: A systematic literature review	Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive ca...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	284,423
2310.05193	Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models	This paper investigates how to better leverage large-scale pre-trained uni-modal models to further enhance discriminative multi-modal learning. Even when fine-tuned with only uni-modal data, these models can outperform previous multi-modal models in certain tasks. It's clear that their incorporation into multi-modal le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	398,035
2108.09112	Continual Learning for Image-Based Camera Localization	For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential. However, such methods assume a stationary data distri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,498
1809.03779	Probabilistic approach to limited-data computed tomography reconstruction	In this work, we consider the inverse problem of reconstructing the internal structure of an object from limited x-ray projections. We use a Gaussian process prior to model the target function and estimate its (hyper)parameters from measured data. In contrast to other established methods, this comes with the advantage ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	107,406
2001.09977	Towards a Human-like Open-Domain Chatbot	We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Ave...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	161,712
2205.09669	Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency	Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial att...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	297,366
1809.01353	IKA: Independent Kernel Approximator	This paper describes a new method for low rank kernel approximation called IKA. The main advantage of IKA is that it produces a function $\psi(x)$ defined as a linear combination of arbitrarily chosen functions. In contrast the approximation produced by Nystr\"om method is a linear combination of kernel evaluations. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,782
2309.16451	Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering	Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,357
2307.00293	AutoST: Training-free Neural Architecture Search for Spiking Transformers	Spiking Transformers have gained considerable attention because they achieve both the energy efficiency of Spiking Neural Networks (SNNs) and the high capacity of Transformers. However, the existing Spiking Transformer architectures, derived from Artificial Neural Networks (ANNs), exhibit a notable architectural gap, r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	376,951
1506.00176	An Open Source Testing Tool for Evaluating Handwriting Input Methods	This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,628
1905.11011	Robustness of accelerated first-order algorithms for strongly convex optimization problems	We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation. Specifically, for unconstrained, smooth, strongly convex optimization problems, we examine the mean-squared error in the optimization variable when the iterates are perturbed by additive white noise. This t...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	132,303
2412.08219	Neural Operator Feedback for a First-Order PIDE with Spatially-Varying State Delay	A transport PDE with a spatial integral and recirculation with constant delay has been a benchmark for neural operator approximations of PDE backstepping controllers. Introducing a spatially-varying delay into the model gives rise to a gain operator defined through integral equations which the operator's input -- the v...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	515,997
2207.00232	Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence	Extracting cybersecurity entities such as attackers and vulnerabilities from unstructured network texts is an important part of security analysis. However, the sparsity of intelligence data resulted from the higher frequency variations and the randomness of cybersecurity entity names makes it difficult for current meth...	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	305,690
2112.15523	Transfer learning for cancer diagnosis in histopathological images	Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture performs better for a given dataset. In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathol...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	273,812
2106.03227	Neural Tangent Kernel Maximum Mean Discrepancy	We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD. This connection enables us to develop a computationally efficient and memory-efficient approach to compute the MMD statistic and perform NTK based two-sample tests towa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,232
2108.07975	Unsupervised Image Generation with Infinite Generative Adversarial Networks	Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision. Generative Adversarial Networks (GANs) as an implicit approach have achieved great successes in this direction and therefore been emp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,088
2101.10263	Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis	Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificia...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	216,877
2306.04186	Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks	Self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. One goal of audio self-supervised pre-training is to transfer knowledge to downstream audio tasks, generally including clip-level and frame-level tasks. While frame-level tasks are important for fine-grained acoustic sc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,641
2006.12486	Locally Masked Convolution for Autoregressive Models	High-dimensional generative models have many applications including image compression, multimedia generation, anomaly detection and data completion. State-of-the-art estimators for natural images are autoregressive, decomposing the joint distribution over pixels into a product of conditionals parameterized by a deep ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	183,610
2205.06618	The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation	Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference. The chosen set is typically determined by separately trained alignment model parameters, independent of the source-sentence c...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	296,299
2304.06121	FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings	An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	357,861
2208.14349	WikiLink: an encyclopedia-based semantic network for design innovation	Data-driven design and innovation is a process to reuse and provide valuable and useful information. However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information. Besides, existing studies build the edges of a semantic network only on either st...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	315,295
2412.17837	Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding	Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation tasks are under-explored in LLMs. In this paper, we present EthioEmo, a multi-label emotion classification dataset for four Ethiopian languages, namely, Amharic (amh...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	520,141
2211.10889	Metadata Caching in Presto: Towards Fast Data Processing	Presto is an open-source distributed SQL query engine for OLAP, aiming for "SQL on everything". Since open-sourced in 2013, Presto has been consistently gaining popularity in large-scale data analytics and attracting adoption from a wide range of enterprises. From the development and operation of Presto, we witnessed a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	331,489
2104.11125	ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training	Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and have demonstrated high compression ratios. However, most existing methods do no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,828
2110.12532	Fronthaul Compression for Uplink Massive MIMO using Matrix Decomposition	Massive MIMO opens up attractive possibilities for next generation wireless systems with its large number of antennas offering spatial diversity and multiplexing gain. However, the fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries IQ samples to the Baseband Unit (BBU) of the base station c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	262,874
2307.10751	Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots	Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	380,675
2009.09558	Efficient Design of Subblock Energy-Constrained Codes and Sliding Window-Constrained Codes	The subblock energy-constrained codes (SECCs) and sliding window-constrained codes (SWCCs) have recently attracted attention due to various applications in communcation systems such as simultaneous energy and information transfer. In a SECC, each codewod is divided into smaller non-overlapping windows, called subblocks...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	196,615
2104.05480	Towards Crowd-aware Indoor Path Planning (Extended Version)	Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) f...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	229,742
2202.02881	Approximate Policy Iteration with Bisimulation Metrics	Bisimulation metrics define a distance measure between states of a Markov decision process (MDP) based on a comparison of reward sequences. Due to this property they provide theoretical guarantees in value function approximation (VFA). In this work we first prove that bisimulation and $\pi$-bisimulation metrics can be ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	278,984
2006.07931	Solos: A Dataset for Audio-Visual Music Analysis	In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual self-supervised task. These ...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	182,011
2209.07299	Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion	Knowledge Graph Completion (KGC) has been recently extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. Previous works often design KGC models closely coupled with specific graph structures, which inevitably results in two drawbacks: 1...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	317,694
2207.01674	GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval	This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy ({\bf Gaz}e-based {\bf B}ert model for...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	306,248
2206.08977	BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation	We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	303,378
2412.04506	Arctic-Embed 2.0: Multilingual Retrieval Without Compromise	This paper presents the training methodology of Arctic-Embed 2.0, a set of open-source text embedding models built for accurate and efficient multilingual retrieval. While prior works have suffered from degraded English retrieval quality, Arctic-Embed 2.0 delivers competitive retrieval quality on multilingual and Engli...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	514,450
2204.12440	Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining	Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, wh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,474
1910.14395	Great New Design: How Do We Talk about Media Architecture in Social Media	In social media, we communicate through pictures, videos, short codes, links, partial phrases. It is a rich, and digitally documented communication channel that relies on a multitude of media and forms. These channels are sorted by algorithms as organizers of discourse, mostly with the goal of channeling attention. In ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	151,639
1811.05768	Parser Extraction of Triples in Unstructured Text	The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subject-predicate-object...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	113,383
2209.06338	Collective Adaptation in Multi-Agent Systems: How Predator Confusion Shapes Swarm-Like Behaviors	Popular hypotheses about the origins of collective adaptation are related to two basic behaviours: protection from predators and a combined search for food resources. Among the anti-predator explanations, the predator confusion hypothesis suggests that groups of individuals moving in a swarm aim to overwhelm the predat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	317,359
1511.02455	(Yet) Another Theoretical Model of Thinking	This paper presents a theoretical, idealized model of the thinking process with the following characteristics: 1) the model can produce complex thought sequences and can be generalized to new inputs, 2) it can receive and maintain input information indefinitely for the generation of thoughts and later use, and 3) it su...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	48,638
2007.10859	Multi-label Thoracic Disease Image Classification with Cross-Attention Networks	Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology image classification task is significantly more challenging as it is far more expensive to collect the t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,396

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