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
2502.02431	Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants	Recent advancements in deep learning optimization have introduced new algorithms, such as Schedule-Free optimizers, AdEMAMix, MARS and Lion which modify traditional momentum mechanisms. In a separate line of work, theoretical acceleration of stochastic gradient descent (SGD) in noise-dominated regime has been achieved ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,310
2404.16563	Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark	Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for rigorously evaluating the capabilities of LLMs on time series understan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	449,546
2205.11751	Video Capsule Endoscopy and Ingestible Electronics: Emerging Trends in Sensors, Circuits, Materials, Telemetry, Optics, and Rapid Reading Software	Real-time monitoring of the gastrointestinal tract in a safe and comfortable manner is valuable for the diagnosis and therapy of many diseases. Within this realm, our review captures the trends in ingestible capsule systems with a focus on hardware and software technologies used for capsule endoscopy and remote patient...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	298,265
2203.03391	A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control	Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning. Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot in diverse manipulation tasks. Several prior works...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	284,068
2011.05554	TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting	Urban flow forecasting is a challenging task, given the inherent periodic characteristics of urban flow patterns. To capture the periodicity, existing urban flow prediction approaches are often designed with closeness, period, and trend components extracted from the urban flow sequence. However, these three components ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	205,960
2407.20643	Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer	Despite advancements in methodologies, immunohistochemistry (IHC) remains the most utilized ancillary test for histopathologic and companion diagnostics in targeted therapies. However, objective IHC assessment poses challenges. Artificial intelligence (AI) has emerged as a potential solution, yet its development requir...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,228
1909.05767	Unitary Shift Operators on a Graph	A unitary shift operator (GSO) for signals on a graph is introduced, which exhibits the desired property of energy preservation over both backward and forward graph shifts. For rigour, the graph differential operator is also derived in an analytical form. The commutativity relation of the shift operator with the Fourie...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	145,200
2103.01263	Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image	The most common technique for generating B-mode ultrasound (US) images is delay and sum (DAS) beamforming, where the signals received at the transducer array are sampled before an appropriate delay is applied. This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,554
2007.05500	Scientific Discovery by Generating Counterfactuals using Image Translation	Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent. Here we investigate if explanation techniques can also be used as a mechanism for scientific discovery. We make three contributions: first, we propose a framework to convert predi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	186,700
2111.09378	MPF6D: Masked Pyramid Fusion 6D Pose Estimation	Object pose estimation has multiple important applications, such as robotic grasping and augmented reality. We present a new method to estimate the 6D pose of objects that improves upon the accuracy of current proposals and can still be used in real-time. Our method uses RGB-D data as input to segment objects and estim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	266,988
2408.17073	Approximately Invertible Neural Network for Learned Image Compression	Learned image compression have attracted considerable interests in recent years. It typically comprises an analysis transform, a synthesis transform, quantization and an entropy coding model. The analysis transform and synthesis transform are used to encode an image to latent feature and decode the quantized feature to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,569
2108.07186	Robust Trimmed k-means	Clustering is a fundamental tool in unsupervised learning, used to group objects by distinguishing between similar and dissimilar features of a given data set. One of the most common clustering algorithms is k-means. Unfortunately, when dealing with real-world data many traditional clustering algorithms are compromised...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	250,854
2010.11909	Contrastive Self-Supervised Learning for Wireless Power Control	We propose a new approach for power control in wireless networks using self-supervised learning. We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so t...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	202,470
2007.04309	Self-Supervised Policy Adaptation during Deployment	In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new enviro...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	186,320
2304.08820	Motion-state Alignment for Video Semantic Segmentation	In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,831
2407.04990	Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data	Several machine learning schemes have attempted to perform the detection of spam messages. However, those schemes mostly require a huge amount of labeled data. The existing techniques addressing the lack of data availability have issues with effectiveness and robustness. Therefore, this paper proposes a conditional sem...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	470,779
2502.02859	Gap-Dependent Bounds for Federated $Q$-learning	We present the first gap-dependent analysis of regret and communication cost for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov decision processes (MDPs). Existing FRL methods focus on worst-case scenarios, leading to $\sqrt{T}$-type regret bounds and communication cost bounds with a $\log T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,497
2004.11210	Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients	Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation which is computationally expensive. In this paper, we present a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,851
2007.06227	Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection	The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,945
2010.08021	MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention	This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -- text, audio and video -- in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the ...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	201,027
2203.04424	SLAM-Supported Self-Training for 6D Object Pose Estimation	Recent progress in object pose prediction provides a promising path for robots to build object-level scene representations during navigation. However, as we deploy a robot in novel environments, the out-of-distribution data can degrade the prediction performance. To mitigate the domain gap, we can potentially perform s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	284,463
2208.07722	Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory	Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,134
2408.06766	Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage	Rigorous testing of machine learning models is necessary for trustworthy deployments. We present a novel black-box approach for generating test-suites for robust testing of deep neural networks (DNNs). Most existing methods create test inputs based on maximizing some "coverage" criterion/metric such as a fraction of ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,336
2203.09044	Convert, compress, correct: Three steps toward communication-efficient DNN training	In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses three processing steps for the network gradients: (i) quantization through floating-point conversion,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,005
2304.08868	Soft-Output Deep Neural Network-Based Decoding	Deep neural network (DNN)-based channel decoding is widely considered in the literature. The existing solutions are investigated for the case of hard output, i.e. when the decoder returns the estimated information word. At the same time, soft-output decoding is of critical importance for iterative receivers and decoder...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	358,850
2401.06825	Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification	Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet challenging retrieval task. The key challenges in USL-VI-ReID are to effectively generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations. Recently, clustered pseud...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,309
2301.06874	Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images	With their combined spectral depth and geometric resolution, hyperspectral remote sensing images embed a wealth of complex, non-linear information that challenges traditional computer vision techniques. Yet, deep learning methods known for their representation learning capabilities prove more suitable for handling such...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	340,766
1701.08731	On the Computation of the Shannon Capacity of a Discrete Channel with Noise	Muroga [M52] showed how to express the Shannon channel capacity of a discrete channel with noise [S49] as an explicit function of the transition probabilities. His method accommodates channels with any finite number of input symbols, any finite number of output symbols and any transition probability matrix. Silverman [...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,513
2009.06797	Competing AI: How does competition feedback affect machine learning?	This papers studies how competition affects machine learning (ML) predictors. As ML becomes more ubiquitous, it is often deployed by companies to compete over customers. For example, digital platforms like Yelp use ML to predict user preference and make recommendations. A service that is more often queried by users, pe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	195,747
2406.06589	PatentEval: Understanding Errors in Patent Generation	In this work, we introduce a comprehensive error typology specifically designed for evaluating two distinct tasks in machine-generated patent texts: claims-to-abstract generation, and the generation of the next claim given previous ones. We have also developed a benchmark, PatentEval, for systematically assessing langu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	462,672
1403.3665	A Low-Complexity Algorithm for Throughput Maximization in Wireless Powered Communication Networks	This paper investigates a wireless powered communication network (WPCN) under the protocol of harvest-then-transmit,where a hybrid access point with constant power supply replenishes the passive user nodes by wireless power transfer in the downlink,then each user node transmit independent information to the hybrid AP i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,589
1705.10941	Spectral Norm Regularization for Improving the Generalizability of Deep Learning	We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,502
2407.12383	Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models	Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomplete erasure, consume a lot of computing resources, and inadvertently ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	473,900
2409.00269	Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content	Cosine similarity between two documents can be computed using token embeddings formed by Large Language Models (LLMs) such as GPT-4, and used to categorize those documents across a range of uses. However, these similarities are ultimately dependent on the corpora used to train these LLMs, and may not reflect subjective...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	484,850
2311.02492	Forecasting Post-Wildfire Vegetation Recovery in California using a Convolutional Long Short-Term Memory Tensor Regression Network	The study of post-wildfire plant regrowth is essential for developing successful ecosystem recovery strategies. Prior research mainly examines key ecological and biogeographical factors influencing post-fire succession. This research proposes a novel approach for predicting and analyzing post-fire plant recovery. We de...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	405,463
1910.02400	A Linear LMP Model for Active and Reactive Power with Power Loss	Pricing the reactive power is more necessary than ever before because of the increasing challenge of renewable energy integration on reactive power balance and voltage control. However, reactive power price is hard to be efficiently calculated because of the non-linear nature of optimal AC power flow equation. This pap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	148,233
1403.5607	Bayesian Optimization with Unknown Constraints	Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general ca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	31,742
2401.00004	Informational non-reductionist theory of consciousness that providing maximum accuracy of reality prediction	The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	418,842
1912.10604	Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction	Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured inf...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	158,363
2401.12187	WARM: On the Benefits of Weight Averaged Reward Models	Aligning large language models (LLMs) with human preferences through reinforcement learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward model (RM) to achieve seemingly high rewards without meeting the underlying objectives. We identify two primary challenges when designing RMs to mitiga...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	423,284
2302.14719	Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction	Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	348,411
1709.05436	Scene-centric Joint Parsing of Cross-view Videos	Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are tha...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	80,867
2401.07218	Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency	An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous ``events''. It has advantages over conventional cameras in those scenes with high-speed motions and challenging lighting conditions because of the high temporal resolution, high dynamic range, low ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,458
2408.10334	A Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers	In recent years, large language models (LLMs) have made significant progress in the field of code generation. However, as more and more users rely on these models for software development, the security risks associated with code generation models have become increasingly significant. Studies have shown that traditional...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,811
2207.00419	Self-Supervised Learning for Videos: A Survey	The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, obtaining annotations is expensive and requires great effort, which is especially challenging for videos. Moreover, the use of human-generated annotations leads to models with biased learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	305,749
2201.03237	Tree-based Search Graph for Approximate Nearest Neighbor Search	Nearest neighbor search supports important applications in many domains, such as database, machine learning, computer vision. Since the computational cost for accurate search is too high, the community turned to the research of approximate nearest neighbor search (ANNS). Among them, graph-based algorithm is one of the ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	274,803
2411.04723	Exploring the Stability Gap in Continual Learning: The Role of the Classification Head	Continual learning (CL) has emerged as a critical area in machine learning, enabling neural networks to learn from evolving data distributions while mitigating catastrophic forgetting. However, recent research has identified the stability gap -- a phenomenon where models initially lose performance on previously learned...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	506,392
1702.01208	A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution	Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor data quality, ER is a challenging task for any automated process. As a remedy, human-powered ER via ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	67,769
2403.15245	Reasoning-Enhanced Object-Centric Learning for Videos	Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world. Recently, slot-based video models have demonstrated remarkable proficiency in segmenting and tracking o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	440,464
2410.02372	Fast Crystal Tensor Property Prediction: A General O(3)-Equivariant Framework Based on Polar Decomposition	Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining $O(3)$ group tensor equivariance. This equivariance constraint often introduces tremendous computa...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	494,250
1805.08719	Parsimonious Bayesian deep networks	Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PB...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,237
2306.16322	Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models	Large language models (LLMs) have demonstrated impressive performance on various downstream tasks without requiring fine-tuning, including ChatGPT, a chat-based model built on top of LLMs such as GPT-3.5 and GPT-4. Despite having a lower training proportion compared to English, these models also exhibit remarkable capa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	376,339
0710.2852	Generating models for temporal representations	We discuss the use of model building for temporal representations. We chose Polish to illustrate our discussion because it has an interesting aspectual system, but the points we wish to make are not language specific. Rather, our goal is to develop theoretical and computational tools for temporal model building tasks i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	788
2211.08310	Identification of medical devices using machine learning on distribution feeder data for informing power outage response	Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,565
2001.05582	The Error Probability of Maximum-Likelihood Decoding over Two Deletion Channels	This paper studies the problem of reconstructing a word given several of its noisy copies. This setup is motivated by several applications, among them is reconstructing strands in DNA-based storage systems. Under this paradigm, a word is transmitted over some fixed number of identical independent channels and the goal ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,583
1603.09701	Paired Threshold Graphs	Threshold graphs are recursive deterministic network models that have been proposed for describing certain economic and social interactions. One drawback of this graph family is that it has limited generative attachment rules. To mitigate this problem, we introduce a new class of graphs termed Paired Threshold (PT) gra...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	53,952
2408.10261	Relational Graph Convolutional Networks Do Not Learn Sound Rules	Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	481,783
0804.0686	Discrimination of two channels by adaptive methods and its application to quantum system	The optimal exponential error rate for adaptive discrimination of two channels is discussed. In this problem, adaptive choice of input signal is allowed. This problem is discussed in various settings. It is proved that adaptive choice does not improve the exponential error rate in these settings. These results are appl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,534
1904.03416	Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks	Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech repr...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,711
1810.09098	Stochastic Gradient MCMC for State Space Models	State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to depende...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,984
1507.01073	Convex Factorization Machine for Regression	We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Specifically, we employ a linear+quadratic model and regularize the linear term with the $\ell_2$-regularizer and the quadratic term with the trace norm regularizer. Then, we formulate the CFM o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,817
2011.14334	Audio-visual Speech Separation with Adversarially Disentangled Visual Representation	Speech separation aims to separate individual voice from an audio mixture of multiple simultaneous talkers. Although audio-only approaches achieve satisfactory performance, they build on a strategy to handle the predefined conditions, limiting their application in the complex auditory scene. Towards the cocktail party ...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,746
1808.05906	Story Disambiguation: Tracking Evolving News Stories across News and Social Streams	Following a particular news story online is an important but difficult task, as the relevant information is often scattered across different domains/sources (e.g., news articles, blogs, comments, tweets), presented in various formats and language styles, and may overlap with thousands of other stories. In this work we ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	105,437
2303.13019	Construction Methods Based on Minimum Weight Distribution for Polar Codes with Successive Cancellation List Decoding	Minimum weight distribution (MWD) is an important metric to calculate the first term of union bound called minimum weight union bound (MWUB). In this paper, we first prove the maximum likelihood (ML) performance approaches MWUB as signal-to-noise ratio (SNR) goes to infinity and provide the deviation when MWD and SNR a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	353,509
2005.06792	Linear Quadratic Gaussian Mean-Field Controls of Social Optima	This paper investigates a class of unified stochastic linear quadratic Gaussian (LQG) social optima problems involving a large number of weakly-coupled interactive agents under a {generalized} setting. For each individual agent, the control and state process enters both diffusion and drift terms in its linear dynamics,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	177,113
2111.01726	Instructive artificial intelligence (AI) for human training, assistance, and explainability	We propose a novel approach to explainable AI (XAI) based on the concept of "instruction" from neural networks. In this case study, we demonstrate how a superhuman neural network might instruct human trainees as an alternative to traditional approaches to XAI. Specifically, an AI examines human actions and calculates v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	264,648
0704.2092	A Note on the Inapproximability of Correlation Clustering	We consider inapproximability of the correlation clustering problem defined as follows: Given a graph $G = (V,E)$ where each edge is labeled either "+" (similar) or "-" (dissimilar), correlation clustering seeks to partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	52
2312.10329	Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off	Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hin...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	416,115
1906.03088	Improving Relation Extraction by Pre-trained Language Representations	Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of rela...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	134,277
1803.04062	Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back	Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,386
2204.04044	Confidence Score for Unsupervised Foreground Background Separation of Document Images	Foreground-background separation is an important problem in document image analysis. Popular unsupervised binarization methods (such as the Sauvola's algorithm) employ adaptive thresholding to classify pixels as foreground or background. In this work, we propose a novel approach for computing confidence scores of the c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,518
1904.09234	Software Tools for Big Data Resources in Family Names Dictionaries	This paper describes the design and development of specific software tools used during the creation of Family Names in Britain and Ireland (FaNBI) research project, started by the University of the West of England in 2010 and finished successfully in 2016. First, the overview of the project and methodology is provided....	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	128,319
2402.11071	Fisher-Riemann geometry for nonparametric probability densities	In this article we aim to obtain the Fisher Riemann geodesics for nonparametric families of probability densities as a weak limit of the parametric case with increasing number of parameters.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	430,221
1004.1001	The Graph Traversal Pattern	A graph is a structure composed of a set of vertices (i.e.nodes, dots) connected to one another by a set of edges (i.e.links, lines). The concept of a graph has been around since the late 19$^\text{th}$ century, however, only in recent decades has there been a strong resurgence in both theoretical and applied graph res...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	6,094
2204.09633	SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction	Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, Surv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,506
2006.11937	Learning of Discrete Graphical Models with Neural Networks	Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,416
2103.12258	Hallucination of speech recognition errors with sequence to sequence learning	Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions. When plain text data is to be used to train systems for spoken language understanding or ASR, a proven strategy to reduce said mismatch and prevent degr...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	226,107
1104.5466	Notes on a New Philosophy of Empirical Science	This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the len...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,160
2412.09055	Hyperbolic-constraint Point Cloud Reconstruction from Single RGB-D Images	Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods often rely on expensive CAD models and complex geometric priors. Effec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,344
1906.00823	Data-driven Estimation of Sinusoid Frequencies	Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,521
2411.11371	Rethinking Thinking Tokens: Understanding Why They Underperform in Practice	Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs marginally improves performance and consistently underperforms compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. We hy...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	509,033
2412.09026	Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model	A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recover normal patterns exclusively, thus reporting abnormal patterns as outliers. Yet, existing attempts neglect the various formations of anomal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,327
2008.09346	SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation	Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,687
2007.13118	UIAI System for Short-Duration Speaker Verification Challenge 2020	In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and u...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,027
2311.12521	Classification of Tabular Data by Text Processing	Natural Language Processing technology has advanced vastly in the past decade. Text processing has been successfully applied to a wide variety of domains. In this paper, we propose a novel framework, Text Based Classification(TBC), that uses state of the art text processing techniques to solve classification tasks on t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	409,363
2401.10353	Inconsistent dialogue responses and how to recover from them	One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem. In this work, we study methods to assess and bolster utterance consistency of chat systems. A dataset is first developed for studying the inconsistencies, where i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	422,601
2207.03853	Decision Trees for Analyzing Influences on the Accuracy of Indoor Localization Systems	Absolute position accuracy is the key performance criterion of an Indoor Localization System (ILS). Since ILS are heterogeneous and complex cyber-physical systems, the localization accuracy depends on various influences from the environment, system configuration, and the application processes. To determine the position...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	306,994
2110.04121	On the Limitations of Multimodal VAEs	Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhibit a gap in generative quality compared to unimodal VAEs, which are completely unsupervised. In an attempt to explain this gap, we uncover ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,764
2304.10333	Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition	To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain. However, the existing UniDA methods rely on source samples with correct an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,378
2212.05908	Instance-Conditional Timescales of Decay for Non-Stationary Learning	Slow concept drift is a ubiquitous, yet under-studied problem in practical machine learning systems. In such settings, although recent data is more indicative of future data, naively prioritizing recent instances runs the risk of losing valuable information from the past. We propose an optimization-driven approach towa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,932
1307.7447	Wireless Information and Power Transfer in Two-Way Amplify-and-Forward Relaying Channels	The various wireless networks have made the ambient radio frequency signals around the world. Wireless information and power transfer enables the devices to recycle energy from these ambient radio frequency signals and process information simultaneously. In this paper, we develop a wireless information and power transf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,108
2209.08532	SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection	We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,161
2207.05466	A Benchmark dataset for predictive maintenance	The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,547
1905.09512	True scale-free networks hidden by finite size effects	We analyze about two hundred naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statistical testing of the validity of power law distributions o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	131,751
2302.01595	Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties	Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics. We propose a data-driven deep reinforcement learning (DRL) framework to learn proactive, context-aware, defense count...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	343,677
2408.00312	Adversarial Text Rewriting for Text-aware Recommender Systems	Text-aware recommender systems incorporate rich textual features, such as titles and descriptions, to generate item recommendations for users. The use of textual features helps mitigate cold-start problems, and thus, such recommender systems have attracted increased attention. However, we argue that the dependency on i...	false	false	false	true	false	true	true	false	false	false	false	false	true	false	false	false	false	false	477,790
1807.05351	ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies	The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is a...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	false	102,911
1311.4121	Application of Rough Set Theory in Data Mining	Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. Data mining is a discipline that has an important contribution to data analysis, discovery of new meaningful knowledge, and autonomous decision making. The rough set theory offers a viable approach for decision rul...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	28,469
1001.2947	Design and Analysis of Multi-User SDMA Systems with Noisy Limited CSIT Feedback	In this paper, we consider spatial-division multiple-access (SDMA) systems with one base station with multiple antennae and a number of single antenna mobiles under noisy limited CSIT feedback. We propose a robust noisy limited feedback design for SDMA systems. The solution consists of a real-time robust SDMA precoding...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,425
2406.02760	Stable MPC with maximal terminal sets and quadratic terminal costs	This paper develops a technique for computing a quadratic terminal cost for linear model predictive controllers that is valid for all states in the maximal control invariant set. This maximizes the set of recursively feasible states for the controller, ensures asymptotic stability using standard proofs, and allows for ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	460,920

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