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
2106.05241	Multi-Facet Clustering Variational Autoencoders	Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	240,026
2309.04579	EGOFALLS: A visual-audio dataset and benchmark for fall detection using egocentric cameras	Falls are significant and often fatal for vulnerable populations such as the elderly. Previous works have addressed the detection of falls by relying on data capture by a single sensor, images or accelerometers. In this work, we rely on multimodal descriptors extracted from videos captured by egocentric cameras. Our pr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	390,772
2305.07132	Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization	This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an i...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,781
2308.09141	Semi-sparsity Priors for Image Structure Analysis and Extraction	Image structure-texture decomposition is a long-standing and fundamental problem in both image processing and computer vision fields. In this paper, we propose a generalized semi-sparse regularization framework for image structural analysis and extraction, which allows us to decouple the underlying image structures fro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,171
2010.04941	When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models	We address hypernymy detection, i.e., whether an is-a relationship exists between words (x, y), with the help of large textual corpora. Most conventional approaches to this task have been categorized to be either pattern-based or distributional. Recent studies suggest that pattern-based ones are superior, if large-scal...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,932
2501.14734	Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents	This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static workflows, inefficient stateful computations, and lack of human intervention by leveraging LangGraph's graph-based workflow construct...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	527,240
2105.14177	The Jacobi sums over Galois rings of arbitrary characters and their applications in constructing asymptotically optimal codebooks	Codebooks with small maximum cross-correlation amplitudes are used to distinguish the signals from different users in CDMA communication systems. In this paper, we first study the Jacobi sums over Galois rings of arbitrary characteristics and completely determine their absolute values, which extends the work in [34], w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	237,563
1601.07224	Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)	This Bachelor's thesis, written in Russian, is devoted to a relatively new direction in the field of machine learning and artificial intelligence, namely probabilistic programming. The thesis gives a brief overview to the already existing probabilistic programming languages: Church, Venture, and Anglican. It also descr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	51,394
1902.01544	An Ensemble SVM-based Approach for Voice Activity Detection	Voice activity detection (VAD), used as the front end of speech enhancement, speech and speaker recognition algorithms, determines the overall accuracy and efficiency of the algorithms. Therefore, a VAD with low complexity and high accuracy is highly desirable for speech processing applications. In this paper, we propo...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,681
2102.07367	A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum	This paper proposes a new algorithm -- the \underline{S}ingle-timescale Do\underline{u}ble-momentum \underline{St}ochastic \underline{A}pprox\underline{i}matio\underline{n} (SUSTAIN) -- for tackling stochastic unconstrained bilevel optimization problems. We focus on bilevel problems where the lower level subproblem is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,088
2004.08917	On the dynamics emerging from pandemics and infodemics	This position paper discusses emerging behavioral, social, and economic dynamics related to the COVID-19 pandemic and puts particular emphasis on two emerging issues: First, delayed effects (or second strikes) of pandemics caused by dread risk effects are discussed whereby two factors which might influence the existenc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	173,209
1803.00001	The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel	In this article we study the field of Hilbertian metrics and positive definit (pd) kernels on probability measures, they have a real interest in kernel methods. Firstly we will make a study based on the Alpha-Beta-divergence to have a Hilbercan metric by proposing an improvement of this divergence by constructing it so...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,565
2302.02209	A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs	Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for simple graphs, our understanding remains incomplete in the context of knowledge graphs. Our goal is to provide a systematic understanding of t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	343,904
2502.05148	An Annotated Reading of 'The Singer of Tales' in the LLM Era	The Parry-Lord oral-formulaic theory was a breakthrough in understanding how oral narrative poetry is learned, composed, and transmitted by illiterate bards. In this paper, we provide an annotated reading of the mechanism underlying this theory from the lens of large language models (LLMs) and generative artificial int...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	531,460
2102.02969	Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery	Restricted isometry property (RIP), essentially stating that the linear measurements are approximately norm-preserving, plays a crucial role in studying low-rank matrix recovery problem. However, RIP fails in the robust setting, when a subset of the measurements are grossly corrupted with noise. In this work, we propos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,583
2410.11278	UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba	Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer architectures, which utilize attention mechanisms to capture temporal dependencies. However, these met...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	498,479
2102.04237	Interval Analysis of Worst-case Stationary Moments for Stochastic Chemical Reactions with Uncertain Parameters	The dynamics of cellular chemical reactions are variable due to stochastic noise from intrinsic and extrinsic sources. The intrinsic noise is the intracellular fluctuations of molecular copy numbers caused by the probabilistic encounter of molecules and is modeled by the chemical master equation. The extrinsic noise, o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	219,041
1906.02745	Automated Classification of Seizures against Nonseizures: A Deep Learning Approach	In current clinical practice, electroencephalograms (EEG) are reviewed and analyzed by well-trained neurologists to provide supports for therapeutic decisions. The way of manual reviewing is labor-intensive and error prone. Automatic and accurate seizure/nonseizure classification methods are needed. One major problem i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,162
2006.09199	AVLnet: Learning Audio-Visual Language Representations from Instructional Videos	Current methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognition (ASR) transcripts. In this work, we introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared au...	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	182,461
1306.5268	Static and Dynamic Aspects of Scientific Collaboration Networks	Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	25,384
2407.10238	Asymptotic Normality of Generalized Low-Rank Matrix Sensing via Riemannian Geometry	We prove an asymptotic normality guarantee for generalized low-rank matrix sensing -- i.e., matrix sensing under a general convex loss $\bar\ell(\langle X,M\rangle,y^)$, where $M\in\mathbb{R}^{d\times d}$ is the unknown rank-$k$ matrix, $X$ is a measurement matrix, and $y^$ is the corresponding measurement. Our analy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	472,895
2309.10517	Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption	Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	393,042
2107.00314	Backtracking (the) Algorithms on the Hamiltonian Cycle Problem	Even though the Hamiltonian cycle problem is NP-complete, many of its problem instances aren't. In fact, almost all the hard instances reside in one area: near the Koml\'os-Szemer\'edi bound, of $\frac{1}{2}\ v\cdot ln(v) + \frac{1}{2}\ v\cdot ln( ln(v))$ edges, where randomly generated graphs have an approximate 50\% ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	244,113
2110.00023	Mining for Strong Gravitational Lenses with Self-supervised Learning	We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,255
2111.11758	The Impact of Data Distribution on Q-learning with Function Approximation	We study the interplay between the data distribution and Q-learning-based algorithms with function approximation. We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms. We connect different lines of resear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,764
1401.6333	The Sampling-and-Learning Framework: A Statistical View of Evolutionary Algorithms	Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance. This paper presents an attempt towards revealing their general power from a statistical view of EAs. By s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	30,334
2404.07917	DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation	This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. Developed with a focus on real-world engineering challenges, DesignQA uniquely combines multimodal data-...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	446,017
2201.12414	Posterior Matching for Arbitrary Conditioning	Arbitrary conditioning is an important problem in unsupervised learning, where we seek to model the conditional densities $p(\mathbf{x}_u \mid \mathbf{x}_o)$ that underly some data, for all possible non-intersecting subsets $o, u \subset \{1, \dots , d\}$. However, the vast majority of density estimation only focuses o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,631
1006.5677	Shape of Traveling Densities with Extremum Statistical Complexity	In this paper, we analyze the behavior of statistical complexity in several systems where two identical densities that travel in opposite direction cross each other. Besides the crossing between two Gaussian, rectangular and triangular densities studied in a previous work, we also investigate in detail the crossing bet...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,917
2004.04721	Translation Artifacts in Cross-lingual Transfer Learning	Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique. In this paper, we sho...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	171,973
2204.04944	Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders	Semantic segmentation of point clouds generates comprehensive understanding of scenes through densely predicting the category for each point. Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,852
2307.02672	GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations	Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifications, particularly for safety-critical applications. Existing detection methods usually only focus on adversarial attacks or out-of-distribution samples as reasons for false predictions. However, general...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	377,766
2204.02634	Federated Reinforcement Learning with Environment Heterogeneity	We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environment heterogeneity, which means $n$ environments corresponding to these $n$ agent...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,029
2502.07687	Large Language Models as Proxies for Theories of Human Linguistic Cognition	We consider the possible role of current large language models (LLMs) in the study of human linguistic cognition. We focus on the use of such models as proxies for theories of cognition that are relatively linguistically-neutral in their representations and learning but differ from current LLMs in key ways. We illustra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	532,712
1412.2994	Discontinuous phase transition in an open-ended Naming Game	In this work we study on a 2-dimensional square lattice a recent version of the Naming Game, an agent-based model used for describing the emergence of linguistic structures. The system is open-ended and agents can invent new words all along the evolution of the game, picking them up from a pool characterised by a Gauss...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,249
2409.12184	Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings	Deploying Multi-Modal Large Language Models (MLLMs) in healthcare is hindered by their high computational demands and significant memory requirements, which are particularly challenging for resource-constrained devices like the Nvidia Jetson Xavier. This problem is particularly evident in remote medical settings where ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	489,478
2109.08865	Interest-oriented Universal User Representation via Contrastive Learning	User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application. In this paper, we attempt to improve universal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,050
2405.08475	Representing Information on DNA using Patterns Induced by Enzymatic Labeling	Enzymatic DNA labeling is a powerful tool with applications in biochemistry, molecular biology, biotechnology, medical science, and genomic research. This paper contributes to the evolving field of DNA-based data storage by presenting a formal framework for modeling DNA labeling in strings, specifically tailored for da...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	454,107
2211.16482	Chaining Simultaneous Thoughts for Numerical Reasoning	Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning over text should be an essential skill of AI systems. To derive precise equations to solve numerical reasoning problems, previous work focused on modeling the structures of equations, and has proposed various structured decoder...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	333,643
2409.01012	Improved Diversity-Promoting Collaborative Metric Learning for Recommendation	Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challengi...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	485,174
2008.13361	Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection	Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies with discrete-event sequences. However, it still remains an extremely difficult task due to several ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,826
1909.07771	Arrow, Hausdorff, and Ambiguities in the Choice of Preferred States in Complex Systems	Arrow's `impossibility' theorem asserts that there are no satisfactory methods of aggregating individual preferences into collective preferences in many complex situations. This result has ramifications in economics, politics, i.e., the theory of voting, and the structure of tournaments. By identifying the objects of c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	145,772
1703.04105	Combining Residual Networks with LSTMs for Lipreading	We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size targ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,838
2402.18102	Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging	Passive, compact, single-shot 3D sensing is useful in many application areas such as microscopy, medical imaging, surgical navigation, and autonomous driving where form factor, time, and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance, in an ultra-compact form factor, and in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,283
2311.01091	Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding	Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption. As a multimodal task, an essential aspect of PNG is the visual-linguistic interaction between image and caption. The previous two-stage method aggregates visual contexts from offline...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	404,916
2412.05393	HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Design	With Large Language Models (LLMs) recently demonstrating impressive proficiency in code generation, it is promising to extend their abilities to Hardware Description Language (HDL). However, LLMs tend to generate single HDL code blocks rather than hierarchical structures for hardware designs, leading to hallucinations,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	514,811
1002.2182	Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering	Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to imp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	5,671
1610.04027	Compressive Cyclostationary Spectrum Sensing with a Constant False Alarm Rate	Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. Since information about these users' signals is no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,331
2006.08762	Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize	Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer-generated imagery to computer-aided design in research and development. However, solving the partial differential equations of incompressible fluids is a challenging task and traditional numerical approximation scheme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,290
1212.6235	Real and Complex Monotone Communication Games	Noncooperative game-theoretic tools have been increasingly used to study many important resource allocation problems in communications, networking, smart grids, and portfolio optimization. In this paper, we consider a general class of convex Nash Equilibrium Problems (NEPs), where each player aims to solve an arbitrary...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	20,631
1409.4829	Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification	Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circuits. In stochastic spectral methods, one needs to determine a set of orthonormal polynomials and a proper numerical quadrature rule. The form...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	36,115
2408.15263	S4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation	Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data to learn domain invariant features for cross-scene classification. Compared to natural images, numerous spectral bands of HSIs provide abunda...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	483,870
2410.22967	Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector	The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats, thus developing Anomaly Detection Systems (ADSs) that can adapt to evolving or new attacks is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,850
1904.08921	Deep Parametric Shape Predictions using Distance Fields	Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	128,218
1402.3484	Simulation and Bisimulation over Multiple Time Scales in a Behavioral Setting	This paper introduces a new behavioral system model with distinct external and internal signals possibly evolving on different time scales. This allows to capture abstraction processes or signal aggregation in the context of control and verification of large scale systems. For this new system model different notions of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,877
1912.06172	Coevolution of Generative Adversarial Networks	Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model, such as the training stability and the hand-design of architectures. Neuroevolution is a technique that can be used to provide the automatic...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	157,278
1811.02356	Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation	Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to most monolingual tasks, insufficient data is an issue for code-switching. To mitigate the issue without...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	112,569
1910.13724	Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events	Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning. However, such systems have only been applied to chunks of sounds for classification or verification. In this paper, we aim to achieve few-shot detection ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,462
2001.04428	On the synthesis of control policies from noisy example datasets: a probabilistic approach	In this note we consider the problem of synthesizing optimal control policies for a system from noisy datasets. We present a novel algorithm that takes as input the available dataset and, based on these inputs, computes an optimal policy for possibly stochastic and nonlinear systems that also satisfies actuation constr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,248
2312.15731	Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement	The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focus on designing a complex interaction mechanism between the query and support feature. However, unlike humans who can rapidly learn new things from limited sam...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,115
2411.01629	Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity	Adding noise is easy; what about denoising? Diffusion is easy; what about reverting a diffusion? Diffusion-based generative models aim to denoise a Langevin diffusion chain, moving from a log-concave equilibrium measure $\nu$, say isotropic Gaussian, back to a complex, possibly non-log-concave initial measure $\mu$. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,159
2312.10942	ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton	Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e.g., pedestrian predictions and marketing bidding. Badminton represents a fascinating example of a multifaceted turn-based sport, requiring both sophisticated tactic developments and alternate-dep...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	416,376
2403.03537	On the Second-Order Asymptotics of the Hoeffding Test and Other Divergence Tests	Consider a binary statistical hypothesis testing problem, where $n$ independent and identically distributed random variables $Z^n$ are either distributed according to the null hypothesis $P$ or the alternative hypothesis $Q$, and only $P$ is known. A well-known test that is suitable for this case is the so-called Hoeff...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	435,236
1904.13281	CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation	Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusion-weighted magnetic resonance imaging (DWI). It has also been proposed that computed tomography perfusion (CTP) could alternatively be used to triage stroke patients, given improvements in speed and availability, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	129,349
2003.02695	Compute-and-Forward Network Coding Design over Multi-Source Multi-Relay Channels	Network coding is a new and promising paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Compute-and-forward strategy is one category of network coding in which a relay will decode and forward a linear combination of source messages according to the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	167,016
2202.13664	Neural Adaptive SCEne Tracing	Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost. While the most recent generation of such methods has made progress on the rendering (inference) times, very little progress has been m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,708
2206.02797	FedNST: Federated Noisy Student Training for Automatic Speech Recognition	Federated Learning (FL) enables training state-of-the-art Automatic Speech Recognition (ASR) models on user devices (clients) in distributed systems, hence preventing transmission of raw user data to a central server. A key challenge facing practical adoption of FL for ASR is obtaining ground-truth labels on the client...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	301,040
2303.17732	Optimal Input Gain: All You Need to Supercharge a Feed-Forward Neural Network	Linear transformation of the inputs alters the training performance of feed-forward networks that are otherwise equivalent. However, most linear transforms are viewed as a pre-processing operation separate from the actual training. Starting from equivalent networks, it is shown that pre-processing inputs using linear t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	355,323
1811.06437	Contextual Care Protocol using Neural Networks and Decision Trees	A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	113,525
2411.19064	Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph	Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial e...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	512,105
2305.16114	Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning	Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic -- scal...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	367,918
2305.17594	Fully Automatic Gym Exercises Recording: An IoT Solution	In recent years, working out in the gym has gotten increasingly more data-focused and many gym enthusiasts are recording their exercises to have a better overview of their historical gym activities and to make a better exercise plan for the future. As a side effect, this recording process has led to a lot of time spent...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	368,668
1406.5797	Constructions of A Large Class of Optimum Constant Weight Codes over F_2	A new method of constructing optimum constant weight codes over F_2 based on a generalized $(u, u+v)$ construction is presented. We present a new method of constructing superimposed code $C_{(s_1,s_2,\cdots,s_I)}^{(h_1, h_2, \cdots, h_I)}$ bound. and presented a large class of optimum constant weight codes over F_2 tha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,067
2203.11669	Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook	Our opinions and views of life can be shaped by how we perceive the opinions of others on social media like Facebook. This dependence has increased during COVID-19 periods when we have fewer means to connect with others. However, fake news related to COVID-19 has become a significant problem on Facebook. Bengali is the...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	286,995
2004.14356	AxCell: Automatic Extraction of Results from Machine Learning Papers	Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,857
2411.14375	Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!	Most reinforcement learning (RL) platforms use high-level programming languages, such as OpenAI Gymnasium using Python. These frameworks provide various API and benchmarks for testing RL algorithms in different domains, such as autonomous driving (AD) and robotics. These platforms often emphasise the design of RL algor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	510,133
2407.12138	Monocular pose estimation of articulated surgical instruments in open surgery	This work presents a novel approach to monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, symmetries, occlusions, and lack of annotated real-world data. The method leverages synthetic data generation and domain adaptation techniques to overcome thes...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	473,779
1805.08320	The Swarmathon: An Autonomous Swarm Robotics Competition	The Swarmathon is a swarm robotics programming challenge that engages college students from minority-serving institutions in NASA's Journey to Mars. Teams compete by programming a group of robots to search for, pick up, and drop off resources in a collection zone. The Swarmathon produces prototypes for robot swarms tha...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	98,102
2211.08016	Contextual Transformer for Offline Meta Reinforcement Learning	The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is still hindered by several challenges in Reinforcement Learning (RL), including the lack of self-supervised pretraining algorithms based on o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	330,450
1010.4876	Optimal Packet Scheduling on an Energy Harvesting Broadcast Link	The minimization of transmission completion time for a given number of bits per user in an energy harvesting communication system, where energy harvesting instants are known in an offline manner is considered. An achievable rate region with structural properties satisfied by the 2-user AWGN Broadcast Channel capacity r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,003
2104.08829	Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity	The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concept...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	false	231,005
2010.11634	Online Time-Varying Topology Identification via Prediction-Correction Algorithms	Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging tas...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	202,339
1907.05208	Explicitly Conditioned Melody Generation: A Case Study with Interdependent RNNs	Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional infor...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	138,299
1304.3443	The Estimation of Subjective Probabilities via Categorical Judgments of Uncertainty	Theoretically as well as experimentally it is investigated how people represent their knowledge in order to make decisions or to share their knowledge with others. Experiment 1 probes into the ways how people 6ather information about the frequencies of events and how the requested response mode, that is, numerical vs. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,882
2101.05645	Ensemble of LSTMs and feature selection for human action prediction	As robots are becoming more and more ubiquitous in human environments, it will be necessary for robotic systems to better understand and predict human actions. However, this is not an easy task, at times not even for us humans, but based on a relatively structured set of possible actions, appropriate cues, and the righ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	215,489
1909.03140	Geometry-Aware Video Object Detection for Static Cameras	In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video surveillance scenarios, and scene pseudo depth maps can therefore be inferred easily from the object scale on the image plane. We make the foll...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,381
2203.01303	An Analysis of Ensemble Sampling	Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem. Thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,330
0910.1014	Building upon Fast Multipole Methods to Detect and Model Organizations	Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help with the calc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	4,645
2204.06309	Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information	Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and th...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	291,303
1911.04634	Interference and Efficient Transmission Range via V2V Communication at Roads Traffic Intersections	Vehicle-to-Vehicle (V2V) communication technology has dramatically promoted many promising applications to enhance traffic safety, mobility, and sustainability. However, However, we still lack the understanding of some fundamental properties of V2V technology under urban traffic conditions, such as interference at traf...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	153,037
1702.07371	Feasibility of Principal Component Analysis in hand gesture recognition system	Nowadays actions are increasingly being handled in electronic ways, instead of physical interaction. From earlier times biometrics is used in the authentication of a person. It recognizes a person by using a human trait associated with it like eyes (by calculating the distance between the eyes) and using hand gestures,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,771
2204.13366	Semantic Information Recovery in Wireless Networks	Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and thus...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	293,794
2105.00101	Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization	The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this pape...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,085
2412.08651	Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection	Code-switching-where multilingual speakers alternately switch between languages during conversations-still poses significant challenges to end-to-end (E2E) automatic speech recognition (ASR) systems due to phenomena of both acoustic and semantic confusion. This issue arises because ASR systems struggle to handle the ra...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	516,191
2410.11100	Characterizing the MrDeepFakes Sexual Deepfake Marketplace	The prevalence of sexual deepfake material has exploded over the past several years. Attackers create and utilize deepfakes for many reasons: to seek sexual gratification, to harass and humiliate targets, or to exert power over an intimate partner. In part enabling this growth, several markets have emerged to support t...	true	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	498,382
cs/0609030	Space Division Multiple Access with a Sum Feedback Rate Constraint	On a multi-antenna broadcast channel, simultaneous transmission to multiple users by joint beamforming and scheduling is capable of achieving high throughput, which grows double logarithmically with the number of users. The sum rate for channel state information (CSI) feedback, however, increases linearly with the numb...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	539,674
1701.01094	Minimally-Supervised Attribute Fusion for Data Lakes	Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes, e.g., category, across diverse datasets from different geographies or retail chains, even after disparate data is technically ingested into...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	66,357
2304.13182	Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking	Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real-world problems. Additionally, we propose an effective scheme for clos...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	360,493
2002.05777	Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities	Combining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive predictors at the same time. Existing attempts uniting the two modeling approaches are, however, limited to very specific combinations and, m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,999
2406.10773	Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles	Large language models (LLMs) are increasingly being utilised across a range of tasks and domains, with a burgeoning interest in their application within the field of journalism. This trend raises concerns due to our limited understanding of LLM behaviour in this domain, especially with respect to political bias. Existi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	464,543

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