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
2207.05730	1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022	In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student netw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,643
2011.10407	Smart obervation method with wide field small aperture telescopes for real time transient detection	Wide field small aperture telescopes (WFSATs) are commonly used for fast sky survey. Telescope arrays composed by several WFSATs are capable to scan sky several times per night. Huge amount of data would be obtained by them and these data need to be processed immediately. In this paper, we propose ARGUS (Astronomical t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,503
2202.08675	Winograd Convolution: A Perspective from Fault Tolerance	Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than the computing efficiency, we observe its great potential in improving NN fault tolerance and evaluate its fault tolerance comprehensively f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	280,955
2401.17093	StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis	To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes. This paper posits that an alternative representation of images, vec...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	425,098
1506.03663	Design, Analysis and Evaluation of Control Algorithms for Applications in Smart Grids	In many countries, the currently observable transformation of the power supply system from a centrally controlled system towards a complex "system of systems", comprising lots of autonomously interacting components, leads to a significant amount of research regarding novel control concepts. To facilitate the structured...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	44,083
2207.03556	A Learn-and-Control Strategy for Jet-Based Additive Manufacturing	In this paper, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model's parameters are obtained by training the network through back propagation using inp...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	306,883
1703.04813	Learned Optimizers that Scale and Generalize	Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	69,956
1907.09735	Deep Learning Assisted User Identification in Massive Machine-Type Communications	In this paper, we propose a deep learning aided list approximate message passing (AMP) algorithm to further improve the user identification performance in massive machine type communications. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	139,462
2102.11137	Program Synthesis Guided Reinforcement Learning for Partially Observed Environments	A key challenge for reinforcement learning is solving long-horizon planning problems. Recent work has leveraged programs to guide reinforcement learning in these settings. However, these approaches impose a high manual burden on the user since they must provide a guiding program for every new task. Partially observed e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	221,338
1708.02146	Rank modulation codes for DNA storage	Synthesis of DNA molecules offers unprecedented advances in storage technology. Yet, the microscopic world in which these molecules reside induces error patterns that are fundamentally different from their digital counterparts. Hence, to maintain reliability in reading and writing, new coding schemes must be developed....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	78,528
cs/0209008	The partition semantics of questions, syntactically	Groenendijk and Stokhof (1984, 1996; Groenendijk 1999) provide a logically attractive theory of the semantics of natural language questions, commonly referred to as the partition theory. Two central notions in this theory are entailment between questions and answerhood. For example, the question "Who is going to the pa...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	537,695
2401.04312	Prompt-based Multi-interest Learning Method for Sequential Recommendation	Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existing methods mainly consist of a multi-interest extractor that embeds the multiple user interests based on the user interactions, and a multi-i...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	420,385
2411.07581	Semantic segmentation on multi-resolution optical and microwave data using deep learning	Presently, deep learning and convolutional neural networks (CNNs) are widely used in the fields of image processing, image classification, object identification and many more. In this work, we implemented convolutional neural network based modified U-Net model and VGG-UNet model to automatically identify objects from s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,595
1906.09313	A Cyclically-Trained Adversarial Network for Invariant Representation Learning	Recent studies show that deep neural networks are vulnerable to adversarial examples which can be generated via certain types of transformations. Being robust to a desired family of adversarial attacks is then equivalent to being invariant to a family of transformations. Learning invariant representations then naturall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,112
2006.09042	Fine-Tuning DARTS for Image Classification	Neural Architecture Search (NAS) has gained attraction due to superior classification performance. Differential Architecture Search (DARTS) is a computationally light method. To limit computational resources DARTS makes numerous approximations. These approximations result in inferior performance. We propose to fine-tun...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	182,406
2404.09016	Theoretical research on generative diffusion models: an overview	Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on the specific application areas without concentrating on the research about the a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	446,510
2408.01962	The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations	Calls to use open generative language models in academic research have highlighted the need for reproducibility and transparency in scientific research. However, the impact of generative AI extends well beyond academia, as corporations and public interest organizations have begun integrating these models into their dat...	true	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	true	478,440
2410.01943	CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL	In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-time compute in multi-agent modeling to improve candidate generation and selection. CHASE-SQL leverages LLMs' intrinsic knowledge to generate...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	494,021
1811.04067	Benefits of Coded Placement for Networks with Heterogeneous Cache Sizes	In this work, we study coded placement in caching systems where the users have unequal cache sizes and demonstrate its performance advantage. In particular, we propose a caching scheme with coded placement for three-user systems that outperforms the best caching scheme with uncoded placement. In our proposed scheme, us...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	112,988
2011.07044	Tactile SLAM: Real-time inference of shape and pose from planar pushing	Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	206,423
2407.12528	On the Complexity of Identification in Linear Structural Causal Models	Learning the unknown causal parameters of a linear structural causal model is a fundamental task in causal analysis. The task, known as the problem of identification, asks to estimate the parameters of the model from a combination of assumptions on the graphical structure of the model and observational data, represente...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	473,970
1405.1782	When are dynamic relaying strategies necessary in half-duplex wireless networks?	We study a simple question: when are dynamic relaying strategies essential in optimizing the diversity-multiplexing tradeoff (DMT) in half-duplex wireless relay networks? This is motivated by apparently two contrasting results even for a simple 3 node network, with a single half-duplex relay. When all channels are assu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,912
2202.12642	New Antenna Selection Schemes for Full-Duplex Cooperative MIMO-NOMA Systems	In this paper, we address the antenna selection (AS) problem in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay bridges the connection between the multi-antenna base station and NOMA far user. Specifically, two AS schemes, namely max-$\SUu$ and max-$\SUuu$, are...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	282,318
2406.12428	PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems	Multimodal language models that process both text and speech have a potential for applications in spoken dialogue systems. However, current models face two major challenges in response generation latency: (1) generating a spoken response requires the prior generation of a written response, and (2) speech sequences are ...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	465,402
2306.05857	How Sparse Can We Prune A Deep Network: A Fundamental Limit Viewpoint	Network pruning is a commonly used measure to alleviate the storage and computational burden of deep neural networks. However, the fundamental limit of network pruning is still lacking. To close the gap, in this work we'll take a first-principles approach, i.e. we'll directly impose the sparsity constraint on the loss ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,360
2211.14343	Less Data, More Knowledge: Building Next Generation Semantic Communication Networks	Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a s...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	true	332,791
2201.00929	Designing for Robustness in Electric Grids via a General Effective Resistance Measure	We propose a mathematical framework for designing robust networks of coupled phase-oscillators by leveraging a vulnerability measure proposed by Tyloo et. al that quantifies how much a small perturbation to a phase-oscillator's natural frequency impacts the system's global synchronized frequencies. Given a fixed comple...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	274,095
2408.15020	Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection	Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,770
1209.5039	Creation of Digital Test Form for Prepress Department	The main problem in colour management in prepress department is lack of availability of literature on colour management and knowledge gap between prepress department and press department. So a digital test from has been created by Adobe Photoshop to analyse the ICC profile and to create a new profile and this analysed ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	18,697
1601.06296	Think before you collect: Setting up a data collection approach for social media studies	This chapter discusses important challenges of designing the data collection setup for social media studies. It outlines how it is necessary to carefully think about which data to collect and to use, and to recognize the effects that a specific data collection approach may have on the types of analyses that can be carr...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	51,255
2308.04086	Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation	Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video pl...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	384,289
1411.7149	A Fuzzy Syllogistic Reasoning Schema for Generalized Quantifiers	In this paper, a new approximate syllogistic reasoning schema is described that expands some of the approaches expounded in the literature into two ways: (i) a number of different types of quantifiers (logical, absolute, proportional, comparative and exception) taken from Theory of Generalized Quantifiers and similarit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	37,899
2501.19153	Test-Time Training Scaling for Chemical Exploration in Drug Design	Chemical language models for molecular design have the potential to find solutions to multi-parameter optimization problems in drug discovery via reinforcement learning (RL). A key requirement to achieve this is the capacity to "search" chemical space to identify all molecules of interest. Here, we propose a challengin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,034
2012.04023	The Spectral-Domain $\mathcal{W}_2$ Wasserstein Distance for Elliptical Processes and the Spectral-Domain Gelbrich Bound	In this short note, we introduce the spectral-domain $\mathcal{W}_2$ Wasserstein distance for elliptical stochastic processes in terms of their power spectra. We also introduce the spectral-domain Gelbrich bound for processes that are not necessarily elliptical.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,317
2305.19157	Sensor Fault Detection and Compensation with Performance Prescription for Robotic Manipulators	This paper focuses on sensor fault detection and compensation for robotic manipulators. The proposed method features a new adaptive observer and a new terminal sliding mode control law established on a second-order integral sliding surface. The method enables sensor fault detection without the need to know the bounds o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	369,400
2412.07787	Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis	Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex gen...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true	515,808
2403.17852	Counterfactual Fairness through Transforming Data Orthogonal to Bias	Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite substantial research on counterfactual fairness, methods to reduce the impact of multiv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	441,659
2301.01821	Parameter-Efficient Fine-Tuning Design Spaces	Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether certain design patterns exi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	339,333
2208.08883	Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces	We propose a data-driven method for controlling the frequency and convergence rate of black-box nonlinear dynamical systems based on the Koopman operator theory. With the proposed method, a policy network is trained such that the eigenvalues of a Koopman operator of controlled dynamics are close to the target eigenvalu...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	313,521
2409.18330	DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors	Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,196
1702.01187	Named Entity Evolution Recognition on the Blogosphere	Advancements in technology and culture lead to changes in our language. These changes create a gap between the language known by users and the language stored in digital archives. It affects user's possibility to firstly find content and secondly interpret that content. In previous work we introduced our approach for N...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	67,764
2208.14738	Scatter Points in Space: 3D Detection from Multi-view Monocular Images	3D object detection from monocular image(s) is a challenging and long-standing problem of computer vision. To combine information from different perspectives without troublesome 2D instance tracking, recent methods tend to aggregate multiview feature by sampling regular 3D grid densely in space, which is inefficient. I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,407
2410.01335	Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models	Model merging, such as model souping, is the practice of combining different models with the same architecture together without further training. In this work, we present a model merging methodology that addresses the difficulty of fine-tuning Large Language Models (LLMs) for target tasks in non-English languages, wher...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	493,723
1903.03269	A Deep Generative Model of Speech Complex Spectrograms	This paper proposes an approach to the joint modeling of the short-time Fourier transform magnitude and phase spectrograms with a deep generative model. We assume that the magnitude follows a Gaussian distribution and the phase follows a von Mises distribution. To improve the consistency of the phase values in the time...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	123,689
1605.07363	Spatio-Temporal Image Boundary Extrapolation	Boundary prediction in images as well as video has been a very active topic of research and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on predicting boundaries for observed frames, our work aims at predicting boundaries ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,288
2310.08751	Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints	Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization. One popular approach to handling these complex scenarios i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,512
cs/0103012	Meaning Sort - Three examples: dictionary construction, tagged corpus construction, and information presentation system	It is often useful to sort words into an order that reflects relations among their meanings as obtained by using a thesaurus. In this paper, we introduce a method of arranging words semantically by using several types of `{\sf is-a}' thesauri and a multi-dimensional thesaurus. We also describe three major applications ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,303
2204.13635	SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion	Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,884
1501.02728	Minimal paths between communities induced by geographical networks	In this work we investigate the betweenness centrality in geographical networks and its relationship with network communities. We show that vertices with large betweenness define what we call characteristic betweenness paths in both modeled and real-world geographical networks. We define a geographical network model th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	39,213
1810.12136	Phase Harmonic Correlations and Convolutional Neural Networks	A major issue in harmonic analysis is to capture the phase dependence of frequency representations, which carries important signal properties. It seems that convolutional neural networks have found a way. Over time-series and images, convolutional networks often learn a first layer of filters which are well localized i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,690
2302.04360	Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation	Rearrangement-based nonprehensile manipulation still remains as a challenging problem due to the high-dimensional problem space and the complex physical uncertainties it entails. We formulate this class of problems as a coupled problem of local rearrangement and global action optimization by incorporating free-space tr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	344,666
1910.04919	From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images	Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a chall...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	148,904
2305.03835	Spatiotemporal Transformer for Stock Movement Prediction	Financial markets are an intriguing place that offer investors the potential to gain large profits if timed correctly. Unfortunately, the dynamic, non-linear nature of financial markets makes it extremely hard to predict future price movements. Within the US stock exchange, there are a countless number of factors that ...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	362,525
2108.02572	SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis	Deep learning has achieved great success in a wide spectrum of multimedia applications such as image classification, natural language processing and multimodal data analysis. Recent years have seen the development of many deep learning frameworks that provide a high-level programming interface for users to design model...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	249,376
2406.11147	Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG	Vulnerability detection is essential for software quality assurance. In recent years, deep learning models (especially large language models) have shown promise in vulnerability detection. In this work, we propose a novel LLM-based vulnerability detection technique Vul-RAG, which leverages knowledge-level retrieval-aug...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	464,739
1811.07253	Quantifying Uncertainties in Natural Language Processing Tasks	Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	113,720
2308.16739	Parsing is All You Need for Accurate Gait Recognition in the Wild	Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait repre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,094
2208.01220	GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction	There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,106
2112.06879	Multi-Robot On-site Shared Analytics Information and Computing	Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or internet may be nonexistent. In this paper we...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	271,319
2501.03700	AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features	Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images. Existing methods often depend on external depth estimators or expensive sensors, which increase computational complexity and hinder real-time performance. To overcome these limi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	522,968
2111.05175	Area Rate Efficiency in Multi-Link Molecular Communications	We consider a multi-link diffusion-based molecular communication (MC) system where multiple spatially distributed transmitter (TX)-receiver (RX) pairs establish point-to-point communication links employing the same type of signaling molecules. To exploit the full potential of such a system, an in-depth understanding of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	265,714
2409.13787	Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification	With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have been shown to achieve high accuracy. However, most of these models are trained using labeled data from seen domains. It is difficult for these models to mainta...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	490,172
1310.4349	An Improved Majority-Logic Decoder Offering Massively Parallel Decoding for Real-Time Control in Embedded Systems	We propose an easy-to-implement hard-decision majority-logic decoding algorithm for Reed-Muller codes RM(r,m) with m >= 3, m/2 >= r >= 1. The presented algorithm outperforms the best known majority-logic decoding algorithms and offers highly parallel decoding. The result is of special importance for safety- and time-cr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	27,809
2305.01443	Scalable Mask Annotation for Video Text Spotting	Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames. However, current datasets available for this task rely on quadrilateral ground truth annotations, which may result in including ex...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	361,675
2402.00996	mmID: High-Resolution mmWave Imaging for Human Identification	Achieving accurate human identification through RF imaging has been a persistent challenge, primarily attributed to the limited aperture size and its consequent impact on imaging resolution. The existing imaging solution enables tasks such as pose estimation, activity recognition, and human tracking based on deep neura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,806
2212.02753	Safe Inverse Reinforcement Learning via Control Barrier Function	Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to l...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	334,873
2103.10951	Paint by Word	We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	225,613
2206.07327	Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition	Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems designed for normal speech. Their practical application to atypical task domains such as elderly and disordered speech across languages is often limited by...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	302,700
1304.6736	Networks in Cognitive Science	Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural net- works to spreading activation models of semantic mem- ory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as d...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,196
2008.06995	Efficient Knowledge Graph Validation via Cross-Graph Representation Learning	Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	191,963
2310.08529	GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models	In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their quality and generalization are limited as trainable 3D data is expensive and hard to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	399,420
2203.08472	Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects	In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. This task contrasts with the one considered by most existing deep learning methods which typically assume that the testing objects have been observed during training. To handle the unseen objects, we f...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	285,804
2310.09495	Learning In-between Imagery Dynamics via Physical Latent Spaces	We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps. The complex nature of image data and the lack of temporal information pose significant challenges in capturing the unique evolving patterns. Our proposed method focuses on estimating the intermediary ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	399,800
2307.13055	MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning	In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data. This scenario is particularly challenging because graph neural networks (GNNs) have been shown to be sensitive to distributional shifts, even when labels are available. To address this c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	381,464
2406.01928	History-Aware Planning for Risk-free Autonomous Navigation on Unknown Uneven Terrain	It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This structure unifies the p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	460,528
2407.01574	cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM	The three-dimensional structure of a protein plays a key role in determining its function. Methods like AlphaFold have revolutionized protein structure prediction based only on the amino-acid sequence. However, proteins often appear in multiple different conformations, and it is highly relevant to resolve the full conf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	469,366
1704.04347	Exploiting Cross-Sentence Context for Neural Machine Translation	In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is sum...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,794
2011.12715	Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication	Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	208,251
2412.04898	Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement	Deep learning models rely heavily on large volumes of labeled data to achieve high performance. However, real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process. Instance-dependent label noise (IDN), where the probability of a label being corru...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	514,619
1903.07744	A Geometrical Method for Low-Dimensional Representations of Simulations	We propose a new data analysis approach for the efficient post-processing of bundles of finite element data from numerical simulations. The approach is based on the mathematical principles of symmetry. We consider the case where simulations of an industrial product are contained in the space of surface meshes embedde...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	124,682
2110.03562	Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions	We introduce the task of weakly supervised learning for detecting human and object interactions in videos. Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and the object. To address these challen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	259,545
1912.04479	Homograph Disambiguation Through Selective Diacritic Restoration	Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Omitting diacritics leads to an increase in the number of homographs: different words with the same spelling. Diacritic restoration could theo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	156,854
2012.01944	Multi-Label Contrastive Learning for Abstract Visual Reasoning	For a long time the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in application of deep learning (DL) methods led, as in many other domains, to surpassing human abstract reasoning performance, specifically in the most popular type of such problems ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	209,590
2006.09554	Isometric Graph Neural Networks	Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph. Geometric techniques to extract such representations have poor scaling over large graph size, and recent advances in Graph Neural Network (GNN) algorithms have limited...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,581
2201.01349	RASS: Risk-Aware Swarm Storage	In robotics, data acquisition often plays a key part in unknown environment exploration. For example, storing information about the topography of the explored terrain or the natural dangers in the environment can inform the decision-making process of the robots. Therefore, it is crucial to store these data safely and t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	274,219
2108.09615	Apache Submarine: A Unified Machine Learning Platform Made Simple	As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity. However, existing machine learning platforms are ill-equipped to address the "Machi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	251,659
1607.08434	Video Registration in Egocentric Vision under Day and Night Illumination Changes	With the spread of wearable devices and head mounted cameras, a wide range of application requiring precise user localization is now possible. In this paper we propose to treat the problem of obtaining the user position with respect to a known environment as a video registration problem. Video registration, i.e. the ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,159
2309.09470	Face-Driven Zero-Shot Voice Conversion with Memory-based Face-Voice Alignment	This paper presents a novel task, zero-shot voice conversion based on face images (zero-shot FaceVC), which aims at converting the voice characteristics of an utterance from any source speaker to a newly coming target speaker, solely relying on a single face image of the target speaker. To address this task, we propose...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	392,622
2210.12910	Specializing Multi-domain NMT via Penalizing Low Mutual Information	Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An ideal multi-domain NMT should learn distinctive domain characteristics simultaneously, however, grasping the domain peculiarity is a non-tr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	325,962
2403.20287	Benchmarking Counterfactual Image Generation	Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data generation process. S...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	442,693
1105.5307	Efficient Learning of Sparse Invariant Representations	We propose a simple and efficient algorithm for learning sparse invariant representations from unlabeled data with fast inference. When trained on short movies sequences, the learned features are selective to a range of orientations and spatial frequencies, but robust to a wide range of positions, similar to complex ce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	10,508
1806.05024	Self-Supervised Feature Learning by Learning to Spot Artifacts	We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts, and then to extract features from its intermediate layers that can be transferred to other data domains and tasks. To gen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,370
1910.10900	UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands	To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand. We propose UniGrasp, an effici...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	150,624
1409.3821	Computational Implications of Reducing Data to Sufficient Statistics	Given a large dataset and an estimation task, it is common to pre-process the data by reducing them to a set of sufficient statistics. This step is often regarded as straightforward and advantageous (in that it simplifies statistical analysis). I show that -on the contrary- reducing data to sufficient statistics can ch...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	36,008
2402.04668	A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data	The burden of diseases is rising worldwide, with unequal treatment efficacy for patient populations that are underrepresented in clinical trials. Healthcare, however, is driven by the average population effect of medical treatments and, therefore, operates in a "one-size-fits-all" approach, not necessarily what best fi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,551
2206.07658	Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers	We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,828
1702.07545	Optimal Energy Beamforming under Per-Antenna Power Constraint	Energy beamforming (EB) is a key technique to enhance the efficiency of wireless power transfer (WPT). In this paper, we study the optimal EB under per-antenna power constraint (PAC) which is more practical than the conventional sum-power constraint (SPC). We consider a multi antenna energy transmitter (ET) with PAC th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,805
1601.06859	Optimal Dynamic Routing for the Wireless Relay Channel	Consider a communication network with a source, a relay and a destination. Each time interval, the source may dynamically choose between a few possible coding schemes, based on the channel state, traffic pattern and its own queue status. For example, the source may choose between a direct route to the destination and a...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	51,346
1108.0342	Black-Box Complexities of Combinatorial Problems	Black-box complexity is a complexity theoretic measure for how difficult a problem is to be optimized by a general purpose optimization algorithm. It is thus one of the few means trying to understand which problems are tractable for genetic algorithms and other randomized search heuristics. Most previous work on blac...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	11,532
2410.21494	Towards Multi-dimensional Explanation Alignment for Medical Classification	The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, as well as issues related to efficienc...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	503,269

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