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
2210.08473	1st Place Solution in Google Universal Images Embedding	This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to conduct training and fine-tuning; 2) The idea of a better ensemble in the pool of models that make embedding; 3) The potential trade-off between ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,164
1106.5594	The Swiss Board Directors Network in 2009	We study the networks formed by the directors of the most important Swiss boards and the boards themselves for the year 2009. The networks are obtained by projection from the original bipartite graph. We highlight a number of important statistical features of those networks such as degree distribution, weight distribut...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,043
1906.06050	Neural Response Generation with Meta-Words	We present open domain response generation with meta-words. A meta-word is a structured record that describes various attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	135,199
2309.15259	SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers	Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this cha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	394,899
1511.05219	How much does your data exploration overfit? Controlling bias via information usage	Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next dep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	49,012
2404.09203	Advanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review	This review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) in enhancing modern power systems. It delves into the challenges posed by the integration of renewables, smart grids, and increasing energy demands, focusing on evolutionary algorithms, swarm intelli...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	446,575
2408.03394	Faster Model Predictive Control via Self-Supervised Initialization Learning	Optimization for robot control tasks, spanning various methodologies, includes Model Predictive Control (MPC). However, the complexity of the system, such as non-convex and non-differentiable cost functions and prolonged planning horizons often drastically increases the computation time, limiting MPC's real-world appli...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	478,995
2304.10642	Word Sense Induction with Knowledge Distillation from BERT	Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	359,494
2410.17715	Continual Learning on a Data Diet	Continual Learning (CL) methods usually learn from all available data. However, this is not the case in human cognition which efficiently focuses on key experiences while disregarding the redundant information. Similarly, not all data points in a dataset have equal potential; some can be more informative than others. T...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	501,584
1501.02246	The Effect of Wedge Tip Angles on Stress Intensity Factors in the Contact Problem between Tilted Wedge and a Half Plane with an Edge Crack Using Digital Image Correlation	The first and second mode stress intensity factors (SIFs) of a contact problem between a half-plane with an edge crack and an asymmetric tilted wedge were obtained using experimental method of Digital Image Correlation (DIC). In this technique, displacement and strain fields can be measured using two digital images of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,160
2304.00111	Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing	Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	355,558
2012.14970	Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments	In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applications are often highl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	213,657
1701.08718	Memory Augmented Neural Networks with Wormhole Connections	Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the ef...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	67,510
2502.04795	Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition	Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period, a stage when human language acquisition is particularly efficient, into the training ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	531,325
1403.7123	User Cooperation in Wireless Powered Communication Networks	This paper studies user cooperation in the emerging wireless powered communication network (WPCN) for throughput optimization. For the purpose of exposition, we consider a two-user WPCN, in which one hybrid access point (H-AP) broadcasts wireless energy to two distributed users in the downlink (DL) and the users transm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,873
2402.02690	Competitive Equilibrium in Microgrids With Dynamic Loads	In this paper, we consider microgrids that interconnect prosumers with distributed energy resources and dynamic loads. Prosumers are connected through the microgrid to trade energy and gain profit while respecting the network constraints. We establish a local energy market by defining a competitive equilibrium which ba...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	426,685
1711.05133	Reinforcement Learning in a large scale photonic Recurrent Neural Network	Photonic Neural Network implementations have been gaining considerable attention as a potentially disruptive future technology. Demonstrating learning in large scale neural networks is essential to establish photonic machine learning substrates as viable information processing systems. Realizing photonic Neural Network...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	84,503
2104.13659	Exploiting Degeneracy in Belief Propagation Decoding of Quantum Codes	Quantum information needs to be protected by quantum error-correcting codes due to imperfect physical devices and operations. One would like to have an efficient and high-performance decoding procedure for the class of quantum stabilizer codes. A potential candidate is Pearl's belief propagation (BP), but its performan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	232,573
1811.12819	Structure-Preserving Constrained Optimal Trajectory Planning of a Wheeled Inverted Pendulum	The Wheeled Inverted Pendulum (WIP) is an underactuated, nonholonomic mechatronic system, and has been popularized commercially as the Segway. Designing a control law for motion planning, that incorporates the state and control constraints, while respecting the configuration manifold, is a challenging problem. In this ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	115,111
2307.06555	Deep Network Approximation: Beyond ReLU to Diverse Activation Functions	This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{CE...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	379,104
1904.11490	RepPoints: Point Set Representation for Object Detection	Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of objec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,872
2312.17076	Minimally-intrusive Navigation in Dense Crowds with Integrated Macro and Micro-level Dynamics	In mobile robot navigation, despite advancements, the generation of optimal paths often disrupts pedestrian areas. To tackle this, we propose three key contributions to improve human-robot coexistence in shared spaces. Firstly, we have established a comprehensive framework to understand disturbances at individual and f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	418,614
1808.09933	Certified Mapper: Repeated testing for acyclicity and obstructions to the nerve lemma	The Mapper algorithm does not include a check for whether the cover produced conforms to the requirements of the nerve lemma. To perform a check for obstructions to the nerve lemma, statistical considerations of multiple testing quickly arise. In this paper, we propose several statistical approaches to finding obstru...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,297
2110.03170	TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network	Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have shown promising results for tasks like 3D shape classification and segmentation. This work proposes a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	259,393
1911.08794	Natural Language Generation Challenges for Explainable AI	Good quality explanations of artificial intelligence (XAI) reasoning must be written (and evaluated) for an explanatory purpose, targeted towards their readers, have a good narrative and causal structure, and highlight where uncertainty and data quality affect the AI output. I discuss these challenges from a Natural La...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	154,312
2105.12021	Inner Approximations of the Positive-Semidefinite Cone via Grassmannian Packings	We investigate the problem of finding inner ap-proximations of positive semidefinite (PSD) cones. We developa novel decomposition framework of the PSD cone by meansof conical combinations of smaller dimensional sub-cones. Weshow that many inner approximation techniques could besummarized within this framework, includin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	236,889
2306.02268	Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection	Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. However, many of these methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. Furthermore, in the literature, it has been observed ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	370,825
2111.06980	GraSSNet: Graph Soft Sensing Neural Networks	In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Mo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	266,237
2307.02007	Remote Sensing Image Change Detection with Graph Interaction	Modern remote sensing image change detection has witnessed substantial advancements by harnessing the potent feature extraction capabilities of CNNs and Transforms.Yet,prevailing change detection techniques consistently prioritize extracting semantic features related to significant alterations,overlooking the viability...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	377,550
2002.08538	Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems	We consider the problem of learning stabilizable systems governed by nonlinear state equation $h_{t+1}=\phi(h_t,u_t;\theta)+w_t$. Here $\theta$ is the unknown system dynamics, $h_t $ is the state, $u_t$ is the input and $w_t$ is the additive noise vector. We study gradient based algorithms to learn the system dynamics ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	164,785
2102.13493	ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation	Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have demonstrated remarkable results by adopting two-stream or 3D CNN architectures. However, these methods typically operate in a non-real-time...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,079
2308.08459	Knowledge Prompt-tuning for Sequential Recommendation	Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this i...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	385,909
2401.03337	MTAC: Hierarchical Reinforcement Learning-based Multi-gait Terrain-adaptive Quadruped Controller	Urban search and rescue missions require rapid first response to minimize loss of life and damage. Often, such efforts are assisted by humanitarian robots which need to handle dynamic operational conditions such as uneven and rough terrains, especially during mass casualty incidents like an earthquake. Quadruped robots...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	420,065
2112.07242	On the Impact of Channel Estimation on the Design and Analysis of IRSA based Systems	Irregular repetition slotted aloha (IRSA) is a distributed grant-free random access protocol where users transmit multiple replicas of their packets to a base station (BS). The BS recovers the packets using successive interference cancellation. In this paper, we first derive channel estimates for IRSA, exploiting the s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	271,423
2109.05113	Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics	Generating stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This paper presents an alternative methodology, where we propose the addition of musculoskeletal models directly into the gait generation proc...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,662
0803.1600	Understanding Retail Productivity by Simulating Management Practise	Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail producti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	1,423
2405.12856	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language	Machine learning practitioners often face significant challenges in formally integrating their prior knowledge and beliefs into predictive models, limiting the potential for nuanced and context-aware analyses. Moreover, the expertise needed to integrate this prior knowledge into probabilistic modeling typically limits ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	455,677
1704.01891	On Multi-source Networks: Enumeration, Rate Region Computation, and Hierarchy	Recent algorithmic developments have enabled computers to automatically determine and prove the capacity regions of small hypergraph networks under network coding. A structural theory relating network coding problems of different sizes is developed to make best use of this newfound computational capability. A formal no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,342
2005.02160	Printing and Scanning Attack for Image Counter Forensics	Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks, ranging from simple double J...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	175,792
2409.11516	Learning-Augmented Frequency Estimation in Sliding Windows	We show how to utilize machine learning approaches to improve sliding window algorithms for approximate frequency estimation problems, under the ``algorithms with predictions'' framework. In this dynamic environment, previous learning-augmented algorithms are less effective, since properties in sliding window resolutio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	489,189
2209.11748	GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation	Robots have been used in all sorts of automation, and yet the design of robots remains mainly a manual task. We seek to provide design tools to automate the design of robots themselves. An important challenge in robot design automation is the large and complex design search space which grows exponentially with the numb...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	319,283
2011.12131	A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces	The design and implementation of low-profile antennas has been analyzed in past decades from different perspectives while the purpose is to have a small size in the device, and an adequate electromagnetic behavior. This work pursues a methodology to identify small antennas and consequently presents some similarities. M...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,067
1701.06884	Combination Networks with End-user-caches: Novel Achievable and Converse Bounds under Uncoded Cache Placement	Caching is an efficient way to reduce network traffic congestion during peak hours by storing some content at the users' local caches. For the shared-link network with end-user-caches, Maddah-Ali and Niesen proposed a two-phase coded caching strategy. In practice, users may communicate with the server through intermedi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,209
2312.05021	A Negative Result on Gradient Matching for Selective Backprop	With increasing scale in model and dataset size, the training of deep neural networks becomes a massive computational burden. One approach to speed up the training process is Selective Backprop. For this approach, we perform a forward pass to obtain a loss value for each data point in a minibatch. The backward pass is ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	413,921
2003.02341	QED: using Quality-Environment-Diversity to evolve resilient robot swarms	In swarm robotics, any of the robots in a swarm may be affected by different faults, resulting in significant performance declines. To allow fault recovery from randomly injected faults to different robots in a swarm, a model-free approach may be preferable due to the accumulation of faults in models and the difficulty...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	166,910
2411.17406	CoA: Chain-of-Action for Generative Semantic Labels	Recent advances in vision-language models (VLM) have demonstrated remarkable capability in image classification. These VLMs leverage a predefined set of categories to construct text prompts for zero-shot reasoning. However, in more open-ended domains like autonomous driving, using a predefined set of labels becomes imp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,430
2412.11711	MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning	Extensive research has been conducted to explore the capability of Large Language Models (LLMs) for table reasoning and has significantly improved the performance on existing benchmarks. However, tables and user questions in real-world applications are more complex and diverse, presenting an unignorable gap compared to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,548
2411.04445	Large Sets of Quasi-Complementary Sequences From Polynomials over Finite Fields and Gaussian Sums	Perfect complementary sequence sets (PCSSs) are widely used in multi-carrier code-division multiple-access (MC-CDMA) communication systems. However, the set size of a PCSS is upper bounded by the number of row sequences of each two-dimensional matrix in the PCSS. Then quasi-complementary sequence sets (QCSSs) were prop...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	506,272
2310.04536	Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes	This work extends a previous work in regime detection, which allowed trading positions to be profitably adjusted when a new regime was detected, to ex ante prediction of regimes, leading to substantial performance improvements over the earlier model, over all three asset classes considered (equities, commodities, and f...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	397,700
2306.13325	Differentiable Display Photometric Stereo	Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo, which employs a conventional monitor as an illumination source, has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	375,242
2009.11649	Prescribed-Time Fully Distributed Nash Equilibrium Seeking in Noncooperative Games	In this paper, we investigate a prescribed-time and fully distributed Nash Equilibrium (NE) seeking problem for continuous-time noncooperative games. By exploiting pseudo-gradient play and consensus-based schemes, various distributed NE seeking algorithms are presented over either fixed or switching communication topol...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,221
2412.04703	Transformers Struggle to Learn to Search	Search is an ability foundational in many important tasks, and recent studies have shown that large language models (LLMs) struggle to perform search robustly. It is unknown whether this inability is due to a lack of data, insufficient model parameters, or fundamental limitations of the transformer architecture. In thi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	514,522
1905.00495	A Modular Framework for Motion Planning using Safe-by-Design Motion Primitives	We present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. A...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	129,480
2212.06250	ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes	The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data. In this paper, we curate a large-scale and complementary dataset extending both the aforementioned ones by associating all objects mentioned in a referential sentence to their underlying instances inside a 3D scene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	336,045
2110.07235	HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media	We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and $2$ male professional actors performing various full-body movements and expressi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	260,911
1307.5827	Cooperative Energy Harvesting Networks with Spatially Random Users	This paper considers a cooperative network with multiple source-destination pairs and one energy harvesting relay. The outage probability experienced by users in this network is characterized by taking the spatial randomness of user locations into consideration. In addition, the cooperation among users is modeled as a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,982
2007.00886	Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds	Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent parad...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	185,257
2205.01414	Multimodal Detection of Unknown Objects on Roads for Autonomous Driving	Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomou...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	294,573
2203.04967	UNeXt: MLP-based Rapid Medical Image Segmentation Network	UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-heavy, computationally complex and slow to use. To this end, we p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,670
2111.15182	Easy Semantification of Bioassays	Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling ve...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	268,849
2111.05409	Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras	In this paper, we propose a novel pipeline for the 3D reconstruction of the full body from egocentric viewpoints. 3-D reconstruction of the human body from egocentric viewpoints is a challenging task as the view is skewed and the body parts farther from the cameras are occluded. One such example is the view from camera...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	265,781
1511.05297	On the interplay of network structure and gradient convergence in deep learning	The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied. However, our understanding of how the asymptotic convergence of backpropagation in deep architectures is related to the structural properties of the network and other design...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	49,031
2209.10163	DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation	Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverag...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	318,773
1603.06216	Skew-t inference with improved covariance matrix approximation	Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our earlier proposed filter and smoother using the mean field variational Bayes approximation of the posterior distribution to a skew-t likelihood ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	53,465
2501.14660	Mean-field limit from general mixtures of experts to quantum neural networks	In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main result establishes the propagation of chaos for a MoE as the number of experts diverges. We demonstrate that the corresponding empirical measure of their parameters is close to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,206
2110.09994	DPFM: Deep Partial Functional Maps	We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain, given hand-crafted shape descriptors. In this paper, we propose the first learning method aim...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	261,999
0706.3295	Lower bounds on the minimum average distance of binary codes	New lower bounds on the minimum average Hamming distance of binary codes are derived. The bounds are obtained using linear programming approach.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	349
2404.02257	SnAG: Scalable and Accurate Video Grounding	Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalability -- they have been optimized for grounding only a few text queries within short videos, and fail to scale up to long videos with hundreds...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,781
2406.05986	Neural-g: A Deep Learning Framework for Mixing Density Estimation	Mixing (or prior) density estimation is an important problem in machine learning and statistics, especially in empirical Bayes $g$-modeling where accurately estimating the prior is necessary for making good posterior inferences. In this paper, we propose neural-$g$, a new neural network-based estimator for $g$-modeling...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,384
2010.10047	Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods	Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler method of numerical discretization. From the numerical discretization perspective, Strong Stability Preserving (SSP) methods are more advanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,766
1608.01859	Wireless-Powered Two-Way Relaying with Power Splitting-based Energy Accumulation	This paper investigates a wireless-powered two-way relay network (WP-TWRN), in which two sources exchange information with the aid of one amplify-and-forward (AF) relay. Contrary to the conventional two-way relay networks, we consider the scenario that the AF relay has no embedded energy supply, and it is equipped with...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	59,477
2203.14940	Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model	Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,182
2410.21326	Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease	Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring technologies are power-hungry, rely on extensive amounts of labeled data, and operate ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	503,204
2410.20469	Graph Neural Networks on Discriminative Graphs of Words	In light of the recent success of Graph Neural Networks (GNNs) and their ability to perform inference on complex data structures, many studies apply GNNs to the task of text classification. In most previous methods, a heterogeneous graph, containing both word and document nodes, is constructed using the entire corpus a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,824
2407.21029	Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification	To advance formal verification of stochastic systems against temporal logic requirements for handling unknown dynamics, researchers have been designing data-driven approaches inspired by breakthroughs in the underlying machine learning techniques. As one promising research direction, abstraction-based solutions based o...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	477,365
2308.07615	Self-supervised Hypergraphs for Learning Multiple World Interpretations	We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task hypergraph. We also show how we can use the hypergraph to improve a powerful pretrained VisTransformer model without any additional labeled ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	385,586
2306.17514	A behaviouristic approach to representing processes and procedures in the OASIS 2 ontology	Foundational ontologies devoted to the effective representation of processes and procedures are not widely investigated at present, thereby limiting the practical adoption of semantic approaches in real scenarios where the precise instructions to follow must be considered. Also, the representation ought to include how ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	376,729
1506.05636	Translational and Scaling Formation Maneuver Control via a Bearing-Based Approach	This paper studies distributed maneuver control of multi-agent formations in arbitrary dimensions. The objective is to control the translation and scale of the formation while maintaining the desired formation pattern. Unlike conventional approaches where the target formation is defined by relative positions or distanc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	44,322
2001.11981	Lifted Reed-Solomon Codes with Application to Batch Codes	Guo, Kopparty and Sudan have initiated the study of error-correcting codes derived by lifting of affine-invariant codes. Lifted Reed-Solomon (RS) codes are defined as the evaluation of polynomials in a vector space over a field by requiring their restriction to every line in the space to be a codeword of the RS code. I...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,215
2409.18139	Robust optimization and uncertainty quantification in the nonlinear mechanics of an elevator brake system	This paper deals with nonlinear mechanics of an elevator brake system subjected to uncertainties. A deterministic model that relates the braking force with uncertain parameters is deduced from mechanical equilibrium conditions. In order to take into account parameters variabilities, a parametric probabilistic approach ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	492,121
1802.07370	SufiSent - Universal Sentence Representations Using Suffix Encodings	Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a method to learn such representations by encoding the suffixes of word sequences in a sentence and training on the Stanford Natural Language Inference (SNLI) dataset. We demonstrate the effecti...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	90,875
2312.07753	Polynomial-based Self-Attention for Table Representation learning	Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these, Transformer-based ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	415,039
2312.02671	Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow	This paper presents a method for finding a sparse representation of Barron functions. Specifically, given an $L^2$ function $f$, the inverse scale space flow is used to find a sparse measure $\mu$ minimising the $L^2$ loss between the Barron function associated to the measure $\mu$ and the function $f$. The convergence...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	412,967
1902.07370	Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision	Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short sequences and require ground-truth annotations of each video frame/sequence, which is quite costly and time-consuming. In this paper, given only ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,966
2207.03620	More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity	Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,916
2007.05474	A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems	The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower le...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	186,691
2006.03531	A Meta-Bayesian Model of Intentional Visual Search	We propose a computational model of visual search that incorporates Bayesian interpretations of the neural mechanisms that underlie categorical perception and saccade planning. To enable meaningful comparisons between simulated and human behaviours, we employ a gaze-contingent paradigm that required participants to cla...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,340
2108.01338	Position Control and Variable-Height Trajectory Tracking of a Soft Pneumatic Legged Robot	Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomoti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	248,999
2210.12183	Weighted Coordinates Poset Block Codes	Given $[n]=\{1,2,\ldots,n\}$, a partial order $\preceq$ on $[n]$, a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i) = k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, the direct sum $ \mathbb{F}_{q}^{k_1} \oplus \mathbb{F}_{q}^{k_2}\oplus \ldots \oplus \mathbb{F}_{q}^{k_n} $ of $ \mathbb{F}_q^N $, and a weight fu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	325,617
2107.03648	Deep Learning Based Image Retrieval in the JPEG Compressed Domain	Content-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image representations i.e. local and global image features have been studied in the literature. Extracting these features from pixel images and compar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,217
2012.11101	ResizeMix: Mixing Data with Preserved Object Information and True Labels	Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks. Recent data mixing based augmentation strategies have achieved great success. Especially, CutMix uses a simple but effective method to impr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,529
1111.7170	REX: Explaining Relationships between Entity Pairs	Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft, and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	13,250
2309.04257	A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions	Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	390,662
1206.4952	Space-Efficient Sampling from Social Activity Streams	In order to efficiently study the characteristics of network domains and support development of network systems (e.g. algorithms, protocols that operate on networks), it is often necessary to sample a representative subgraph from a large complex network. Although recent subgraph sampling methods have been shown to work...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	16,758
2309.08739	Concept explainability for plant diseases classification	Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	392,299
2403.08943	LMStyle Benchmark: Evaluating Text Style Transfer for Chatbots	Since the breakthrough of ChatGPT, large language models (LLMs) have garnered significant attention in the research community. With the development of LLMs, the question of text style transfer for conversational models has emerged as a natural extension, where chatbots may possess their own styles or even characters. H...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	437,551
2406.00507	Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization	Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. Two strategies are designed to perform this iterative process: Prompt Chaining and Stepwise Prompt. Prompt chaining orchestrates ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	459,889
2102.13143	Robust Pollen Imagery Classification with Generative Modeling and Mixup Training	Deep learning approaches have shown great success in image classification tasks and can aid greatly towards the fast and reliable classification of pollen grain aerial imagery. However, often-times deep learning methods in the setting of natural images can suffer generalization problems and yield poor performance on un...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	221,959
1304.1097	A Randomized Approximation Algorithm of Logic Sampling	In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of exact probabilistic inference on belief n...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,450
2412.12230	The impact of AI on engineering design procedures for dynamical systems	Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniq...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	517,809

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