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
2006.09000	How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks	Explainable AI (XAI) aims to provide interpretations for predictions made by learning machines, such as deep neural networks, in order to make the machines more transparent for the user and furthermore trustworthy also for applications in e.g. safety-critical areas. So far, however, no methods for quantifying uncertain...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	182,387
2411.14125	RestorerID: Towards Tuning-Free Face Restoration with ID Preservation	Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradation is heavy. Current reference-guided face restoration approaches either require face alignment or personalized test-tuning, which are unfa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,049
2306.14017	A Cyber-HIL for Investigating Control Systems in Ship Cyber Physical Systems under Communication Issues and Cyber Attacks	This paper presents a novel Cyber-Hardware-in-the-Loop (Cyber-HIL) platform for assessing control operation in ship cyber-physical systems. The proposed platform employs cutting-edge technologies, including Docker containers, real-time simulator $OPAL-RT$, and network emulator $ns3$, to create a secure and controlled t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	375,497
1203.4626	Active sequential hypothesis testing	Consider a decision maker who is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration. Due to the sequential nature of the problem, the decision maker relies on his current info...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,050
1701.06181	The Optimality of Partial Clique Covering for Index Coding	Partial clique covering is one of the most basic coding schemes for index coding problems, generalizing clique and cycle covering on the side information digraph and further reducing the achievable broadcast rate. In this paper, we start with partition multicast, a special case of partial clique covering with cover num...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,082
2402.12368	A synthetic data approach for domain generalization of NLI models	Natural Language Inference (NLI) remains an important benchmark task for LLMs. NLI datasets are a springboard for transfer learning to other semantic tasks, and NLI models are standard tools for identifying the faithfulness of model-generated text. There are several large scale NLI datasets today, and models have impro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,818
2206.14069	Equivariant Priors for Compressed Sensing with Unknown Orientation	In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements. Thus, prior knowledge about the signal of interest and its structure is required. Additionally, in many scenarios, the signal has an unknown orientation prior to measurements. To address such recovery pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,167
1307.2482	Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms	We study distributed optimization where nodes cooperatively minimize the sum of their individual, locally known, convex costs $f_i(x)$'s, $x \in {\mathbb R}^d$ is global. Distributed augmented Lagrangian (AL) methods have good empirical performance on several signal processing and learning applications, but there is li...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,720
1906.09884	Channel-by-Channel Demosaicking Networks with Embedded Spectral Correlation	Demosaicking is standardly the first step in today's Image Signal Processing (ISP) pipeline of digital cameras. It reconstructs image RGB values from the spatially and spectrally sparse Color Filter Array (CFA) samples, which are the original raw data digitized from electrical signals. High quality and low cost demosai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	136,302
2004.09694	Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification	Skin disease classification from images is crucial to dermatological diagnosis. However, identifying skin lesions involves a variety of aspects in terms of size, color, shape, and texture. To make matters worse, many categories only contain very few samples, posing great challenges to conventional machine learning algo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,418
2209.06251	Data-Driven Gain Scheduling Control of Linear Parameter-Varying Systems using Quadratic Matrix Inequalities	This paper synthesizes a gain-scheduled controller to stabilize all possible Linear Parameter-Varying (LPV) plants that are consistent with measured input/state data records. Inspired by prior work in data informativity and LTI stabilization, a set of Quadratic Matrix Inequalities is developed to represent the noise se...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	317,330
2210.09723	Textual Entailment Recognition with Semantic Features from Empirical Text Representation	Textual entailment recognition is one of the basic natural language understanding(NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural language processing(NLP) techniques to automatically recognize the textual entailment. A text entails a hypothesis if and only if the true va...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	324,640
2406.03853	Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism	The recent advancements in large language models (LLMs) have been extraordinary, yet the escalating inference costs associated with them present challenges in real-world applications. To address these challenges, we propose a novel approach called Early-exiting Speculative Decoding (EESD) with lossless acceleration. Sp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	461,419
2502.06768	Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions	In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (ARMs), MDMs trade off complexity at training time with flexibility at inference time. At training time, they must learn to solve an exponenti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,229
2410.16668	Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling	Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,130
2307.03761	DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems	In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying conditions. These complex dynamics make fault detection particularly challenging. While previous methods effectively model these dynamics, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	378,143
1310.1221	Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model	Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In this paper we propose a novel method to provide a scalable encoding of an image acquired by means of compressed sensing techniques. Two bit-str...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	true	27,546
2404.10499	Robust Noisy Label Learning via Two-Stream Sample Distillation	Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selection or label correction to deal with noisy labels during the model training process. In this paper, we design a simple yet effective sample s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	447,128
2404.12045	RAM: Towards an Ever-Improving Memory System by Learning from Communications	We introduce an innovative RAG-based framework with an ever-improving memory. Inspired by humans'pedagogical process, RAM utilizes recursively reasoning-based retrieval and experience reflections to continually update the memory and learn from users' communicative feedback, namely communicative learning. Extensive expe...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	447,717
2104.07378	Tracking entities in technical procedures -- a new dataset and baselines	We introduce TechTrack, a new dataset for tracking entities in technical procedures. The dataset, prepared by annotating open domain articles from WikiHow, consists of 1351 procedures, e.g., "How to connect a printer", identifies more than 1200 unique entities with an average of 4.7 entities per procedure. We evaluate ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	230,394
1808.04560	Deep Retinex Decomposition for Low-Light Enhancement	Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-based methods have carefully designed hand-crafted constraints and parameters for this highly ill-posed decomposition, which may be limited b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,182
1804.09521	Fair Division Under Cardinality Constraints	We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of the entire set of goods---i.e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each ca...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	95,987
0904.4542	A Generalized Cut-Set Bound	In this paper, we generalize the well known cut-set bound to the problem of lossy transmission of functions of arbitrarily correlated sources over a discrete memoryless multiterminal network.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,608
1603.09638	Detection under Privileged Information	For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at runt...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	53,948
2209.05020	Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs	We investigate efficient learning from higher-order graph convolution and learning directly from adjacency matrices for node classification. We revisit the scaled graph residual network and remove ReLU activation from residual layers and apply a single weight matrix at each residual layer. We show that the resulting mo...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,966
2112.09060	Towards Robust Real-time Audio-Visual Speech Enhancement	The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing. For example, during the cocktail party situation, the human auditory cortex contextually integrates audio-visual (AV) cues in order to better perceive speech. Recent studies have ...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,018
1706.07966	Irregular Convolutional Neural Networks	Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like ${3\times3}$,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,927
1410.2144	k-Mixing Properties of Multidimensional Cellular Automata	This paper investigates the $k$-mixing property of a multidimensional cellular automaton. Suppose $F$ is a cellular automaton with the local rule $f$ defined on a $d$-dimensional convex hull $\mathcal{C}$ which is generated by an apex set $C$. Then $F$ is $k$-mixing with respect to the uniform Bernoulli measure for all...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,593
1302.6906	Tradition and Innovation in Scientists' Research Strategies	What factors affect a scientist's choice of research problem? Qualitative research in the history, philosophy, and sociology of science suggests that this choice is shaped by an "essential tension" between the professional demand for productivity and a conflicting drive toward risky innovation. We examine this tension ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	22,487
2207.05284	High-Order Leader-Follower Tracking Control under Limited Information Availability	Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high-order dynamics. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	307,479
2303.07304	Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research	The paper looks at the role of large language models in academic knowledge creation based on a scoping review (2018 to January 2023) of how researchers have previously used the language model GPT to assist in the performance of academic knowledge creation tasks beyond data analysis. These tasks include writing, editing...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	351,207
1703.10344	Automated News Suggestions for Populating Wikipedia Entity Pages	Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20\% of the references are from online news sources. However, many entity...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	70,904
2106.15373	DRILL-- Deep Reinforcement Learning for Refinement Operators in $\mathcal{ALC}$	Approaches based on refinement operators have been successfully applied to class expression learning on RDF knowledge graphs. These approaches often need to explore a large number of concepts to find adequate hypotheses. This need arguably stems from current approaches relying on myopic heuristic functions to guide the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	243,746
1609.07701	Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results	Machine translation between Arabic and Hebrew has so far been limited by a lack of parallel corpora, despite the political and cultural importance of this language pair. Previous work relied on manually-crafted grammars or pivoting via English, both of which are unsatisfactory for building a scalable and accurate MT sy...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	61,472
1511.00271	Stochastic Top-k ListNet	ListNet is a well-known listwise learning to rank model and has gained much attention in recent years. A particular problem of ListNet, however, is the high computation complexity in model training, mainly due to the large number of object permutations involved in computing the gradients. This paper proposes a stochast...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	48,392
2311.00629	Formal Translation from Reversing Petri Nets to Coloured Petri Nets	Reversible computation is an emerging computing paradigm that allows any sequence of operations to be executed in reverse order at any point during computation. Its appeal lies in its potential for lowpower computation and its relevance to a wide array of applications such as chemical reactions, quantum computation, ro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	404,719
2411.06136	Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm over Wireless MIMO Fading Channels	This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	506,982
2312.07935	Comparing YOLOv8 and Mask RCNN for object segmentation in complex orchard environments	Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compare...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,126
2101.06172	Empirical Evaluation of Supervision Signals for Style Transfer Models	Text style transfer has gained increasing attention from the research community over the recent years. However, the proposed approaches vary in many ways, which makes it hard to assess the individual contribution of the model components. In style transfer, the most important component is the optimization technique used...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	215,631
1901.05103	DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation	Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Funct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,724
1705.09474	Zero-Shot Learning with Generative Latent Prototype Model	Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most existing ZSL methods exploit deterministic transfer learning via an in-between semantic embedding space. In this paper, we try to attack this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,210
2202.09447	A Mental-Model Centric Landscape of Human-AI Symbiosis	There has been significant recent interest in developing AI agents capable of effectively interacting and teaming with humans. While each of these works try to tackle a problem quite central to the problem of human-AI interaction, they tend to rely on myopic formulations that obscure the possible inter-relatedness and ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	281,190
2106.09671	Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs	Dot product embeddings take a graph and construct vectors for nodes such that dot products between two vectors give the strength of the edge. Dot products make a strong transitivity assumption, however, many important forces generating graphs in the real world lead to non-transitive relationships. We remove the transit...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	241,744
2405.17016	$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation	Continuous diffusion models have demonstrated their effectiveness in addressing the inherent uncertainty and indeterminacy in monocular 3D human pose estimation (HPE). Despite their strengths, the need for large search spaces and the corresponding demand for substantial training data make these models prone to generati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,717
2304.08147	Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks	We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the multi-input case. Our approach is applicable to a special class of input-affine discrete-time systems, which includes a class of bilinear r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	358,606
2403.07355	Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems	This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework whil...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	436,850
2311.17860	On the Verification of the Correctness of a Subgraph Construction Algorithm	We automatically verify the crucial steps in the original proof of correctness of an algorithm which, given a geometric graph satisfying certain additional properties removes edges in a systematic way for producing a connected graph in which edges do not (geometrically) intersect. The challenge in this case is represen...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	411,442
2111.10698	Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming	Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	267,418
2306.09179	Neural World Models for Computer Vision	Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying objective. Such world models hold strong promises for planning in complex ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	373,705
2405.15936	Zero-Shot Spam Email Classification Using Pre-trained Large Language Models	This paper investigates the application of pre-trained large language models (LLMs) for spam email classification using zero-shot prompting. We evaluate the performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4) on the well-known SpamAssassin dataset. Two classification approaches are explored:...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	457,172
1709.05940	Normal Integration: A Survey	The need for efficient normal integration methods is driven by several computer vision tasks such as shape-from-shading, photometric stereo, deflectometry, etc. In the first part of this survey, we select the most important properties that one may expect from a normal integration method, based on a thorough study of tw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,987
1911.12448	Soft Anchor-Point Object Detection	Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,390
2210.04607	A Snapshot of the Frontiers of Client Selection in Federated Learning	Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	322,523
2405.20452	Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures	We present new results to model and understand the role of encoder-decoder design in machine learning (ML) from an information-theoretic angle. We use two main information concepts, information sufficiency (IS) and mutual information loss (MIL), to represent predictive structures in machine learning. Our first main res...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	459,353
1504.00434	Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach	We consider the problem of coordinated multi- cell downlink beamforming in massive multiple input multiple output (MIMO) systems consisting of N cells, Nt antennas per base station (BS) and K user terminals (UTs) per cell. Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO systems which requ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,704
1806.09025	Considerations for a PAP Smear Image Analysis System with CNN Features	It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the resulting single cell PAP-smear images. We propose a patch based approach using CNN fo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,272
2406.16026	CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis	Purpose: This study aims to propose and investigate the feasibility of using Kolmogorov-Arnold Network (KAN) for CEST MRI data analysis (CEST-KAN). Methods: CEST MRI data were acquired from twelve healthy volunteers at 3T. Data from ten subjects were used for training, while the remaining two were reserved for testing....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,966
1711.10558	Intent-Aware Contextual Recommendation System	Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	85,621
1407.5949	Deep Recurrent Neural Networks for Time Series Prediction	Ability of deep networks to extract high level features and of recurrent networks to perform time-series inference have been studied. In view of universality of one hidden layer network at approximating functions under weak constraints, the benefit of multiple layers is to enlarge the space of dynamical systems approxi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	34,819
1811.07615	An efficient density-based clustering algorithm using reverse nearest neighbour	Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering algorithm. However, its cluster recovery capabilities depend on the combination of the t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,827
2210.01966	Joint Reconfigurable Intelligent Surface Location and Passive Beamforming Optimization for Maximizing the Secrecy-Rate	The physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS) assisted wireless networks, where a source transmits its confidential information to a legitimate destination with the aid of a single small RIS in the presence of a malicious eavesdropper. A new joint RIS location and passiv...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	321,471
2304.10737	Schooling to Exploit Foolish Contracts	We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions. SCooLS incorporates two key elements: semi-supervised learning and graph neural networks (GNNs). Semi-supervised learning produces mo...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	359,536
2206.05380	Learning Imbalanced Datasets with Maximum Margin Loss	A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization. For a goo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	301,984
2310.00642	From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information	The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios. In such complicated environments, reinforcement learning (RL), a reward-oriented strategy for optimum control, has...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	396,075
2208.14600	ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices	With the popularity of mobile devices, e.g., smartphone and wearable devices, lighter and faster model is crucial for the application of video super resolution. However, most previous lightweight models tend to concentrate on reducing lantency of model inference on desktop GPU, which may be not energy efficient in curr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,363
2110.09702	A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems	Existing text- and image-based multimodal dialogue systems use the traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has an utterance-level encoder to model utterance representation and a context-level encoder to model context representation. Although pioneer efforts have shown promising perfor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,894
2304.00451	Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild	Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	355,694
2402.05546	Offline Actor-Critic Reinforcement Learning Scales to Large Models	We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on a large dataset con...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	427,900
1910.01197	Automatic Group Cohesiveness Detection With Multi-modal Features	Group cohesiveness is a compelling and often studied composition in group dynamics and group performance. The enormous number of web images of groups of people can be used to develop an effective method to detect group cohesiveness. This paper introduces an automatic group cohesiveness prediction method for the 7th Emo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,865
2104.00239	Positive Sample Propagation along the Audio-Visual Event Line	Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. In order to learn discriminative features for a classifier, it is pivotal to identify the helpful (or positive) audio-visual s...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	227,926
2002.03444	Robust binary classification with the 01 loss	The 01 loss is robust to outliers and tolerant to noisy data compared to convex loss functions. We conjecture that the 01 loss may also be more robust to adversarial attacks. To study this empirically we have developed a stochastic coordinate descent algorithm for a linear 01 loss classifier and a single hidden layer 0...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,266
2202.01197	VOS: Learning What You Don't Know by Virtual Outlier Synthesis	Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous approaches rely on rea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	278,401
2108.04352	Attribute Guided Sparse Tensor-Based Model for Person Re-Identification	Visual perception of a person is easily influenced by many factors such as camera parameters, pose and viewpoint variations. These variations make person Re-Identification (ReID) a challenging problem. Nevertheless, human attributes usually stand as robust visual properties to such variations. In this paper, we propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,972
2208.06382	Layers, Folds, and Semi-Neuronal Information Processing	What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	312,699
2211.10934	Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation	Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	331,507
1211.3302	Rational Instability in the Natural Coalition Forming	We are investigating a paradigm of instability in coalition forming among countries, which indeed is intrinsic to any collection of individual groups or other social aggregations. Coalitions among countries are formed by the respective attraction or repulsion caused by the historical bond propensities between the count...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,734
2502.00739	Orlicz-Sobolev Transport for Unbalanced Measures on a Graph	Moving beyond $L^p$ geometric structure, Orlicz-Wasserstein (OW) leverages a specific class of convex functions for Orlicz geometric structure. While OW remarkably helps to advance certain machine learning approaches, it has a high computational complexity due to its two-level optimization formula. Recently, Le et al. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,528
2407.04631	An autoencoder for compressing angle-resolved photoemission spectroscopy data	Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advance...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,640
1305.2490	Combining Drift Analysis and Generalized Schema Theory to Design Efficient Hybrid and/or Mixed Strategy EAs	Hybrid and mixed strategy EAs have become rather popular for tackling various complex and NP-hard optimization problems. While empirical evidence suggests that such algorithms are successful in practice, rather little theoretical support for their success is available, not mentioning a solid mathematical foundation tha...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	24,520
1810.07483	O2A: One-shot Observational learning with Action vectors	We present O2A, a novel method for learning to perform robotic manipulation tasks from a single (one-shot) third-person demonstration video. To our knowledge, it is the first time this has been done for a single demonstration. The key novelty lies in pre-training a feature extractor for creating a perceptual representa...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	110,648
2501.16945	ToolFactory: Automating Tool Generation by Leveraging LLM to Understand REST API Documentations	LLM-based tool agents offer natural language interfaces, enabling users to seamlessly interact with computing services. While REST APIs are valuable resources for building such agents, they must first be transformed into AI-compatible tools. Automatically generating AI-compatible tools from REST API documents can great...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	528,163
1905.04394	Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks	Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	130,447
1805.05696	Neuromodulation of Neuromorphic Circuits	We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the physiological neuromodulation of a single neuron. The methodology is general in that it only relies on a parallel interconnection of elementary voltage-controlled current sources. In contrast to controlling a nonlinear ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	97,475
2011.08968	Contrastive Weight Regularization for Large Minibatch SGD	The minibatch stochastic gradient descent method (SGD) is widely applied in deep learning due to its efficiency and scalability that enable training deep networks with a large volume of data. Particularly in the distributed setting, SGD is usually applied with large batch size. However, as opposed to small-batch SGD, n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,041
1801.09176	Mitigating Pilot Contamination in Multi-cell Hybrid Millimeter Wave Systems	In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple-output (MIMO) network adopting the channel estimation algorithm proposed in [1] for channel estimation. Due to the reuse of orthogonal pilot symbols among different cells, the cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,059
2502.11718	ChineseSimpleVQA -- "See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models	The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models' knowledge capacity and reliability. In this paper, we introduce the first factuality-based visual question-answering benchmark in Chinese, named Chin...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	534,511
1005.3093	A remark about orthogonal matching pursuit algorithm	In this note, we investigate the theoretical properties of Orthogonal Matching Pursuit (OMP), a class of decoder to recover sparse signal in compressed sensing. In particular, we show that the OMP decoder can give $(p,q)$ instance optimality for a large class of encoders with $1\leq p\leq q \leq 2$ and $(p,q)\neq (2,2)...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,508
2402.00033	LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition	The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	425,443
2201.12622	Hand Gesture Recognition of Dumb Person Using one Against All Neural Network	We propose a new technique for recognition of dumb person hand gesture in real world environment. In this technique, the hand image containing the gesture is preprocessed and then hand region is segmented by convergent the RGB color image to L.a.b color space. Only few statistical features are used to classify the segm...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	277,713
1808.08582	Sensor-based, time-critical mobility of autonomous robots in cluttered spaces: a harmonic potential approach	This paper suggests an integrated navigation system for an unmanned ground vehicle operating in an unknown cluttered environment. The navigator supports time-critical mobility making it possible for a mobile robot to reach a target from the first attempt without the need for a dedicated exploration and mapping stage. T...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	105,985
2408.12056	Enhancing Automated Program Repair with Solution Design	Automatic Program Repair (APR) endeavors to autonomously rectify issues within specific projects, which generally encompasses three categories of tasks: bug resolution, new feature development, and feature enhancement. Despite extensive research proposing various methodologies, their efficacy in addressing real issues ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	482,560
1909.12552	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box functions. In this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,162
2412.20438	Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations	The financial sector, a pivotal force in economic development, increasingly uses the intelligent technologies such as natural language processing to enhance data processing and insight extraction. This research paper through a review process of the time span of 2018-2023 explores the use of text mining as natural langu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,228
2108.13512	Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups	With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond 5G and towards 6G systems. This work looks into a future scenario in which there are multiple groups with different learning purposes and participating in different FL processes. We ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,816
1906.05799	Deep Reinforcement Learning for Cyber Security	The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	135,126
1902.06792	Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data	Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose framew...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	121,840
1910.11399	Comparison of Quality Indicators in User-generated Content Using Social Media and Scholarly Text	Predicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	150,765
1908.10763	Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual	Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to associate with the label on a particular dataset, but are not useful in general, e.g., negation words indicate contradiction. As exposed by several recent challenge datasets, these models perfor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,206
2501.03235	Neural networks consisting of DNA	Neural networks based on soft and biological matter constitute an interesting potential alternative to traditional implementations based on electric circuits. DNA is a particularly promising system in this context due its natural ability to store information. In recent years, researchers have started to construct neura...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	522,806
2211.08074	Predicting Eye Gaze Location on Websites	World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user interaction, which is primarily done by analyzing users' behavior, especially users' eye-gaze locations. However, gathering these data is still...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,470

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