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
1401.2398	An Elias Bound on the Bhattacharyya Distance of Codes for Channels with a Zero-Error Capacity	In this paper, we propose an upper bound on the minimum Bhattacharyya distance of codes for channels with a zero-error capacity. The bound is obtained by combining an extension of the Elias bound introduced by Blahut, with an extension of a bound previously introduced by the author, which builds upon ideas of Gallager,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,738
2004.04216	Generating Counter Narratives against Online Hate Speech: Data and Strategies	Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from fur...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	171,811
2110.09354	An Analysis and Implementation of the HDR+ Burst Denoising Method	HDR+ is an image processing pipeline presented by Google in 2016. At its core lies a denoising algorithm that uses a burst of raw images to produce a single higher quality image. Since it is designed as a versatile solution for smartphone cameras, it does not necessarily aim for the maximization of standard denoising m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,782
2105.06752	Classifying Long Clinical Documents with Pre-trained Transformers	Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria. Phenotyping typically involves classifying long clinical documents that contain thousands of tokens. At the same time, recent state-of-art transformer-based pre-trained language models limit the input to a few hu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	235,221
2409.00872	Self-evolving Agents with reflective and memory-augmented abilities	Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this research, we propose a novel framework by integrating iterative feedback, reflective mechanisms, and a memory optimization mechanism based o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	485,110
2403.16037	Knowledge-aware Dual-side Attribute-enhanced Recommendation	\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and further fail to leverage the \textit{preference-attribute connection} to...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	440,844
2409.15637	Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale	LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives (e.g., arranging an online meeting). However, accuracy is still far from satisfactory, partly due to a lack of large-scale, direct demonstrations for digital tasks. Obtaining supervised data from humans is cos...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,994
2307.13824	Offline Reinforcement Learning with On-Policy Q-Function Regularization	The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly regularizing the learning polic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	381,711
1801.09271	Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data	This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	89,074
2103.02999	A framework for power line inspection tasks with multi-robot systems from signal temporal logic specifications	Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy different requirements expressed in terms of potentia...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	223,136
2404.00380	DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation	Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything. However, WSS faces challenges related to minor classes since those are overlooked in images with adjacent multiple classe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	442,894
2204.13695	Bilinear value networks	The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a, g) using...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	293,904
2207.04772	Whois? Deep Author Name Disambiguation using Bibliographic Data	As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital librar...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	307,306
1704.05796	Network Dissection: Quantifying Interpretability of Deep Visual Representations	We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	72,076
2007.06811	A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection	Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream. They do not deeply explore the effect of the depth map itself. In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,135
1112.1528	Chargaff's "Grammar of Biology": New Fractal-like Rules	Chargaff once said that "I saw before me in dark contours the beginning of a grammar of Biology". In linguistics, "grammar" is the set of natural language rules, but we do not know for sure what Chargaff meant by "grammar" of Biology. Nevertheless, assuming the metaphor, Chargaff himself started a "grammar of Biology" ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	13,351
0801.0678	Implementation of perception and action at nanoscale	Real time combination of nanosensors and nanoactuators with virtual reality environment and multisensorial interfaces enable us to efficiently act and perceive at nanoscale. Advanced manipulation of nanoobjects and new strategies for scientific education are the key motivations. We have no existing intuitive representa...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	1,119
2101.11984	Machine learning for cloud resources management -- An overview	Nowadays, an important topic that is considered a lot is how to integrate Machine Learning(ML) to cloud resources management. In this study, our goal is to explore the most important cloud resources management issues that have been combined with ML and which present many promising results. To accomplish this, we used c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	217,450
1011.1662	A New Sufficient Condition for 1-Coverage to Imply Connectivity	An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully the active nodes must maintain both sensing coverage and network connectivity, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	8,164
2211.00448	Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition	The goal of this work is background-robust continuous sign language recognition. Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are filmed in studios with a static monochromatic background. However, signing is not limited only to studios in the real world. In order to an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	327,873
1902.00423	Do We Train on Test Data? Purging CIFAR of Near-Duplicates	The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets in computer vision and are often used to evaluate novel methods and model architectures in the field of deep learning. However, we find that 3.3% and 10% of the images from the test sets of these datasets have duplicates in the trainin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,398
2006.08476	Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data. We demonstrate that for broad classes o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,206
1904.00962	Large Batch Optimization for Deep Learning: Training BERT in 76 minutes	Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning ra...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	126,026
2108.08262	SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks	Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	251,197
2204.05891	A DNN Framework for Learning Lagrangian Drift With Uncertainty	Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this const...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,174
2012.14785	Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer	Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However, the shortage of annotation and the variance of the data among different vendors and medical centers restrict the performance of advanced dee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	213,609
2401.12480	IDPro: Flexible Interactive Video Object Segmentation by ID-queried Concurrent Propagation	Interactive Video Object Segmentation (iVOS) is a challenging task that requires real-time human-computer interaction. To improve the user experience, it is important to consider the user's input habits, segmentation quality, running time and memory consumption.However, existing methods compromise user experience with ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,394
2306.10134	Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth	Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (MARL) algorithms often provide agents with a binary choice: either transmitting a fixed number of byt...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	374,116
2310.08390	Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning	Metric learning plays a critical role in training image retrieval and classification. It is also a key algorithm in representation learning, e.g., for feature learning and its alignment in metric space. Hyperbolic embedding has been recently developed. Compared to the conventional Euclidean embedding in most of the pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,373
2007.06437	A Provably Efficient Sample Collection Strategy for Reinforcement Learning	One of the challenges in online reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize its behavior. Whether we optimize for regret, sample complexity, state-space coverage or model estimation, we need to strike a different exp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,015
2203.06026	The Role of ImageNet Classes in Fr\'echet Inception Distance	Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling. While remarkably successful, the metric is known to sometimes disagree with human judgement. We investigate a root cause of these discrepancies, and visualize what FID "looks at" in generated images. We show ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	284,993
2004.00137	Revisiting Few-shot Activity Detection with Class Similarity Control	Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot te...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	170,525
1407.5716	Massive-MIMO Meets HetNet: Interference Coordination Through Spatial Blanking	In this paper, we study the downlink performance of a heterogeneous cellular network (HetNet) where both macro and small cells share the same spectrum and hence interfere with each other. We assume that the users are concentrated at certain areas in the cell, i.e., they form hotspots. While some of the hotspots are ass...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,798
2501.09994	Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse Thermography	AI-driven pulse thermography (PT) has become a crucial tool in non-destructive testing (NDT), enabling automatic detection of hidden anomalies in various industrial components. Current state-of-the-art techniques feed segmentation and depth estimation networks compressed PT sequences using either Principal Component An...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	525,348
2212.14128	Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction	Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recogni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,512
2109.06016	On the Optimal Memory-Load Tradeoff of Coded Caching for Location-Based Content	Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	255,019
2306.15253	MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation	Humans talk in daily conversations while aligning and negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	375,963
2311.05440	A Practical Approach to Novel Class Discovery in Tabular Data	The problem of Novel Class Discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD has recently received a lot of attention from the community, it is often solved on computer vision problems and under unrealistic condition...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,582
2407.16193	CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation	3D point clouds captured from real-world sensors frequently encompass noisy points due to various obstacles, such as occlusion, limited resolution, and variations in scale. These challenges hinder the deployment of pre-trained point cloud recognition models trained on clean point clouds, leading to significant performa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,499
2308.13739	Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion	Vignetting commonly occurs as a degradation in images resulting from factors such as lens design, improper lens hood usage, and limitations in camera sensors. This degradation affects image details, color accuracy, and presents challenges in computational photography. Existing vignetting removal algorithms predominantl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,034
2410.17275	Automated Quality Control System for Canned Tuna Production using Artificial Vision	This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a photoelectric sensor. A robotic arm classifies the metal cans accordi...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	501,401
2112.08037	LookinGood^{\pi}: Real-time Person-independent Neural Re-rendering for High-quality Human Performance Capture	We propose LookinGood^{\pi}, a novel neural re-rendering approach that is aimed to (1) improve the rendering quality of the low-quality reconstructed results from human performance capture system in real-time; (2) improve the generalization ability of the neural rendering network on unseen people. Our key idea is to ut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,677
2408.12266	Accounts of using the Tustin-Net architecture on a rotary inverted pendulum	In this report we investigate the use of the Tustin neural network architecture (Tustin-Net) for the identification of a physical rotary inverse pendulum. This physics-based architecture is of particular interest as it builds on the known relationship between velocities and positions. We here aim at discussing the adva...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	482,659
2411.04735	Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing	Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex racing behaviors from expert demonstrations using hierarchical policy abstractions. At the trajectory level, our policy model predicts a dense d...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	506,398
2103.12517	Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments	We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	226,201
2104.10493	End-to-end Biomedical Entity Linking with Span-based Dictionary Matching	Disease name recognition and normalization, which is generally called biomedical entity linking, is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	231,601
1112.5493	Critical Data Compression	A new approach to data compression is developed and applied to multimedia content. This method separates messages into components suitable for both lossless coding and 'lossy' or statistical coding techniques, compressing complex objects by separately encoding signals and noise. This is demonstrated by compressing the ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	13,570
1801.00317	"Like Sheep Among Wolves": Characterizing Hateful Users on Twitter	Hateful speech in Online Social Networks (OSNs) is a key challenge for companies and governments, as it impacts users and advertisers, and as several countries have strict legislation against the practice. This has motivated work on detecting and characterizing the phenomenon in tweets, social media posts and comments....	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	87,542
2006.06071	Affective Movement Generation using Laban Effort and Shape and Hidden Markov Models	Body movements are an important communication medium through which affective states can be discerned. Movements that convey affect can also give machines life-like attributes and help to create a more engaging human-machine interaction. This paper presents an approach for automatic affective movement generation that ma...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	181,298
2307.05834	Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing	Recently, DARPA launched the ShELL program, which aims to explore how experience sharing can benefit distributed lifelong learning agents in adapting to new challenges. In this paper, we address this issue by conducting both theoretical and empirical research on distributed multi-task reinforcement learning (RL), where...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	378,870
0904.3894	On Capacity Computation for the Two-User Binary Multiple-Access Channel	This paper deals with the problem of computing the boundary of the capacity region for the memoryless two-user binary-input binary-output multiple-access channel ((2,2;2)-MAC), or equivalently, the computation of input probability distributions maximizing weighted sum-rate. This is equivalent to solving a difficult non...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,590
2002.02064	No-Regret Prediction in Marginally Stable Systems	We consider the problem of online prediction in a marginally stable linear dynamical system subject to bounded adversarial or (non-isotropic) stochastic perturbations. This poses two challenges. Firstly, the system is in general unidentifiable, so recent and classical results on parameter recovery do not apply. Secondl...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	162,814
2408.00606	U2UData: A Large-scale Cooperative Perception Dataset for Swarm UAVs Autonomous Flight	Modern perception systems for autonomous flight are sensitive to occlusion and have limited long-range capability, which is a key bottleneck in improving low-altitude economic task performance. Recent research has shown that the UAV-to-UAV (U2U) cooperative perception system has great potential to revolutionize the aut...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	477,899
2310.01633	Distributionally Robust Path Integral Control	We consider a continuous-time continuous-space stochastic optimal control problem, where the controller lacks exact knowledge of the underlying diffusion process, relying instead on a finite set of historical disturbance trajectories. In situations where data collection is limited, the controller synthesized from empir...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,496
2203.12828	Matrix Pontryagin principle approach to controllability metrics maximization under sparsity constraints	Controllability maximization problem under sparsity constraints is a node selection problem that selects inputs that are effective for control in order to minimize the energy to control for desired state. In this paper we discuss the equivalence between the sparsity constrained controllability metrics maximization prob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	287,406
1306.3888	The SP theory of intelligence: an overview	This article is an overview of the "SP theory of intelligence". The theory aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives 'New' informa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,261
2107.05830	ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement	Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can be enlightened subjectively according to diverse preferences by each individual. To tackle these two challenges, this paper presents a novel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,900
1906.03509	Outlier Exposure with Confidence Control for Out-of-Distribution Detection	Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algori...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	134,400
2405.20390	Quantitative Convergences of Lie Group Momentum Optimizers	Explicit, momentum-based dynamics that optimize functions defined on Lie groups can be constructed via variational optimization and momentum trivialization. Structure preserving time discretizations can then turn this dynamics into optimization algorithms. This article investigates two types of discretization, Lie Heav...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	459,321
1107.1900	Behavior patterns of online users and the effect on information filtering	Understanding the structure and evolution of web-based user-object bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users' behavior and the effect on recommendation process. Empirical analysis on t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,231
2004.02147	BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation	The low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, which leads to a considerable accuracy decrease. We propose to treat these spatial details and categorical sema...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,139
1402.4566	Transduction on Directed Graphs via Absorbing Random Walks	In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications. Different from existing research efforts that either only deal with undirected graphs or circumvent directionality...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	30,976
2305.04449	DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects	Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determi...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	362,772
1912.00565	An Integrative Data-Driven Physics-Inspired Approach to Traffic Congestion Control	This paper offers an integrative data-driven physics-inspired approach to model and control traffic congestion in a resilient and efficient manner. While existing physics-based approaches commonly assign density and flow traffic states by using the Fundamental Diagram, this paper specifies the flow-density relation usi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	155,812
2401.04152	Cross-Speaker Encoding Network for Multi-Talker Speech Recognition	End-to-end multi-talker speech recognition has garnered great interest as an effective approach to directly transcribe overlapped speech from multiple speakers. Current methods typically adopt either 1) single-input multiple-output (SIMO) models with a branched encoder, or 2) single-input single-output (SISO) models ba...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	420,349
1903.09408	Rule-Based Translation of Application-Level QoS Constraints into SDN Configurations for the IoT	In this paper, we propose an approach for the automated translation of application-level requirements regarding the logical workflow and its QoS into a configuration of the underlying network substrate. Our goal is to facilitate the integration of QoS constraints in the development of industrial IoT applications to mak...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	125,056
2103.02205	Gradual Fine-Tuning for Low-Resource Domain Adaptation	Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-stage process can yield substantial...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	222,886
1805.11348	Uncertainty Gated Network for Land Cover Segmentation	The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	98,901
2201.08574	Classroom Slide Narration System	Slide presentations are an effective and efficient tool used by the teaching community for classroom communication. However, this teaching model can be challenging for blind and visually impaired (VI) students. The VI student required personal human assistance for understand the presented slide. This shortcoming motiva...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	276,386
1703.02591	Energy-Aware Disk Storage Management: Online Approach with Application in DBMS	Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which was found to be the major consumer of energy in the software stack of modern data centers. Among all databa...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	69,579
1803.05428	A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music	The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-te...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,635
2102.11447	Data Engineering for Everyone	Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data than individual teams of data engineers can readily produce, which presents a severe challenge to ML deployment at scale. Much like the soft...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,420
2109.05750	Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization	Image harmonization aims to modify the color of the composited region with respect to the specific background. Previous works model this task as a pixel-wise image-to-image translation using UNet family structures. However, the model size and computational cost limit the ability of their models on edge devices and high...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,928
1905.05377	A human-inspired recognition system for premodern Japanese historical documents	Recognition of historical documents is a challenging problem due to the noised, damaged characters and background. However, in Japanese historical documents, not only contains the mentioned problems, pre-modern Japanese characters were written in cursive and are connected. Therefore, character segmentation based method...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	130,705
2309.03825	Prime and Modulate Learning: Generation of forward models with signed back-propagation and environmental cues	Deep neural networks employing error back-propagation for learning can suffer from exploding and vanishing gradient problems. Numerous solutions have been proposed such as normalisation techniques or limiting activation functions to linear rectifying units. In this work we follow a different approach which is particula...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,523
2210.12091	Physical Layer Security in Random NOMA-Enabled Heterogeneous Networks	The performance of physical layer secrecy approach in non-orthogonal multiple access (NOMA)-enabled heterogeneous networks (HetNets) is analyzed in this paper. A $K$-tier multi-cell HetNet is considered, comprising NOMA adopted in all tiers. The base stations, legitimate users (in a two-user NOMA setup), and passive ea...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	325,580
2208.14197	A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies	As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital m...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	315,249
1907.13566	Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions	We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaternions representing planar motions, the nonlinear structure of the SE(2) group is inherently considered. To solve the on-manifold least squares...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	140,401
cmp-lg/9501003	An HPSG Parser Based on Description Logics	In this paper I present a parser based on Description Logics (DL) for a German HPSG -style fragment. The specified parser relies mainly on the inferential capabilities of the underlying DL system. Given a preferential default extension for DL disambiguation is achieved by choosing the parse containing a qualitatively m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,259
2402.15942	Minimum energy density steering of linear systems with Gromov-Wasserstein terminal cost	In this paper, we newly formulate and solve the optimal density control problem with Gromov-Wasserstein (GW) terminal cost in discrete-time linear Gaussian systems. Differently from the Wasserstein or Kullback-Leibler distances employed in the existing works, the GW distance quantifies the difference in shapes of the d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	432,344
2002.02903	Subsampling Winner Algorithm for Feature Selection in Large Regression Data	Feature selection from a large number of covariates (aka features) in a regression analysis remains a challenge in data science, especially in terms of its potential of scaling to ever-enlarging data and finding a group of scientifically meaningful features. For example, to develop new, responsive drug targets for ovar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,073
2105.04184	Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation	Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	234,419
2305.02606	Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training	Pre-training on large-scale open-domain dialogue data can substantially improve the performance of dialogue models. However, the pre-trained dialogue model's ability to utilize long-range context is limited due to the scarcity of long-turn dialogue sessions. Most dialogues in existing pre-training corpora contain fewer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	362,105
2310.09789	FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy	Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,949
2101.08937	Prior Preference Learning from Experts:Designing a Reward with Active Inference	Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. An agent will choose an action that leads to its prior preference for a future observation. In this paper, we clai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,453
1310.2375	Web Usage Mining: Pattern Discovery and Forecasting	Web usage mining: automatic discovery of patterns in clickstreams and associated data collected or generated as a result of user interactions with one or more Web sites. This paper describes web usage mining for our college log files to analyze the behavioral patterns and profiles of users interacting with a Web site. ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	27,663
2308.08079	Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation	Subsurface datasets inherently possess big data characteristics such as vast volume, diverse features, and high sampling speeds, further compounded by the curse of dimensionality from various physical, engineering, and geological inputs. Among the existing dimensionality reduction (DR) methods, nonlinear dimensionality...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,756
2310.03684	SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks	Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content. To address this vulnerability, we propose SmoothLLM, the first algorithm ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	397,374
2201.11462	Multiple-antenna Placement Delivery Array for Cache-aided MISO Systems	We consider the cache-aided multiple-input single-output (MISO) broadcast channel, which consists of a server with $L$ antennas and $K$ single-antenna users, where the server contains $N$ files of equal length and each user is equipped with a local cache of size $M$ files. Each user requests an arbitrary file from libr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	277,305
2408.06736	Speculations on Uncertainty and Humane Algorithms	The appreciation and utilisation of risk and uncertainty can play a key role in helping to solve some of the many ethical issues that are posed by AI. Understanding the uncertainties can allow algorithms to make better decisions by providing interrogatable avenues to check the correctness of outputs. Allowing algorithm...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	480,324
2406.04336	On the Expressive Power of Spectral Invariant Graph Neural Networks	Incorporating spectral information to enhance Graph Neural Networks (GNNs) has shown promising results but raises a fundamental challenge due to the inherent ambiguity of eigenvectors. Various architectures have been proposed to address this ambiguity, referred to as spectral invariant architectures. Notable examples i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	461,638
2201.04922	Uplink-Downlink Duality and Precoding Strategies with Partial CSI in Cell-Free Wireless Networks	We consider a scalable user-centric wireless network with dynamic cluster formation as defined by Bj\"ornsson and Sanguinetti. After having shown the importance of dominant channel subspace information for uplink (UL) pilot decontamination and having examined different UL combining schemes in our previous work, here we...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	275,235
2204.05172	Event Transformer	The event camera's low power consumption and ability to capture microsecond brightness changes make it attractive for various computer vision tasks. Existing event representation methods typically convert events into frames, voxel grids, or spikes for deep neural networks (DNNs). However, these approaches often sacrifi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,938
2110.08355	Clean or Annotate: How to Spend a Limited Data Collection Budget	Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling. There are two common strategies to manage the impact of such noise. The first involves aggregating redundant annotations, but comes at the expense of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,360
2502.00222	The Free Termination Property of Queries Over Time	Building on prior work on distributed databases and the CALM Theorem, we define and study the question of free termination: in the absence of distributed coordination, what query properties allow nodes in a distributed (database) system to unilaterally terminate execution even though they may receive additional data or...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	529,265
1701.09076	Combined Thermal Control and GNC: An Enabling Technology for CubeSat Surface Probes and Small Robots	Advances in GNC, particularly from miniaturized control electronics, reaction-wheels and attitude determination sensors make it possible to design surface probes and small robots to perform surface exploration and science on low-gravity environments. These robots would use their reaction wheels to roll, hop and tumble ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	67,583
2002.10244	Fractional-Order Models for the Static and Dynamic Analysis of Nonlocal Plates	This study presents the analytical formulation and the finite element solution of fractional order nonlocal plates under both Mindlin and Kirchoff formulations. By employing consistent definitions for fractional-order kinematic relations, the governing equations and the associated boundary conditions are derived based ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	165,335
2109.03088	Power Management of Microgrid Integrated with Electric Vehicles in Residential Parking Station	Lately, increasing number of electric vehicles (EVs) in residential parking station has become an important issue, because excessive number of EVs can destabilize the power system during peak hours with high charging power requested. When the power system of the residential parking station takes the structure of microg...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	253,951
2111.11750	S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding	Contrastive learning has been studied for improving the performance of learning sentence embeddings. The current state-of-the-art method is the SimCSE, which takes dropout as the data augmentation method and feeds a pre-trained transformer encoder the same input sentence twice. The corresponding outputs, two sentence e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	267,762
1202.3778	Sparse Topical Coding	We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood functio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	14,450

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