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
1702.05345	Universal Spatiotemporal Sampling Sets for Discrete Spatially Invariant Evolution Systems	Let $(I,+)$ be a finite abelian group and $\mathbf{A}$ be a circular convolution operator on $\ell^2(I)$. The problem under consideration is how to construct minimal $\Omega \subset I$ and $l_i$ such that $Y=\{\mathbf{e}_i, \mathbf{A}\mathbf{e}_i, \cdots, \mathbf{A}^{l_i}\mathbf{e}_i: i\in \Omega\}$ is a frame for $\el...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,382
2208.04379	A Systematic Evaluation of Response Selection for Open Domain Dialogue	Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art neural chatbots cannot produce satisfying responses for every turn in a dialog. A practical solution is to generate multiple response candi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	312,093
2104.01754	Potential Convolution: Embedding Point Clouds into Potential Fields	Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications, such as shape classification, scene segmentation and so on. However, they still suffer from some drawbacks. For continuous kernels, the ina...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,464
1210.4866	A Bayesian Approach to Constraint Based Causal Inference	We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical guarantees but are susceptible to propagating erroneous decisions, while others are very adept at handling and representing uncertainty, but n...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	19,192
2309.05028	SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views	In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,932
2406.04138	The 3D-PC: a benchmark for visual perspective taking in humans and machines	Visual perspective taking (VPT) is the ability to perceive and reason about the perspectives of others. It is an essential feature of human intelligence, which develops over the first decade of life and requires an ability to process the 3D structure of visual scenes. A growing number of reports have indicated that dee...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,535
2306.07174	Augmenting Language Models with Long-Term Memory	Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs. To address this, we propose a framework, Language Models Augmented with Long-Term Memory (LongMem), which enables LLMs to memorize long his...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,908
2412.03563	From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents	Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and fa...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	514,004
2203.06318	Deformable VisTR: Spatio temporal deformable attention for video instance segmentation	Video instance segmentation (VIS) task requires classifying, segmenting, and tracking object instances over all frames in a video clip. Recently, VisTR has been proposed as end-to-end transformer-based VIS framework, while demonstrating state-of-the-art performance. However, VisTR is slow to converge during training, r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,069
2211.08400	Air Pollution Hotspot Detection and Source Feature Analysis using Cross-domain Urban Data	Air pollution is a major global environmental health threat, in particular for people who live or work near pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. Detecting and characterizing pollution ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,600
2312.07560	AI-driven Structure Detection and Information Extraction from Historical Cadastral Maps (Early 19th Century Franciscean Cadastre in the Province of Styria) and Current High-resolution Satellite and Aerial Imagery for Remote Sensing	Cadastres from the 19th century are a complex as well as rich source for historians and archaeologists, whose use presents them with great challenges. For archaeological and historical remote sensing, we have trained several Deep Learning models, CNNs as well as Vision Transformers, to extract large-scale data from thi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,976
2208.00553	Search for or Navigate to? Dual Adaptive Thinking for Object Navigation	"Search for" or "Navigate to"? When finding an object, the two choices always come up in our subconscious mind. Before seeing the target, we search for the target based on experience. After seeing the target, we remember the target location and navigate to. However, recently methods in object navigation field almost on...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	310,894
2305.16474	FairDP: Certified Fairness with Differential Privacy	This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP) guarantee to protect training data. The key idea of FairDP is to train models for distinct individual groups independently, add noise to eac...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	false	368,102
2204.07763	UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio	We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augment...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,836
2108.00548	A Reinforcement Learning Approach for Scheduling in mmWave Networks	We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	248,749
2312.17649	Investigating the Effects of Sparse Attention on Cross-Encoders	Cross-encoders are effective passage and document re-rankers but less efficient than other neural or classic retrieval models. A few previous studies have applied windowed self-attention to make cross-encoders more efficient. However, these studies did not investigate the potential and limits of different attention pat...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	418,818
2210.01063	On Stability and Generalization of Bilevel Optimization Problem	(Stochastic) bilevel optimization is a frequently encountered problem in machine learning with a wide range of applications such as meta-learning, hyper-parameter optimization, and reinforcement learning. Most of the existing studies on this problem only focused on analyzing the convergence or improving the convergence...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,117
1810.01466	Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics	We developed and used a collection of statistical methods (unsupervised machine learning) to extract relevant information from a Twitter supplied data set consisting of alleged Russian trolls who (allegedly) attempted to influence the 2016 US Presidential election. These unsupervised statistical methods allow fast iden...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	109,398
2306.15154	Contrastive Meta-Learning for Few-shot Node Classification	Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we refer to the task of classifying nodes in classes with a few labeled nodes as the few-shot node classif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	375,926
2010.08209	Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation	Computer vision technology is widely used in biological and medical data analysis and understanding. However, there are still two major bottlenecks in the field of cell membrane segmentation, which seriously hinder further research: lack of sufficient high-quality data and lack of suitable evaluation criteria. In order...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,107
2407.09658	BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Federated learning, while being a promising approach for collaborative model training, is susceptible to poisoning attacks due to its decentralized nature. Backdoor attacks, in particular, have shown remarkable stealthiness, as they selectively compromise predictions for inputs containing triggers. Previous endeavors t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	472,671
2006.01595	Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data	Recognizing sounds is a key aspect of computational audio scene analysis and machine perception. In this paper, we advocate that sound recognition is inherently a multi-modal audiovisual task in that it is easier to differentiate sounds using both the audio and visual modalities as opposed to one or the other. We prese...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,815
2306.05783	Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation	New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines rangin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,335
2006.02480	V2V communication-based rail collision avoidance system for urban light rail vehicles	In this paper, we document a design, implementation, and field tests of a vehicle-to-vehicle (V2V) communication-enabled rail collision avoidance system (RCAS) for urban light rail vehicles---trams. The RCAS runs onboard a tram and issues an acoustic warning to a tram driver if a collision with another tram is imminent...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	180,046
2101.11194	Equivalence of Non-Perfect Secret Sharing and Symmetric Private Information Retrieval with General Access Structure	We study the equivalence between non-perfect secret sharing (NSS) and symmetric private information retrieval (SPIR) with arbitrary response and collusion patterns. NSS and SPIR are defined with an access structure, which corresponds to the authorized/forbidden sets for NSS and the response/collusion patterns for SPIR....	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	217,189
2207.14398	Analysis and Computation of Multidimensional Linear Complexity of Periodic Arrays	Linear complexity is an important parameter for arrays that are used in applications related to information security. In this work we survey constructions of two and three dimensional arrays, and present new results on the multidimensional linear complexity of periodic arrays obtained using the definition and method pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	310,569
2112.12872	Sparsified Secure Aggregation for Privacy-Preserving Federated Learning	Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation protocols incur a significant communication overhead, which can become a major bottleneck in real-wor...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	273,072
2410.12655	Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks	Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to policy development. An important and popular technique for examining how amino acids make up these characteristics of the protein sequences wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,127
2203.15305	Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization	This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using the more efficient online algorithms through time-varying optimization. The idea lies in that when quadratic programming (QP) or other convex optimization algorithms needed in t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	288,334
2405.10847	Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling Using Torque Vectoring	This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tra...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	454,906
2310.10462	Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems	Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,235
2501.12810	Machine Learning Modeling for Multi-order Human Visual Motion Processing	Our research aims to develop machines that learn to perceive visual motion as do humans. While recent advances in computer vision (CV) have enabled DNN-based models to accurately estimate optical flow in naturalistic images, a significant disparity remains between CV models and the biological visual system in both arch...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	526,443
2412.19128	Semantic Residual for Multimodal Unified Discrete Representation	Recent research in the domain of multimodal unified representations predominantly employs codebook as representation forms, utilizing Vector Quantization(VQ) for quantization, yet there has been insufficient exploration of other quantization representation forms. Our work explores more precise quantization methods and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	520,724
2107.02868	Principles for Evaluation of AI/ML Model Performance and Robustness	The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these sys...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,965
2207.07743	HOME: High-Order Mixed-Moment-based Embedding for Representation Learning	Minimum redundancy among different elements of an embedding in a latent space is a fundamental requirement or major preference in representation learning to capture intrinsic informational structures. Current self-supervised learning methods minimize a pair-wise covariance matrix to reduce the feature redundancy and pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	308,293
2010.03412	Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation	While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a un...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	199,395
2209.00486	Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics	The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures....	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	false	false	315,596
2209.06185	HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning	Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly-labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,319
2407.12815	SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparison	Large language models (LLMs) have gained significant attention due to their ability to mimic human language. Identifying texts generated by LLMs is crucial for understanding their capabilities and mitigating potential consequences. This paper analyzes datasets of varying text lengths: small, medium, and large. We compa...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	474,092
2405.03537	Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation	Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	452,219
2212.00624	Safe Control Design for Unknown Nonlinear Systems with Koopman-based Fixed-Time Identification	We consider the problem of safe control design for a class of nonlinear, control-affine systems subject to an unknown, additive, nonlinear disturbance. Leveraging recent advancements in the application of Koopman operator theory to the field of system identification and control, we introduce a novel fixed-time identifi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	334,132
2010.00117	Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning	While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training d...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	198,187
2203.08450	The Devil Is in the Details: Window-based Attention for Image Compression	Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,797
1910.10245	Global Capacity Measures for Deep ReLU Networks via Path Sampling	Classical results on the statistical complexity of linear models have commonly identified the norm of the weights $\\|w\\|$ as a fundamental capacity measure. Generalizations of this measure to the setting of deep networks have been varied, though a frequently identified quantity is the product of weight norms of each la...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,424
2002.04793	ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems	We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but integrates more ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	163,704
2302.12794	HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis	This paper describes our participation in SemEval-2023 Task 9, Intimacy Analysis of Multilingual Tweets. We fine-tune some of the most popular transformer models with the training dataset and synthetic data generated by different data augmentation techniques. During the development phase, our best results were obtained...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	347,694
2407.13229	Disturbance Observer for Estimating Coupled Disturbances	High-precision control for nonlinear systems is impeded by the low-fidelity dynamical model and external disturbance. Especially, the intricate coupling between internal uncertainty and external disturbance is usually difficult to be modeled explicitly. Here we show an effective and convergent algorithm enabling accura...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	474,296
2312.16143	On the Trajectories of SGD Without Replacement	This article examines the implicit regularization effect of Stochastic Gradient Descent (SGD). We consider the case of SGD without replacement, the variant typically used to optimize large-scale neural networks. We analyze this algorithm in a more realistic regime than typically considered in theoretical works on SGD, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,277
1502.06601	Optimization-Based Linear Network Coding for General Connections of Continuous Flows	For general connections, the problem of finding network codes and optimizing resources for those codes is intrinsically difficult and little is known about its complexity. Most of the existing solutions rely on very restricted classes of network codes in terms of the number of flows allowed to be coded together, and ar...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,503
1706.06418	Design and optimal springs stiffness estimation of a Modular OmniCrawler in-pipe climbing Robot	This paper discusses the design of a novel compliant in-pipe climbing modular robot for small diameter pipes. The robot consists of a kinematic chain of 3 OmniCrawler modules with a link connected in between 2 adjacent modules via compliant joints. While the tank-like crawler mechanism provides good traction on low fri...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	75,675
2403.06341	RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation	Distributed as an open source library since 2013, RTAB-Map started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement Simultaneous Localization and Mapping (SLAM) on various robots and mobile platforms. As each...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	436,401
2407.19528	Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment Analysis	Sentiment analysis is the process of identifying and categorizing people's emotions or opinions regarding various topics. Analyzing political sentiment is critical for understanding the complexities of public opinion processes, especially during election seasons. It gives significant information on voter preferences, a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	476,827
1903.12220	The Algorithmic Automation Problem: Prediction, Triage, and Human Effort	In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains. The discussion around these developments, however, has implicitly equated the specific task of prediction with the gener...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	125,662
2401.11500	Integration of Large Language Models in Control of EHD Pumps for Precise Color Synthesis	This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems. We propose a novel framework that employs fine-tuned LLMs to interpret natural language commands and convert them into specifi...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	423,030
2401.07164	3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding	Neural implicit surface representations are currently receiving a lot of interest as a means to achieve high-fidelity surface reconstruction at a low memory cost, compared to traditional explicit representations.However, state-of-the-art methods still struggle with excessive memory usage and non-smooth surfaces. This i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	421,439
2108.09201	Performance Bounds for Sampling and Remote Estimation of Gauss-Markov Processes over a Noisy Channel with Random Delay	In this study, we generalize a problem of sampling a scalar Gauss Markov Process, namely, the Ornstein-Uhlenbeck (OU) process, where the samples are sent to a remote estimator and the estimator makes a causal estimate of the observed realtime signal. In recent years, the problem is solved for stable OU processes. We pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	251,536
2004.14489	Interactive Video Stylization Using Few-Shot Patch-Based Training	In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are sty...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	174,899
2106.15406	A Comprehensive Survey of Incentive Mechanism for Federated Learning	Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning proc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	243,758
2405.20568	Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases	As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	459,410
1201.3740	Contractive Interference Functions and Rates of Convergence of Distributed Power Control Laws	The standard interference functions introduced by Yates have been very influential on the analysis and design of distributed power control laws. While powerful and versatile, the framework has some drawbacks: the existence of fixed-points has to be established separately, and no guarantees are given on the rate of conv...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	13,869
2312.11939	Time-Series Contrastive Learning against False Negatives and Class Imbalance	As an exemplary self-supervised approach for representation learning, time-series contrastive learning has exhibited remarkable advancements in contemporary research. While recent contrastive learning strategies have focused on how to construct appropriate positives and negatives, in this study, we conduct theoretical ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	416,790
1901.07521	Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study	We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necess...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	119,233
2412.10453	Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach	In recent years, significant advancements have been made in deep learning-based object detection algorithms, revolutionizing basic computer vision tasks, notably in object detection, tracking, and segmentation. This paper delves into the intricate domain of Small-Object-Detection (SOD) within satellite imagery, highlig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,941
2303.16275	Writing Assistants Should Model Social Factors of Language	Intelligent writing assistants powered by large language models (LLMs) are more popular today than ever before, but their further widespread adoption is precluded by sub-optimal performance. In this position paper, we argue that a major reason for this sub-optimal performance and adoption is a singular focus on the inf...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	354,805
2312.04261	New ternary self-orthogonal codes and related LCD codes from weakly regular plateaued functions	A linear code is said to be self-orthogonal if it is contained in its dual. Self-orthogonal codes are of interest because of their important applications, such as for constructing linear complementary dual (LCD) codes and quantum codes. In this paper, we construct several new families of ternary self-orthogonal codes b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	413,612
2005.02587	Modeling nanoconfinement effects using active learning	Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore sizes are less than 50 nm. At this scale, the fluid properties are affected by na...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,918
2404.01849	EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking	As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	443,612
2409.06890	Learning Deep Kernels for Non-Parametric Independence Testing	The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful tool for nonparametric detection of dependence between random variables. It crucially depends, however, on the selection of reasonable kernels; commonly-used choices like the Gaussian kernel, or the kernel that yields the distance covariance, are sufficien...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,310
2203.01155	Top-N Recommendation Algorithms: A Quest for the State-of-the-Art	Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures. Several recent research works however indicate that the reported improvements over the years sometimes "don't add up", and t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	283,269
2107.07098	Hida-Mat\'ern Kernel	We present the class of Hida-Mat\'ern kernels, which is the canonical family of covariance functions over the entire space of stationary Gauss-Markov Processes. It extends upon Mat\'ern kernels, by allowing for flexible construction of priors over processes with oscillatory components. Any stationary kernel, including ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,309
1910.12748	A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettes	The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more sever...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,186
2410.03154	Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights	This study explores the learnability of memory-less and memory-augmented RNNs, which are theoretically equivalent to Pushdown Automata. Empirical results show that these models often fail to generalize on longer sequences, relying more on precision than mastering symbolic grammar. Experiments on fully trained and compo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	494,651
2203.06560	Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior	Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Pr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	285,154
1911.10876	Towards robust word embeddings for noisy texts	Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	154,969
1609.01594	An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials	To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators. However, a major obstacle is the vast ...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	60,616
2308.04704	A Feature Set of Small Size for the PDF Malware Detection	Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Theref...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	384,521
2104.00358	Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems	This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users' interests. In particular, we collected a set of nine potential pitfalls, based on the literature and our own experiences as researchers working at the inters...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	227,977
2411.03527	PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices	Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround time in the photonic circuit design process. Neural operators offer a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,931
1504.06015	Super-Resolution of Mutually Interfering Signals	We consider simultaneously identifying the membership and locations of point sources that are convolved with different low-pass point spread functions, from the observation of their superpositions. This problem arises in three-dimensional super-resolution single-molecule imaging, neural spike sorting, multi-user channe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,351
1605.06555	Bot or Not? Deciphering Time Maps for Tweet Interarrivals	This exploratory study used the R Statistical Software to perform Monte Carlo simulation of time maps, which characterize events based on the elapsed time since the last event and the time that will transpire until the next event, and compare them to time maps from real Twitter users. Time maps are used to explore diff...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	56,152
1912.02478	Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue	The training of task-oriented dialogue systems is often confronted with the lack of annotated data. In contrast to previous work which augments training data through expensive crowd-sourcing efforts, we propose four different automatic approaches to data augmentation at both the word and sentence level for end-to-end t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	156,359
2012.06675	Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)	We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider a Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	211,177
1604.05453	An entropic characterization of long memory stationary process	Long memory or long range dependency is an important phenomenon that may arise in the analysis of time series or spatial data. Most of the definitions of long memory of a stationary process $X=\{X_1, X_2,\cdots,\}$ are based on the second-order properties of the process. The excess entropy of a stationary process is th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,808
2003.13960	Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model	We study how to train a student deep neural network for visual recognition by distilling knowledge from a blackbox teacher model in a data-efficient manner. Progress on this problem can significantly reduce the dependence on large-scale datasets for learning high-performing visual recognition models. There are two majo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	170,372
1801.02021	Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks	Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only av...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,846
2312.14299	Fairness in Submodular Maximization over a Matroid Constraint	Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning. Some of these applications involve decision-making over datapoints with sensitive attributes such as gender or race. In such settings, it is crucial to guarantee that the selected solution is fairly...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	417,579
2310.10074	SoTTA: Robust Test-Time Adaptation on Noisy Data Streams	Test-time adaptation (TTA) aims to address distributional shifts between training and testing data using only unlabeled test data streams for continual model adaptation. However, most TTA methods assume benign test streams, while test samples could be unexpectedly diverse in the wild. For instance, an unseen object or ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,077
2204.03495	Covariance matrix preparation for quantum principal component analysis	Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a density matrix. These algorithms assume that the covariance matrix can be encoded ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,318
2202.06856	Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization	A common explanation for the failure of deep networks to generalize out-of-distribution is that they fail to recover the "correct" features. We challenge this notion with a simple experiment which suggests that ERM already learns sufficient features and that the current bottleneck is not feature learning, but robust re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,356
2304.13649	A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Knowledge-Intensive Visual Question Answering (KI-VQA) refers to answering a question about an image whose answer does not lie in the image. This paper presents a new pipeline for KI-VQA tasks, consisting of a retriever and a reader. First, we introduce DEDR, a symmetric dual encoding dense retrieval framework in which...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	360,654
2101.06125	The Impact of Post-editing and Machine Translation on Creativity and Reading Experience	This article presents the results of a study involving the translation of a fictional story from English into Catalan in three modalities: machine-translated (MT), post-edited (MTPE) and translated without aid (HT). Each translation was analysed to evaluate its creativity. Subsequently, a cohort of 88 Catalan participa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	215,616
2005.12729	Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO	We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO). Specifically, we investigate the consequences of "code-level optimizations:" algorithm augmentations found only in...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	178,810
2004.14223	The computational framework for continuum-kinematics-inspired peridynamics	Peridynamics (PD) is a non-local continuum formulation. The original version of PD was restricted to bond-based interactions. Bond-based PD is geometrically exact and its kinematics are similar to classical continuum mechanics (CCM). However, it cannot capture the Poisson effect correctly. This shortcoming was addresse...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	174,811
1606.07585	Representing Extended Finite State Machines for SDL by A Novel Control Model of Discrete Event Systems	This paper discusses EFSM for SDL and transforms EFSM into a novel control model of discrete event systems. We firstly propose a control model of discrete event systems, where the event set is made up of several conflicting pairs and control is implemented to select one event of the pair. Then we transform EFSM for SDL...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	57,755
2406.06953	SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo Matching	Due to the difficulty in obtaining real samples and ground truth, the generalization performance and domain adaptation performance are critical for the feasibility of stereo matching methods in practical applications. However, there are significant distributional discrepancies among different domains, which pose challe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	462,833
2206.01473	Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation	Convolutional Neural Network (CNN) have been widely used in image classification. Over the years, they have also benefited from various enhancements and they are now considered as state of the art techniques for image like data. However, when they are used for regression to estimate some function value from images, few...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,489
1602.06136	Ordonnancement d'entit\'es pour la rencontre du web des documents et du web des donn\'ees	The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new appl...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	52,330
1908.01885	Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments	3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	140,880
2007.01452	Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks	This paper proposes a new mean-field framework for over-parameterized deep neural networks (DNNs), which can be used to analyze neural network training. In this framework, a DNN is represented by probability measures and functions over its features (that is, the function values of the hidden units over the training dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,431
2403.15837	Centered Masking for Language-Image Pre-Training	We introduce Gaussian masking for Language-Image Pre-Training (GLIP) a novel, straightforward, and effective technique for masking image patches during pre-training of a vision-language model. GLIP builds on Fast Language-Image Pre-Training (FLIP), which randomly masks image patches while training a CLIP model. GLIP re...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	440,769

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