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
2003.01267	Single-Shot Pose Estimation of Surgical Robot Instruments' Shafts from Monocular Endoscopic Images	Surgical robots are used to perform minimally invasive surgery and alleviate much of the burden imposed on surgeons. Our group has developed a surgical robot to aid in the removal of tumors at the base of the skull via access through the nostrils. To avoid injuring the patients, a collision-avoidance algorithm that dep...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	166,588
1711.07410	Disentangling Factors of Variation by Mixing Them	We propose an approach to learn image representations that consist of disentangled factors of variation without exploiting any manual labeling or data domain knowledge. A factor of variation corresponds to an image attribute that can be discerned consistently across a set of images, such as the pose or color of objects...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,985
2101.03300	Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus	Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of mal...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	214,888
2102.09583	Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling	With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	220,821
1907.06923	The Bregman-Tweedie Classification Model	This work proposes the Bregman-Tweedie classification model and analyzes the domain structure of the extended exponential function, an extension of the classic generalized exponential function with additional scaling parameter, and related high-level mathematical structures, such as the Bregman-Tweedie loss function an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,741
1411.2897	Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic Operator	This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their actions in sele...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	37,459
2312.17527	Data-Driven Template-Free Invariant Generation	Automatic verification of concurrent programs faces state explosion due to the exponential possible interleavings of its sequential components coupled with large or infinite state spaces. An alternative is deductive verification, where given a candidate invariant, we establish inductive invariance and show that any sta...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	418,784
2412.07794	Automatic answering of scientific questions using the FACTS-V1 framework: New methods in research to increase efficiency through the use of AI	The use of artificial intelligence (AI) offers various possibilities to expand and support educational research. Specifically, the implementation of AI can be used to develop new frameworks to establish new research tools that accelerate and meaningfully expand the efficiency of data evaluation and interpretation (Buck...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	515,812
2307.02849	NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic	Reasoning has been a central topic in artificial intelligence from the beginning. The recent progress made on distributed representation and neural networks continues to improve the state-of-the-art performance of natural language inference. However, it remains an open question whether the models perform real reasoning...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	377,838
2001.03956	Interpretable feature subset selection: A Shapley value based approach	For feature selection and related problems, we introduce the notion of classification game, a cooperative game, with features as players and hinge loss based characteristic function and relate a feature's contribution to Shapley value based error apportioning (SVEA) of total training error. Our major contribution is ($...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	160,111
2011.08346	Refining Automatic Speech Recognition System for older adults	Building a high quality automatic speech recognition (ASR) system with limited training data has been a challenging task particularly for a narrow target population. Open-sourced ASR systems, trained on sufficient data from adults, are susceptible on seniors' speech due to acoustic mismatch between adults and seniors. ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	206,840
1904.03736	Unsupervised Dialog Structure Learning	Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems. We propose to extract dialog structu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	126,822
2308.12284	D4: Improving LLM Pretraining via Document De-Duplication and Diversification	Over recent years, an increasing amount of compute and data has been poured into training large language models (LLMs), usually by doing one-pass learning on as many tokens as possible randomly selected from large-scale web corpora. While training on ever-larger portions of the internet leads to consistent performance ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	387,485
1805.04777	Convolutional CRFs for Semantic Segmentation	For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,309
2211.09778	I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision	Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether it is possible to learn those skills from text data and then...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	331,095
2212.10129	Minimizing interference-to-signal ratios in multi-cell networks	In contemporary wireless communication networks, base-stations are organized into coordinated clusters (called cells) to jointly serve the users. However, such fixed systems are plagued by the so-called cell-edge problem: near the boundaries, the interference between neighboring clusters can result in very poor interfe...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	337,348
1707.04095	Is writing style predictive of scientific fraud?	The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators. The results seem to suggest that writing style is predictive of scientific fraud. We revisit these initial experiments, and show that the ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	76,990
2301.04391	Grassroots Systems: Concept, Examples, Implementation and Applications	Informally, a grassroots system is a distributed system that can have multiple instances, independent of each other and of any global resources, that can interoperate once interconnected. Grassroots applications are potentially important as they may allow people to conduct their social, economic, civic, and political l...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	true	340,047
2406.03157	A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction	Accurate prediction of financial time series is a key concern for market economy makers and investors. The article selects online store sales and Australian beer sales as representatives of non-stationary, trending, and seasonal financial time series, and constructs a new SGVMD-ARIMA combination model in a non-linear c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,118
2210.03850	Toward an Over-parameterized Direct-Fit Model of Visual Perception	In this paper, we revisit the problem of computational modeling of simple and complex cells for an over-parameterized and direct-fit model of visual perception. Unlike conventional wisdom, we highlight the difference in parallel and sequential binding mechanisms between simple and complex cells. A new proposal for abst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,195
2106.01072	Evidence-based Factual Error Correction	This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We de...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	238,371
2101.12629	Quarter and Full Car Models Optimisation of Passive and Active Suspension System Using Genetic Algorithm	This study evaluates a suspension design of a passenger car to obtain maximum rider's comfort when the vehicle is subjected to different road profile or road surface condition. The challenge will be on finding a balance between the rider's comfort and vehicle handling to optimize design parameters. The study uses a sim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	217,610
2409.06716	Detailed delineation of the fetal brain in diffusion MRI via multi-task learning	Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain in-utero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable ana...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	487,242
2407.15656	Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN	Penetration testing is the process of searching for security weaknesses by simulating an attack. It is usually performed by experienced professionals, where scanning and attack tools are applied. By automating the execution of such tools, the need for human interaction and decision-making could be reduced. In this work...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	475,270
2008.02793	Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications	The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was chal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,715
2307.08318	Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge	Purpose: Navigation guidance is a key requirement for a multitude of lung interventions using video bronchoscopy. State-of-the-art solutions focus on lung biopsies using electromagnetic tracking and intraoperative image registration w.r.t. preoperative CT scans for guidance. The requirement of patient-specific CT scans...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	379,762
1506.02554	DUAL-LOCO: Distributing Statistical Estimation Using Random Projections	We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the depend...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	43,937
2210.08001	Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks	We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampling layers. It is widely applicable as it can be integrated into any c...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	323,941
2410.03601	How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework	Discrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for discrete diffusion models remains less well-understood. In this work, we propose a comprehensive framework for the error analysis of discrete...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	494,871
2405.20082	Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations	Existing approaches for learning representations of time-series keep the temporal arrangement of the time-steps intact with the presumption that the original order is the most optimal for learning. However, non-adjacent sections of real-world time-series may have strong dependencies. Accordingly, we raise the question:...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	459,187
2410.20678	A Machine Learning-Driven Wireless System for Structural Health Monitoring	The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,912
1912.11711	Controllable and Progressive Image Extrapolation	Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no control of the content generation process. In this work, we study conditional image extrapolation to synthesize new images guided by the input s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,643
1801.04012	Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data	A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	88,197
1902.09849	Multi-Scale Quasi-RNN for Next Item Recommendation	How to better utilize sequential information has been extensively studied in the setting of recommender systems. To this end, architectural inductive biases such as Markov-Chains, Recurrent models, Convolutional networks and many others have demonstrated reasonable success on this task. This paper proposes a new neural...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	122,524
2012.15370	OSTeC: One-Shot Texture Completion	The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	213,756
2401.05115	Unpacking Human-AI interactions: From interaction primitives to a design space	This paper aims to develop a semi-formal design space for Human-AI interactions, by building a set of interaction primitives which specify the communication between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can provide an abstrac...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	420,647
2007.12088	Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application	We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion datasets while, based on the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,727
1707.01877	Changing Views on Curves and Surfaces	Visual events in computer vision are studied from the perspective of algebraic geometry. Given a sufficiently general curve or surface in 3-space, we consider the image or contour curve that arises by projecting from a viewpoint. Qualitative changes in that curve occur when the viewpoint crosses the visual event surfac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,610
0811.0048	Conjectural Equilibrium in Water-filling Games	This paper considers a non-cooperative game in which competing users sharing a frequency-selective interference channel selfishly optimize their power allocation in order to improve their achievable rates. Previously, it was shown that a user having the knowledge of its opponents' channel state information can make for...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	2,594
2104.14360	Video Salient Object Detection via Adaptive Local-Global Refinement	Video salient object detection (VSOD) is an important task in many vision applications. Reliable VSOD requires to simultaneously exploit the information from both the spatial domain and the temporal domain. Most of the existing algorithms merely utilize simple fusion strategies, such as addition and concatenation, to m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,798
1810.03488	A Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers	We propose a coded distributed computing scheme based on Raptor codes to address the straggler problem. In particular, we consider a scheme where each server computes intermediate values, referred to as droplets, that are either stored locally or sent over the network. Once enough droplets are collected, the computatio...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	109,822
2402.03732	Deep Outdated Fact Detection in Knowledge Graphs	Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. I...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	427,161
2106.00497	Omnizart: A General Toolbox for Automatic Music Transcription	We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the life-cycle of deep learning-based AMT, and is designed for ease of use with a compact command-line interface. To the best of our knowledge, O...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	238,142
2403.02360	Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling	Federated learning, as a promising distributed learning paradigm, enables collaborative training of a global model across multiple network edge clients without the need for central data collecting. However, the heterogeneity of edge data distribution drags the model towards the local minima, which can be distant from t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	434,781
2311.12071	Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning	Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with undersampled measurements o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	409,193
2411.09344	Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing	Remote sensing (RS) involves the acquisition of data about objects or areas from a distance, primarily to monitor environmental changes, manage resources, and support planning and disaster response. A significant challenge in RS segmentation is the scarcity of high-quality labeled images due to the diversity and comple...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,220
2004.02788	Self-Supervised Scene De-occlusion	Natural scene understanding is a challenging task, particularly when encountering images of multiple objects that are partially occluded. This obstacle is given rise by varying object ordering and positioning. Existing scene understanding paradigms are able to parse only the visible parts, resulting in incomplete and u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,347
0705.0450	VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs	Performance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	146
2308.06332	Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology	The rapid accessibility of portable and affordable retinal imaging devices has made early differential diagnosis easier. For example, color funduscopy imaging is readily available in remote villages, which can help to identify diseases like age-related macular degeneration (AMD), glaucoma, or pathological myopia (PM). ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	385,096
1803.01118	Some Considerations on Learning to Explore via Meta-Reinforcement Learning	We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$ deliver better performanc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	91,818
1706.08675	Proceedings of the First International Workshop on Deep Learning and Music	Proceedings of the First International Workshop on Deep Learning and Music, joint with IJCNN, Anchorage, US, May 17-18, 2017	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	76,031
2209.07162	Brain Imaging Generation with Latent Diffusion Models	Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datase...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,649
2306.04004	Randomized Schur Complement Views for Graph Contrastive Learning	We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements and treats the corresponding graphs as augmented views. We discuss the benefits of our approach, provi...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,562
2309.02050	Machine learning of network inference enhancement from noisy measurements	Inferring networks from observed time series data presents a clear glimpse into the interconnections among nodes. Network inference models, when dealing with real-world open cases, especially in the presence of observational noise, experience a sharp decline in performance, significantly undermining their practical app...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	389,911
2207.09828	${\mathcal K}$-monotonicity and feedback synthesis for incrementally stable networks	We discuss the role of monotonicity in enabling numerically tractable modular control design for networked nonlinear systems. We first show that the variational systems of monotone systems can be embedded into positive systems. Utilizing this embedding, we show how to solve a network stabilization problem by enforcing ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	309,042
2301.08305	The Invasion of Ukraine Viewed through TikTok: A Dataset	We present a dataset of video descriptions, comments, and user statistics, from the social media platform TikTok, centred around the invasion of Ukraine in 2022, an event that launched TikTok into the geopolitical arena. User activity on the platform around the invasion exposed myriad political behaviours and dynamics ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	341,161
2405.00693	Leveraging Large Language Models in Human-Robot Interaction: A Critical Analysis of Potential and Pitfalls	The emergence of large language models (LLM) and, consequently, vision language models (VLM) has ignited new imaginations among robotics researchers. At this point, the range of applications to which LLM and VLM can be applied in human-robot interaction (HRI), particularly socially assistive robots (SARs), is uncharter...	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	451,027
2104.09134	Restoration of Video Frames from a Single Blurred Image with Motion Understanding	We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurred image, in this work, we tackle a more challenging task i.e. video restoration from a blurred image. We formulate video restoration from a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,129
2303.06200	Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints	Dynamic Programming (DP) suffers from the well-known ``curse of dimensionality'', further exacerbated by the need to compute expectations over process noise in stochastic models. This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	350,733
1601.01944	Nonparametric semi-supervised learning of class proportions	The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate posterior probabilities of positive and negative classes for a previously unseen data point. This problem can be decomposed into two steps: (i) t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,785
2112.08327	Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog	The wide applicability of pretrained transformer models (PTMs) for natural language tasks is well demonstrated, but their ability to comprehend short phrases of text is less explored. To this end, we evaluate different PTMs from the lens of unsupervised Entity Linking in task-oriented dialog across 5 characteristics --...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,759
2210.12654	Cross-document Event Coreference Search: Task, Dataset and Modeling	The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search, focusing in this paper on event c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,843
1301.2158	Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach	In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,914
2410.07961	QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design	Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control over quantum states. De...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	496,879
2305.16979	Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays	Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be difficult due to time delays that can compromise system performance and stability. Enhancing the synchr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	368,354
2501.17105	Optimal control over Markovian wireless communication channels under generalized packet dropout compensation	Control loops closed over wireless links greatly benefit from accurate estimates of the communication channel condition. To this end, the finite-state Markov channel model allows for reliable channel state estimation. This paper develops a Markov jump linear system representation for wireless networked control with per...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	528,211
1306.2685	Flexible sampling of discrete data correlations without the marginal distributions	Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction. More recently, the framework of copula modeling has gained popularity due to its modular parametrization of joint distributions. Among o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,147
2112.05151	Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI	Deep learning-based diagnostic performance increases with more annotated data, but large-scale manual annotations are expensive and labour-intensive. Experts evaluate diagnostic images during clinical routine, and write their findings in reports. Leveraging unlabelled exams paired with clinical reports could overcome t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,758
1903.03295	Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos	Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors. We propose a new method to model the normal patterns of human movements in surveillance video for anomaly detection using dynamic skeleton features. We decompose the skeletal movements into two sub-c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,699
2110.00512	Optic Disc Segmentation using Disk-Centered Patch Augmentation	The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a color fundus image is straightforward, accurately segmenting its boundary at the pixel level is very...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	258,415
2407.17839	Long-term Fairness in Ride-Hailing Platform	Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and fairness issues in ride-hailing have been neglected. Fairness issues in ride-hailing, including significant earning differences between dri...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	476,138
2207.04713	GMN: Generative Multi-modal Network for Practical Document Information Extraction	Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. Although recent literature has already achieved competitive results, these approaches usually fail when dealing with complex documents with noisy OCR results or mutative layouts. This pap...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	307,291
1711.09453	An Analytical Framework for Coverage in Cellular Networks Leveraging Vehicles	This paper analyzes an emerging architecture of cellular network utilizing both planar base stations uniformly distributed in Euclidean plane and base stations located on roads. An example of this architecture is that where, in addition to conventional planar cellular base stations and users, vehicles also play the rol...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	85,406
2110.05208	Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm	Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	260,204
1010.1042	Hidden Markov Models with Multiple Observation Processes	We consider a hidden Markov model with multiple observation processes, one of which is chosen at each point in time by a policy---a deterministic function of the information state---and attempt to determine which policy minimises the limiting expected entropy of the information state. Focusing on a special case, we pro...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	7,799
2308.00521	SurveyLM: A platform to explore emerging value perspectives in augmented language models' behaviors	This white paper presents our work on SurveyLM, a platform for analyzing augmented language models' (ALMs) emergent alignment behaviors through their dynamically evolving attitude and value perspectives in complex social contexts. Social Artificial Intelligence (AI) systems, like ALMs, often function within nuanced soc...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	382,968
1907.07509	A Link Between the Multiplicative and Additive Functional Asplund's Metrics	Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided probing in the Logarithmic Image Processing (LIP) framework. Two metrics were defined, namely the LIP-multiplicative Asplund's metric which is robust to variations of object thickness (or ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,896
2309.10001	CaSAR: Contact-aware Skeletal Action Recognition	Skeletal Action recognition from an egocentric view is important for applications such as interfaces in AR/VR glasses and human-robot interaction, where the device has limited resources. Most of the existing skeletal action recognition approaches use 3D coordinates of hand joints and 8-corner rectangular bounding boxes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,827
2006.11757	Methodology for Building Synthetic Datasets with Virtual Humans	Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the qu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	183,361
2010.06408	Penalized model-based clustering of fMRI data	Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,476
1703.06182	Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability	Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	70,185
1912.03449	Fully Dense Neural Network for the Automatic Modulation Recognition	Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of preprocessing, but also consume a lot of memory. In order to directly use in-phase and quadrature (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,594
1302.1211	Quantum Lyapunov Control Based on the Average Value of an Imaginary Mechanical Quantity	The convergence of closed quantum systems in the degenerate cases to the desired target state by using the quantum Lyapunov control based on the average value of an imaginary mechanical quantity is studied. On the basis of the existing methods which can only ensure the single-control Hamiltonian systems converge toward...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	21,788
2306.00390	Learning Gaussian Mixture Representations for Tensor Time Series Forecasting	Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e.g., transportation demands and air pollutants). Compared to modeling time series or multivari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,992
1702.04538	A core-set approach for distributed quadratic programming in big-data classification	A new challenge for learning algorithms in cyber-physical network systems is the distributed solution of big-data classification problems, i.e., problems in which both the number of training samples and their dimension is high. Motivated by several problem set-ups in Machine Learning, in this paper we consider a specia...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	68,273
1806.09770	Completely Distributed Guaranteed-performance Consensualization for High-order Multiagent Systems with Switching Topologies	The guaranteed-performance consensualization for high-order linear and nonlinear multiagent systems with switching topologies is respectively realized in a completely distributed manner in the sense that consensus design criteria are independent of interaction topologies and switching motions. The current paper firstly...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	101,415
2201.00583	Evaluation and comparison of SEA torque controllers in a unified framework	Series elastic actuators (SEA) with their inherent compliance offer a safe torque source for robots that are interacting with various environments, including humans. These applications have high requirements for the SEA torque controllers, both in the torque response as well as interaction behavior with its the environ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	274,006
2203.10545	Parallel Instance Query Network for Named Entity Recognition	Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one ty...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,584
2502.07846	Memory Analysis on the Training Course of DeepSeek Models	We present a theoretical analysis of GPU memory consumption during the training of DeepSeek models such as DeepSeek-v2 and DeepSeek-v3. Our primary objective is to clarify the device-level memory requirements associated with various distributed training configurations. Specifically, we examine critical factors influenc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	532,796
2304.03456	Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning	Linear probing (LP) (and $k$-NN) on the upstream dataset with labels (e.g., ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate the quality of visual representations learned via self-supervised learning (SSL). Although existing SSL methods have shown good performances u...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	356,817
2004.04125	Wisdom of Crowds Detects COVID-19 Severity Ahead of Officially Available Data	During the unfolding of a crisis, it is crucial to determine its severity, yet access to reliable data is challenging. We investigate the relation between geolocated Tweet Intensity of initial COVID-19 related tweet at the beginning of the pandemic across Italian, Spanish and USA regions and mortality in the region a m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	171,792
2402.15011	A Conversational Brain-Artificial Intelligence Interface	We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to acco...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	431,954
2405.10414	A Reliability Theory of Compromise Decisions for Large-Scale Stochastic Programs	Stochastic programming models can lead to very large-scale optimization problems for which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a sampling-based solution methodology in which case the reliability of the resulting decisions may be suspect. For such instances, it is advisabl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,747
2306.13891	Estimating the Causal Effect of Early ArXiving on Paper Acceptance	What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and apply methods from causal inference to estimate the effect of a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	375,442
2203.07941	Reachability In Simple Neural Networks	We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and specifications over the input/output dimension given by conjunctions of linear inequalities. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	285,615
2407.19262	Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications	Understanding whether and to what extent large language models (LLMs) have memorised training data has important implications for the reliability of their output and the privacy of their training data. In order to cleanly measure and disentangle memorisation from other phenomena (e.g. in-context learning), we create an...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	476,708
1910.08919	Sampling Strategies for Data-Driven Inference of Input-Output System Properties	Due to their relevance in controller design, we consider the problem of determining the $\mathcal{L}^2$-gain, passivity properties and conic relations of an input-output system. While, in practice, the input-output relation is often undisclosed, input-output data tuples can be sampled by performing (numerical) experime...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	150,018
2305.12804	The defender's perspective on automatic speaker verification: An overview	Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are s...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	366,200
1905.07097	The Discussion on Shannon channel capacity formula from the viewpoint of signal uncertainty and Research on the Technique of Breaking through the Shannon Limit	In this paper, firstly, the Shannon channel capacity formula is briefly stated, and the relationship between the formula and the signal uncertainty principle is analyzed in order to prepare for deriving the formula which is able to break through the Shannon channel capacity. Then, as a practical example of breaking the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	131,148
2105.09222	More Similar Values, More Trust? -- the Effect of Value Similarity on Trust in Human-Agent Interaction	As AI systems are increasingly involved in decision making, it also becomes important that they elicit appropriate levels of trust from their users. To achieve this, it is first important to understand which factors influence trust in AI. We identify that a research gap exists regarding the role of personal values in t...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	236,004

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