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
2005.07652	Efficiently Learning Adversarially Robust Halfspaces with Noise	We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions on the adversarial perturbation sets under which halfspaces are efficiently robustly learnable. In the presence of random label noise, we gi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	177,338
2501.00856	Advances in UAV Avionics Systems Architecture, Classification and Integration: A Comprehensive Review and Future Perspectives	Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identifi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	521,827
2502.10173	Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model	Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships betw...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,759
2308.07117	iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN	The inverse short-time Fourier transform network (iSTFTNet) has garnered attention owing to its fast, lightweight, and high-fidelity speech synthesis. It obtains these characteristics using a fast and lightweight 1D CNN as the backbone and replacing some neural processes with iSTFT. Owing to the difficulty of a 1D CNN ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,393
2311.04834	Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction	We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD). Motivated by the effectiveness of Masked Image Modeling (MIM), we propose Masked Bounding Box Reconstruction (MBBR), a variation of MIM where a percentage of the entities/objects wi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,362
2411.01025	FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection	Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH). However, existing FISH image classification methods face challenges due to signal variability, the need for costly manual annotations and fail to adequately address the intrinsic uncertainty. We in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,879
2405.10221	Scalarisation-based risk concepts for robust multi-objective optimisation	Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs whose outputs are both desirable for the decision maker, whilst also being robust to the underlying uncertainties in the problem. In this w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,681
2104.08692	MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs	Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,921
2202.03706	Temporal Walk Centrality: Ranking Nodes in Evolving Networks	We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random w...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	279,315
1301.1003	Charting the Tractability Frontier of Certain Conjunctive Query Answering	An uncertain database is defined as a relational database in which primary keys need not be satisfied. A repair (or possible world) of such database is obtained by selecting a maximal number of tuples without ever selecting two distinct tuples with the same primary key value. For a Boolean query q, the decision problem...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	20,828
2304.04646	ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning	Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,299
1604.08171	Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?	Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give free samples or discounts to, up front. A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	55,178
1910.05339	DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services	Large scale cloud services use Key Performance Indicators (KPIs) for tracking and monitoring performance. They usually have Service Level Objectives (SLOs) baked into the customer agreements which are tied to these KPIs. Dependency failures, code bugs, infrastructure failures, and other problems can cause performance r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	149,029
2410.17629	Graph Signal Adaptive Message Passing	This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Signal Processing methods that apply the same filter to the entire graph, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,554
2308.09103	Efficient collision avoidance for autonomous vehicles in polygonal domains	This research focuses on trajectory planning problems for autonomous vehicles utilizing numerical optimal control techniques. The study reformulates the constrained optimization problem into a nonlinear programming problem, incorporating explicit collision avoidance constraints. We present three novel, exact formulatio...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	386,155
2407.01256	From Coupling to Resilience: Quantifying the Impact of Interconnection in Energy Carrier Grids	Due to the increasing share of renewable energy resources and the emergence of couplings of different energy carrier grids, which may support the electricity networks by providing additional flexibility, conducting research on the properties of multi-energy systems is necessary. Primarily to keep stable grid operation ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	469,209
2308.04380	Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination	Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of <anchor, positive, negative> is important for effectively training the model, e.g., hard negatives make the model learn efficiently and effectively. However, we observe th...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	384,389
2106.15315	Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics	Commercial retrospective video analytics platforms have increasingly adopted general interfaces to support the custom queries and convolutional neural networks (CNNs) that different applications require. However, existing optimizations were designed for settings where CNNs were platform- (not user-) determined, and fai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	true	243,705
2309.04911	A Review of Machine Learning-based Security in Cloud Computing	Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confiden...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	390,894
2110.02813	Accelerated First Order Methods for Variational Imaging	In this thesis, we offer a thorough investigation of different regularisation terms used in variational imaging problems, together with detailed optimisation processes of these problems. We begin by studying smooth problems and partially non-smooth problems in the form of Tikhonov denoising and Total Variation (TV) den...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	259,258
2201.10222	Explanatory Learning: Beyond Empiricism in Neural Networks	We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomously learning to interpret them. In EL, the burden of interpreting symbols is not left to humans or rigid human-coded compilers, as done in P...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	276,914
2407.16216	A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More	With advancements in self-supervised learning, the availability of trillions tokens in a pre-training corpus, instruction fine-tuning, and the development of large Transformers with billions of parameters, large language models (LLMs) are now capable of generating factual and coherent responses to human queries. Howeve...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,509
1312.4149	Autonomous Quantum Perceptron Neural Network	Recently, with the rapid development of technology, there are a lot of applications require to achieve low-cost learning. However the computational power of classical artificial neural networks, they are not capable to provide low-cost learning. In contrast, quantum neural networks may be representing a good computatio...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	29,104
2011.09768	Scene text removal via cascaded text stroke detection and erasing	Recent learning-based approaches show promising performance improvement for scene text removal task. However, these methods usually leave some remnants of text and obtain visually unpleasant results. In this work, we propose a novel "end-to-end" framework based on accurate text stroke detection. Specifically, we decoup...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,298
2005.02601	Performance Limit and Coding Schemes for Resistive Random-Access Memory Channels	Resistive random-access memory (ReRAM) is a promising candidate for the next generation non-volatile memory technology due to its simple read/write operations and high storage density. However, its crossbar array structure causes a severe interference effect known as the "sneak path." In this paper, we propose channel ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	175,926
1508.00116	Extending SROIQ with Constraint Networks and Grounded Circumscription	Developments in semantic web technologies have promoted ontological encoding of knowledge from diverse domains. However, modelling many practical domains requires more expressiveness than what the standard description logics (most prominently SROIQ) support. In this paper, we extend the expressive DL SROIQ with constra...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	45,635
1711.00331	Semantic Structure and Interpretability of Word Embeddings	Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	83,698
2206.02062	Performance Analysis of SPAD-Based Optical Wireless Communication with OFDM	In recent years, there has been a growing interest in the use of single-photon avalanche diode (SPAD) in optical wireless communication (OWC). SPAD operates in the Geiger mode and can act as a photon counting receiver obviating the need for a transimpedance amplifier (TIA). Although a SPAD receiver can provide higher s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	300,729
2501.05605	Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method	Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactio...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	523,653
2108.07610	DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,967
2204.02256	The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions	The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar constraint (NEC). However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is h...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	289,873
2106.08295	A White Paper on Neural Network Quantization	While neural networks have advanced the frontiers in many applications, they often come at a high computational cost. Reducing the power and latency of neural network inference is key if we want to integrate modern networks into edge devices with strict power and compute requirements. Neural network quantization is one...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	241,245
2312.12463	Open Vocabulary Semantic Scene Sketch Understanding	We study the underexplored but fundamental vision problem of machine understanding of abstract freehand scene sketches. We introduce a sketch encoder that results in semantically-aware feature space, which we evaluate by testing its performance on a semantic sketch segmentation task. To train our model we rely only on ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,966
2106.11482	Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics	Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,407
2106.13864	Nonuniform Defocus Removal for Image Classification	We propose and study the single-frame anisoplanatic deconvolution problem associated with image classification using machine learning algorithms, named the nonuniform defocus removal (NDR) problem. Mathematical analysis of the NDR problem is done and the so-called defocus removal (DR) algorithm for solving it is propos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,206
2202.07595	Bayesian Optimisation for Active Monitoring of Air Pollution	Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,603
2204.03559	Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy	With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. The ultimate benefit is improved access to video datasets, e.g. in healthcare settings. Recent literature has proposed deep network-based architectures to perform facial swaps and repo...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	290,349
1705.02936	Link Prediction using Top-$k$ Shortest Distances	In this paper, we apply an efficient top-$k$ shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-$k$ distances as a similarity measure outperf...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	true	73,086
2006.00555	Transferring Inductive Biases through Knowledge Distillation	Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly representative of the conditions at test time. However, defining, designing and efficiently adapting inductive biases is not necessarily straightfo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	179,503
2209.06006	Exploiting Semantic Communication for Non-Orthogonal Multiple Access	A novel semantics-empowered two-user uplink non-orthogonal multiple access (NOMA) framework is proposed for resource efficiency enhancement. More particularly, a secondary far user (F-user) employs the semantic communication (SemCom) while a primary near user (N-user) employs the conventional bit-based communication (B...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	317,266
1406.5751	Computing on Masked Data: a High Performance Method for Improving Big Data Veracity	The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cry...	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	34,062
2312.09015	Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection	(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs. The identification of latent communities holds practical significance in various domains, from soci...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	415,563
2305.12966	Hierarchical Integration Diffusion Model for Realistic Image Deblurring	Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,279
2011.11088	Data Mining Techniques in Predicting Breast Cancer	Background and Objective: Breast cancer, which accounts for 23% of all cancers, is threatening the communities of developing countries because of poor awareness and treatment. Early diagnosis helps a lot in the treatment of the disease. The present study conducted in order to improve the prediction process and extract ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	207,716
1311.6045	Build Electronic Arabic Lexicon	There are many known Arabic lexicons organized on different ways, each of them has a different number of Arabic words according to its organization way. This paper has used mathematical relations to count a number of Arabic words, which proofs the number of Arabic words presented by Al Farahidy. The paper also presents...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	28,615
2310.16675	Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks	Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series. The decision on whe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	402,826
2008.13102	Blackmarket-driven Collusion on Online Media: A Survey	Online media platforms have enabled users to connect with individuals, organizations, and share their thoughts. Other than connectivity, these platforms also serve multiple purposes - education, promotion, updates, awareness, etc. Increasing the reputation of individuals in online media (aka Social growth) is thus esse...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	193,766
2410.03941	AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models	Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the model to a specific domain, character, style, or concept. However, due to the limited data utilized during training, the fine-tuned model pe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,053
1612.00390	Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks	Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end-to-end trainable composite Convolutional Long...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,875
2310.02945	Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques	This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods. The performance of the PPO algorithm is evaluated using MATLAB Simulink co-simulation, and the results demonstrate that the most effi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,053
1802.04082	Towards self-adaptable robots: from programming to training machines	We argue that hardware modularity plays a key role in the convergence of Robotics and Artificial Intelligence (AI). We introduce a new approach for building robots that leads to more adaptable and capable machines. We present the concept of a self-adaptable robot that makes use of hardware modularity and AI techniques ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	90,139
2204.13662	ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation	Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). This is not yet the case for machines. In part this is because there exist no datasets with ground-truth 3D annotations for the study of physic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,892
2312.15838	SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security	In this paper, we introduce SecQA, a novel dataset tailored for evaluating the performance of Large Language Models (LLMs) in the domain of computer security. Utilizing multiple-choice questions generated by GPT-4 based on the "Computer Systems Security: Planning for Success" textbook, SecQA aims to assess LLMs' unders...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	418,151
2107.04576	Analytical Inverter-Based Distributed Generator Model for Power Flow Analysis	Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous. Existing distribution system tools predominately model distributed generation sources as either negative PQ loads or as a PV generator and then employed a PV-PQ switching algorithm to mimic V...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,506
2206.03436	A Benchmark for Federated Hetero-Task Learning	To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks. We also present B-FHTL, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,284
1401.5891	Hierarchical pixel clustering for image segmentation	In the paper a piecewise constant image approximations of sequential number of pixel clusters or segments are treated. A majorizing of optimal approximation sequence by hierarchical sequence of image approximations is studied. Transition from pixel clustering to image segmentation by reducing of segment numbers in clus...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	30,267
2204.00630	Extremely Low-light Image Enhancement with Scene Text Restoration	Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, we found out that most of these methods could not sufficiently recover the image details, for instance, the texts in the scene. In this paper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,330
2001.03807	Decentralized sequential active hypothesis testing and the MAC feedback capacity	We consider the problem of decentralized sequential active hypothesis testing (DSAHT), where two transmitting agents, each possessing a private message, are actively helping a third agent--and each other--to learn the message pair over a discrete memoryless multiple access channel (DM-MAC). The third agent (receiver) o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,066
2305.07868	Bridging History with AI A Comparative Evaluation of GPT 3.5, GPT4, and GoogleBARD in Predictive Accuracy and Fact Checking	The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	364,061
2211.00216	Distributed Graph Neural Network Training: A Survey	Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	true	327,795
2301.12003	Minimizing Trajectory Curvature of ODE-based Generative Models	Recent ODE/SDE-based generative models, such as diffusion models, rectified flows, and flow matching, define a generative process as a time reversal of a fixed forward process. Even though these models show impressive performance on large-scale datasets, numerical simulation requires multiple evaluations of a neural ne...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	342,345
2502.03540	Path Planning for Masked Diffusion Model Sampling	In this paper, we explore how token unmasking order influences generative quality in masked diffusion models (MDMs). We derive an expanded evidence lower bound (ELBO) that introduces a planner to select which tokens to unmask at each step. Our analysis reveals that alternative unmasking strategies can enhance generatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,759
2212.04537	Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks	Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,475
2409.10570	Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Watermarking	Pre-training language models followed by fine-tuning on specific tasks is standard in NLP, but traditional models often underperform when applied to the medical domain, leading to the development of specialized medical pre-trained language models (Med-PLMs). These models are valuable assets but are vulnerable to misuse...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	488,801
1905.12028	Image Alignment in Unseen Domains via Domain Deep Generalization	Image alignment across domains has recently become one of the realistic and popular topics in the research community. In this problem, a deep learning-based image alignment method is usually trained on an available largescale database. During the testing steps, this trained model is deployed on unseen images collected ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,624
2412.20289	Causal Discovery on Dependent Binary Data	The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. We propose a decorrelation-based approa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,161
1912.13156	Hiding Information in Big Data based on Deep Learning	The current approach of information hiding based on deep learning model can not directly use the original data as carriers, which means the approach can not make use of the existing data in big data to hiding information. We proposed a novel method of information hiding in big data based on deep learning. Our method us...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	159,018
2308.05780	Optical Script Identification for multi-lingual Indic-script	Script identification and text recognition are some of the major domains in the application of Artificial Intelligence. In this era of digitalization, the use of digital note-taking has become a common practice. Still, conventional methods of using pen and paper is a prominent way of writing. This leads to the classifi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	384,910
1901.00673	High Precision Variational Bayesian Inference of Sparse Linear Networks	Sparse networks can be found in a wide range of applications, such as biological and communication networks. Inference of such networks from data has been receiving considerable attention lately, mainly driven by the need to understand and control internal working mechanisms. However, while most available methods have ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	117,828
2403.03675	ZF Beamforming Tensor Compression for Massive MIMO Fronthaul	In the rapidly evolving landscape of 5G and beyond 5G (B5G) mobile cellular communications, efficient data compression and reconstruction strategies become paramount, especially in massive multiple-input multiple-output (MIMO) systems. A critical challenge in these systems is the capacity-limited fronthaul, particularl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	435,299
2305.09011	The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)	Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	364,479
1911.00465	ARSM Gradient Estimator for Supervised Learning to Rank	We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to the multivariate categorical variables with an unbiased and low-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,822
1405.0917	Anatomy of Scientific Evolution	The quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society, yet many studies are limited to qualitative and small-scale approaches. Here, we investigate scientific evolution through systematic analysis of a massive corpus of digitized English te...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	32,822
2403.12986	BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning	Semi-supervised Learning (SSL) reduces the need for extensive annotations in deep learning, but the more realistic challenge of imbalanced data distribution in SSL remains largely unexplored. In Class Imbalanced Semi-supervised Learning (CISSL), the bias introduced by unreliable pseudo-labels can be exacerbated by imba...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	439,425
1609.03529	Examining Representational Similarity in ConvNets and the Primate Visual Cortex	We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex. We find that with increasing depth and validation performance, ConvNet features are closer to corti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,896
1802.04929	Context-Specific Validation of Data-Driven Models	With an increasing use of data-driven models to control robotic systems, it has become important to develop a methodology for validating such models before they can be deployed to design a controller for the actual system. Specifically, it must be ensured that the controller designed for a learned model would perform a...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	90,341
1909.01583	Gerrymandering: A Briber's Perspective	We initiate the study of bribery problem in the context of gerrymandering and reverse gerrymandering. In our most general problem, the input is a set of voters having votes over a set of alternatives, a graph on the voters, a partition of voters into connected districts, cost of every voter for changing her district, a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	true	143,946
1911.04679	Object-Centric Task and Motion Planning in Dynamic Environments	We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid only as long as the environment is static and perception and control ar...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	153,055
1910.05276	exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models	Large language models can produce powerful contextual representations that lead to improvements across many NLP tasks. Since these models are typically guided by a sequence of learned self attention mechanisms and may comprise undesired inductive biases, it is paramount to be able to explore what the attention has lear...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	149,010
2103.11778	A Total-Variation Sparseness-Promoting Method for the Synthesis of Contiguously Clustered Linear Arrays	By exploiting an innovative total-variation compressive sensing (TV-CS) formulation, a new method for the synthesis of physically contiguous clustered linear arrays is presented. The computation of the feed network excitations is recast as the maximization of the gradient sparsity of the excitation vector subject to ma...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	225,931
2303.02151	Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners	Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. We then question, if the more diverse pre-training k...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	349,235
2304.12418	A hybrid quantum-classical approach for inference on restricted Boltzmann machines	Boltzmann machine is a powerful machine learning model with many real-world applications, for example by constructing deep belief networks. Statistical inference on a Boltzmann machine can be carried out by sampling from its posterior distribution. However, uniform sampling from such a model is not trivial due to an ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,197
2408.14381	Learning Tree-Structured Composition of Data Augmentation	Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transformations sequentially to the data. Existing augmentation methods such as RandAugment randomly sample from a list of pre-selected transformatio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	483,515
1811.01268	ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale	Enterprises are increasingly deploying large camera networks for video analytics. Many target applications entail a common problem template: searching for and tracking an object or activity of interest (e.g. a speeding vehicle, a break-in) through a large camera network in live video. Such cross-camera analytics is com...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	112,315
2306.07745	Kernelized Reinforcement Learning with Order Optimal Regret Bounds	Reinforcement learning (RL) has shown empirical success in various real world settings with complex models and large state-action spaces. The existing analytical results, however, typically focus on settings with a small number of state-actions or simple models such as linearly modeled state-action value functions. To ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	373,134
2011.08148	Causal motifs and existence of endogenous cascades in directed networks with application to company defaults	Motivated by the detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered by an endogenous or an exogenous event, that the underlying network is directed an...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	206,796
2406.11778	Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning	The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computational neuroscience, has received significant attention due to both its intricate nature and the brain's exceptional performance in this area...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	465,051
2012.06280	Acoustic Leak Detection in Water Networks	In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment. Based on recordings from seven contact microphones attached to the water supply network of a municipal suburb, we trained several sh...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,063
2201.10232	Learning Controllers from Data via Approximate Nonlinearity Cancellation	We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabili...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	276,916
2208.02126	Noise tolerance of learning to rank under class-conditional label noise	Often, the data used to train ranking models is subject to label noise. For example, in web-search, labels created from clickstream data are noisy due to issues such as insufficient information in item descriptions on the SERP, query reformulation by the user, and erratic or unexpected user behavior. In practice, it is...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	311,384
1804.08774	Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding	Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,833
2311.13613	Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning	Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset. Most existing dataset pruning methods rely on snapshot-based criteria to identify representative samples, often resulting in poor generalization across various pruning and cross-architecture scenarios....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	409,801
1904.11876	Simulating Execution Time of Tensor Programs using Graph Neural Networks	Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration. Searching the configuration space exhaustively is typically infeasible in practice. In line with recent research using TVM, we propose to learn a surrogate model to overcome this issue. The model is trained...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,969
2107.00887	HO-3D_v3: Improving the Accuracy of Hand-Object Annotations of the HO-3D Dataset	HO-3D is a dataset providing image sequences of various hand-object interaction scenarios annotated with the 3D pose of the hand and the object and was originally introduced as HO-3D_v2. The annotations were obtained automatically using an optimization method, 'HOnnotate', introduced in the original paper. HO-3D_v3 pro...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,307
2408.15616	Beyond Levenshtein: Leveraging Multiple Algorithms for Robust Word Error Rate Computations And Granular Error Classifications	The Word Error Rate (WER) is the common measure of accuracy for Automatic Speech Recognition (ASR). Transcripts are usually pre-processed by substituting specific characters to account for non-semantic differences. As a result of this normalisation, information on the accuracy of punctuation or capitalisation is lost. ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	484,006
2502.11984	Blank Space: Adaptive Causal Coding for Streaming Communications Over Multi-Hop Networks	In this work, we introduce Blank Space AC-RLNC (BS), a novel Adaptive and Causal Network Coding (AC-RLNC) solution designed to mitigate the triplet trade-off between throughput-delay-efficiency in multi-hop networks. BS leverages the network's physical limitations considering the bottleneck from each node to the destin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	534,632
2106.08502	Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent	We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric. Although the objective is geodesically non-convex, Riemannian GD empirically converges rapidly, in fact faster than off-the-shelf methods such as Euclidean GD and SDP solvers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,316
2406.04745	Confidence-aware Contrastive Learning for Selective Classification	Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the mo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	461,822
2003.08337	Weakly Supervised PET Tumor Detection Using Class Response	One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited due to the lack of annotated data. Using a weakly supervised learning is a promising way to address this problem, however, it is challenging t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	168,702
2303.02243	Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks	Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced computational costs during inference and can be trained directly from observational data. Existing methods, however, cannot extrapolate accurately...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	349,269

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