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
1912.02163	Regression with Uncertainty Quantification in Large Scale Complex Data	While several methods for predicting uncertainty on deep networks have been recently proposed, they do not readily translate to large and complex datasets. In this paper we utilize a simplified form of the Mixture Density Networks (MDNs) to produce a one-shot approach to quantify uncertainty in regression problems. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,274
2405.12806	MOSS: Motion-based 3D Clothed Human Synthesis from Monocular Video	Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current methodologies often overlook the influence of motion on surface deformation, re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,664
1901.08824	Joint shape learning and segmentation for medical images using a minimalistic deep network	Recently, state-of-the-art results have been achieved in semantic segmentation using fully convolutional networks (FCNs). Most of these networks employ encoder-decoder style architecture similar to U-Net and are trained with images and the corresponding segmentation maps as a pixel-wise classification task. Such framew...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,588
2411.03677	Physical Layer Deception in OFDM Systems	As a promising technology, physical layer security (PLS) enhances security by leveraging the physical characteristics of communication channels. However, it commonly takes the legitimate user more effort to secure its data, compared to that required by the eavesdropper to intercept the communication. To address this im...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	505,992
1901.05049	Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools	Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	118,715
2202.10415	Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users	Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	281,516
1510.03710	Hybrid Dialog State Tracker	This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	47,857
2310.15816	Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era	This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assisted models inspired by, and templated upon, the theory of Approximate Inertial Manifolds (AIMs); the particular motivation is the so-called...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,463
2402.02827	PowerGraph: A power grid benchmark dataset for graph neural networks	Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, developing grid analysis algorithms is important for...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	426,744
2409.20059	Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis	Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical metrics. Researchers have therefore utilized neural metrics through quality-informed decoding strategies, achieving better results than likelih...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,978
2412.12238	Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD)	In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	517,814
1904.11134	Summarizing Data Succinctly with the Most Informative Itemsets	Knowledge discovery from data is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and therefore, what results we would find interesting and/or surprising. Given new knowledge about the data, our expectations will change. Hence, in order to avoid redundant result...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	128,792
1706.00038	Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks	Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. This paper proposes a novel framework for training deep convolutional neural networks from noisy labeled datasets that can be obtained cheaply. The problem is formulated using an undirected graphical model that represe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,547
2205.06111	Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language	To solve difficult tasks, humans ask questions to acquire knowledge from external sources. In contrast, classical reinforcement learning agents lack such an ability and often resort to exploratory behavior. This is exacerbated as few present-day environments support querying for knowledge. In order to study how agents ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	296,139
1406.7525	Fusion Based Holistic Road Scene Understanding	This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, we propose an approach that jointly tackles object-level image segmentation and semantic region labeling within a conditional random field (CRF) framework. Specifically, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	34,227
2104.13180	Controlling earthquake-like instabilities using artificial intelligence	Earthquakes are lethal and costly. This study aims at avoiding these catastrophic events by the application of injection policies retrieved through reinforcement learning. With the rapid growth of artificial intelligence, prediction-control problems are all the more tackled by function approximation models that learn h...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	232,424
2008.11574	Leveraging Kernelized Synergies on Shared Subspace for Precision Grasp and Dexterous Manipulation	Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands, increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelize...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	193,314
2410.08743	Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization	3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	497,251
1506.02739	Connotation Frames: A Data-Driven Investigation	Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: somet...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	43,965
2407.06189	Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision	The performance of Large Vision Language Models (LVLMs) is dependent on the size and quality of their training datasets. Existing video instruction tuning datasets lack diversity as they are derived by prompting large language models with video captions to generate question-answer pairs, and are therefore mostly descri...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	471,303
2012.11431	Amplifying the Anterior-Posterior Difference via Data Enhancement -- A More Robust Deep Monocular Orientation Estimation Solution	Existing deep-learning based monocular orientation estimation algorithms faces the problem of confusion between the anterior and posterior parts of the objects, caused by the feature similarity of such parts in typical objects in traffic scenes such as cars and pedestrians. While difficult to solve, the problem may lea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,639
1505.00542	A deterministic algorithm for the distance and weight distribution of binary nonlinear codes	Given a binary nonlinear code, we provide a deterministic algorithm to compute its weight and distance distribution, and in particular its minimum weight and its minimum distance, which takes advantage of fast Fourier techniques. This algorithm's performance is similar to that of best-known algorithms for the average c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,743
1508.02103	Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning	Maier et al. (2010) introduced the relational causal model (RCM) for representing and inferring causal relationships in relational data. A lifted representation, called abstract ground graph (AGG), plays a central role in reasoning with and learning of RCM. The correctness of the algorithm proposed by Maier et al. (201...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	45,861
2305.01163	Federated Neural Radiance Fields	The ability of neural radiance fields or NeRFs to conduct accurate 3D modelling has motivated application of the technique to scene representation. Previous approaches have mainly followed a centralised learning paradigm, which assumes that all training images are available on one compute node for training. In this pap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,582
2106.11952	Unsupervised Object-Level Representation Learning from Scene Images	Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training on ImageNet. However, its success highly relies on the object-centric priors of ImageNet, i.e., different augmented views of the same image correspond to the same object. Such a heavily curated constraint becomes immediately in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,571
1901.09807	Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection	This paper considers a low-complexity iterative Linear Minimum Mean Square Error (LMMSE) multi-user detector for the Multiple-Input and Multiple-Output system with Non-Orthogonal Multiple Access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,844
2407.05175	TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks	This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical infor...	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,853
2408.16944	FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning	Few-shot imitation learning relies on only a small amount of task-specific demonstrations to efficiently adapt a policy for a given downstream tasks. Retrieval-based methods come with a promise of retrieving relevant past experiences to augment this target data when learning policies. However, existing data retrieval m...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	484,512
2111.14574	On the rate of convergence of a classifier based on a Transformer encoder	Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,635
2404.14071	From Rigid to Soft Robotic Approaches for Minimally Invasive Neurosurgery	Robotic assistance has significantly improved the outcomes of open microsurgery and rigid endoscopic surgery, however is yet to make an impact in flexible endoscopic neurosurgery. Some of the most common intracranial procedures for treatment of hydrocephalus and tumors stand to benefit from increased dexterity and redu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	448,564
1411.4194	ROSS User's Guide and Reference Manual (Version 1.0)	The ROSS method is a new approach in the area of knowledge representation that is useful for many artificial intelligence and natural language understanding representation and reasoning tasks. (ROSS stands for "Representation", "Ontology", "Structure", "Star" language). ROSS is a physical symbol-based representational ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	37,602
2408.00421	Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction	Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties -- including absorption, distribution, metabolism and excretion (ADME)-- are crucial in the early stages of drug development since they provide an unders...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	477,834
2002.00544	Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network	We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN)...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	162,387
2207.07016	Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets	Accurate three-dimensional perception is a fundamental task in several computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains ins...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,075
1701.02362	Visualizing Residual Networks	Residual networks are the current state of the art on ImageNet. Similar work in the direction of utilizing shortcut connections has been done extremely recently with derivatives of residual networks and with highway networks. This work potentially challenges our understanding that CNNs learn layers of local features th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,540
1807.01697	Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations	In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performan...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	102,114
2004.02640	Coronavirus Detection and Analysis on Chest CT with Deep Learning	The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,293
1612.02859	Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment	This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,294
2109.07854	Context-aware Padding for Semantic Segmentation	Zero padding is widely used in convolutional neural networks to prevent the size of feature maps diminishing too fast. However, it has been claimed to disturb the statistics at the border. As an alternative, we propose a context-aware (CA) padding approach to extend the image. We reformulate the padding problem as an i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	255,677
2210.12865	Knowledge Transfer from Answer Ranking to Answer Generation	Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This allows for synthesizing the information from multiple candidates into a concise, natural-sounding answer. However, creati...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	325,939
2208.04204	Origami-based Zygote structure enables pluripotent shape-transforming deployable structure	We propose an algorithmic framework of a pluripotent structure evolving from a simple compact structure into diverse complex 3-D structures for designing the shape transformable, reconfigurable, and deployable structures and robots. Our algorithmic approach suggests a way of transforming a compact structure consisting ...	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	312,027
2306.02167	The effects of increasing velocity on the tractive performance of planetary rovers	An emerging paradigm is being embraced in the conceptualization of future planetary exploration missions. Ambitious objectives and increasingly demanding mission constraints stress the importance associated with faster surface mobility. Driving speeds approaching or surpassing 1 m/s have been rarely used and their effe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	370,782
2312.04464	Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation	To tackle long planning horizon problems in reinforcement learning with general function approximation, we propose the first algorithm, termed as UCRL-WVTR, that achieves both \emph{horizon-free} and \emph{instance-dependent}, since it eliminates the polynomial dependency on the planning horizon. The derived regret bou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,684
2209.10807	SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning	Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural n...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	318,977
2303.04118	A Multiplicative Value Function for Safe and Efficient Reinforcement Learning	An emerging field of sequential decision problems is safe Reinforcement Learning (RL), where the objective is to maximize the reward while obeying safety constraints. Being able to handle constraints is essential for deploying RL agents in real-world environments, where constraint violations can harm the agent and the ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	349,964
0906.0531	Medium Access Control Protocols With Memory	Many existing medium access control (MAC) protocols utilize past information (e.g., the results of transmission attempts) to adjust the transmission parameters of users. This paper provides a general framework to express and evaluate distributed MAC protocols utilizing a finite length of memory for a given form of feed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	3,814
1912.10364	Learning to Impute: A General Framework for Semi-supervised Learning	Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies. In this paper, we take a more direct approach for semi-supervised learning and propose lea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	158,306
1804.11294	Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup	In this work, we propose a special cascade network for image segmentation, which is based on the U-Net networks as building blocks and the idea of the iterative refinement. The model was mainly applied to achieve higher recognition quality for the task of finding borders of the optic disc and cup, which are relevant to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,341
2411.17912	Can LLMs plan paths in the real world?	As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planning scenarios in various settings and with various difficulties. Our experiments showed that all LLMs made numerous error...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	511,650
2212.13562	An effectivization of the law of large numbers for algorithmically random sequences and its absolute speed limit of convergence	The law of large numbers is one of the fundamental properties which algorithmically random infinite sequences ought to satisfy. In this paper, we show that the law of large numbers can be effectivized for an arbitrary Schnorr random infinite sequence, with respect to an arbitrary computable Bernoulli measure. Moreover,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	338,339
2209.09602	Comparing Shape-Constrained Regression Algorithms for Data Validation	Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches that are able to consider the prior knowledge of domain experts to produce dependable, trustworthy assessments of data quality. Prior knowled...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,565
1412.6577	Modeling Compositionality with Multiplicative Recurrent Neural Networks	We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	38,662
2210.04400	Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching	Distance teaching has become popular these years because of the COVID-19 epidemic. However, both students and teachers face several challenges in distance teaching, like being easy to distract. We proposed Focus+, a system designed to detect learners' status with the latest AI technology from their web camera to solve ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	322,443
1812.07105	Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis	In this work, we propose an advanced AI based grading system for OCT images. The proposed system is a very deep fully convolutional attentive classification network trained with end to end advanced transfer learning with online random augmentation. It uses quasi random augmentation that outputs confidence values for di...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	116,750
2210.06970	Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation	Most research on natural language processing treats bias as an absolute concept: Based on a (probably complex) algorithmic analysis, a sentence, an article, or a text is classified as biased or not. Given the fact that for humans the question of whether a text is biased can be difficult to answer or is answered contrad...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	323,525
2202.04768	Boosting Graph Neural Networks by Injecting Pooling in Message Passing	There has been tremendous success in the field of graph neural networks (GNNs) as a result of the development of the message-passing (MP) layer, which updates the representation of a node by combining it with its neighbors to address variable-size and unordered graphs. Despite the fruitful progress of MP GNNs, their pe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	279,656
2206.03181	Detecting Global Community Structure in a COVID-19 Activity Correlation Network	The global pandemic of COVID-19 over the last 2.5 years have produced an enormous amount of epidemic/public health datasets, which may also be useful for studying the underlying structure of our globally connected world. Here we used the Johns Hopkins University COVID-19 dataset to construct a correlation network of co...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	301,172
2210.09126	Verifiable and Provably Secure Machine Unlearning	Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted. While many machine unlearning methods have been proposed, none of them enable users to audit the procedure. Furthermore, recent work shows a user is u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,426
1909.11387	Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks	In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy probability and the two dimensional velocity...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	146,801
1408.0881	Volumes of logistic regression models with applications to model selection	Logistic regression models with $n$ observations and $q$ linearly-independent covariates are shown to have Fisher information volumes which are bounded below by $\pi^q$ and above by ${n \choose q} \pi^q$. This is proved with a novel generalization of the classical theorems of Pythagoras and de Gua, which is of independ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,119
1911.13214	Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory	This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm. Similarly to checkpoint-ing techniques coming from the literature on Automatic Differentiation, it consists in dynamically selecting the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	155,612
2106.06499	Policy Gradient Bayesian Robust Optimization for Imitation Learning	The difficulty in specifying rewards for many real-world problems has led to an increased focus on learning rewards from human feedback, such as demonstrations. However, there are often many different reward functions that explain the human feedback, leaving agents with uncertainty over what the true reward function is...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	240,502
2405.05547	2-16 GHz Multifrequency X-Cut Lithium Niobate NEMS Resonators on a Single Chip	This work presents the design, fabrication, and testing of X-Cut Lithium Niobate (LN) acoustic nanoelectromechanical (NEMS) Laterally Vibrating Resonators (LVRs) and Degenerate LVRs (d-LVRs) operating in the S0 (YZ30) and SH0 (YZ-10) modes between 2 to 16 GHz range, monolithically fabricated on a single chip. The NEMS ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	452,961
1810.10821	On the Convergence of the Polarization Process in the Noisiness/Weak-$\ast$ Topology	Let $W$ be a channel where the input alphabet is endowed with an Abelian group operation, and let $(W_n)_{n\geq 0}$ be Ar{\i}kan's channel-valued polarization process that is obtained from $W$ using this operation. We prove that the process $(W_n)_{n\geq 0}$ converges almost surely to deterministic homomorphism channel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	111,374
1907.06013	Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners	This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment information such as raw poin...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	138,500
2010.10266	Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation	Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	201,836
2409.03475	An Effective Current Limiting Strategy to Enhance Transient Stability of Virtual Synchronous Generator	VSG control has emerged as a crucial technology for integrating renewable energy sources. However, renewable energy have limited tolerance to overcurrent, necessitating the implementation of current limiting (CL)strategies to mitigate the overcurrent. The introduction of different CL strategies can have varying impacts...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	486,062
1905.07852	Boundary Loss for Remote Sensing Imagery Semantic Segmentation	In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing da...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	131,343
2011.07462	Nonlinearity Characteristic of High Impedance Fault at Resonant Distribution Networks: Theoretical Basis to Identify the Faulty Feeder	Feeder identification is indispensable for distribution networks to locate faults at a specific feeder, especially when measuring de-vices are insufficient for precise locations. For the high imped-ance fault (HIF), the feeder identification is much more compli-cated and related approaches are still in the early stage....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	206,569
2404.00886	MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control	Traffic signal control has a great impact on alleviating traffic congestion in modern cities. Deep reinforcement learning (RL) has been widely used for this task in recent years, demonstrating promising performance but also facing many challenges such as limited performances and sample inefficiency. To handle these cha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	443,149
2208.02856	Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange	Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data. Nevertheless, in many applications, it is impractical to assume existence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,593
2310.03984	AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems	The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimization process is the environment non-stationarity stemming from the continual and complex evolution of user behavior patterns over time, such ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	397,499
1409.0838	A Supervisory Control Approach to Dynamic Cyber-Security	An analytical approach for a dynamic cyber-security problem that captures progressive attacks to a computer network is presented. We formulate the dynamic security problem from the defender's point of view as a supervisory control problem with imperfect information, modeling the computer network's operation by a discre...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	35,769
2002.10742	Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem	Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy. In several domains, however, data is scarce or expensive to retrieve, while a substantial amount of expert knowledge is available. It seems reasonable that if we can inject this additional informati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	165,503
1812.04048	Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks	Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network consensus optimization problems, one of the most well-known approaches is the distributed gradient descent method (DGD). However, in networks ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	116,127
2202.03423	Backdoor Defense via Decoupling the Training Process	Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples. The attacked model behaves normally on benign samples, whereas its prediction will be maliciously changed when the backdoor is ac...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	279,198
1605.08201	Towards optimal nonlinearities for sparse recovery using higher-order statistics	We consider machine learning techniques to develop low-latency approximate solutions to a class of inverse problems. More precisely, we use a probabilistic approach for the problem of recovering sparse stochastic signals that are members of the $\ell_p$-balls. In this context, we analyze the Bayesian mean-square-error ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,401
2206.03800	Optimal User Load and Energy Efficiency in User-Centric Cell-Free Wireless Networks	Cell-free massive MIMO is a variant of multiuser MIMO and massive MIMO, in which the total number of antennas $LM$ is distributed among the $L$ remote radio units (RUs) in the system, enabling macrodiversity and joint processing. Due to pilot contamination and system scalability, each RU can only serve a limited number...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	301,412
1705.01936	Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels	Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,901
2412.08875	Brain-inspired AI Agent: The Way Towards AGI	Artificial General Intelligence (AGI), widely regarded as the fundamental goal of artificial intelligence, represents the realization of cognitive capabilities that enable the handling of general tasks with human-like proficiency. Researchers in brain-inspired AI seek inspiration from the operational mechanisms of the ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	516,259
2107.09899	Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection	Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,158
2412.08890	Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries	We introduce Lexico, a novel KV cache compression method that leverages sparse coding with a universal dictionary. Our key finding is that key-value cache in modern LLMs can be accurately approximated using sparse linear combination from a small, input-agnostic dictionary of ~4k atoms, enabling efficient compression ac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,264
2312.10242	A Survey of Classical And Quantum Sequence Models	Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,074
1409.1628	Extended Delivery Time Analysis for Cognitive Packet Transmission with Application to Secondary Queuing Analysis	Cognitive radio transceiver can opportunistically access the underutilized spectrum resource of primary systems for new wireless services. With interleave implementation, the secondary transmission may be interrupted by the primary user's transmission. To facilitate the delay analysis of such secondary transmission for...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	35,844
2101.04170	Resolution-Based Distillation for Efficient Histology Image Classification	Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image cl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,077
2501.17450	NF-MKV Net: A Constraint-Preserving Neural Network Approach to Solving Mean-Field Games Equilibrium	Neural network-based methods for solving Mean-Field Games (MFGs) equilibria have garnered significant attention for their effectiveness in high-dimensional problems. However, many algorithms struggle with ensuring that the evolution of the density distribution adheres to the required mathematical constraints. This pape...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,343
2004.14996	Segatron: Segment-Aware Transformer for Language Modeling and Understanding	Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated f...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,079
2310.20443	Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines	In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented on...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	true	404,393
2007.06305	Mixed-state entanglement from local randomized measurements	We propose a method for detecting bipartite entanglement in a many-body mixed state based on estimating moments of the partially transposed density matrix. The estimates are obtained by performing local random measurements on the state, followed by post-processing using the classical shadows framework. Our method can b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	186,978
2009.08610	Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation	Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,290
1911.10556	Specific Absorption Rate-Aware Beamforming in MISO Downlink SWIPT Systems	This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	154,870
1906.08889	SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks	Recently end-to-end unsupervised deep learning methods have achieved an effect beyond geometric methods for visual depth and ego-motion estimation tasks. These data-based learning methods perform more robustly and accurately in some of the challenging scenes. The encoder-decoder network has been widely used in the dept...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	136,006
2502.02338	Geometric Neural Process Fields	This paper addresses the challenge of Neural Field (NeF) generalization, where models must efficiently adapt to new signals given only a few observations. To tackle this, we propose Geometric Neural Process Fields (G-NPF), a probabilistic framework for neural radiance fields that explicitly captures uncertainty. We for...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	530,270
1908.03313	Using Semantic Role Knowledge for Relevance Ranking of Key Phrases in Documents: An Unsupervised Approach	In this paper, we investigate the integration of sentence position and semantic role of words in a PageRank system to build a key phrase ranking method. We present the evaluation results of our approach on three scientific articles. We show that semantic role information, when integrated with a PageRank system, can bec...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	141,217
2007.13027	Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding	Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain, application, and context. This paper investigates the effect of seven text proces...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	188,999
1803.04840	Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion	Today's Automatic Speech Recognition systems only rely on acoustic signals and often don't perform well under noisy conditions. Performing multi-modal speech recognition - processing acoustic speech signals and lip-reading video simultaneously - significantly enhances the performance of such systems, especially in nois...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	92,528
2001.08615	Knowledge Graphs for Innovation Ecosystems	Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge a...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	161,337
2105.09909	PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection	Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the cortical microcircuits and uses spiking neural networks (SNN) that can be directly realized on neuromorphic hardware. In this paper, we pres...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	236,200
1908.07721	Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text	Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	142,354
2502.02723	Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives	We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in truncating activations, rather than merely using activations as an optimization distance. Building on this principle, we address three crit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,444

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