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
1202.5599	On the Ingleton-Violations in Finite Groups	Given $n$ discrete random variables, its entropy vector is the $2^n-1$ dimensional vector obtained from the joint entropies of all non-empty subsets of the random variables. It is well known that there is a one-to-one correspondence between such an entropy vector and a certain group-characterizable vector obtained from...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,568
2302.08631	Practical Contextual Bandits with Feedback Graphs	While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which interpolates between the full information and bandit regimes, provides a promising framework to mitigate the statistical complexity of lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,113
2407.20095	Crafting Generative Art through Genetic Improvement: Managing Creative Outputs in Diverse Fitness Landscapes	Generative art is a rules-driven approach to creating artistic outputs in various mediums. For example, a fluid simulation can govern the flow of colored pixels across a digital display or a rectangle placement algorithm can yield a Mondrian-style painting. Previously, we investigated how genetic improvement, a sub-fie...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	477,044
2008.04481	Transformer with Bidirectional Decoder for Speech Recognition	Attention-based models have made tremendous progress on end-to-end automatic speech recognition(ASR) recently. However, the conventional transformer-based approaches usually generate the sequence results token by token from left to right, leaving the right-to-left contexts unexploited. In this work, we introduce a bidi...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	191,239
2501.07069	Hierarchical Superpixel Segmentation via Structural Information Theory	Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding. Existing graph-based superpixel segmentation methods typically concentrate on the relationships between a given pixel and its directly adjacent pixels while overl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,252
1310.7048	Scaling SVM and Least Absolute Deviations via Exact Data Reduction	The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting classifier. Motivated by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,004
2209.05139	Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation	Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent al...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	317,006
2108.05722	MT-ORL: Multi-Task Occlusion Relationship Learning	Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image. We observe two key issues in existing works: firstly, lack of an architecture which can exploit the limited amount of coupling in the decoder stage between the two subtasks, namely occlusion boundary ext...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	250,396
2103.02768	Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction	The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide people with clinical expertise with domain-relevant evidence about why a prediction should be trusted. We firs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,059
2410.02511	Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration	With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning. Although pursuing novelty, diversity, or uncertainty attracts increasing attention, redundant efforts brought by exploration without proper guidance choices poses a practical issue for the c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	494,308
2404.14322	A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Module	Lung segmentation in chest X-ray images is of paramount importance as it plays a crucial role in the diagnosis and treatment of various lung diseases. This paper presents a novel approach for lung segmentation in chest X-ray images by integrating U-net with attention mechanisms. The proposed method enhances the U-net a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	448,636
1710.08590	Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection	The rapid development of the mobile communications requires ever higher spectral efficiency. The non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Amongst several NOMA schemes, the sparse code multiple access (SCMA) has been shown...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,100
2404.11665	Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers	Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. However, their high computational complexity and vulnerability to adversarial attacks are ongoing challenges. In this letter, approximate multipliers are used to explore DNN robustness improvement again...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	447,564
2210.01662	DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems	An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	321,345
1811.08021	CM Sequence based Trajectory Modeling with Destination	In some problems there is information about the destination of a moving object. An example is an airliner flying from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. To emphasize that the motion trajectories end up at the destination, we call them \...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	113,926
1202.3735	Noisy-OR Models with Latent Confounding	Given a set of experiments in which varying subsets of observed variables are subject to intervention, we consider the problem of identifiability of causal models exhibiting latent confounding. While identifiability is trivial when each experiment intervenes on a large number of variables, the situation is more complic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	14,407
1712.09376	Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors	We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior) classifier, i.e., a randomized classifier obtained by a risk-sensitive perturbation of the weights of a learned classifier. Entropy-SGD works by optimizing the bound's p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	87,342
2402.04420	Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways	As machine learning applications proliferate, we need an understanding of their potential for harm. However, current fairness metrics are rarely grounded in human psychological experiences of harm. Drawing on the social psychology of stereotypes, we use a case study of gender stereotypes in image search to examine how ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	427,443
2205.14224	Will Bilevel Optimizers Benefit from Loops	Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve solving one or two sub-problems, and consequently, whether we solve these problems with loops (that take many iterations) or without loops (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,260
2303.02604	Two-Stage Grasping: A New Bin Picking Framework for Small Objects	This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and pushing are performed in the second stage. A small object bin picking s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	349,422
1907.00431	Influence measures in subnetworks using vertex centrality	This work deals with the issue of assessing the influence of a node in the entire network and in the subnetwork to which it belongs as well, adapting the classical idea of vertex centrality. We provide a general definition of relative vertex centrality measure with respect to the classical one, referred to the whole ne...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	137,039
0811.0726	Improved Capacity Scaling in Wireless Networks With Infrastructure	This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks of $n$ randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,631
2404.17023	Out-of-Distribution Detection using Maximum Entropy Coding	Given a default distribution $P$ and a set of test data $x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was likely that $x^M$ was generated by $P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-L\"{o}f randomness. In this paper we seek to general...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	449,704
2112.13099	Fine-Tuning Data Structures for Analytical Query Processing	We introduce a framework for automatically choosing data structures to support efficient computation of analytical workloads. Our contributions are twofold. First, we introduce a novel low-level intermediate language that can express the algorithms behind various query processing paradigms such as classical joins, grou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	273,139
1912.05945	Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples	We address the problem of adversarial examples in machine learning where an adversary tries to misguide a classifier by making functionality-preserving modifications to original samples. We assume a black-box scenario where the adversary has access to only the feature set, and the final hard-decision output of the clas...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	157,234
1512.08814	Combined statistical and model based texture features for improved image classification	This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture methods and classifying the patterns using a naive Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different textur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,547
1903.07833	Fisher Discriminative Least Squares Regression for Image Classification	Discriminative least squares regression (DLSR) has been shown to achieve promising performance in multi-class image classification tasks. Its key idea is to force the regression labels of different classes to move in opposite directions by means of the proposed the joint use of the $\epsilon$-draggings technique, yield...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,714
1901.02399	Service Rate Region of Content Access from Erasure Coded Storage	We consider storage systems in which $K$ files are stored over $N$ nodes. A node may be systematic for a particular file in the sense that access to it gives access to the file. Alternatively, a node may be coded, meaning that it gives access to a particular file only when combined with other nodes (which may be coded ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,194
0906.2820	Equalization for Non-Coherent UWB Systems with Approximate Semi-Definite Programming	In this paper, we propose an approximate semi-definite programming framework for demodulation and equalization of non-coherent ultra-wide-band communication systems with inter-symbol-interference. It is assumed that the communication systems follow non-linear second-order Volterra models. We formulate the demodulation ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,889
2112.12592	Flow and Transport in Three-Dimensional Discrete Fracture Matrix Models using Mimetic Finite Difference on a Conforming Multi-Dimensional Mesh	We present a comprehensive workflow to simulate single-phase flow and transport in fractured porous media using the discrete fracture matrix approach. The workflow has three primary parts: (1) a method for conforming mesh generation of and around a three-dimensional fracture network, (2) the discretization of the gover...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	273,010
2009.05527	On Multitask Loss Function for Audio Event Detection and Localization	Audio event localization and detection (SELD) have been commonly tackled using multitask models. Such a model usually consists of a multi-label event classification branch with sigmoid cross-entropy loss for event activity detection and a regression branch with mean squared error loss for direction-of-arrival estimatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,346
2303.03470	Partial-Information, Longitudinal Cyber Attacks on LiDAR in Autonomous Vehicles	What happens to an autonomous vehicle (AV) if its data are adversarially compromised? Prior security studies have addressed this question through mostly unrealistic threat models, with limited practical relevance, such as white-box adversarial learning or nanometer-scale laser aiming and spoofing. With growing evidence...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	349,738
2310.20145	Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs	Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimization process, such as machining errors, execution noise, or contextual variability. This uncertainty...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,271
2107.12078	6DCNN with roto-translational convolution filters for volumetric data processing	In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data. 6DCNN also includes SE(3)-equivariant message-passing and nonlinear activation operations constructed in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,795
2010.09235	Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream	Conventional spoken language understanding (SLU) consist of two stages, the first stage maps speech to text by automatic speech recognition (ASR), and the second stage maps text to intent by natural language understanding (NLU). End-to-end SLU maps speech directly to intent through a single deep learning model. Previou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	201,457
1702.07772	Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment	Purpose: Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video ana...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,835
0704.2509	Signal Set Design for Full-Diversity Low-Decoding-Complexity Differential Scaled-Unitary STBCs	The problem of designing high rate, full diversity noncoherent space-time block codes (STBCs) with low encoding and decoding complexity is addressed. First, the notion of $g$-group encodable and $g$-group decodable linear STBCs is introduced. Then for a known class of rate-1 linear designs, an explicit construction of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	64
1904.08668	An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval	The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN graph size due to the high-quantity of new connections between nodes in the graph, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,145
2407.19196	Why Misinformation is Created? Detecting them by Integrating Intent Features	Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinforma...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	false	476,684
1201.0676	Knowledge epidemics and population dynamics models for describing idea diffusion	The diffusion of ideas is often closely connected to the creation and diffusion of knowledge and to the technological evolution of society. Because of this, knowledge creation, exchange and its subsequent transformation into innovations for improved welfare and economic growth is briefly described from a historical poi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,672
1210.7102	3D Face Recognition using Significant Point based SULD Descriptor	In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,416
2410.01105	M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions	Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging sensors, e.g., color...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	493,614
1510.04493	Sparsity-aware Possibilistic Clustering Algorithms	In this paper two novel possibilistic clustering algorithms are presented, which utilize the concept of sparsity. The first one, called sparse possibilistic c-means, exploits sparsity and can deal well with closely located clusters that may also be of significantly different densities. The second one, called sparse ada...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	47,922
2112.03203	A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods	In recent years, text summarization methods have attracted much attention again thanks to the researches on neural network models. Most of the current text summarization methods based on neural network models are supervised methods which need large-scale datasets. However, large-scale datasets are difficult to obtain i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	270,114
2211.17104	Agent-Cells with DNA Programming: A Dynamic Decentralized System	This paper introduces a new concept. We intend to give life to a software agent. A software agent is a computer program that acts on a user's behalf. We put a DNA inside the agent. DNA is a simple text, a whole roadmap of a network of agents or a system with details. A Dynamic Numerical Abstract of a multiagent system....	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	true	333,862
1501.04370	Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling	We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	39,369
2005.14330	Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images	Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conventionally via tedious and time-consuming manual radiographic procedures that are prone to subjectivity and observational variability. Relia...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	179,238
1908.03684	Bayesian Loss for Crowd Count Estimation with Point Supervision	In crowd counting datasets, each person is annotated by a point, which is usually the center of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of-the-art methods are based on density map estimation, which convert the sparse point annotations into a "ground truth" density map t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,295
2411.00109	Prospective Learning: Learning for a Dynamic Future	In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably approximately correct (PAC) learning, largely ignores time. As a consequence, existing strategies to address the dynamic nature of data and goals...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	504,434
2105.00201	Designing Games for Enabling Co-creation with Social Agents	Digital tools have long been used for supporting children's creativity. Digital games that allow children to create artifacts and express themselves in a playful environment serve as efficient Creativity Support Tools (or CSTs). Creativity is also scaffolded by social interactions with others in their environment. In o...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	233,127
2305.10572	Tensor Products and Hyperdimensional Computing	Following up on a previous analysis of graph embeddings, we generalize and expand some results to the general setting of vector symbolic architectures (VSA) and hyperdimensional computing (HDC). Importantly, we explore the mathematical relationship between superposition, orthogonality, and tensor product. We establish ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,130
1708.09492	Automatically Generating Commit Messages from Diffs using Neural Machine Translation	Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	79,794
1302.4948	Testing Identifiability of Causal Effects	This paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. Whe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,222
2302.00192	Density peak clustering using tensor network	Tensor networks, which have been traditionally used to simulate many-body physics, have recently gained significant attention in the field of machine learning due to their powerful representation capabilities. In this work, we propose a density-based clustering algorithm inspired by tensor networks. We encode classical...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	343,133
1910.04992	A sub-Riemannian model of the visual cortex with frequency and phase	In this paper we present a novel model of the primary visual cortex (V1) based on orientation, frequency and phase selective behavior of the V1 simple cells. We start from the first level mechanisms of visual perception: receptive profiles. The model interprets V1 as a fiber bundle over the 2-dimensional retinal plane ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,940
1811.05826	Char2char Generation with Reranking for the E2E NLG Challenge	This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing delexicalization (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2se...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	113,394
2309.01156	Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics	Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	389,569
2408.11948	Topological Representational Similarity Analysis in Brains and Beyond	Understanding how the brain represents and processes information is crucial for advancing neuroscience and artificial intelligence. Representational similarity analysis (RSA) has been instrumental in characterizing neural representations, but traditional RSA relies solely on geometric properties, overlooking crucial to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,517
2404.07904	HGRN2: Gated Linear RNNs with State Expansion	Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However, the recurrent state size of HGRN remains relatively small, limiting its expressiveness. To address this issue, we introduce a simple outer ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	446,015
2301.09025	Nichtverbales Verhalten sozialer Roboter: Bewegungen, deren Bedeutung und die Technik dahinter	Nichtverbale Signale sind ein elementarer Bestandteil der menschlichen Kommunikation. Sie erf\"ullen eine Vielzahl von Funktionen bei der Kl\"arung von Mehrdeutigkeiten, der subtilen Aushandlung von Rollen oder dem Ausdruck dessen, was im Inneren der Gespr\"achspartner vorgeht. Viele Studien mit sozial-interaktiven Rob...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	341,376
2202.01856	Data-Driven Optimal Control via Linear Transfer Operators: A Convex Approach	This paper is concerned with data-driven optimal control of nonlinear systems. We present a convex formulation to the optimal control problem (OCP) with a discounted cost function. We consider OCP with both positive and negative discount factor. The convex approach relies on lifting nonlinear system dynamics in the spa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	278,606
2410.12130	Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning	The development of Large Language Models (LLMs) has significantly advanced various AI applications in commercial and scientific research fields, such as scientific literature summarization, writing assistance, and knowledge graph construction. However, a significant challenge is the high risk of hallucination during LL...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	498,864
1503.00604	Robust Group Linkage	We study the problem of group linkage: linking records that refer to entities in the same group. Applications for group linkage include finding businesses in the same chain, finding conference attendees from the same affiliation, finding players from the same team, etc. Group linkage faces challenges not present for tr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	40,725
2202.03140	OPP-Miner: Order-preserving sequential pattern mining	A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	279,087
2201.05000	Automated Reinforcement Learning: An Overview	Reinforcement Learning and recently Deep Reinforcement Learning are popular methods for solving sequential decision making problems modeled as Markov Decision Processes. RL modeling of a problem and selecting algorithms and hyper-parameters require careful considerations as different configurations may entail completel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	275,251
2006.01563	Exploring Cross-sentence Contexts for Named Entity Recognition with BERT	Named entity recognition (NER) is frequently addressed as a sequence classification task where each input consists of one sentence of text. It is nevertheless clear that useful information for the task can often be found outside of the scope of a single-sentence context. Recently proposed self-attention models such as ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,810
1706.08317	Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks	Temporal landmarks have been proved to be a helpful mechanism to deal with temporal planning problems, specifically to improve planners performance and handle problems with deadline constraints. In this paper, we show the strength of using temporal landmarks to handle the state trajectory constraints of PDDL3.0. We ana...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	75,976
1908.01146	Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality	On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, t...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	140,688
2404.15347	Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction	Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learn...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	449,058
2402.17533	Black-box Adversarial Attacks Against Image Quality Assessment Models	The goal of No-Reference Image Quality Assessment (NR-IQA) is to predict the perceptual quality of an image in line with its subjective evaluation. To put the NR-IQA models into practice, it is essential to study their potential loopholes for model refinement. This paper makes the first attempt to explore the black-box...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,039
1905.02176	Computation of Circular Area and Spherical Volume Invariants via Boundary Integrals	We show how to compute the circular area invariant of planar curves, and the spherical volume invariant of surfaces, in terms of line and surface integrals, respectively. We use the Divergence Theorem to express the area and volume integrals as line and surface integrals, respectively, against particular kernels; our r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	129,909
2408.15823	Benchmarking foundation models as feature extractors for weakly-supervised computational pathology	Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is currently limited literature independently evaluating these foundation models on truly external cohorts and clinically-relevant tasks to un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,091
2307.11138	Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure	Accurate error estimation is crucial in model order reduction, both to obtain small reduced-order models and to certify their accuracy when deployed in downstream applications such as digital twins. In existing a posteriori error estimation approaches, knowledge about the time integration scheme is mandatory, e.g., the...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	380,815
2112.05313	Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction	Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,804
1803.00389	Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework	In this paper, we address the problem of denoising images degraded by Poisson noise. We propose a new patch-based approach based on best linear prediction to estimate the underlying clean image. A simplified prediction formula is derived for Poisson observations, which requires the covariance matrix of the underlying c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,665
1904.01730	Sequencing and Scheduling for Multi-User Machine-Type Communication	In this paper, we propose joint sequencing and scheduling optimization for uplink machine-type communication (MTC). We consider multiple energy-constrained MTC devices that transmit data to a base station following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in T...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	126,228
1801.07357	CHALET: Cornell House Agent Learning Environment	We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	88,771
cs/9506102	Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs	This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the n...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,312
2006.00467	End-to-End Change Detection for High Resolution Drone Images with GAN Architecture	Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,480
1504.04103	Faster Algorithms for Testing under Conditional Sampling	There has been considerable recent interest in distribution-tests whose run-time and sample requirements are sublinear in the domain-size $k$. We study two of the most important tests under the conditional-sampling model where each query specifies a subset $S$ of the domain, and the response is a sample drawn from $S$ ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	42,103
1309.6851	Treedy: A Heuristic for Counting and Sampling Subsets	Consider a collection of weighted subsets of a ground set N. Given a query subset Q of N, how fast can one (1) find the weighted sum over all subsets of Q, and (2) sample a subset of Q proportionally to the weights? We present a tree-based greedy heuristic, Treedy, that for a given positive tolerance d answers such cou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	27,313
2201.08559	Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages	We develop a Causal-Deep Neural Network (CDNN) model trained in two stages to infer causal impact estimates at an individual unit level. Using only the pre-treatment features in stage 1 in the absence of any treatment information, we learn an encoding for the covariates that best represents the outcome. In the $2^{nd}$...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,383
1909.01587	Topological Coding and Topological Matrices Toward Network Overall Security	A mathematical topology with matrix is a natural representation of a coding relational structure that is found in many fields of the world. Matrices are very important in computation of real applications, s ce matrices are easy saved in computer and run quickly, as well as matrices are convenient to deal with communiti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	143,949
2407.12331	I2AM: Interpreting Image-to-Image Latent Diffusion Models via Attribution Maps	Large-scale diffusion models have made significant advancements in the field of image generation, especially through the use of cross-attention mechanisms that guide image formation based on textual descriptions. While the analysis of text-guided cross-attention in diffusion models has been extensively studied in recen...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	473,875
1905.10158	Preventing wind turbine tower natural frequency excitation with a quasi-LPV model predictive control scheme	With the ever increasing power rates of wind turbines, more advanced control techniques are needed to facilitate tall towers that are low-weight and cost effective, but in effect more flexible. Such soft-soft tower configurations generally have their fundamental side-side frequency in the below-rated operational domain...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	131,980
2405.14365	JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models	Mathematical reasoning is an important capability of large language models~(LLMs) for real-world applications. To enhance this capability, existing work either collects large-scale math-related texts for pre-training, or relies on stronger LLMs (\eg GPT-4) to synthesize massive math problems. Both types of work general...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	456,388
2111.10780	FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection	Existing anchor-base oriented object detection methods have achieved amazing results, but these methods require some manual preset boxes, which introduces additional hyperparameters and calculations. The existing anchor-free methods usually have complex architectures and are not easy to deploy. Our goal is to propose a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,446
2201.13302	Eris: Measuring discord among multidimensional data sources	Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	277,941
2411.11006	BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation	We introduce BackdoorMBTI, the first backdoor learning toolkit and benchmark designed for multimodal evaluation across three representative modalities from eleven commonly used datasets. BackdoorMBTI provides a systematic backdoor learning pipeline, encompassing data processing, data poisoning, backdoor training, and e...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	508,889
2109.07359	Modular Neural Ordinary Differential Equations	The laws of physics have been written in the language of dif-ferential equations for centuries. Neural Ordinary Differen-tial Equations (NODEs) are a new machine learning architecture which allows these differential equations to be learned from a dataset. These have been applied to classical dynamics simulations in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	255,494
1609.05158	Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network	Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,086
1808.03147	A New Optimization Layer for Real-Time Bidding Advertising Campaigns	While it is relatively easy to start an online advertising campaign, obtaining a high Key Performance Indicator (KPI) can be challenging. A large body of work on this subject has already been performed and platforms known as DSPs are available on the market that deal with such an optimization. From the advertiser's poi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	104,887
2307.02641	Active Class Selection for Few-Shot Class-Incremental Learning	For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. There...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	377,757
2203.05836	Efficient and Robust Semantic Mapping for Indoor Environments	A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucia...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	284,931
2411.11069	Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification	Video-based visible-infrared person re-identification (VVI-ReID) is challenging due to significant modality feature discrepancies. Spatial-temporal information in videos is crucial, but the accuracy of spatial-temporal information is often influenced by issues like low quality and occlusions in videos. Existing methods...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,907
1611.02247	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic	Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their high sample complexity. Batch policy gradient methods offer stable learning, but at the cost of high variance, which often requires large b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,528
2410.13490	Novelty-based Sample Reuse for Continuous Robotics Control	In reinforcement learning, agents collect state information and rewards through environmental interactions, essential for policy refinement. This process is notably time-consuming, especially in complex robotic simulations and real-world applications. Traditional algorithms usually re-engage with the environment after ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	499,555
2304.04095	A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry	We study the mixing time of Metropolis-Adjusted Langevin algorithm (MALA) for sampling a target density on $\mathbb{R}^d$. We assume that the target density satisfies $\psi_\mu$-isoperimetry and that the operator norm and trace of its Hessian are bounded by $L$ and $\Upsilon$ respectively. Our main result establishes t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	357,073
2310.12303	Document-Level Language Models for Machine Translation	Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence-level aligned, without document-level meta information available. In this work, we set out to build context-aware translation systems utili...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	400,966
2404.02429	AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset	Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits, most algorithm development research in offline reinforcement learning still relies on game tasks with synthetic datasets. To address such li...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	443,853

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