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
2102.06380	Neural Inverse Text Normalization	While there have been several contributions exploring state of the art techniques for text normalization, the problem of inverse text normalization (ITN) remains relatively unexplored. The best known approaches leverage finite state transducer (FST) based models which rely on manually curated rules and are hence not sc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	219,730
2409.15831	Introducing Anisotropic Fields for Enhanced Diversity in Crowd Simulation	Large crowds exhibit intricate behaviors and significant emergent properties, yet existing crowd simulation systems often lack behavioral diversity, resulting in homogeneous simulation outcomes. To address this limitation, we propose incorporating anisotropic fields (AFs) as a fundamental structure for depicting the un...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	491,087
2011.07439	Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee	Sparse deep learning aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions. Although tremendous empirical successes have been achieved, most sparse deep learning algorithms are lacking of theoretical support. On the other hand, another...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,555
1211.7232	Real Time Enhanced Random Sampling of Online Social Networks	Social graphs can be easily extracted from Online Social Networks. However these networks are getting larger from day to day. Sampling methods used to evaluate graph information cannot accurately extract graph properties. Furthermore Social Networks are limiting the access to their data, making the crawling process eve...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	20,039
2303.01959	PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees	Point cloud classification is an essential component in many security-critical applications such as autonomous driving and augmented reality. However, point cloud classifiers are vulnerable to adversarially perturbed point clouds. Existing certified defenses against adversarial point clouds suffer from a key limitation...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	349,173
2308.06432	Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration	Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions. Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	385,134
1002.0378	A Grey-Box Approach to Automated Mechanism Design	Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	5,586
2311.08669	On the Calibration of Multilingual Question Answering LLMs	Multilingual pre-trained Large Language Models (LLMs) are incredibly effective at Question Answering (QA), a core task in Natural Language Understanding, achieving high accuracies on several multilingual benchmarks. However, little is known about how well their confidences are calibrated. In this paper, we comprehensiv...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	407,821
2107.03891	Technical Report for Valence-Arousal Estimation in ABAW2 Challenge	In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW2 ICCV-2021 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use a two stream model to learn emotion features from ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,282
1805.04252	Adaptive Selection of Deep Learning Models on Embedded Systems	The recent ground-breaking advances in deep learning networks ( DNNs ) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	97,206
0804.0188	Support Vector Machine Classification with Indefinite Kernels	We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learnin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	1,515
2108.04633	Channel Modeling and Channel Estimation for Holographic Massive MIMO with Planar Arrays	In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlation fading. This is more emphasized when a large number of antennas is densely deployed, known as holographic massive MIMO (multiple-input multiple-output). In the first part of this letter, we develop a channel model for ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	250,072
2410.01686	Positional Attention: Expressivity and Learnability of Algorithmic Computation	There is a growing interest in the ability of neural networks to execute algorithmic tasks (e.g., arithmetic, summary statistics, and sorting). The goal of this work is to better understand the role of attention in Transformers for algorithmic execution. Its importance for algorithmic execution has been studied theoret...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	493,891
1702.08360	Neural Map: Structured Memory for Deep Reinforcement Learning	A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution ov...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	68,968
2208.08999	k-Dimensional Agreement in Multiagent Systems	Given a network of agents, we study the problem of designing a distributed algorithm that computes k independent weighted means of the network's initial conditions (namely, the agents agree on a k-dimensional space). Akin to average consensus, this problem finds applications in distributed computing and sensing, where ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	313,559
2203.00458	A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction	The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptabili...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	283,007
2411.12355	DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding	The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redundancy and correspondence in videos have hindered the performance potential of existing methods. Through statistical learning on current datase...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,394
2103.02696	On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction	Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite their great success, training GCNs on large graphs suffers from computational and memory issues. A potential path to circumvent these obstacles is sampling-based...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	223,034
2306.11134	OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems	In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers. Despite this interest, the field is still emerging, and the lack of open-source R&D platforms may impede the exploration of LLM-based recommendations. This paper intr...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	374,485
2010.09859	A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments under Severe Odometry Drift	Exploration is a fundamental problem in robot autonomy. A major limitation, however, is that during exploration robots oftentimes have to rely on on-board systems alone for state estimation, accumulating significant drift over time in large environments. Drift can be detrimental to robot safety and exploration performa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	201,682
2303.10778	Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths	This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps us...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	352,580
2401.00466	Online Symbolic Music Alignment with Offline Reinforcement Learning	Symbolic Music Alignment is the process of matching performed MIDI notes to corresponding score notes. In this paper, we introduce a reinforcement learning (RL)-based online symbolic music alignment technique. The RL agent - an attention-based neural network - iteratively estimates the current score position from local...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,007
1909.09437	Underwater Image Super-Resolution using Deep Residual Multipliers	We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,259
2002.03776	Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference	In this paper we introduce the DMR -- a prototype-based method and network architecture for deep learning which is using a decision tree (DT)-based inference and synthetic data to balance the classes. It builds upon the recently introduced xDNN method addressing more complex multi-class problems, specifically when clas...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	163,402
1506.04135	Reducing offline evaluation bias of collaborative filtering algorithms	Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	44,130
1707.06892	Fog Radio Access Networks: Mobility Management, Interference Mitigation and Resource Optimization	In order to make Internet connections ubiquitous and autonomous in our daily lives, maximizing the utilization of radio resources and social information is one of the major research topics in future mobile communication technologies. Fog radio access network (FRAN) is regarded as a promising paradigm for the fifth gene...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,506
2110.15066	Thermodynamics of Evolution and the Origin of Life	We outline a phenomenological theory of evolution and origin of life by combining the formalism of classical thermodynamics with a statistical description of learning. The maximum entropy principle constrained by the requirement for minimization of the loss function is employed to derive a canonical ensemble of organis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,749
1912.13480	On the Difference Between the Information Bottleneck and the Deep Information Bottleneck	Combining the Information Bottleneck model with deep learning by replacing mutual information terms with deep neural nets has proved successful in areas ranging from generative modelling to interpreting deep neural networks. In this paper, we revisit the Deep Variational Information Bottleneck and the assumptions neede...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	159,094
1905.10809	Minimum Age TDMA Scheduling	We consider a transmission scheduling problem in which multiple systems receive update information through a shared Time Division Multiple Access (TDMA) channel. To provide timely delivery of update information, the problem asks for a schedule that minimizes the overall age of information. We call this problem the Min-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	132,201
2407.02744	Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models	Reconstructing high-fidelity magnetic resonance (MR) images from under-sampled k-space is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy. However, traditional posterior sampling met...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,862
2111.03630	Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation	Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a nove...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	265,228
2501.03518	Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer	Quantum annealing (QA) has attracted research interest as a sampler and combinatorial optimization problem (COP) solver. A recently proposed sampling-based solver for QA significantly reduces the required number of qubits, being capable of large COPs. In relation to this, a trainable sampling-based COP solver has been ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,904
2212.03044	On the Importance of Clinical Notes in Multi-modal Learning for EHR Data	Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that jointly using clinical notes with electronic health record (EHR) data improved predictive performance for patient monitoring in the intensive car...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,977
2305.10399	End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics	High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this \textit{inverse prob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,036
2403.11015	Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach	In systems biology, attractor landscape analysis of gene regulatory networks is recognized as a powerful computational tool for studying various cellular states from proliferation and differentiation to senescence and apoptosis. Therefore, accurate identification of attractors plays a critical role in determination of ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	438,480
1805.05603	Neural Classification of Malicious Scripts: A study with JavaScript and VBScript	Malicious scripts are an important computer infection threat vector. Our analysis reveals that the two most prevalent types of malicious scripts include JavaScript and VBScript. The percentage of detected JavaScript attacks are on the rise. To address these threats, we investigate two deep recurrent models, LaMP (LSTM ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	97,462
0812.2575	Face Detection Using Adaboosted SVM-Based Component Classifier	Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task. To obtain a set of effective SVM-weaklearner Cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	2,795
2112.02162	A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing	Modern herbicide application in agricultural settings typically relies on either large scale sprayers that dispense herbicide over crops and weeds alike or portable sprayers that require labor intensive manual operation. The former method results in overuse of herbicide and reduction in crop yield while the latter is o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	269,745
1905.09897	Robust guarantees for learning an autoregressive filter	The optimal predictor for a linear dynamical system (with hidden state and Gaussian noise) takes the form of an autoregressive linear filter, namely the Kalman filter. However, a fundamental problem in reinforcement learning and control theory is to make optimal predictions in an unknown dynamical system. To this end, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,870
1303.0742	Multivariate Temporal Dictionary Learning for EEG	This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	22,616
1207.0036	The Kullback-Leibler Divergence as a Lyapunov Function for Incentive Based Game Dynamics	It has been shown that the Kullback-Leibler divergence is a Lyapunov function for the replicator equations at evolutionary stable states, or ESS. In this paper we extend the result to a more general class of game dynamics. As a result, sufficient conditions can be given for the asymptotic stability of rest points for t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	17,118
2107.12775	Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease	With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	248,004
2311.06634	Back to Basics: Fast Denoising Iterative Algorithm	We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknown. We examine three ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,014
2008.01300	Weakly Supervised Construction of ASR Systems with Massive Video Data	Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts. Although several unsupervised pre-training models have been proposed, applying such models direct...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	190,271
1504.07968	Learning Contextualized Music Semantics from Tags via a Siamese Network	Music information retrieval faces a challenge in modeling contextualized musical concepts formulated by a set of co-occurring tags. In this paper, we investigate the suitability of our recently proposed approach based on a Siamese neural network in fighting off this challenge. By means of tag features and probabilistic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	42,598
2107.07150	Tailor: Generating and Perturbing Text with Semantic Controls	Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, which is expensive and hard to generalize. We present Tailor, a semantically-controlled text generation system. Tailor builds on a pretrained se...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	246,331
2002.04599	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations	Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied sensitivity-based adversarial examples introduce semantically-small changes to an input that result in a different model prediction. This paper studies a complementary failure mode, invariance-based adversarial examples, tha...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	163,639
2109.08002	SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models	Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-pur...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	255,733
2401.06031	GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model	Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly, its promising performance has led to the GAN-based adversarial attack methods in the white-box and black-box attack scenarios. The importanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,993
1405.2294	Nonparametric Detection of Anomalous Data Streams	A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,966
1305.3939	Analysis Of Interest Points Of Curvelet Coefficients Contributions Of Microscopic Images And Improvement Of Edges	This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study local...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,648
2404.17884	Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models	This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iteratively forecasting the temporal evolution of flow dynamics. Three autoregressive models were compared: a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	450,043
2103.13041	Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization	Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,377
2307.09435	SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs	In recent years, large-scale pre-trained speech language models (SLMs) have demonstrated remarkable advancements in various generative speech modeling applications, such as text-to-speech synthesis, voice conversion, and speech enhancement. These applications typically involve mapping text or speech inputs to pre-train...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	380,171
1111.1564	Particle Swarm Optimization Framework for Low Power Testing of VLSI Circuits	Power dissipation in sequential circuits is due to increased toggling count of Circuit under Test, which depends upon test vectors applied. If successive test vectors sequences have more toggling nature then it is sure that toggling rate of flip flops is higher. Higher toggling for flip flops results more power dissipa...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	12,940
2108.05165	Stable Marriage Problems with Ties and Incomplete Preferences: An Empirical Comparison of ASP, SAT, ILP, CP, and Local Search Methods	We study a variation of the Stable Marriage problem, where every man and every woman express their preferences as preference lists which may be incomplete and contain ties. This problem is called the Stable Marriage problem with Ties and Incomplete preferences (SMTI). We consider three optimization variants of SMTI, Ma...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	250,231
2307.03266	Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging	Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation ha...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	377,972
2502.05879	Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models	Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Language Models (LLMs) have shown promise in addressing mental health challenges, including the detection of depression through text-based analys...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	531,821
2110.10599	Video Instance Segmentation by Instance Flow Assembly	Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into the video domain. This is because they usually deal with features or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	262,210
2409.09748	Explore the Hallucination on Low-level Perception for MLLMs	The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in visual perception and understanding. However, these models also exhibit hallucinations, which limit their reliability as AI systems, especi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	488,455
2410.06502	Chemistry-Inspired Diffusion with Non-Differentiable Guidance	Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the condition, or (ii) implicitly, using a property predictor. However, training property predictors or c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,232
2408.06799	On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga	A good understanding of player preferences is crucial for increasing content relevancy, especially in mobile games. This paper illustrates the use of attentive models for producing item recommendations in a mobile game scenario. The methodology comprises a combination of supervised and unsupervised approaches to create...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,350
2212.04501	Learning Video Representations from Large Language Models	We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,468
2502.00330	From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation	Recent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more demonstrating examples beyond the conventional few-shot setup in the context can lead to performance benefits. However, despite its promise, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	529,321
2402.07069	Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine	We present LARL-RM (Large language model-generated Automaton for Reinforcement Learning with Reward Machine) algorithm in order to encode high-level knowledge into reinforcement learning using automaton to expedite the reinforcement learning. Our method uses Large Language Models (LLM) to obtain high-level domain-speci...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	428,556
2408.03339	The Ontoverse: Democratising Access to Knowledge Graph-based Data Through a Cartographic Interface	As the number of scientific publications and preprints is growing exponentially, several attempts have been made to navigate this complex and increasingly detailed landscape. These have almost exclusively taken unsupervised approaches that fail to incorporate domain knowledge and lack the structural organisation requir...	true	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	true	478,977
2402.15602	Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions	We study the asymptotic error of score-based diffusion model sampling in large-sample scenarios from a non-parametric statistics perspective. We show that a kernel-based score estimator achieves an optimal mean square error of $\widetilde{O}\left(n^{-1} t^{-\frac{d+2}{2}}(t^{\frac{d}{2}} \vee 1)\right)$ for the score f...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	432,206
1805.00737	Distributed watermarking for secure control of microgrids under replay attacks	The problem of replay attacks in the communication network between Distributed Generation Units (DGUs) of a DC microgrid is examined. The DGUs are regulated through a hierarchical control architecture, and are networked to achieve secondary control objectives. Following analysis of the detectability of replay attacks b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,502
1907.00534	Large Area 3D Human Pose Detection Via Stereo Reconstruction in Panoramic Cameras	We propose a novel 3D human pose detector using two panoramic cameras. We show that transforming fisheye perspectives to rectilinear views allows a direct application of two-dimensional deep-learning pose estimation methods, without the explicit need for a costly re-training step to compensate for fisheye image distort...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	137,080
1705.07615	AIXIjs: A Software Demo for General Reinforcement Learning	Reinforcement learning is a general and powerful framework with which to study and implement artificial intelligence. Recent advances in deep learning have enabled RL algorithms to achieve impressive performance in restricted domains such as playing Atari video games (Mnih et al., 2015) and, recently, the board game Go...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	73,870
2411.09220	Transferable Adversarial Attacks against ASR	Given the extensive research and real-world applications of automatic speech recognition (ASR), ensuring the robustness of ASR models against minor input perturbations becomes a crucial consideration for maintaining their effectiveness in real-time scenarios. Previous explorations into ASR model robustness have predomi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,175
2212.00223	Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework	In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora. Decades of study of the field of BioNLP has produced a plethora of algorithms, systems and datasets. However, our experience has been that no single ope...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,977
2207.07889	You Should Look at All Objects	Feature pyramid network (FPN) is one of the key components for object detectors. However, there is a long-standing puzzle for researchers that the detection performance of large-scale objects are usually suppressed after introducing FPN. To this end, this paper first revisits FPN in the detection framework and reveals ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,360
0708.0224	Multisource Bayesian sequential change detection	Suppose that local characteristics of several independent compound Poisson and Wiener processes change suddenly and simultaneously at some unobservable disorder time. The problem is to detect the disorder time as quickly as possible after it happens and minimize the rate of false alarms at the same time. These problems...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	515
2404.00272	HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification	Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it inc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	442,846
2401.12048	HomeRobot Open Vocabulary Mobile Manipulation Challenge 2023 Participant Report (Team KuzHum)	We report an improvements to NeurIPS 2023 HomeRobot: Open Vocabulary Mobile Manipulation (OVMM) Challenge reinforcement learning baseline. More specifically, we propose more accurate semantic segmentation module, along with better place skill policy, and high-level heuristic that outperforms the baseline by 2.4% of ove...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	423,240
1807.04715	Orthogonal Matching Pursuit for Text Classification	In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low s...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	102,780
2303.01801	Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept	Several theoretical works have shown that solitons -- waves that self-maintain constant shape and velocity as they propagate -- can be used as a physical computational reservoir, a concept where machine learning algorithms designed for digital computers are replaced by analog physical systems that exhibit nonlinear dyn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	349,115
2412.09117	Reconfigurable Intelligent Surface for Internet of Robotic Things	With the rapid development of artificial intelligence, robotics, and Internet of Things, multi-robot systems are progressively acquiring human-like environmental perception and understanding capabilities, empowering them to complete complex tasks through autonomous decision-making and interaction. However, the Internet...	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	516,369
2106.04152	PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning	Learning good feature representations is important for deep reinforcement learning (RL). However, with limited experience, RL often suffers from data inefficiency for training. For un-experienced or less-experienced trajectories (i.e., state-action sequences), the lack of data limits the use of them for better feature ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,608
1510.01628	Large-scale subspace clustering using sketching and validation	The nowadays massive amounts of generated and communicated data present major challenges in their processing. While capable of successfully classifying nonlinearly separable objects in various settings, subspace clustering (SC) methods incur prohibitively high computational complexity when processing large-scale data. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	47,645
2302.06914	Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention	Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	345,578
1907.01216	Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA	Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attack detection methods have been proposed, however they ar...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	137,263
2104.10051	Semantic similarity metrics for learned image registration	We propose a semantic similarity metric for image registration. Existing metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our approach learns dataset-specific features that drive the optimization of a learning-b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	231,444
2402.19427	Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models	Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model that mixes gated linear recurrences with local attention. Hawk exceeds the reported performa...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	433,797
2105.14278	An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works	Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	237,612
1709.08271	3D Camouflaging Object using RGB-D Sensors	This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer s eyes is a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,441
1905.11759	Manipulating a Learning Defender and Ways to Counteract	In Stackelberg security games when information about the attacker's payoffs is uncertain, algorithms have been proposed to learn the optimal defender commitment by interacting with the attacker and observing their best responses. In this paper, we show that, however, these algorithms can be easily manipulated if the at...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	132,534
1711.00867	The (Un)reliability of saliency methods	Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step ---adding a constant shift to the input data--- to show that a transformatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,791
2205.14790	Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret	The stochastic multi-armed bandit setting has been recently studied in the non-stationary regime, where the mean payoff of each action is a non-decreasing function of the number of rounds passed since it was last played. This model captures natural behavioral aspects of the users which crucially determine the performan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	299,488
2412.17700	MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification	Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	520,082
1902.07017	Detector-in-Detector: Multi-Level Analysis for Human-Parts	Vision-based person, hand or face detection approaches have achieved incredible success in recent years with the development of deep convolutional neural network (CNN). In this paper, we take the inherent correlation between the body and body parts into account and propose a new framework to boost up the detection perf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,897
2403.05045	Are Human Conversations Special? A Large Language Model Perspective	This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three use cases of LLMs: interactions over web content, code, and mathematical texts. By analyzing attention distance, dispersion, and interdepen...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	435,840
2310.11230	Zipformer: A faster and better encoder for automatic speech recognition	The Conformer has become the most popular encoder model for automatic speech recognition (ASR). It adds convolution modules to a transformer to learn both local and global dependencies. In this work we describe a faster, more memory-efficient, and better-performing transformer, called Zipformer. Modeling changes includ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,565
2404.10201	Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages	We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in\mathbb{R}^d$. We propose a new multi-message protocol that achieves the optimal error using $\tilde{\mathcal{O}}\left(\min(n\varepsilon^2,d)\right)$ messages per user. Moreover, we...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	446,991
2209.09400	Polynomial-Time Reachability for LTI Systems with Two-Level Lattice Neural Network Controllers	In this paper, we consider the computational complexity of bounding the reachable set of a Linear Time-Invariant (LTI) system controlled by a Rectified Linear Unit (ReLU) Two-Level Lattice (TLL) Neural Network (NN) controller. In particular, we show that for such a system and controller, it is possible to compute the e...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	318,488
2009.02043	Data Readiness for Natural Language Processing	This document concerns data readiness in the context of machine learning and Natural Language Processing. It describes how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis methods. The contents of the document is based on the practical challenges and f...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	true	false	194,454
2403.14696	MOTIV: Visual Exploration of Moral Framing in Social Media	We present a visual computing framework for analyzing moral rhetoric on social media around controversial topics. Using Moral Foundation Theory, we propose a methodology for deconstructing and visualizing the \textit{when}, \textit{where}, and \textit{who} behind each of these moral dimensions as expressed in microblog...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	440,210
1612.06000	Sample-efficient Deep Reinforcement Learning for Dialog Control	Representing a dialog policy as a recurrent neural network (RNN) is attractive because it handles partial observability, infers a latent representation of state, and can be optimized with supervised learning (SL) or reinforcement learning (RL). For RL, a policy gradient approach is natural, but is sample inefficient. I...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	65,756
1803.01420	Detecting Correlations with Little Memory and Communication	We study the problem of identifying correlations in multivariate data, under information constraints: Either on the amount of memory that can be used by the algorithm, or the amount of communication when the data is distributed across several machines. We prove a tight trade-off between the memory/communication complex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,873

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