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
2407.05355	VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool	Multimodal large language models (MLLMs) are flourishing, but mainly focus on images with less attention than videos, especially in sub-fields such as prompt engineering, video chain-of-thought (CoT), and instruction tuning on videos. Therefore, we try to explore the collection of CoT datasets in videos to lead to vide...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	470,938
2209.01813	Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices	We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and the pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation bas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,015
2312.01840	An AI-based solution for the cold start and data sparsity problems in the recommendation systems	In recent years, the amount of data available on the internet and the number of users who utilize the Internet have increased at an unparalleled pace. The exponential development in the quantity of digital information accessible and the number of Internet users has created the possibility for information overload, impe...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	412,610
1906.03543	apricot: Submodular selection for data summarization in Python	We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greedy selection algorithm that offers strong theoretical guarantees on the quality of the selected set. Two submodular set functions are impleme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,411
1402.6133	Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings	Sample size determination for a data set is an important statistical process for analyzing the data to an optimum level of accuracy and using minimum computational work. The applications of this process are credible in every domain which deals with large data sets and high computational work. This study uses Bayesian a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	31,153
2411.10163	Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions	Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, existing benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. In ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	508,524
2411.04319	Towards Optimizing SQL Generation via LLM Routing	Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	506,220
2407.02271	Improving Explainability of Softmax Classifiers Using a Prototype-Based Joint Embedding Method	We propose a prototype-based approach for improving explainability of softmax classifiers that provides an understandable prediction confidence, generated through stochastic sampling of prototypes, and demonstrates potential for out of distribution detection (OOD). By modifying the model architecture and training to ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	469,655
1909.08243	Quantified Constraint Handling Rules	We shift the QCSP (Quantified Constraint Satisfaction Problems) framework to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic binder and access to user-defined constraints. QCSP offers a natural framework to express PSPACE problems as finite two-players games. But to define a QCSP model, th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	145,921
2309.16105	Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise	We consider the problem of private distributed multi-party multiplication. It is well-established that Shamir secret-sharing coding strategies can enable perfect information-theoretic privacy in distributed computation via the celebrated algorithm of Ben Or, Goldwasser and Wigderson (the "BGW algorithm"). However, perf...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	395,215
2402.10200	Chain-of-Thought Reasoning Without Prompting	In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	429,865
2311.08258	Unprecedented reach and rich online journeys drive hate and extremism globally	Hate and extremism cannot be controlled globally without understanding how they operate at scale. Both have escalated dramatically during the Israel-Hamas and Ukraine-Russia wars. Here we show how the online hate-extremism system is now operating at unprecedented scale across 26 social media platforms of all sizes, aud...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	407,650
1509.05096	Out of vocabulary words decrease, running texts prevail and hashtags coalesce: Twitter as an evolving sociolinguistic system	Twitter is one of the most popular social media. Due to the ease of availability of data, Twitter is used significantly for research purposes. Twitter is known to evolve in many aspects from what it was at its birth; nevertheless, how it evolved its own linguistic style is still relatively unknown. In this paper, we st...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	47,003
2306.09666	A Smooth Binary Mechanism for Efficient Private Continual Observation	In privacy under continual observation we study how to release differentially private estimates based on a dataset that evolves over time. The problem of releasing private prefix sums of $x_1,x_2,x_3,\dots \in\{0,1\}$ (where the value of each $x_i$ is to be private) is particularly well-studied, and a generalized form ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	373,921
1805.01811	Failure Prediction for Autonomous Driving	The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is important that automated cars foresee problems ahead as early as possible. This is also ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,707
2006.05622	P-ADMMiRNN: Training RNN with Stable Convergence via An Efficient and Paralleled ADMM Approach	It is hard to train Recurrent Neural Network (RNN) with stable convergence and avoid gradient vanishing and exploding problems, as the weights in the recurrent unit are repeated from iteration to iteration. Moreover, RNN is sensitive to the initialization of weights and bias, which brings difficulties in training. The ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,142
2309.15830	OrthoPlanes: A Novel Representation for Better 3D-Awareness of GANs	We present a new method for generating realistic and view-consistent images with fine geometry from 2D image collections. Our method proposes a hybrid explicit-implicit representation called \textbf{OrthoPlanes}, which encodes fine-grained 3D information in feature maps that can be efficiently generated by modifying 2D...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	395,135
2204.02662	Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs	The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,042
2411.12989	Data Watermarking for Sequential Recommender Systems	In the era of large foundation models, data has become a crucial component for building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequenti...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	509,627
1911.06509	Improved algorithm for neuronal ensemble inference by Monte Carlo method	Neuronal ensemble inference is one of the significant problems in the study of biological neural networks. Various methods have been proposed for ensemble inference from their activity data taken experimentally. Here we focus on Bayesian inference approach for ensembles with generative model, which was proposed in rece...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,560
1912.02153	Walking on the Edge: Fast, Low-Distortion Adversarial Examples	Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	156,270
2006.09701	Adversarial Defense by Latent Style Transformations	Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs. The intuition behind our approac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	182,631
1702.04342	BranchHull: Convex bilinear inversion from the entrywise product of signals with known signs	We consider the bilinear inverse problem of recovering two vectors, $x$ and $w$, in $\mathbb{R}^L$ from their entrywise product. For the case where the vectors have known signs and belong to known subspaces, we introduce the convex program BranchHull, which is posed in the natural parameter space that does not require ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,247
2310.02029	Between accurate prediction and poor decision making: the AI/ML gap	Intelligent agents rely on AI/ML functionalities to predict the consequence of possible actions and optimise the policy. However, the effort of the research community in addressing prediction accuracy has been so intense (and successful) that it created the illusion that the more accurate the learner prediction (or cla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,681
2304.09919	The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages	Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two. Many Christian organizations are dedicated to the task of translating the Holy Bible into languages that lack a modern translat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	359,221
1807.09341	Learning Plannable Representations with Causal InfoGAN	In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans -- a plausible sequence of observations that transition a dynamical system ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false	103,700
2011.08723	Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution	In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems. For the stability analysis we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	206,971
2408.05636	Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion	Speculative decoding has emerged as a widely adopted method to accelerate large language model inference without sacrificing the quality of the model outputs. While this technique has facilitated notable speed improvements by enabling parallel sequence verification, its efficiency remains inherently limited by the reli...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	479,871
2401.07042	GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection	Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	421,396
2409.18473	Efficient Top-k s-Biplexes Search over Large Bipartite Graphs	In a bipartite graph, a subgraph is an $s$-biplex if each vertex of the subgraph is adjacent to all but at most $s$ vertices on the opposite set. The enumeration of $s$-biplexes from a given graph is a fundamental problem in bipartite graph analysis. However, in real-world data engineering, finding all $s$-biplexes is ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	492,274
2102.08585	Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View	In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record. We thus try to evaluate the generation faithfulness with two entity-centric metrics: table record coverage and the ratio of hallucinated entities in...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	220,511
1603.05486	A flexible state space model for learning nonlinear dynamical systems	We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions. The coefficients in the basis function expansions are learned from data. Using a connection to Gaussian processes we also develop priors on the coefficients, for tuning the model flexibi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	53,365
2409.18989	SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks	This paper introduces SC-Phi2, a fine-tuned StarCraft II small language model for macromanagement tasks. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less power and memory to run. To teach Microsoft's Phi2 model abou...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	492,492
1811.12642	AI Neurotechnology for Aging Societies -- Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods	Dementia and especially Alzheimer's disease (AD) are the most common causes of cognitive decline in elderly people. A spread of the above mentioned mental health problems in aging societies is causing a significant medical and economic burden in many countries around the world. According to a recent World Health Organi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	115,064
2011.12747	Symmetry-Aware Actor-Critic for 3D Molecular Design	Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,260
1406.1055	Lattice Codes for the Binary Deletion Channel	The construction of deletion codes for the Levenshtein metric is reduced to the construction of codes over the integers for the Manhattan metric by run length coding. The latter codes are constructed by expurgation of translates of lattices. These lattices, in turn, are obtained from Construction~A applied to binary co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,593
2107.07582	Prediction of Blood Lactate Values in Critically Ill Patients: A Retrospective Multi-center Cohort Study	Purpose. Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate wheth...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	246,464
1109.3714	High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity	Although the standard formulations of prediction problems involve fully-observed and noiseless data drawn in an i.i.d. manner, many applications involve noisy and/or missing data, possibly involving dependence, as well. We study these issues in the context of high-dimensional sparse linear regression, and propose novel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,204
2411.15296	MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs	As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops multimodal perception and reasoning capabilities that are impressive, such...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	510,559
1610.07671	Sparser Sparse Roadmaps	We present methods for offline generation of sparse roadmap spanners that result in graphs 79% smaller than existing approaches while returning solutions of equivalent path quality. Our method uses a hybrid approach to sampling that combines traditional graph discretization with random sampling. We present techniques t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	62,821
2301.07074	SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations	Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an expensive effort, especially for 3D images. One potential solution is to...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	340,822
2309.09274	Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking	The expansion of online social media platforms has led to a surge in online content consumption. However, this has also paved the way for disseminating false claims and misinformation. As a result, there is an escalating demand for a substantial workforce to sift through and validate such unverified claims. Currently, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	392,544
2403.06269	FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing	Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling in diffusion models poses challenges for efficient video editing. Existing appr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,374
2202.05255	Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials	Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wavefunction. H...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,819
1404.2074	Renewable Powered Cellular Networks: Energy Field Modeling and Network Coverage	Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,175
2107.05191	The Impact of Three-phase Impedances on the Stability of DER systems	In this work we explore impedance-based interactions that arise when inverter-connected distributed energy resources (DERs) inject real and reactive power to regulate voltage and power flows on three-phase unbalanced distribution grids. We consider two inverter control frameworks that compute power setpoints: a mix of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,702
2110.04507	TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations	Deep reinforcement learning (DRL) has achieved super-human performance on complex video games (e.g., StarCraft II and Dota II). However, current DRL systems still suffer from challenges of multi-agent coordination, sparse rewards, stochastic environments, etc. In seeking to address these challenges, we employ a footbal...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	259,925
1704.07466	Learning from Ontology Streams with Semantic Concept Drift	Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	72,356
2501.13594	Text-to-SQL based on Large Language Models and Database Keyword Search	Text-to-SQL prompt strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks. However, when applied to real-world databases, their performance is significantly less than for these benchmarks, especially for Natural Language (NL) questions requiring complex filters and join...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	526,748
2412.08973	Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?	Cross-modal contrastive distillation has recently been explored for learning effective 3D representations. However, existing methods focus primarily on modality-shared features, neglecting the modality-specific features during the pre-training process, which leads to suboptimal representations. In this paper, we theore...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	516,304
2410.01627	Intent Detection in the Age of LLMs	Intent detection is a critical component of task-oriented dialogue systems (TODS) which enables the identification of suitable actions to address user utterances at each dialog turn. Traditional approaches relied on computationally efficient supervised sentence transformer encoder models, which require substantial trai...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	493,857
2006.00731	Second-Order Provable Defenses against Adversarial Attacks	A robustness certificate is the minimum distance of a given input to the decision boundary of the classifier (or its lower bound). For {\it any} input perturbations with a magnitude smaller than the certificate value, the classification output will provably remain unchanged. Exactly computing the robustness certificate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,559
2009.06156	AutoML for Multilayer Perceptron and FPGA Co-design	State-of-the-art Neural Network Architectures (NNAs) are challenging to design and implement efficiently in hardware. In the past couple of years, this has led to an explosion in research and development of automatic Neural Architecture Search (NAS) tools. AutomML tools are now used to achieve state of the art NNA desi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	195,543
0910.1849	Color Image Clustering using Block Truncation Algorithm	With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	4,694
2305.00382	Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database	Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). Our approach combines named entity recognition ...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	true	361,317
2411.15657	Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data	Open-vocabulary 3D object detection has recently attracted considerable attention due to its broad applications in autonomous driving and robotics, which aims to effectively recognize novel classes in previously unseen domains. However, existing point cloud-based open-vocabulary 3D detection models are limited by their...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,708
2407.09722	Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference	Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-token generation at each decoding step. While previous methods such as speculative decoding mitigate these inefficiencies by producing multi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	472,694
2402.08763	Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks	Endeavors in indoor robotic navigation rely on the accuracy of segmentation models to identify free space in RGB images. However, deep learning models are vulnerable to adversarial attacks, posing a significant challenge to their real-world deployment. In this study, we identify vulnerabilities within the hidden layers...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	429,223
2203.12208	Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection	Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods in the training dataset. This work addresses the generaliz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,181
1607.00548	Active Object Localization in Visual Situations	We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,097
1803.07351	Discrete Potts Model for Generating Superpixels on Noisy Images	Many computer vision applications, such as object recognition and segmentation, increasingly build on superpixels. However, there have been so far few superpixel algorithms that systematically deal with noisy images. We propose to first decompose the image into equal-sized rectangular patches, which also sets the maxim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,034
1410.1160	On Benchmarking Intrusion Detection Systems in Virtualized Environments	Modern intrusion detection systems (IDSes) for virtualized environments are deployed in the virtualization layer with components inside the virtual machine monitor (VMM) and the trusted host virtual machine (VM). Such IDSes can monitor at the same time the network and host activities of all guest VMs running on top of ...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	36,534
2405.20892	MALT: Multi-scale Action Learning Transformer for Online Action Detection	Online action detection (OAD) aims to identify ongoing actions from streaming video in real-time, without access to future frames. Since these actions manifest at varying scales of granularity, ranging from coarse to fine, projecting an entire set of action frames to a single latent encoding may result in a lack of loc...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	459,567
1701.03383	Secure communications with cooperative jamming: Optimal power allocation and secrecy outage analysis	This paper studies the secrecy rate maximization problem of a secure wireless communication system, in the presence of multiple eavesdroppers. The security of the communication link is enhanced through cooperative jamming, with the help of multiple jammers. First, a feasibility condition is derived to achieve a positiv...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,697
2206.14882	LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood	Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high-dimensional data. Many of them rely on a non-parametric nearest neighbors approach which suffers from the curse of dimensionality. We attempt to address that challenge by proposing a novel approach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,415
0712.2789	Trading in Risk Dimensions (TRD)	Previous work, mostly published, developed two-shell recursive trading systems. An inner-shell of Canonical Momenta Indicators (CMI) is adaptively fit to incoming market data. A parameterized trading-rule outer-shell uses the global optimization code Adaptive Simulated Annealing (ASA) to fit the trading system to histo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	1,049
1905.12969	Enriched Mixtures of Gaussian Process Experts	Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussian process (GP) experts, this results in a powerful and highly flexible model. We focus on alternative mixtures of GP experts, which model th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,957
2401.12455	Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management	We present a multi-agent Deep Reinforcement Learning (DRL) framework for managing large transportation infrastructure systems over their life-cycle. Life-cycle management of such engineering systems is a computationally intensive task, requiring appropriate sequential inspection and maintenance decisions able to reduce...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	423,380
1508.04211	Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data	We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a repa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	46,105
2303.09461	ChatGPT Participates in a Computer Science Exam	We asked ChatGPT to participate in an undergraduate computer science exam on ''Algorithms and Data Structures''. The program was evaluated on the entire exam as posed to the students. We hand-copied its answers onto an exam sheet, which was subsequently graded in a blind setup alongside those of 200 participating stude...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	352,053
2208.02442	FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning	The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	311,467
2110.10205	MultiHead MultiModal Deep Interest Recommendation Network	With the development of information technology, human beings are constantly producing a large amount of information at all times. How to obtain the information that users are interested in from the large amount of information has become an issue of great concern to users and even business managers. In order to solve th...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	262,063
1810.13044	Scalable Laplacian K-modes	We advocate Laplacian K-modes for joint clustering and density mode finding, and propose a concave-convex relaxation of the problem, which yields a parallel algorithm that scales up to large datasets and high dimensions. We optimize a tight bound (auxiliary function) of our relaxation, which, at each iteration, amounts...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	111,889
2012.11400	Unifying Homophily and Heterophily Network Transformation via Motifs	Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network analysis tasks. Most existing HOP definitions are based on either homophily to place close and highly interconnected nodes tightly in embed...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,628
2303.07131	Evolutionary quantum feature selection	Effective feature selection is essential for enhancing the performance of artificial intelligence models. It involves identifying feature combinations that optimize a given metric, but this is a challenging task due to the problem's exponential time complexity. In this study, we present an innovative heuristic called E...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,133
1804.09401	Generative Temporal Models with Spatial Memory for Partially Observed Environments	In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic envi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,967
1809.07778	Moderate deviation analysis of majorisation-based resource interconversion	We consider the problem of interconverting a finite amount of resources within all theories whose single-shot transformation rules are based on a majorisation relation, e.g. the resource theories of entanglement and coherence (for pure state transformations), as well as thermodynamics (for energy-incoherent transformat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	108,352
2010.08596	Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling	Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task. To tackle this challenge, we present a novel po...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	201,225
2108.11811	When should agents explore?	Exploration remains a central challenge for reinforcement learning (RL). Virtually all existing methods share the feature of a monolithic behaviour policy that changes only gradually (at best). In contrast, the exploratory behaviours of animals and humans exhibit a rich diversity, namely including forms of switching be...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	252,299
2501.09597	Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining	Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in using neural networks to accelerate physics simulations, and also a growing body of work on applying neural networks directly to irregular me...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	525,196
1809.02855	iDriveSense: Dynamic Route Planning Involving Roads Quality Information	Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	107,157
1906.04111	A New Ratio Image Based CNN Algorithm For SAR Despeckling	In SAR domain many application like classification, detection and segmentation are impaired by speckle. Hence, despeckling of SAR images is the key for scene understanding. Usually despeckling filters face the trade-off of speckle suppression and information preservation. In the last years deep learning solutions for s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,607
2210.13968	Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle	This paper considers a convex composite optimization problem with affine constraints, which includes problems that take the form of minimizing a smooth convex objective function over the intersection of (simple) convex sets, or regularized with multiple (simple) functions. Motivated by high-dimensional applications in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	326,380
2112.01236	Local Justice and the Algorithmic Allocation of Societal Resources	AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations. Recently, there have been several proposals for how to design objectives for these systems that attempt to achieve some combination o...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	269,417
1506.05889	Constrained adaptive sensing	Suppose that we wish to estimate a vector $\mathbf{x} \in \mathbb{C}^n$ from a small number of noisy linear measurements of the form $\mathbf{y} = \mathbf{A x} + \mathbf{z}$, where $\mathbf{z}$ represents measurement noise. When the vector $\mathbf{x}$ is sparse, meaning that it has only $s$ nonzeros with $s \ll n$, on...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,348
1804.09601	Optimizing gas networks using adjoint gradients	An increasing amount of gas-fired power plants are currently being installed in modern power grids worldwide. This is due to their low cost and the inherent flexibility offered to the electrical network, particularly in the face of increasing renewable generation. However, the integration and operation of gas generator...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	96,008
2012.07495	Deep Learning for Material recognition: most recent advances and open challenges	Recognizing material from color images is still a challenging problem today. While deep neural networks provide very good results on object recognition and has been the topic of a huge amount of papers in the last decade, their adaptation to material images still requires some works to reach equivalent accuracies. Neve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,474
2310.09441	MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments	Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,769
1212.4991	A Physical Layer Secured Key Distribution Technique for IEEE 802.11g Wireless Networks	Key distribution and renewing in wireless local area networks is a crucial issue to guarantee that unauthorized users are prevented from accessing the network. In this paper, we propose a technique for allowing an automatic bootstrap and periodic renewing of the network key by exploiting physical layer security princip...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	20,505
1701.01955	Sampled-Data Boundary Feedback Control of 1-D Parabolic PDEs	The paper provides results for the application of boundary feedback control with Zero-Order-Hold (ZOH) to 1-D linear parabolic systems on bounded domains. It is shown that the continuous-time boundary feedback applied in a sample-and-hold fashion guarantees closed-loop exponential stability, provided that the sampling ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	66,483
2306.00029	CodeTF: One-stop Transformer Library for State-of-the-art Code LLM	Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. H...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	369,841
1709.04320	An efficient genetic algorithm for large-scale transmit power control of dense industrial wireless networks	The industrial wireless local area network (IWLAN) is increasingly dense, not only due to the penetration of wireless applications into factories and warehouses, but also because of the rising need of redundancy for robust wireless coverage. Instead of powering on all the nodes with the maximal transmit power, it becom...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	80,636
2212.06034	IMDB Spoiler Dataset	User-generated reviews are often our first point of contact when we consider watching a movie or a TV show. However, beyond telling us the qualitative aspects of the media we want to consume, reviews may inevitably contain undesired revelatory information (i.e. 'spoilers') such as the surprising fate of a character in ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,983
1905.12442	Rank-one Multi-Reference Factor Analysis	In recent years, there is a growing need for processing methods aimed at extracting useful information from large datasets. In many cases the challenge is to discover a low-dimensional structure in the data, often concealed by the existence of nuisance parameters and noise. Motivated by such challenges, we consider the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	132,773
2404.13325	Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations	Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a significant increase in the computational cost and complexity of these simulations due to additional degrees of uncertainty, non-linearity and...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	448,247
2309.16699	Circular-Line Trajectory Tracking Controller for Mobile Robot using Multi-Pixy2 Sensors	This study suggests a novel tracking method that employs three Pixy2 sensors to identify the desired line trajectories instead of traditional perceiving means. Firstly, the kinematic model of the mobile robot is derived from the information gathered by three Pixy2 sensors. Secondly, the sliding mode controller is imple...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	395,459
2204.12784	Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks	Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations thr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	293,601
2501.07294	Dataset-Agnostic Recommender Systems	[This is a position paper and does not contain any empirical or theoretical results] Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and config...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	524,344
1701.00376	A time-variant channel prediction and feedback framework for interference alignment	In interference channels, channel state information (CSI) can be exploited to reduce the interference signal dimensions and thus achieve the optimal capacity scaling, i.e. degrees of freedom, promised by the interference alignment technique. However, imperfect CSI, due to channel estimation error, imperfect CSI feedbac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,266
2110.07868	FedMe: Federated Learning via Model Exchange	Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients. Numerous methods have been proposed to cope with the data heterogeneity issue in federated learning. Existing solutions require a m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,161

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.