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
2410.06884	Adaptive Refinement Protocols for Distributed Distribution Estimation under $\ell^p$-Losses	Consider the communication-constrained estimation of discrete distributions under $\ell^p$ losses, where each distributed terminal holds multiple independent samples and uses limited number of bits to describe the samples. We obtain the minimax optimal rates of the problem in most parameter regimes. An elbow effect of ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	496,383
1909.01807	ICDM 2019 Knowledge Graph Contest: Team UWA	We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest. Our system uses a pipeline-based approach to extract a set of triples from a given document. It offers a simple and effective solution to the challenge of knowledge graph construction from domain-specific text. It also pro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,015
2105.11866	GraphFM: Graph Factorization Machines for Feature Interaction Modeling	Factorization machine (FM) is a prevalent approach to modeling pairwise (second-order) feature interactions when dealing with high-dimensional sparse data. However, on the one hand, FM fails to capture higher-order feature interactions suffering from combinatorial expansion. On the other hand, taking into account inter...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	236,847
2101.05795	A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines	Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,523
2211.14953	OBMeshfree: An optimization-based meshfree solver for nonlocal diffusion and peridynamics models	We present OBMeshfree, an Optimization-Based Meshfree solver for compactly supported nonlocal integro-differential equations (IDEs) that can describe material heterogeneity and brittle fractures. OBMeshfree is developed based on a quadrature rule calculated via an equality constrained least square problem to reproduce ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	333,060
2402.00969	SPARQL Generation with Entity Pre-trained GPT for KG Question Answering	Knowledge Graphs popularity has been rapidly growing in last years. All that knowledge is available for people to query it through the many online databases on the internet. Though, it would be a great achievement if non-programmer users could access whatever information they want to know. There has been a lot of effor...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	true	false	425,796
2401.01065	BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving	The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous image retrieval methods faces several challenges, such as the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	419,208
1905.10040	OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits	We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,930
2501.07197	Lung Cancer detection using Deep Learning	In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in order to gain early detection of tumors, benign or malignant. The work uses this hybrid model by training upon the Computed Tomography scans (CT scans) as dataset. Using deep l...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	524,306
2105.05555	Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time	We study the problem of learning Bayesian networks where an $\epsilon$-fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying graph structure is known. In this work, we present the first nearly-linear time algorithm for this problem with a dimension-independent e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	234,849
2008.00170	Impact and Implementation of Reserved Lanes for Automated Driving on Signalized Urban Arterials	An automated vehicle refers to a vehicle that can achieve a safe movement on a roadway facility without the influence of a human driver. With emerging trend of the connected vehicle concept over the past decade, numerous state-of-the-art applications focusing on automated vehicle-based intersection control have been pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	189,927
2303.03593	ADELT: Transpilation Between Deep Learning Frameworks	We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks. ADELT uniquely decouples code skeleton transpilation and API keyword mapping. For code skeleton transpilation, it uses few-shot prompting on large language models (LLMs), whi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	349,780
1804.06610	End-to-end Graph-based TAG Parsing with Neural Networks	We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,341
2204.11423	Trusted Multi-View Classification with Dynamic Evidential Fusion	Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,144
1602.06052	Strong Backdoors for Default Logic	In this paper, we introduce a notion of backdoors to Reiter's propositional default logic and study structural properties of it. Also we consider the problems of backdoor detection (parameterised by the solution size) as well as backdoor evaluation (parameterised by the size of the given backdoor), for various kinds of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	52,320
1509.04219	Twitter Sentiment Analysis	This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 character...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	46,904
2502.12804	Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?	The application of reinforcement learning (RL) to dynamic resource allocation in optical networks has been the focus of intense research activity in recent years, with almost 100 peer-reviewed papers. We present a review of progress in the field, and identify significant gaps in benchmarking practices and reproducibili...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	535,059
2405.16752	Model Ensembling for Constrained Optimization	There is a long history in machine learning of model ensembling, beginning with boosting and bagging and continuing to the present day. Much of this history has focused on combining models for classification and regression, but recently there is interest in more complex settings such as ensembling policies in reinforce...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	457,582
1905.02655	Attention-based Fusion for Multi-source Human Image Generation	We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images. In this way, we can exploit multiple, possibly complementary images of the same person which are usually available at training and at testing time. T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,017
2403.19494	Regression with Multi-Expert Deferral	Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attention in classification contexts, it presents unique challenges in regression due to the infinite and continuous nature of the label space. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,369
cmp-lg/9707010	Experiences with the GTU grammar development environment	In this paper we describe our experiences with a tool for the development and testing of natural language grammars called GTU (German: Grammatik-Testumgebumg; grammar test environment). GTU supports four grammar formalisms under a window-oriented user interface. Additionally, it contains a set of German test sentences ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,777
1902.04706	Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup	We present a method for fast training of vision based control policies on real robots. The key idea behind our method is to perform multi-task Reinforcement Learning with auxiliary tasks that differ not only in the reward to be optimized but also in the state-space in which they operate. In particular, we allow auxilia...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	121,403
1410.7709	Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection	Huge datasets in cyber security, such as network traffic logs, can be analyzed using machine learning and data mining methods. However, the amount of collected data is increasing, which makes analysis more difficult. Many machine learning methods have not been designed for big datasets, and consequently are slow and di...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	37,097
1808.03343	On Physical Layer Security over Fox's $H$-Function Wiretap Fading Channels	Most of the well-known fading distributions, if not all of them, could be encompassed by Fox's $H$-function fading. Consequently, we investigate the exact and asymptotic behavior of physical layer security (PLS) over Fox's $H$-function fading wiretap channels. In particular, closed-form expressions are derived for secr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	104,919
2106.05739	Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics	Several works in implicit and explicit generative modeling empirically observed that feature-learning discriminators outperform fixed-kernel discriminators in terms of the sample quality of the models. We provide separation results between probability metrics with fixed-kernel and feature-learning discriminators using ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,210
2501.09757	Distilling Multi-modal Large Language Models for Autonomous Driving	Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as planners to improve generalizability to rare events. However, using LLMs at test time introduces high computational costs. To address this,...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	525,261
1901.00456	Cost-sensitive Selection of Variables by Ensemble of Model Sequences	Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new cl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,781
2310.19065	Evaluating LLP Methods: Challenges and Approaches	Learning from Label Proportions (LLP) is an established machine learning problem with numerous real-world applications. In this setting, data items are grouped into bags, and the goal is to learn individual item labels, knowing only the features of the data and the proportions of labels in each bag. Although LLP is a w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,837
2210.12487	MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure	In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ synthetic data with simple reasoning structures. Therefore, it cannot express more complex reasoning processes, such as the rebuttal to a re...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	325,764
2411.13032	"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models	Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	509,652
2305.17455	CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	Recent vision-language models have achieved tremendous advances. However, their computational costs are also escalating dramatically, making model acceleration exceedingly critical. To pursue more efficient vision-language Transformers, this paper introduces Cross-Guided Ensemble of Tokens (CrossGET), a general acceler...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	368,602
2401.14583	Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation	As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attractive and engaging places. However, the accurate recommendation capability relies on the powerful server collecting a vast amount of users' hi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	424,145
2211.14638	Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning	Microscopy images from different imaging conditions, organs, and tissues often have numerous cells with various shapes on a range of backgrounds. As a result, designing a deep learning model to count cells in a source domain becomes precarious when transferring them to a new target domain. To address this issue, manual...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,924
1902.04606	Quantifying the Loss of Information from Binning List-Mode Data	List-mode data is increasingly being uesd in SPECT and PET imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of informa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,373
1710.04623	Analysis of planar ornament patterns via motif asymmetry assumption and local connections	Planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which exhibit translational symmetry in two independent directions. There are exactly $17$ distinct planar symmetry groups. We present a fully automatic method for complete analysis of planar ornaments in $13$ of these groups, specifically, the groups...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,504
1709.05397	Zero-Shot Learning to Manage a Large Number of Place-Specific Compressive Change Classifiers	With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. Previous approaches for change detection are typically based on image differencing and require ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,853
1509.04788	Growing Network Models Having Part Edges Removed/added Randomly	Since network motifs are an important property of networks and some networks have the behaviors of rewiring or reducing or adding edges between old vertices before new vertices entering the networks, we construct our non-randomized model N(t) and randomized model N'(t) that have the predicated fixed subgraphs like moti...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	46,972
2108.10714	Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent Space	Deep learning models have become an increasingly preferred option for biometric recognition systems, such as speaker recognition. SincNet, a deep neural network architecture, gained popularity in speaker recognition tasks due to its parameterized sinc functions that allow it to work directly on the speech signal. The o...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,985
2206.06640	Confidence Score for Source-Free Unsupervised Domain Adaptation	Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samples, which is vulnerable to incorrect pseudo-labels. To differentiate between sam...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	302,445
2202.07170	Fairness Amidst Non-IID Graph Data: A Literature Review	The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and identically distributed (IID). However, real-world data frequently exists in non-IID graph structures tha...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,456
1109.6881	Human-powered Sorts and Joins	Crowdsourcing markets like Amazon's Mechanical Turk (MTurk) make it possible to task people with small jobs, such as labeling images or looking up phone numbers, via a programmatic interface. MTurk tasks for processing datasets with humans are currently designed with significant reimplementation of common workflows and...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	12,420
2104.01414	Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA	In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix....	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	228,339
1906.00414	Pretraining Methods for Dialog Context Representation Learning	This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	133,387
2103.01607	A Brief Survey on Deep Learning Based Data Hiding	Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this work, we conduct a brief yet comprehensive review of existing literature for deep learning based data hiding (deep hiding) by first classify...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	222,686
2301.03319	FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers	When applying automated speech recognition (ASR) for Belgian Dutch (Van Dyck et al. 2021), the output consists of an unsegmented stream of words, without any punctuation. A next step is to perform segmentation and insert punctuation, making the ASR output more readable and easy to manually correct. As far as we know th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	339,758
2404.18270	Pragmatic Formal Verification of Sequential Error Detection and Correction Codes (ECCs) used in Safety-Critical Design	Error Detection and Correction Codes (ECCs) are often used in digital designs to protect data integrity. Especially in safety-critical systems such as automotive electronics, ECCs are widely used and the verification of such complex logic becomes more critical considering the ISO 26262 safety standards. Exhaustive veri...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	450,198
2206.07570	Calibrating Agent-based Models to Microdata with Graph Neural Networks	Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inference methods have emerged as powerful tools for performing this task when the model likelihood function is intractable, as is often the case fo...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false	302,789
1908.07181	Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior	Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent varia...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	142,228
1405.0766	Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence	This tutorial summarizes recent advances in the convex relaxation of the optimal power flow (OPF) problem, focusing on structural properties rather than algorithms. Part I presents two power flow models, formulates OPF and their relaxations in each model, and proves equivalence relations among them. Part II presents su...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	32,799
2007.10546	Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop	This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019. The goal of the report is to disseminate these ideas more broadly, and in turn encourage continuing discussion about how the field could improve along these axes. We f...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	188,302
2201.09986	Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning	Unlike the classical linear model, nonlinear generative models have been addressed sparsely in the literature of statistical learning. This work aims to bringing attention to these models and their secrecy potential. To this end, we invoke the replica method to derive the asymptotic normalized cross entropy in an inver...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	276,838
2408.15649	Hierarchical Blockmodelling for Knowledge Graphs	In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These models, seldom used in the Semantic Web community, decompose a graph into a set of probability distributions. The parameters of these...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	484,023
2006.00303	Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation	Image segmentation is a fundamental vision task and a crucial step for many applications. In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD. Precisely, we define BPD on each pixel as a two-dime...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,431
2311.06597	Understanding Grokking Through A Robustness Viewpoint	Recently, an interesting phenomenon called grokking has gained much attention, where generalization occurs long after the models have initially overfitted the training data. We try to understand this seemingly strange phenomenon through the robustness of the neural network. From a robustness perspective, we show that t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	407,001
1512.03087	Evacuation time estimate for a total pedestrian evacuation using queuing network model and volunteered geographic information	Estimating city evacuation time is a non-trivial problem due to the interaction between thousands of individual agents, giving rise to various collective phenomena, such as bottleneck formation, intermittent flow and stop-and-go waves. We present a mean field approach to draw relationships between road network spatial ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	49,999
2404.14828	GLDPC-PC Codes: Channel Coding Towards 6G Communications	The sixth generation (6G) wireless communication system will improve the key technical indicators by one to two orders of magnitude, and come with some new features. As a crucial technique to enhance the reliability and efficiency of data transmission, the next generation channel coding is not only required to satisfy ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	448,834
1111.5679	Fisher information as a performance metric for locally optimum processing	For a known weak signal in additive white noise, the asymptotic performance of a locally optimum processor (LOP) is shown to be given by the Fisher information (FI) of a standardized even probability density function (PDF) of noise in three cases: (i) the maximum signal-to-noise ratio (SNR) gain for a periodic signal; ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,156
2202.04397	A hypothesis-driven method based on machine learning for neuroimaging data analysis	There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classificat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,548
2012.13231	Pain Assessment based on fNIRS using Bidirectional LSTMs	Assessing pain in patients unable to speak (also called non-verbal patients) is extremely complicated and often is done by clinical judgement. However, this method is not reliable since patients vital signs can fluctuate significantly due to other underlying medical conditions. No objective diagnosis test exists to dat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	213,161
2305.02297	Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime	Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks. While humans are known to efficiently learn new tasks from a few examples, deep learning models struggle with adaptation from few examples. In this work, we look into task adaptation in the low-data r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,981
2109.12950	Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer	Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However, cascaded models are known to be prone to error propagation and model discrepancy problems. Furthermore, there is no possibility of using end-to-end training data in conventional cascaded...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,470
2411.00412	Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation	Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but, even with domain-specific fine-tuning, often produce hallucinations for complex ones. While integrating LLMs with tools can mitigate this reliability issue, models finetuned on tool usage only often over-rely on t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	504,588
2403.01859	CSE: Surface Anomaly Detection with Contrastively Selected Embedding	Detecting surface anomalies of industrial materials poses a significant challenge within a myriad of industrial manufacturing processes. In recent times, various methodologies have emerged, capitalizing on the advantages of employing a network pre-trained on natural images for the extraction of representative features....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	434,610
2012.10873	Sequence-to-Sequence Contrastive Learning for Text Recognition	We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,465
2012.15110	Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training	Deep learning algorithms are responsible for a technological revolution in a variety of tasks including image recognition or Go playing. Yet, why they work is not understood. Ultimately, they manage to classify data lying in high dimension -- a feat generically impossible due to the geometry of high dimensional space a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,699
1905.04708	A New Look at an Old Problem: A Universal Learning Approach to Linear Regression	Linear regression is a classical paradigm in statistics. A new look at it is provided via the lens of universal learning. In applying universal learning to linear regression the hypotheses class represents the label $y\in {\cal R}$ as a linear combination of the feature vector $x^T\theta$ where $x\in {\cal R}^M$, withi...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	130,538
2311.15010	Adapter is All You Need for Tuning Visual Tasks	Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks. Despite their success, existing visual delta-tuning art fails to exceed the upper limit of full fine-tuning on challenging tasks like inst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,346
1710.05241	Robust Decentralized Learning Using ADMM with Unreliable Agents	Many machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	82,607
1312.6931	Multiple routes transmitted epidemics on multiplex networks	This letter investigates the multiple routes transmitted epidemic process on multiplex networks. We propose detailed theoretical analysis that allows us to accurately calculate the epidemic threshold and outbreak size. It is found that the epidemic can spread across the multiplex network even if all the network layers ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	29,418
1907.12022	DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation	Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis. In this paper, we propose DAR-Net, a novel network architecture that focuses on dynamic feature aggregation. The central idea of DAR-Net is generating a self-adaptive pooling skeleton that considers both scene comp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	140,017
2101.00834	Symbolic Control for Stochastic Systems via Finite Parity Games	We consider the problem of computing the maximal probability of satisfying an omega-regular specification for stochastic nonlinear systems evolving in discrete time. The problem reduces, after automata-theoretic constructions, to finding the maximal probability of satisfying a parity condition on a (possibly hybrid) st...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	214,221
1401.4383	On the Hegselmann-Krause conjecture in opinion dynamics	We give an elementary proof of a conjecture by Hegselmann and Krause in opinion dynamics, concerning a symmetric bounded confidence interval model: If there is a truth and all individuals take each other seriously by a positive amount bounded away from zero, then all truth seekers will converge to the truth. Here truth...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	30,066
2009.07649	Verifying Stochastic Hybrid Systems with Temporal Logic Specifications via Model Reduction	We present a scalable methodology to verify stochastic hybrid systems. Using the Mori-Zwanzig reduction method, we construct a finite state Markov chain reduction of a given stochastic hybrid system and prove that this reduced Markov chain is approximately equivalent to the original system in a distributional sense. Ap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	196,006
2305.05589	DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation	Existing Question Answering (QA) systems limited by the capability of answering questions from unseen domain or any out-of-domain distributions making them less reliable for deployment to real scenarios. Most importantly all the existing QA domain adaptation methods are either based on generating synthetic data or pseu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	363,211
1511.08724	On the convergence of cycle detection for navigational reinforcement learning	We consider a reinforcement learning framework where agents have to navigate from start states to goal states. We prove convergence of a cycle-detection learning algorithm on a class of tasks that we call reducible. Reducible tasks have an acyclic solution. We also syntactically characterize the form of the final polic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	49,574
2301.12893	Formalizing Piecewise Affine Activation Functions of Neural Networks in Coq	Verification of neural networks relies on activation functions being piecewise affine (pwa) -- enabling an encoding of the verification problem for theorem provers. In this paper, we present the first formalization of pwa activation functions for an interactive theorem prover tailored to verifying neural networks withi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,709
2405.20412	Audio2Rig: Artist-oriented deep learning tool for facial animation	Creating realistic or stylized facial and lip sync animation is a tedious task. It requires lot of time and skills to sync the lips with audio and convey the right emotion to the character's face. To allow animators to spend more time on the artistic and creative part of the animation, we present Audio2Rig: a new deep ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	459,329
1312.6055	Unit Tests for Stochastic Optimization	Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devised; however, it is unclear whether these algorithms are robust and widely applicable across many different optimization landscapes. In this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,295
2406.02939	Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes	In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to em...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	461,015
2402.12565	A Simple Detection and Identification Scheme For Reconfigurable Intelligent Surfaces	Reconfigurable intelligent surface (RIS)-empowered communication is one of the promising physical layer enabling technologies for the sixth generation (6G) wireless networks due to their unprecedented capabilities in shaping the wireless communication environment. RISs are modeled as passive objects that can not transm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	430,898
1909.03347	Concentration of kernel matrices with application to kernel spectral clustering	We study the concentration of random kernel matrices around their mean. We derive nonasymptotic exponential concentration inequalities for Lipschitz kernels assuming that the data points are independent draws from a class of multivariate distributions on $\mathbb R^d$, including the strongly log-concave distributions u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,444
2211.11190	Cross-Modal Contrastive Learning for Robust Reasoning in VQA	Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data, which prevents their usage in challenging real-world scenarios. In this paper, we propose a simple but effective cross-modal contrastive le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,627
2309.16364	FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation	Neural radiance fields with stochasticity have garnered significant interest by enabling the sampling of plausible radiance fields and quantifying uncertainty for downstream tasks. Existing works rely on the independence assumption of points in the radiance field or the pixels in input views to obtain tractable forms o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,324
2312.04877	Generating Explanations to Understand and Repair Embedding-based Entity Alignment	Entity alignment (EA) seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed entities in vector space and align them via nearest neighbor search. Although embedding-based EA has gained marked success in recent years, i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	413,869
2303.00137	PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing	Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	348,492
2404.05100	Legibot: Generating Legible Motions for Service Robots Using Cost-Based Local Planners	With the increasing presence of social robots in various environments and applications, there is an increasing need for these robots to exhibit socially-compliant behaviors. Legible motion, characterized by the ability of a robot to clearly and quickly convey intentions and goals to the individuals in its vicinity, thr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	444,942
1502.05928	Supervised Dictionary Learning and Sparse Representation-A Review	Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	40,429
2303.18103	Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions	Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	355,462
1805.05396	Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes	We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,417
2501.01196	Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views	In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. Whe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,970
1206.3275	Learning Hidden Markov Models for Regression using Path Aggregation	We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regress...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,533
2304.02853	Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce	This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,580
1808.06562	Class-Aware Fully-Convolutional Gaussian and Poisson Denoising	We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,561
2004.12880	Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)	The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as la...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	174,380
2405.07363	Multilingual Power and Ideology Identification in the Parliament: a Reference Dataset and Simple Baselines	We introduce a dataset on political orientation and power position identification. The dataset is derived from ParlaMint, a set of comparable corpora of transcribed parliamentary speeches from 29 national and regional parliaments. We introduce the dataset, provide the reasoning behind some of the choices during its cre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,682
1611.05780	Gap Safe screening rules for sparsity enforcing penalties	In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	64,079
1103.5002	User Modeling Combining Access Logs, Page Content and Semantics	The paper proposes an approach to modeling users of large Web sites based on combining different data sources: access logs and content of the accessed pages are combined with semantic information about the Web pages, the users and the accesses of the users to the Web site. The assumption is that we are dealing with a l...	true	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	9,756
2502.14047	Towards a Learning Theory of Representation Alignment	It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different representations toward a shared statistical model of reality. In this paper, we propose ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	535,631
2105.15065	Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services	Site Reliability Engineers (SREs) play a key role in issue identification and resolution. After an issue is reported, SREs come together in a virtual room (collaboration platform) to triage the issue. While doing so, they leave behind a wealth of information which can be used later for triaging similar issues. However,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	237,903
2412.07672	FlexLLM: Exploring LLM Customization for Moving Target Defense on Black-Box LLMs Against Jailbreak Attacks	Defense in large language models (LLMs) is crucial to counter the numerous attackers exploiting these systems to generate harmful content through manipulated prompts, known as jailbreak attacks. Although many defense strategies have been proposed, they often require access to the model's internal structure or need addi...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	515,750

Subsets and Splits

No community queries yet

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