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
2501.13106	VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding	In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. The meaning of "vision-centric" is two-fold: the vision-centric training paradigm and vision-centric framework design. The key insight of our...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,561
2009.11277	Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing	An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-lo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,128
1912.07193	Distributed PV Penetration Impact Analysis on Transmission System Voltages using Co-Simulation	With the growing penetrations of distributed energy resources (DERs), it is imperative to evaluate their impacts on transmission system operations. In this paper, an iteratively coupled transmission and distribution (T&D) co-simulation framework is employed to study the impacts of increasing penetrations of distributio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	157,540
1511.06961	On the Linear Algebraic Structure of Distributed Word Representations	In this work, we leverage the linear algebraic structure of distributed word representations to automatically extend knowledge bases and allow a machine to learn new facts about the world. Our goal is to extract structured facts from corpora in a simpler manner, without applying classifiers or patterns, and using only ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	49,357
2008.08810	An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension	Machine reading comprehension (MRC) is a challenging task in natural language processing that makes computers understanding natural language texts and answer questions based on those texts. There are many techniques for solving this problems, and word representation is a very important technique that impact most to the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	192,509
2410.07260	Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI	Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific genes from gene expression values enables a more tailored and personalized treatment approach. However...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,556
1607.07607	Adaptive Nonnegative Matrix Factorization and Measure Comparisons for Recommender Systems	The Nonnegative Matrix Factorization (NMF) of the rating matrix has shown to be an effective method to tackle the recommendation problem. In this paper we propose new methods based on the NMF of the rating matrix and we compare them with some classical algorithms such as the SVD and the regularized and unregularized no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	59,048
1910.02635	A Decentralized Communication Policy for Multi Agent Multi Armed Bandit Problems	This paper proposes a novel policy for a group of agents to, individually as well as collectively, solve a multi armed bandit (MAB) problem. The policy relies solely on the information that an agent has obtained through sampling of the options on its own and through communication with neighbors. The option selection po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,304
2401.13002	Theorem Discovery Amongst Cyclic Polygons	We examine a class of geometric theorems on cyclic 2n-gons. We prove that if we take n disjoint pairs of sides, each pair separated by an even number of polygon sides, then there is a linear combination of the angles between those sides which is constant. We present a formula for the linear combination, which provides ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	423,583
2412.14142	On Calibration in Multi-Distribution Learning	Modern challenges of robustness, fairness, and decision-making in machine learning have led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor is optimized across multiple distributions. We study the calibration properties of MDL to better understand how the predictor performs unifo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	518,587
2209.00355	Gait Recognition in the Wild with Multi-hop Temporal Switch	Existing studies for gait recognition are dominated by in-the-lab scenarios. Since people live in real-world senses, gait recognition in the wild is a more practical problem that has recently attracted the attention of the community of multimedia and computer vision. Current methods that obtain state-of-the-art perform...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,560
1903.04064	Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation	In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity betw...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	123,892
2404.04667	Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology	Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal perfor...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	444,747
2407.07853	Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks	In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually increases the task's difficulty. We integrated our curriculum into the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,927
1903.11431	Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks	Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in know...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,513
2408.02678	Convergence rates of stochastic gradient method with independent sequences of step-size and momentum weight	In large-scale learning algorithms, the momentum term is usually included in the stochastic sub-gradient method to improve the learning speed because it can navigate ravines efficiently to reach a local minimum. However, step-size and momentum weight hyper-parameters must be appropriately tuned to optimize convergence....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,715
2106.03802	Learning to Efficiently Sample from Diffusion Probabilistic Models	Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a powerful family of generative models that can yield high-fidelity samples and competitive log-likelihoods across a range of domains, including image and speech synthesis. Key advantages of DDPMs include ease of training, in contrast to generative advers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,463
2007.00263	Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks	Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	185,070
2009.14737	Improving Auto-Augment via Augmentation-Wise Weight Sharing	The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A pla...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,125
2301.11147	Train Hard, Fight Easy: Robust Meta Reinforcement Learning	A major challenge of reinforcement learning (RL) in real-world applications is the variation between environments, tasks or clients. Meta-RL (MRL) addresses this issue by learning a meta-policy that adapts to new tasks. Standard MRL methods optimize the average return over tasks, but often suffer from poor results in t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,033
2306.07768	Area is all you need: repeatable elements make stronger adversarial attacks	Over the last decade, deep neural networks have achieved state of the art in computer vision tasks. These models, however, are susceptible to unusual inputs, known as adversarial examples, that cause them to misclassify or otherwise fail to detect objects. Here, we provide evidence that the increasing success of advers...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	373,142
2203.07376	HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing	Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advan...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	285,411
2203.13919	Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator	We present a novel multi-channel front-end based on channel shortening with theWeighted Prediction Error (WPE) method followed by a fixed MVDR beamformer used in combination with a recently proposed self-attention-based channel combination (SACC) scheme, for tackling the distant ASR problem. We show that the proposed s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	287,802
2107.08924	Epistemic Neural Networks	Intelligence relies on an agent's knowledge of what it does not know. This capability can be assessed based on the quality of joint predictions of labels across multiple inputs. In principle, ensemble-based approaches produce effective joint predictions, but the computational costs of training large ensembles can becom...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	246,877
2009.09538	Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms	We study the challenging exploration incentive problem in both bandit and reinforcement learning, where the rewards are scale-free and potentially unbounded, driven by real-world scenarios and differing from existing work. Past works in reinforcement learning either assume costly interactions with an environment or pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	196,610
2009.01371	Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS	With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections. While these models perform well on benchmark dataset where low-resolution (LR) images are constructed from high-r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	194,286
2306.04455	RD-Suite: A Benchmark for Ranking Distillation	The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem, often leveraging ranking information from teacher rankers that is absent in traditional classification settings. To date, there is no well-...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	371,752
2408.03732	Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks	Uncertainty quantification enables users to assess the reliability of responses generated by large language models (LLMs). We present a novel Question Rephrasing technique to evaluate the input uncertainty of LLMs, which refers to the uncertainty arising from equivalent variations of the inputs provided to LLMs. This t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	479,130
2005.00043	Fundamental Challenges of Cyber-Physical Systems Security Modeling	Systems modeling practice lacks security analysis tools that can interface with modeling languages to facilitate security by design. Security by design is a necessity in the age of safety critical cyber-physical systems, where security violations can cause hazards. Currently, the overlap between security and safety is ...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	175,101
1807.06089	Repeatability of Multiparametric Prostate MRI Radiomics Features	In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. For such biomarkers to be us...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,053
2201.00570	3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems	We present Distributed Deep Deterministic Policy Gradient (3DPG), a multi-agent actor-critic (MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully distributed during both training and deployment. 3DPG agents calculate local policy gradients based on the most recently available local data (st...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	274,001
2310.00684	How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?	Neural Radiance Fields (NeRFs) are gaining significant interest for online active object reconstruction due to their exceptional memory efficiency and requirement for only posed RGB inputs. Previous NeRF-based view planning methods exhibit computational inefficiency since they rely on an iterative paradigm, consisting ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	396,094
1409.8558	A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis	Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameteriz...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	36,419
0706.3188	A tutorial on conformal prediction	Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability $\epsilon$, together with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a set of labels, typically containing $\hat{y}$, that also contains $y$ with probability $1-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348
2205.15516	Multi-Scan Multi-Sensor Multi-Object State Estimation	If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across multiple scans. In this article, we propose an efficient numerical solution to the multi-scan multi-sensor multi-object estimation problem by computing the (labeled) multi-sensor multi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	299,759
2312.15424	Integrating Renewable Energy Sources as Reserve Providers: Modeling, Pricing, and Properties	In pursuit of carbon neutrality, many countries have adopted renewable portfolio standards to facilitate the integration of renewable energy. However, increasing penetration of renewable energy resources will also pose higher requirements on system flexibility. Allowing renewable themselves to participate in the reserv...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	418,007
2211.09932	Simple Digital Controls from Approximate Plant Models	Two ways of designing low-order discrete-time (i.e. digital) controls for low-order plant (i.e. process) models are considered in this tutorial. The first polynomial method finds the controller coefficients that place the poles of the closed-loop feedback system at specified positions for adroit controls, i.e. for a ra...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	331,149
1705.09052	Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation	Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation. In contrast, web images and their image-level labels are much easier and cheaper to obtain. In this work, we propose ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,137
2302.06055	Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels	With the continuous increment of maritime applications, the development of marine networks for data offloading becomes necessary. However, the limited maritime network resources are very difficult to satisfy real-time demands. Besides, how to effectively handle multiple compute-intensive tasks becomes another intractab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	345,270
1609.01592	CRTS: A type system for representing clinical recommendations	Background: Clinical guidelines and recommendations are the driving wheels of the evidence-based medicine (EBM) paradigm, but these are available primarily as unstructured text and are generally highly heterogeneous in nature. This significantly reduces the dissemination and automatic application of these recommendatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	60,615
2409.13987	Holistic and Historical Instance Comparison for Cervical Cell Detection	Cytology screening from Papanicolaou (Pap) smears is a common and effective tool for the preventive clinical management of cervical cancer, where abnormal cell detection from whole slide images serves as the foundation for reporting cervical cytology. However, cervical cell detection remains challenging due to 1) hazil...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,271
2105.00093	Energy Efficient Reconfigurable Intelligent Surface Enabled Mobile Edge Computing Networks with NOMA	Reconfigurable intelligent surface (RIS) has emerged as a promising technology for achieving high spectrum and energy efficiency in future wireless communication networks. In this paper, we investigate an RIS-aided single-cell multi-user mobile edge computing (MEC) system where an RIS is deployed to support the communi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	233,082
2010.02537	Do Explicit Alignments Robustly Improve Multilingual Encoders?	Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often subopt...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,067
1301.1409	A Dual Number Approach for Numerical Calculation of Velocity and Acceleration in the Spherical 4R Mechanism	This paper proposes a methodology to calculate both the first and second derivatives of a vector function of one variable in a single computation step. The method is based on the nested application of the dual number approach for first order derivatives. It has been implemented in Fortran language, a module which con...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,856
2308.16684	Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack	The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications. Conventional wisdom suggests that not everyone can be an attacker since the process of designing the trigger generation algorithm often involves significant effort and extensive exp...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	389,078
cs/0701083	A Backtracking-Based Algorithm for Computing Hypertree-Decompositions	Hypertree decompositions of hypergraphs are a generalization of tree decompositions of graphs. The corresponding hypertree-width is a measure for the cyclicity and therefore tractability of the encoded computation problem. Many NP-hard decision and computation problems are known to be tractable on instances whose struc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	540,052
1511.06995	Non-Sentential Utterances in Dialogue: Experiments in Classification and Interpretation	Non-sentential utterances (NSUs) are utterances that lack a complete sentential form but whose meaning can be inferred from the dialogue context, such as "OK", "where?", "probably at his apartment". The interpretation of non-sentential utterances is an important problem in computational linguistics since they constitut...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	49,366
1906.04402	Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval	Visual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding functions that map an instance to a single point in the shared space. Unfortunately, injective embedding cannot effectively handle polysemous in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,706
2202.13428	Distribution Preserving Graph Representation Learning	Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end traini...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	282,600
2210.00483	Learning Algorithm Generalization Error Bounds via Auxiliary Distributions	Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method, that leads to new upper bounds on expected generalization errors that are appropriate for supervised learning scenarios. We show that our g...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	320,882
2402.10373	BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains	Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presen...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	429,932
1402.5803	Sparse phase retrieval via group-sparse optimization	This paper deals with sparse phase retrieval, i.e., the problem of estimating a vector from quadratic measurements under the assumption that few components are nonzero. In particular, we consider the problem of finding the sparsest vector consistent with the measurements and reformulate it as a group-sparse optimizatio...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	31,116
2412.19072	Robust Speech and Natural Language Processing Models for Depression Screening	Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly across patients. We have described two deep learning models developed for this purpose. One model is based on acoustics; the other is based on na...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	520,698
2207.10650	Deep Learning of Radiative Atmospheric Transfer with an Autoencoder	As electro-optical energy from the sun propagates through the atmosphere it is affected by radiative transfer effects including absorption, emission, and scattering. Modeling these affects is essential for scientific remote sensing measurements of the earth and atmosphere. For example, hyperspectral imagery is a form o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	309,332
2312.10032	Osprey: Pixel Understanding with Visual Instruction Tuning	Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grained vision-language alignment at pixel level. B...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,973
1407.1454	Distributed Relay Selection Protocols for Simultaneous Wireless Information and Power Transfer	Harvesting energy from the radio-frequency (RF) signal is an exciting solution to replenish energy in energy-constrained wireless networks. In this paper, an amplify-and-forward (AF) based wireless relay network is considered, where the relay nodes need to harvest energy from the source's RF signal to forward informati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,432
1708.09032	Plausibility and probability in deductive reasoning	We consider the problem of rational uncertainty about unproven mathematical statements, remarked on by G\"odel and others. Using Bayesian-inspired arguments we build a normative model of fair bets under deductive uncertainty which draws from both probability and the theory of algorithms. We comment on connections to Ze...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	79,722
1708.06734	Representation Learning by Learning to Count	We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the represent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,372
2501.11416	Mapping network structures and dynamics of decentralised cryptocurrencies: The evolution of Bitcoin (2009-2023)	Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than \$3.50 trillion, with 1 Bitcoin exchanging for more than \$97,000 at the end of November 2024. Monitor...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	525,916
2010.03604	SRLGRN: Semantic Role Labeling Graph Reasoning Network	This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly. The proposed graph is a heterogeneous ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,450
1604.00470	Overlay Text Extraction From TV News Broadcast	The text data present in overlaid bands convey brief descriptions of news events in broadcast videos. The process of text extraction becomes challenging as overlay text is presented in widely varying formats and often with animation effects. We note that existing edge density based methods are well suited for our appli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,034
2208.08624	Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID	Recently unsupervised person re-identification (re-ID) has drawn much attention due to its open-world scenario settings where limited annotated data is available. Existing supervised methods often fail to generalize well on unseen domains, while the unsupervised methods, mostly lack multi-granularity information and ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,419
2410.12175	Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards	Linear temporal logic (LTL) and, more generally, $\omega$-regular objectives are alternatives to the traditional discount sum and average reward objectives in reinforcement learning (RL), offering the advantage of greater comprehensibility and hence explainability. In this work, we study the relationship between these ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	498,886
2409.20038	Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization	Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to desi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	492,970
2403.20246	Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids	Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of t...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,675
2402.09710	Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks	Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistak...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	429,640
2006.06868	SegNBDT: Visual Decision Rules for Segmentation	The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks like segmentation. To address this, prior work combines neural networks with decision trees. However, such models (1) perform poorly when comp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	181,582
2207.14296	EOSC-LIFE WP4 TOOLBOX: Toolbox for sharing of sensitive data -- a concept description	The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing inform...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	310,541
1607.02914	Minimum Description Length Principle in Supervised Learning with Application to Lasso	The minimum description length (MDL) principle in supervised learning is studied. One of the most important theories for the MDL principle is Barron and Cover's theory (BC theory), which gives a mathematical justification of the MDL principle. The original BC theory, however, can be applied to supervised learning only ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,438
2010.01935	Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence	Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction technique for nonnegative data sets. In order to measure the discrepancy between the input data and the low-rank approximation, the Kullback-Leibler (KL) divergence is one of the most widely used objective function for NMF. It correspo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,850
2410.00700	Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models	Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	493,467
2409.19723	Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues	Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts. Current research predominantly treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. In this paper, we propose a nov...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,820
2011.04448	Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic	Future wireless networks will be characterized by heterogeneous traffic requirements. Such requirements can be low-latency or minimum-throughput. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	205,589
2204.03297	A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks	Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational c...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	false	false	290,250
1801.03523	Generative Models for Stochastic Processes Using Convolutional Neural Networks	The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields (such as quantitative finance and physics) to develop a general tool for forecasts and simulations without the need to identify/assume a speci...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	88,104
2406.08188	Attention-Based Learning for Fluid State Interpolation and Editing in a Time-Continuous Framework	In this work, we introduce FluidsFormer: a transformer-based approach for fluid interpolation within a continuous-time framework. By combining the capabilities of PITT and a residual neural network (RNN), we analytically predict the physical properties of the fluid state. This enables us to interpolate substep frames b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	463,380
2406.07539	BAKU: An Efficient Transformer for Multi-Task Policy Learning	Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, where each data point requires physical execution of actions in the real world. Thus, there is a pressing need for architectures that can eff...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	463,095
2401.14694	TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records	Motivation: Electronic Health Records (EHR) represent a comprehensive resource of a patient's medical history. EHR are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	424,191
2404.17729	CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving	Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems. In this work, we aim to push the upper bound of the reasoning capabil...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	449,966
2011.10996	Predictive maintenance on event logs: Application on an ATM fleet	Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical phenomenons such as temperature or vibration which can be directly linked to the d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,687
1911.05585	Structured Sparsification of Gated Recurrent Neural Networks	Recently, a lot of techniques were developed to sparsify the weights of neural networks and to remove networks' structure units, e.g. neurons. We adjust the existing sparsification approaches to the gated recurrent architectures. Specifically, in addition to the sparsification of weights and neurons, we propose sparsif...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	153,306
2210.10182	Landmark Enforcement and Style Manipulation for Generative Morphing	Morph images threaten Facial Recognition Systems (FRS) by presenting as multiple individuals, allowing an adversary to swap identities with another subject. Morph generation using generative adversarial networks (GANs) results in high-quality morphs unaffected by the spatial artifacts caused by landmark-based methods, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,812
2207.12578	A Retrospective on ICSE 2022	The 44th International Conference on Software Engineering (ICSE 2022) was held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Here, we summarize themes of research and the direction of research in the field of software engineering and testing that we observed at the conference.	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	310,049
1602.05536	Backhaul Traffic Balancing and Dynamic Content-Centric Clustering for the Downlink of Fog Radio Access Network	Recently, an evolution of the Cloud Radio Access Network (C-RAN) has been proposed, named as Fog Radio Access Network (F-RAN). Compared to C-RAN, the Radio Units (RUs) in F-CAN are equipped with local caches, which can store some frequently requested files. In the downlink, users requesting the same file form a multica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,268
2405.11120	Latent State Estimation Helps UI Agents to Reason	A common problem for agents operating in real-world environments is that the response of an environment to their actions may be non-deterministic and observed through noise. This renders environmental state and progress towards completing a task latent. Despite recent impressive demonstrations of LLM's reasoning abilit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,010
2407.11772	User Behavior Analysis and Clustering in a MMO Mobile Game: Insights and Recommendations	This study presents a comprehensive analysis of user behavior and clustering in a popular mobile battle royale game, employing temporal and static data mining techniques to uncover distinct player segments. Our methodology encompasses time series K-means clustering, graph-based algorithms (DeepWalk and LINE), and stati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	473,611
1308.3827	Layered Constructions for Low-Delay Streaming Codes	We propose a new class of error correction codes for low-delay streaming communication. We consider an online setup where a source packet arrives at the encoder every $M$ channel uses, and needs to be decoded with a maximum delay of $T$ packets. We consider a sliding-window erasure channel --- $\cC(N,B,W)$ --- which in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,503
2107.03786	Deep Metric Learning Model for Imbalanced Fault Diagnosis	Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. This paper proposes a novel deep metric learning model, where imbalanced fault dat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	245,258
1704.01893	Improved Decoding and Error Floor Analysis of Staircase Codes	Staircase codes play an important role as error-correcting codes in optical communications. In this paper, a low-complexity method for resolving stall patterns when decoding staircase codes is described. Stall patterns are the dominating contributor to the error floor in the original decoding method. Our improvement is...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,343
1107.3784	Applying Data Privacy Techniques on Tabular Data in Uganda	The growth of Information Technology(IT) in Africa has led to an increase in the utilization of communication networks for data transaction across the continent. A growing number of entities in the private sector, academia, and government, have deployed the Internet as a medium to transact in data, routinely posting st...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	11,358
2406.13542	Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models	One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhance the complex instruction-following abilities of LLMs without manual annotation remains unresolved. In this paper, we introduce AutoIF, the...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	465,889
1905.02791	Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems	Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations. However, most NNFF methods for complex multi-element atomic systems in...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	130,047
2004.12097	A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control	In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	174,130
2310.19684	Density Estimation for Entry Guidance Problems using Deep Learning	This work presents a deep-learning approach to estimate atmospheric density profiles for use in planetary entry guidance problems. A long short-term memory (LSTM) neural network is trained to learn the mapping between measurements available onboard an entry vehicle and the density profile through which it is flying. Me...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	404,088
2110.00931	Exploration of Artificial Intelligence-oriented Power System Dynamic Simulators	With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	258,590
1611.06403	Deep Outdoor Illumination Estimation	We present a CNN-based technique to estimate high-dynamic range outdoor illumination from a single low dynamic range image. To train the CNN, we leverage a large dataset of outdoor panoramas. We fit a low-dimensional physically-based outdoor illumination model to the skies in these panoramas giving us a compact set of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,179
1407.3193	Optimally Stabilized PET Image Denoising Using Trilateral Filtering	Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks. Conventional PET denoising methods either over-smooth small-sized structures due to resolution limitation or make inc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	34,600
2012.11105	Resting-state EEG sex classification using selected brain connectivity representation	Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these eff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,531
2301.09017	Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?	Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in various downstream applications. However, whether the learned knowledge by LLMs can be transferred to clinical cardiology remains unknown. In...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	341,373
1204.3391	Rateless Codes with Progressive Recovery for Layered Multimedia Delivery	This paper proposes a novel approach, based on unequal error protection, to enhance rateless codes with progressive recovery for layered multimedia delivery. With a parallel encoding structure, the proposed Progressive Rateless codes (PRC) assign unequal redundancy to each layer in accordance with their importance. Eac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	15,491

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