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
2205.05857	Comparing Open Arabic Named Entity Recognition Tools	The main objective of this paper is to compare and evaluate the performances of three open Arabic NER tools: CAMeL, Hatmi, and Stanza. We collected a corpus consisting of 30 articles written in MSA and manually annotated all the entities of the person, organization, and location types at the article (document) level. O...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,063
1905.02069	Mixing set and bag semantics	The conservativity theorem for nested relational calculus implies that query expressions can freely use nesting and unnesting, yet as long as the query result type is a flat relation, these capabilities do not lead to an increase in expressiveness over flat relational queries. Moreover, Wong showed how such queries can...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	129,887
2208.06906	Limits of an AI program for solving college math problems	Drori et al. (2022) report that "A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level ... [It] automatically answers 81\% of university-level mathematics problems." The system they describe is indeed impressive; however, the above descriptio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	312,863
2302.11520	Guiding Large Language Models via Directional Stimulus Prompting	We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model (e.g., T5) to generate an auxiliary directional stimulus prompt for each input instance. ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	347,235
2212.10190	Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite	We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,369
2205.09067	Automatic Rule Induction for Interpretable Semi-Supervised Learning	Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Meanwhile, pretrained transformer models act as black-box correlation engines that are difficult to explain and sometimes behave unreliably. In this paper, we propose tackling both of these challenges via...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	297,142
1803.03532	Predicting antimicrobial drug consumption using web search data	Consumption of antimicrobial drugs, such as antibiotics, is linked with antimicrobial resistance. Surveillance of antimicrobial drug consumption is therefore an important element in dealing with antimicrobial resistance. Many countries lack sufficient surveillance systems. Usage of web mined data therefore has the pote...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	92,268
2110.10963	Neuro-Symbolic Reinforcement Learning with First-Order Logic	Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework ...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	262,318
1307.1903	Achieving greater Explanatory Power and Forecasting Accuracy with Non-uniform spread Fuzzy Linear Regression	Fuzzy regression models have been applied to several Operations Research applications viz., forecasting and prediction. Earlier works on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,675
2206.09697	Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets	In this report, we combine the idea of Wide ResNets and transfer learning to optimize the architecture of deep neural networks. The first improvement of the architecture is the use of all layers as information source for the last layer. This idea comes from transfer learning, which uses networks pre-trained on other da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,664
2309.14822	OS-net: Orbitally Stable Neural Networks	We introduce OS-net (Orbitally Stable neural NETworks), a new family of neural network architectures specifically designed for periodic dynamical data. OS-net is a special case of Neural Ordinary Differential Equations (NODEs) and takes full advantage of the adjoint method based backpropagation method. Utilizing ODE th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,754
2402.06367	TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records	Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing data in certain clinical variables is not at random but depends on the decisions of clinicians and the state of the patient. Point process is a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,276
1504.05606	The Impact of Physical Channel on Performance of Subspace-Based Channel Estimation in Massive MIMO Systems	A subspace method for channel estimation has been recently proposed [1] for tackling the pilot contamination effect, which is regarded by some researchers as a bottleneck in massive MIMO systems. It was shown in [1] that if the power ratio between the desired signal and interference is kept above a certain value, the r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,291
2406.00987	Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement	Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. However, current GAD methods largely overlook the fairness problem, which might result in discriminatory decisions skewed toward certain demographic groups defined on sensitive a...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	460,115
1610.01874	Neural-based Noise Filtering from Word Embeddings	Word embeddings have been demonstrated to benefit NLP tasks impressively. Yet, there is room for improvement in the vector representations, because current word embeddings typically contain unnecessary information, i.e., noise. We propose two novel models to improve word embeddings by unsupervised learning, in order to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	62,021
2302.08783	SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance	We study Stochastic Gradient Descent with AdaGrad stepsizes: a popular adaptive (self-tuning) method for first-order stochastic optimization. Despite being well studied, existing analyses of this method suffer from various shortcomings: they either assume some knowledge of the problem parameters, impose strong global L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,184
2312.03009	I-PHYRE: Interactive Physical Reasoning	Current evaluation protocols predominantly assess physical reasoning in stationary scenes, creating a gap in evaluating agents' abilities to interact with dynamic events. While contemporary methods allow agents to modify initial scene configurations and observe consequences, they lack the capability to interact with ev...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	413,089
2501.02598	GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation	Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals attest their findings, but their writing is time consuming and requires specialized clinical expertise. The automated generation of r...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	522,552
2411.12841	Data-to-Model Distillation: Data-Efficient Learning Framework	Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a model trained on the full dataset. Despite recent progress, existing dataset distillation methods often struggle with computational efficiency,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	509,565
1612.07435	Partial $\ell_1$ optimization in random linear systems -- phase transitions and large deviations	$\ell_1$ optimization is a well known heuristic often employed for solving various forms of sparse linear problems. In this paper we look at its a variant that we refer to as the \emph{partial} $\ell_1$ and discuss its mathematical properties when used for solving linear under-determined systems of equations. We will f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,942
2312.02051	TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding	This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame, and (2) a sliding video Q-Former t...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	412,672
2102.08145	Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping	To cope with the growing demand for transportation on the railway system, accurate, robust, and high-frequency positioning is required to enable a safe and efficient utilization of the existing railway infrastructure. As a basis for a localization system we propose a complete on-board mapping pipeline able to map robus...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	220,357
2010.01108	Cross-Lingual Transfer Learning for Complex Word Identification	Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words, or groups of words, in texts from different areas of expertise. The purpose of CWI is to highlight problematic structures that non-native speakers would usually find difficult to understand. Our approach uses zero-shot, one-shot...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	198,522
2308.14983	Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation	Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge. Additionally, the complexity of the external environment and the structure of rolling bearings often manifests faults characterized by randomness a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	388,518
1711.11403	KIBS Innovative Entrepreneurship Networks on Social Media	The analysis of the use of social media for innovative entrepreneurship in the context has received little attention in the literature, especially in the context of Knowledge Intensive Business Services (KIBS). Therefore, this paper focuses on bridging this gap by applying text mining and sentiment analysis techniques ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	85,774
2308.03867	From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal	Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real image deraining (RID). To address this dilemma and advance RID, we construct a Large-scale High-quality Paired real rain benchmark (LHP-Rain), ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,193
2409.09916	SFR-RAG: Towards Contextually Faithful LLMs	Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI. The LLMs used in RAG applications are required to faithfully and completely comprehend the provide...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	488,535
2304.04508	HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion	Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	357,246
2112.12073	Two Stream Network for Stroke Detection in Table Tennis	This paper presents a table tennis stroke detection method from videos. The method relies on a two-stream Convolutional Neural Network processing in parallel the RGB Stream and its computed optical flow. The method has been developed as part of the MediaEval 2021 benchmark for the Sport task. Our contribution did not o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	272,872
1406.3816	Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning	Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,872
2403.05235	Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare	The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fairness-Aware Interpretable Modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface t...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	435,928
1908.07013	The Natural Selection of Words: Finding the Features of Fitness	We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	142,180
2108.09151	Group-based Distinctive Image Captioning with Memory Attention	Describing images using natural language is widely known as image captioning, which has made consistent progress due to the development of computer vision and natural language generation techniques. Though conventional captioning models achieve high accuracy based on popular metrics, i.e., BLEU, CIDEr, and SPICE, the a...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	251,514
2205.02818	Generative methods for sampling transition paths in molecular dynamics	Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promise...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,064
2301.08117	Convergence beyond the over-parameterized regime using Rayleigh quotients	In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove Kurdyka-{\L}ojasiewicz inequalities for a broader set of neural network architecture...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,102
1702.08898	Lipschitz Optimisation for Lipschitz Interpolation	Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	69,087
2208.13303	An Adaptive Pilot Model with Reaction Time-Delay	Practical adaptive control implementations where human pilots coexist in the loop are still uncommon, despite their success in handling uncertain dynamical systems. This is owing to their special nonlinear characteristics which lead to unfavorable interactions between pilots and adaptive controllers. To pave the way fo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,018
2011.05019	On the Downlink Performance of RSMA-based UAV Communications	The use of unmanned aerial vehicles (UAVs) as base stations (BSs) is envisaged as a key enabler for the fifth generation (5G) and beyond-5G networks. Specifically, aerial base stations (UAV-BS) are expected to provide ubiquitous connectivity and high spectral efficiency. To this end, we present in this correspondence a...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	205,776
1907.11115	Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions	Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equip...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,784
1707.09798	Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks	Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,077
2409.13929	Failures in Perspective-taking of Multimodal AI Systems	This study extends previous research on spatial representations in multimodal AI systems. Although current models demonstrate a rich understanding of spatial information from images, this information is rooted in propositional representations, which differ from the analog representations employed in human and animal sp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,238
2305.10284	Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks	The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have scores available for all tasks, which is not always practical. In reality, several factors such as the cost of running baseline, private systems, computa...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	364,986
2310.01612	Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation	In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods. Considering that most existing LLMs are not specifically optimized for recommendation tasks, adapt...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	396,489
2112.09448	Distillation of Human-Object Interaction Contexts for Action Recognition	Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard informative f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,158
2306.05668	RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models	The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	372,282
2209.13077	Accelerating the Genetic Algorithm for Large-scale Traveling Salesman Problems by Cooperative Coevolutionary Pointer Network with Reinforcement Learning	In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can accelerate the convergence of optimization. Based on this hypothesis, in the first sta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	319,759
2111.02363	Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features	In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0.026 (0.990 vs 0.964 in seen noise environments) and 0....	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,852
1906.09591	3D Multi-Robot Patrolling with a Two-Level Coordination Strategy	Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	136,213
2401.04143	RHOBIN Challenge: Reconstruction of Human Object Interaction	Modeling the interaction between humans and objects has been an emerging research direction in recent years. Capturing human-object interaction is however a very challenging task due to heavy occlusion and complex dynamics, which requires understanding not only 3D human pose, and object pose but also the interaction be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,343
1904.02634	Sequence Analysis of Learning Behavior in Different Consecutive Activities	The purpose of this research is to study the possibility of identifying students, statistically, by analyzing their behavior in different consecutive activities. In this project, there are three different sorts of activities: animated example, basic example, and parameterized exercises. We extracted the behavior of eac...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	126,479
2103.15388	Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions	Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control. State-of-the-art algorithms have focused on data-driven approaches that infer the system dynamics online and incorporate posterior unce...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	227,189
1907.05146	Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences	Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,286
2305.07430	Expertise-based Weighting for Regression Models with Noisy Labels	Regression methods assume that accurate labels are available for training. However, in certain scenarios, obtaining accurate labels may not be feasible, and relying on multiple specialists with differing opinions becomes necessary. Existing approaches addressing noisy labels often impose restrictive assumptions on the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,898
2101.12501	Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust	Navigation problems under unknown varying conditions are among the most important and well-studied problems in the control field. Classic model-based adaptive control methods can be applied only when a convenient model of the plant or environment is provided. Recent model-free adaptive control methods aim at removing t...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	217,583
2309.09355	Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties	We introduce the elEmBERT model for chemical classification tasks. It is based on deep learning techniques, such as a multilayer encoder architecture. We demonstrate the opportunities offered by our approach on sets of organic, inorganic and crystalline compounds. In particular, we developed and tested the model using ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	392,573
2408.11868	Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores	This paper presents an approach to improve text embedding models through contrastive fine-tuning on small datasets augmented with expert scores. It focuses on enhancing semantic textual similarity tasks and addressing text retrieval problems. The proposed method uses soft labels derived from expert-augmented scores to ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	482,488
2402.07466	VCR: Video representation for Contextual Retrieval	Streamlining content discovery within media archives requires integrating advanced data representations and effective visualization techniques for clear communication of video topics to users. The proposed system addresses the challenge of efficiently navigating large video collections by exploiting a fusion of visual,...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	428,733
2008.07192	How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank	Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,029
1601.04245	Adaptive type-2 fuzzy second order sliding mode control for nonlinear uncertain chaotic system	In this paper, a robust adaptive type-2 fuzzy higher order sliding mode controller is designed to stabilize the unstable periodic orbits of uncertain perturbed chaotic system with internal parameter uncertainties and external disturbances. In Higher Order Sliding Mode Control (HOSMC),the chattering phenomena of the con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	51,001
1803.09565	SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases	Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic dat...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	93,526
1911.06217	On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network	Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	153,484
1912.07561	Constructing a provably adversarially-robust classifier from a high accuracy one	Modern machine learning models with very high accuracy have been shown to be vulnerable to small, adversarially chosen perturbations of the input. Given black-box access to a high-accuracy classifier $f$, we show how to construct a new classifier $g$ that has high accuracy and is also robust to adversarial $\ell_2$-bou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	157,636
1507.07385	Fundamental Bounds on Radio Localization Precision in the Far Field	This paper experimentally and theoretically investigates the fundamental bounds on radio localization precision of far-field Received Signal Strength (RSS) measurements. RSS measurements are proportional to power-flow measurements time-averaged over periods long compared to the coherence time of the radiation. Our expe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,475
1910.14026	Neural networks trained with WiFi traces to predict airport passenger behavior	The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,538
1810.11120	Improving Document Binarization via Adversarial Noise-Texture Augmentation	Binarization of degraded document images is an elementary step in most of the problems in document image analysis domain. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,435
2011.14678	UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation	In this paper, we describe our method for detection of lexical semantic change (i.e., word sense changes over time) for the DIACR-Ita shared task, where we ranked $1^{st}$. We examine semantic differences between specific words in two Italian corpora, chosen from different time periods. Our method is fully unsupervised...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	208,859
2312.08957	Acceptance and Trust: Drivers' First Contact with Released Automated Vehicles in Naturalistic Traffic	This study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and User Experience. Although previous studies in simulated environments prov...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	415,534
1412.6130	Energy Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems with QoS Constraints	It is widely recognized that besides the quality of service (QoS), the energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy efficiency model is first proposed for multiple-input mu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	38,578
2302.11472	Distilling Calibrated Student from an Uncalibrated Teacher	Knowledge distillation is a common technique for improving the performance of a shallow student network by transferring information from a teacher network, which in general, is comparatively large and deep. These teacher networks are pre-trained and often uncalibrated, as no calibration technique is applied to the teac...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	347,220
2102.05737	Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub	Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,526
2412.14684	Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines	As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user-defined requirement...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	518,823
2410.08129	Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency	3D Gaussian Splats (3DGS) have proven a versatile rendering primitive, both for inverse rendering as well as real-time exploration of scenes. In these applications, coherence across camera frames and multiple views is crucial, be it for robust convergence of a scene reconstruction or for artifact-free fly-throughs. Rec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	496,965
2012.07575	Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China	Do mass media influence people's opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	211,509
2102.12911	Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks	Despite the recent successes in computer vision, there remain new avenues to explore. In this work, we propose a new dataset to investigate the effect of self-occlusion on deep neural networks. With TEOS (The Effect of Self-Occlusion), we propose a 3D blocks world dataset that focuses on the geometric shape of 3D objec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,883
2111.06061	Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI	Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more adva...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,976
2402.00067	Online speaker diarization of meetings guided by speech separation	Overlapped speech is notoriously problematic for speaker diarization systems. Consequently, the use of speech separation has recently been proposed to improve their performance. Although promising, speech separation models struggle with realistic data because they are trained on simulated mixtures with a fixed number o...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,468
1512.02896	Where You Are Is Who You Are: User Identification by Matching Statistics	Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting i...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	49,976
2003.08033	Object-Based Image Coding: A Learning-Driven Revisit	The Object-Based Image Coding (OBIC) that was extensively studied about two decades ago, promised a vast application perspective for both ultra-low bitrate communication and high-level semantical content understanding, but it had rarely been used due to the inefficient compact representation of object with arbitrary sh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,616
2205.14039	Group-invariant max filtering	Given a real inner product space $V$ and a group $G$ of linear isometries, we construct a family of $G$-invariant real-valued functions on $V$ that we call max filters. In the case where $V=\mathbb{R}^d$ and $G$ is finite, a suitable max filter bank separates orbits, and is even bilipschitz in the quotient metric. In t...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	299,188
2403.07929	Sketching the Heat Kernel: Using Gaussian Processes to Embed Data	This paper introduces a novel, non-deterministic method for embedding data in low-dimensional Euclidean space based on computing realizations of a Gaussian process depending on the geometry of the data. This type of embedding first appeared in (Adler et al, 2018) as a theoretical model for a generic manifold in high di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	437,100
1008.5204	A Smoothing Stochastic Gradient Method for Composite Optimization	We consider the unconstrained optimization problem whose objective function is composed of a smooth and a non-smooth conponents where the smooth component is the expectation a random function. This type of problem arises in some interesting applications in machine learning. We propose a stochastic gradient descent algo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	7,415
1907.02796	Unsupervised Anomaly Localization using Variational Auto-Encoders	An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a chec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,686
1910.00726	Animating Face using Disentangled Audio Representations	All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone. As we show empirically, one can easily break these systems by simply adding certain background noise to the utterance or changing its emotional tone (to such as sad). To make talking head generation rob...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	147,745
2302.11234	Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory	Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols. Based on this rationale, we propose Cluster Purging, which is an extension of clustering-based outlier detection. This extension allows one to assess the representivity of clus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,139
2407.05540	GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation	Recent advances in learning multi-modal representation have witnessed the success in biomedical domains. While established techniques enable handling multi-modal information, the challenges are posed when extended to various clinical modalities and practical modalitymissing setting due to the inherent modality gaps. To...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,017
2112.12280	Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction	Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,919
1610.04794	Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering	Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transfo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	62,433
1909.06317	A Comparative Study on Transformer vs RNN in Speech Applications	Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural ma...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,342
2306.04968	Actively Supervised Clustering for Open Relation Extraction	Current clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline. The first stage simultaneously learns relation representations and assignments. The second stage manually labels several instances and thus names the relation for each cluster. However, unsupervised objectives struggl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,994
2306.15634	Automatic Annotation of Direct Speech in Written French Narratives	The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	376,087
2303.13386	Compositional Zero-Shot Domain Transfer with Text-to-Text Models	Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	353,646
2005.09971	Hidden Markov Models and their Application for Predicting Failure Events	We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	178,048
2305.04827	Modeling glycemia in humans by means of Grammatical Evolution	Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, several artificial pancreas systems have been proposed and developed, which are increasingly advanced. However there is...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	362,916
2403.10558	Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack	The utilization of personal sensitive data in training face recognition (FR) models poses significant privacy concerns, as adversaries can employ model inversion attacks (MIA) to infer the original training data. Existing defense methods, such as data augmentation and differential privacy, have been employed to mitigat...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	438,242
2407.15910	Development of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic	In recent years, the clandestine nature of darknet activities has presented an escalating challenge to cybersecurity efforts, necessitating sophisticated methods for the detection and classification of network traffic associated with these covert operations. The system addresses the significant challenge of class imbal...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	475,406
2210.11899	A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT	In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. However, MT systems still lack accuracy in some low...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,497
2312.05642	Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach	Federated learning (FL) enables collaboratively training a model while keeping the training data decentralized and private. However, one significant impediment to training a model using FL, especially large models, is the resource constraints of devices with heterogeneous computation and communication capacities as wel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	414,170
2409.05816	Improving Pretraining Data Using Perplexity Correlations	Quality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slow due to the costly pretraining runs required for data selection experiments. We present a framework that avoids these costs and selects high-quality pretraining data wi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	486,904
2106.07963	Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM	This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long- and short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,133
2008.05030	Reliable Post hoc Explanations: Modeling Uncertainty in Explainability	As black box explanations are increasingly being employed to establish model credibility in high-stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,387

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