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
2007.12826	The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training	Modern neural networks are often operated in a strongly overparametrized regime: they comprise so many parameters that they can interpolate the training set, even if actual labels are replaced by purely random ones. Despite this, they achieve good prediction error on unseen data: interpolating the training set does not...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,933
2009.14725	A Vietnamese Dataset for Evaluating Machine Reading Comprehension	Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietn...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	198,122
2002.04023	Upper, Middle and Lower Region Learning for Facial Action Unit Detection	Facial action units (AUs) detection is fundamental to facial expression analysis. As AU occurs only in a small area of the face, region-based learning has been widely recognized useful for AU detection. Most region-based studies focus on a small region where the AU occurs. Focusing on a specific region helps eliminate ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	163,482
2410.03230	Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate	This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of unknown types for a given nonlinear dynamical system. We develop an algorithm, named Dynamic Batch length and Adaptive learning Rate (DBAR), a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	494,692
2202.00807	Federated Learning Challenges and Opportunities: An Outlook	Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	278,267
2203.01606	Ensemble Methods for Robust Support Vector Machines using Integer Programming	In this work we study binary classification problems where we assume that our training data is subject to uncertainty, i.e. the precise data points are not known. To tackle this issue in the field of robust machine learning the aim is to develop models which are robust against small perturbations in the training data. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,448
2209.08522	A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization	Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to their encoding of varia...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	318,157
2411.18162	SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment	With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts. To address this, we propose the Sentiment Cross-Lingual Recogn...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	511,755
1802.02347	SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images	Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,760
cmp-lg/9611003	Data-Oriented Language Processing. An Overview	During the last few years, a new approach to language processing has started to emerge, which has become known under various labels such as "data-oriented parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak 1996a/b; Good...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,675
0806.1343	Temporized Equilibria	This paper has been withdrawn by the author due to a crucial error in the submission action.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	1,888
2211.02558	A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions	The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of adv...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	328,612
2204.13290	On the Normalizing Constant of the Continuous Categorical Distribution	Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has been discovered: the continuous categorical. This family enjoys remarkable mathematical simplicity; its density function resembles that of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,758
2008.13015	Adaptive Exploitation of Pre-trained Deep Convolutional Neural Networks for Robust Visual Tracking	Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	193,738
2310.17357	Controlling Automated Vehicles on Large Lane-free Roundabouts (Extended Version)	Controlling automated vehicles on large lane-free roundabouts is challenging because of the geometrical complexity and frequent conflicts among entering, rotating, and exiting vehicles. This paper proposes a comprehensive methodology to control the vehicles within the roundabout and the connected road branches. The dev...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,100
1907.03344	Majority-logic Decoding with Subspace Designs	Rudolph (1967) introduced one-step majority logic decoding for linear codes derived from combinatorial designs. The decoder is easily realizable in hardware and requires that the dual code has to contain the blocks of so called geometric designs as codewords. Peterson and Weldon (1972) extended Rudolphs algorithm to a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	137,840
2409.02017	AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities	Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher educati...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	485,544
2402.01751	Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's Dementia	Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4 and Bard) are assessed - in their current form, as publicly available - for their ability to recognize Alzheimer's Dementia (AD) and Cognitively Normal (CN) individuals using textual input derived ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	426,196
1406.2210	Memristor models for machine learning	In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,726
1304.2545	For Solving Linear Equations Recombination is a Needless Operation in Time-Variant Adaptive Hybrid Algorithms	Recently hybrid evolutionary computation (EC) techniques are successfully implemented for solving large sets of linear equations. All the recently developed hybrid evolutionary algorithms, for solving linear equations, contain both the recombination and the mutation operations. In this paper, two modified hybrid evolut...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	23,708
1912.00965	AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning	We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based upon neural network architectures. Our approach is based on the recently developed adversarial prediction framework, a distributionally robus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,944
2411.05527	How Good is Your Wikipedia?	Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP. In the context of low-resource languages, however, these quality assumptions are increasingly being scrutinised. This paper critically examines the data quality of Wikipedia in a non-English...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	506,699
2403.11487	Can LLMs Generate Human-Like Wayfinding Instructions? Towards Platform-Agnostic Embodied Instruction Synthesis	We present a novel approach to automatically synthesize "wayfinding instructions" for an embodied robot agent. In contrast to prior approaches that are heavily reliant on human-annotated datasets designed exclusively for specific simulation platforms, our algorithm uses in-context learning to condition an LLM to genera...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	438,723
2203.10078	Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors	Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the image. It depends on the model of the imaging system and on prior ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,391
2209.04725	Anticipating the Unseen Discrepancy for Vision and Language Navigation	Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes it challenging for the agent to generalize well. Previous studies propose data augmentation methods to mitigate the data bias explicitly or i...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	316,866
2310.09195	AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space	Quadrotor motion planning in complex environments leverage the concept of safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically, SFCs are constructed through convex decomposition of the environment's free space into cuboids, convex polyhedra, or spheres. However, when dealing with a quadrotor sw...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	399,685
2308.11791	Data Assimilation for Sign-indefinite Priors: A generalization of Sinkhorn's algorithm	The purpose of this work is to develop a framework to calibrate signed datasets so as to be consistent with specified marginals by suitably extending the Schr\"odinger-Fortet-Sinkhorn paradigm. Specifically, we seek to revise sign-indefinite multi-dimensional arrays in a way that the updated values agree with specified...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	387,275
2412.08031	Constrained Best Arm Identification in Grouped Bandits	We study a grouped bandit setting where each arm comprises multiple independent sub-arms referred to as attributes. Each attribute of each arm has an independent stochastic reward. We impose the constraint that for an arm to be deemed feasible, the mean reward of all its attributes should exceed a specified threshold. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,908
1911.10321	Compressing Representations for Embedded Deep Learning	Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices. To address that issue, we envision sharing the computational costs of inference between local devices and the cloud, taking advantage of the compression performed by the f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,789
2008.01503	Multiple Code Hashing for Efficient Image Retrieval	Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	190,337
2301.03470	Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG	Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic seizures are commonly monitored through electroencephalogram (EEG) recordings due to their routine and low expense collection. The stochastic nature of EEG makes seizure identification via manual inspections pe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	339,817
1707.05436	Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder	Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,231
2201.02517	Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions	Background: Extractive question-answering (EQA) is a useful natural language processing (NLP) application for answering patient-specific questions by locating answers in their clinical notes. Realistic clinical EQA can have multiple answers to a single question and multiple focus points in one question, which are lacki...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	274,567
2205.14522	A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization	Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have developed various approaches to improve the ROUGE score, which is the main evaluation metric for summarization, ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	299,394
2409.20122	Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images	In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model rob...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	492,999
0808.4160	Using Relative Entropy to Find Optimal Approximations: an Application to Simple Fluids	We develop a maximum relative entropy formalism to generate optimal approximations to probability distributions. The central results consist in (a) justifying the use of relative entropy as the uniquely natural criterion to select a preferred approximation from within a family of trial parameterized distributions, and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,247
2403.03126	A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems	Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is beca...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	435,073
2210.07031	Rebalanced Zero-shot Learning	Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,550
2012.07745	Perceptions of YouTube's political influence	YouTube plays an ever more important role as a political medium. Yet, the implications are to-date not well understood and difficult to analyse, since access to YouTube's statistics is limited. To address this gap, we surveyed 124 people about their views and experiences around YouTube's political influence. Our result...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	211,564
2002.11500	Robust Underlay Device-to-Device Communications on Multiple Channels	Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-stateinformation (CSI). In this...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	165,727
2107.02462	FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning	The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR). This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. Unfortunat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,833
2009.01509	User Intention Recognition and Requirement Elicitation Method for Conversational AI Services	In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	194,323
2205.05763	Individual Fairness Guarantees for Neural Networks	We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the $\epsilon$-$\delta$-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the output difference between any pair of $\epsilon$-similar individuals...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,027
2205.15567	Few-Shot Unlearning by Model Inversion	We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model. The target dataset is often assumed to be fully identifiable in a standard unlearning scenario. Such a flawless identification, however, is almost impossible if the training dataset...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,781
1308.3785	Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition	This paper is concerned with the development of Back-propagation Neural Network for Bangla Speech Recognition. In this paper, ten bangla digits were recorded from ten speakers and have been recognized. The features of these speech digits were extracted by the method of Mel Frequency Cepstral Coefficient (MFCC) analysis...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	26,500
2501.19201	Efficient Reasoning with Hidden Thinking	Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Models (MLLMs). However, the verbose nature of textual reasoning introduces significant inefficiencies. In this work, we propose $\textbf{Heima}$ (as hidden llama), an efficie...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	529,055
1710.03778	Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images	We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,368
1408.5350	Structural bias in population-based algorithms	Challenging optimisation problems are abundant in all areas of science. Since the 1950s, scientists have developed ever-diversifying families of black box optimisation algorithms designed to address any optimisation problem, requiring only that quality of a candidate solution is calculated via a fitness function specif...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	35,536
1811.01461	Bias Disparity in Recommendation Systems	Recommender systems have been applied successfully in a number of different domains, such as, entertainment, commerce, and employment. Their success lies in their ability to exploit the collective behavior of users in order to deliver highly targeted, personalized recommendations. Given that recommenders learn from use...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	112,373
1805.04793	Coarse-to-Fine Decoding for Neural Semantic Parsing	Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,314
2106.02743	SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks	Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy conce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,003
1603.05095	Improved Bounds on the Epidemic Threshold of Exact SIS Models on Complex Networks	The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a $2^n$-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	53,320
2206.01278	Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks	A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the method can find a sparse sub-network that can be trained to the same accuracy as the dense network. However, the same does not hold at step...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	300,408
2501.16070	Generalizing Egocentric Temporal Neighborhoods to probe for spatial correlations in temporal networks and infer their topology	Motifs are thought to be some fundamental components of social face-to-face interaction temporal networks. However, the motifs previously considered are either limited to a handful of nodes and edges, or do not include triangles, which are thought to be of critical relevance to understand the dynamics of social systems...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	527,803
2106.10241	An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises	Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers. The effect of DP on the fairness of the resulting trained models is not yet well understood. In this contribution, we systematically study the effects of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	241,951
2206.07050	Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning	This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy? Positive evidence is provided for the first time, focusing on a prototypical computed tomography (CT) setup. We demonstrate that an i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,585
2407.20914	An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User Discrete Passive Beamforming	Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink netw...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	477,331
2011.00819	Self-Concordant Analysis of Generalized Linear Bandits with Forgetting	Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to address them. In contrast to the case of linear bandits, existing algorithms for GLB have two drawbacks undermining their applicability. First, th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	204,387
1610.10079	Bounded Model Checking of State-Space Digital Systems: The Impact of Finite Word-Length Effects on the Implementation of Fixed-Point Digital Controllers Based on State-Space Modeling	The extensive use of digital controllers demands a growing effort to prevent design errors that appear due to finite-word length (FWL) effects. However, there is still a gap, regarding verification tools and methodologies to check implementation aspects of control systems. Thus, the present paper describes an approach,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	63,156
2306.06386	Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation	Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalized learning. Despite these benefits,...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	372,588
2103.05331	Active Testing: Sample-Efficient Model Evaluation	We introduce a new framework for sample-efficient model evaluation that we call active testing. While approaches like active learning reduce the number of labels needed for model training, existing literature largely ignores the cost of labeling test data, typically unrealistically assuming large test sets for model ev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,942
1806.05516	Translations as Additional Contexts for Sentence Classification	In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier. However, such contexts are domain-dependent and thus cannot be used for another classification task with an inappropriate domain. In contrast, we propose the use of translated sentences ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	100,495
1910.12191	Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data	Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	150,997
1812.06861	Taking a Deeper Look at the Inverse Compositional Algorithm	In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax these assumptions by incorporating data-driven priors into this model. More specifically, we ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	116,698
1711.05928	Budget-Constrained Multi-Armed Bandits with Multiple Plays	We study the multi-armed bandit problem with multiple plays and a budget constraint for both the stochastic and the adversarial setting. At each round, exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$). In addition to observing the individual rewards for each arm played, the player also lea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	84,677
2407.03841	On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation	Large Language Models (LLMs) have showcased remarkable capabilities in various Natural Language Processing tasks. For automatic open-domain dialogue evaluation in particular, LLMs have been seamlessly integrated into evaluation frameworks, and together with human evaluation, compose the backbone of most evaluations. Ho...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,309
1608.04959	Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation	We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,904
1607.00502	Threshold Decoding for Disjunctive Group Testing	Let $1 \le s < t$, $N \ge 1$ be integers and a complex electronic circuit of size $t$ is said to be an $s$-active, $\; s \ll t$, and can work as a system block if not more than $s$ elements of the circuit are defective. Otherwise, the circuit is said to be an $s$-defective and should be replaced by a similar $s$-active...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,087
1811.00648	Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities	We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input sce...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	112,151
2409.09633	A Scalable Tabletop Satellite Automation Testbed:Design And Experiments	This paper presents a detailed system design and component selection for the Transforming Proximity Operations and Docking Service (TPODS) module, designed to gain custody of uncontrolled resident space objects (RSOs) via rendezvous and proximity operation (RPO). In addition to serving as a free-flying robotic manipula...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	488,403
1908.01650	Minimal linear codes from characteristic functions	Minimal linear codes have interesting applications in secret sharing schemes and secure two-party computation. This paper uses characteristic functions of some subsets of $\mathbb{F}_q$ to construct minimal linear codes. By properties of characteristic functions, we can obtain more minimal binary linear codes from know...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	140,812
2304.04237	Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention	Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing self-attention methods either adopt sparse global attention or window attention to reduce the computation complexity, which may compromise ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,144
2105.09719	Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics	Deep Reinforcement learning holds the guarantee of empowering self-ruling robots to master enormous collections of conduct abilities with negligible human mediation. The improvements brought by this technique enables robots to perform difficult tasks such as grabbing or reaching targets. Nevertheless, the training proc...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	236,153
1509.05936	STDP as presynaptic activity times rate of change of postsynaptic activity	We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	47,100
1309.4923	Quantum Walks in artificial electric and gravitational Fields	The continuous limit of quantum walks (QWs) on the line is revisited through a recently developed method. In all cases but one, the limit coincides with the dynamics of a Dirac fermion coupled to an artificial electric and/or relativistic gravitational field. All results are carefully discussed and illustrated by numer...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,128
1711.08014	The Riemannian Geometry of Deep Generative Models	Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinear manifolds in the data space. In this paper, we investigate the Riemannian geometry of these generated manifolds. First, we develop efficie...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	85,119
1908.09250	An I + PI Controller Structure for Integrating Processes with Dead-Time: Application to Depth Control of an Autonomous Underwater Vehicle	The paper presents a feedforward plus feedback controller structure with I and PI controllers for control of an integrating process with dead time. Guidelines for controller gain selection based on time domain specifications of damping factor and natural frequency are provided along with simulations indicating the sele...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	142,799
1010.0418	Quantum capacity under adversarial quantum noise: arbitrarily varying quantum channels	We investigate entanglement transmission over an unknown channel in the presence of a third party (called the adversary), which is enabled to choose the channel from a given set of memoryless but non-stationary channels without informing the legitimate sender and receiver about the particular choice that he made. This ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,764
1602.02018	Compressive Spectral Clustering	Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	51,776
2402.19406	On the Scaling Laws of Geographical Representation in Language Models	Language models have long been shown to embed geographical information in their hidden representations. This line of work has recently been revisited by extending this result to Large Language Models (LLMs). In this paper, we propose to fill the gap between well-established and recent literature by observing how geogra...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	433,788
cs/9907004	MAP Lexicon is useful for segmentation and word discovery in child-directed speech	Because of rather fundamental changes to the underlying model proposed in the paper, it has been withdrawn from the archive.	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	540,535
2403.08335	A Sparsity Principle for Partially Observable Causal Representation Learning	Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations. We instead consider a partially observed setting, in which each measurement only provides information about a subse...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	437,298
2411.05850	Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models	Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plaus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,840
1606.05982	Optimal Finite-Length and Asymptotic Index Codes for Five or Fewer Receivers	Index coding models broadcast networks in which a sender sends different messages to different receivers simultaneously, where each receiver may know some of the messages a priori. The aim is to find the minimum (normalised) index codelength that the sender sends. This paper considers unicast index coding, where each r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,506
1109.1087	A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm	Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is b...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	11,991
2109.10259	AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators	Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the existing contrastive learning methods employ pre-defined view generation methods, e.g., node drop or edge perturbation, which usually cannot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,562
2007.12163	Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval	Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods. To this end, we introduce an objective that optimises instead a smoothed approximation of AP, coined Smooth-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,753
2305.16894	Robustness of Multi-Source MT to Transcription Errors	Automatic speech translation is sensitive to speech recognition errors, but in a multilingual scenario, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. In this paper, we hypothesize that leveraging multiple sources will improve translation quality if the sour...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,309
2211.06932	Challenges in Close-Proximity Safe and Seamless Operation of Manned and Unmanned Aircraft in Shared Airspace	We propose developing an integrated system to keep autonomous unmanned aircraft safely separated and behave as expected in conjunction with manned traffic. The main goal is to achieve safe manned-unmanned vehicle teaming to improve system performance, have each (robot/human) teammate learn from each other in various ai...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	330,076
2304.00536	Design of a Jumping Control Framework with Heuristic Landing for Bipedal Robots	Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal robots or other multi-legged robots, bipedal robots place higher requirements for the control strategy given a much smaller footprint. To solv...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	355,726
2203.15501	Deep Learning for Encrypted Traffic Classification and Unknown Data Detection	Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a new Deep Neural Network (DNN) based user activity detection framework is proposed to identify fine grained user activitie...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	288,428
2405.12642	Combining Twitter and Mobile Phone Data to Observe Border-Rush: The Turkish-European Border Opening	Following Turkey's 2020 decision to revoke border controls, many individuals journeyed towards the Greek, Bulgarian, and Turkish borders. However, the lack of verifiable statistics on irregular migration and discrepancies between media reports and actual migration patterns require further exploration. The objective of ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	455,602
1111.0039	Reasoning with Very Expressive Fuzzy Description Logics	It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,840
2009.03976	Anticipatory Human-Robot Path Planning for Search and Rescue	In this work, our goal is to extend the existing search and rescue paradigm by allowing teams of autonomous unmanned aerial vehicles (UAVs) to collaborate effectively with human searchers on the ground. We derive a framework that includes a simulated lost person behavior model, as well as a human searcher behavior mode...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	194,929
2407.11190	In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models	By training deep neural networks on massive archives of digitized text, large language models (LLMs) learn the complex linguistic patterns that constitute historic and contemporary discourses. We argue that LLMs can serve as a valuable tool for sociological inquiry by enabling accurate simulation of respondents from sp...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	473,336
1805.01971	#ILookLikeAnEngineer: Using Social Media Based Hashtag Activism Campaigns as a Lens to Better Understand Engineering Diversity Issues	Each year, significant investment of time and resources is made to improve diversity within engineering across a range of federal and state agencies, private/not-for-profit organizations, and foundations. In spite of decades of investments, efforts have not yielded desired returns - participation by minorities continue...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	96,744
2501.18716	Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release	The goal of this work was to develop a deep network for whole-head segmentation, including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 91 MRIs with volumetric segmentation labels for a diverse set of human subjects (4 normal, 32 traumatic brain inju...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	528,823
2107.06981	Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison	In the paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights in the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner's best configuration for the data at hand, and it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,258
2402.07478	A Comparison of Different Representations of Ordinal Patterns and Their Usability in Data Analysis	We describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability from different angles. Namely we consider digital implementation, inverse patterns and ties between ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	428,740
2206.11517	Utilizing Expert Features for Contrastive Learning of Time-Series Representations	We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformations in previous contrastive learning approaches. We do this since time-series data frequently stems from the industrial or medical field whe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,293

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