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
2412.03856	How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?	E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the sys...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	514,136
2406.06284	An ODMA-Based Unsourced Random Access Scheme with a Multiple Antenna Receiver	We investigate the unsourced random access scheme assuming that the base station is equipped with multiple antennas, and propose a high-performing solution utilizing on-off-division multiple access. We assume that each user spreads its pilot sequence and polar codeword to the pilot and data parts of the transmission fr...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	462,506
1804.04389	Design of Polar Codes in 5G New Radio	Polar codes have attracted the attention of academia and industry alike in the past decade, such that the 5$^\text{th}$ generation wireless systems (5G) standardization process of the 3$^\text{th}$ generation partnership project (3GPP) chose polar codes as a channel coding scheme. In this tutorial, we provide a descrip...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,841
2303.03720	Querying Shortest Path on Large Time-Dependent Road Networks with Shortcuts	Querying the shortest path between two vertexes is a fundamental operation in a variety of applications, which has been extensively studied over static road networks. However, in reality, the travel costs of road segments evolve over time, and hence the road network can be modeled as a time-dependent graph. In this pap...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	349,829
2502.08825	Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts	Time is implicitly embedded in classification process: classifiers are usually built on existing data while to be applied on future data whose distributions (e.g., label and token) may change. However, existing state-of-the-art classification models merely consider the temporal variations and primarily focus on English...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,181
2306.07932	Human-in-the-Loop through Chain-of-Thought	While the emergence of powerful language models along with Chain-of-thought prompting has made automation more and more omnipresent, it sometimes demonstrates its weakness in long-term or multi-step logical reasoning. For example, users don't always get desirable answers for complex mathematical problems without human ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	373,200
2008.03808	Diverse Group Formation Based on Multiple Demographic Features	The goal of group formation is to build a team to accomplish a specific task. Algorithms are employed to improve the effectiveness of the team so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms th...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	191,037
2204.04384	The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization	Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this "hard-to-learn" conc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	290,635
2109.00486	Survey of Low-Resource Machine Translation	We present a survey covering the state of the art in low-resource machine translation research. There are currently around 7000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the ch...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,116
2101.09575	Examining Factors Associated with Twitter Account Suspension Following the 2020 U.S. Presidential Election	Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safegua...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	216,643
2406.07646	Pre-training Feature Guided Diffusion Model for Speech Enhancement	Speech enhancement significantly improves the clarity and intelligibility of speech in noisy environments, improving communication and listening experiences. In this paper, we introduce a novel pretraining feature-guided diffusion model tailored for efficient speech enhancement, addressing the limitations of existing d...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	463,142
2501.07278	Lifelong Learning of Large Language Model based Agents: A Roadmap	Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	524,339
2412.15447	LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction	Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scenarios and expand training data without additional acquisition costs. Gaussian Splatting (GS) facilitates real-time, photorealistic rendering ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	519,116
2212.03476	Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information	Multilingual end-to-end models have shown great improvement over monolingual systems. With the development of pre-training methods on speech, self-supervised multilingual speech representation learning like XLSR has shown success in improving the performance of multilingual automatic speech recognition (ASR). However, ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,138
1505.00564	Structure-Preserving Sparsification of Social Networks	Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different contexts. We contribute the first systematic conceptual and experimental comparison of \textit{edge sparsification} methods on a diverse set of n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	42,747
1905.04107	Towards Emotion Retrieval in Egocentric PhotoStream	The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,374
2408.02761	Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation	Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at infere...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	478,751
1504.05603	Formalizing Preference Utilitarianism in Physical World Models	Most ethical work is done at a low level of formality. This makes practical moral questions inaccessible to formal and natural sciences and can lead to misunderstandings in ethical discussion. In this paper, we use Bayesian inference to introduce a formalization of preference utilitarianism in physical world models, sp...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	42,290
2410.03483	S2C2A: A Flexible Task Space Planning and Control Strategy for Modular Soft Robot Arms	Modular soft robot arms (MSRAs) are composed of multiple independent modules connected in a sequence. Due to their modular structure and high degrees of freedom (DOFs), these modules can simultaneously bend at different angles in various directions, enabling complex deformation. This capability allows MSRAs to perform ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	494,805
1809.08621	Learning and Evaluating Sparse Interpretable Sentence Embeddings	Previous research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints during training time, have the benefit of increased interpretability properties: to some degree, each dimension can be understood by a human a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,546
2011.14134	Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning	In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	208,680
2201.05322	Is it personal? The impact of personally relevant robotic failures (PeRFs) on humans' trust, likeability, and willingness to use the robot	In three laboratory experiments, we examine the impact of personally relevant failures (PeRFs) on perceptions of a collaborative robot. PeR is determined by how much a specific issue applies to a particular person, i.e., it affects one's own goals and values. We hypothesized that PeRFs would reduce trust in the robot a...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	275,357
2112.06736	Roof-Transformer: Divided and Joined Understanding with Knowledge Enhancement	Recent work on enhancing BERT-based language representation models with knowledge graphs (KGs) and knowledge bases (KBs) has yielded promising results on multiple NLP tasks. State-of-the-art approaches typically integrate the original input sentences with KG triples and feed the combined representation into a BERT mode...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,280
2105.07704	Bounds on the Capacity of PIR over Graphs	In the private information retrieval (PIR) problem, a user wants to retrieve a file from a database without revealing any information about the desired file's identity to the servers that store the database. In this paper, we study the PIR capacity of a graph-based replication system, in which each file is stored on tw...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	235,530
2204.03083	Audio-Visual Person-of-Interest DeepFake Detection	Face manipulation technology is advancing very rapidly, and new methods are being proposed day by day. The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. Our key insight is that each person has specific characte...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,182
2311.02538	Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols	Untrimmed videos have interrelated events, dependencies, context, overlapping events, object-object interactions, domain specificity, and other semantics that are worth highlighting while describing a video in natural language. Owing to such a vast diversity, a single sentence can only correctly describe a portion of t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	405,480
2010.13119	A Survey on Churn Analysis	In this paper, I present churn prediction techniques that have been released so far. Churn prediction is used in the fields of Internet services, games, insurance, and management. However, since it has been used intensively to increase the predictability of various industry/academic fields, there is a big difference in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,014
1309.7694	Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles	An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,410
2406.11563	Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI	This perspective piece calls for the study of the new field of Intersymbolic AI, by which we mean the combination of symbolic AI, whose building blocks have inherent significance/meaning, with subsymbolic AI, whose entirety creates significance/effect despite the fact that individual building blocks escape meaning. Can...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	464,944
2308.10871	FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations	Quantization summarizes continuous distributions by calculating a discrete approximation. Among the widely adopted methods for data quantization is Lloyd's algorithm, which partitions the space into Vorono\"i cells, that can be seen as clusters, and constructs a discrete distribution based on their centroids and probab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	386,917
2306.09707	Representation and decomposition of functions in DAG-DNNs and structural network pruning	The conclusions provided by deep neural networks (DNNs) must be carefully scrutinized to determine whether they are universal or architecture dependent. The term DAG-DNN refers to a graphical representation of a DNN in which the architecture is expressed as a direct-acyclic graph (DAG), on which arcs are associated wit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,938
1205.1720	Reconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach	Reconstruction of biochemical reaction networks is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	15,852
2307.00088	Redeeming Data Science by Decision Modelling	With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to researchers in Bayesian methods in AI that are needed to ground the practice of Data Science by borrowing from AI techniques for model form...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,863
1512.01722	Vanishing point attracts gaze in free-viewing and visual search tasks	To investigate whether the vanishing point (VP) plays a significant role in gaze guidance, we ran two experiments. In the first one, we recorded fixations of 10 observers (4 female; mean age 22; SD=0.84) freely viewing 532 images, out of which 319 had VP (shuffled presentation; each image for 4 secs). We found that the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,849
1206.6480	A Dantzig Selector Approach to Temporal Difference Learning	LSTD is a popular algorithm for value function approximation. Whenever the number of features is larger than the number of samples, it must be paired with some form of regularization. In particular, L1-regularization methods tend to perform feature selection by promoting sparsity, and thus, are well-suited for high-dim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	17,015
1804.05772	Design and implementation of a wireless instrument adapter	The evaluation of new methods for control and manipulation in minimally invasive robotic surgery requires a realistic setup. To decouple the evaluation of methods from overall clinical systems, we propose an instrument adapter for the S line EndoWrist\c{opyright} instruments of the da Vinci surgical system. The adapter...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	95,140
2102.00381	A Unified Light Framework for Real-time Fault Detection of Freight Train Images	Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements. Despite the promising results for deep learning based approaches, the performance of these fault detectors on freight train images, are far ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,759
2310.12055	Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning	In inverse reinforcement learning (IRL), the central objective is to infer underlying reward functions from observed expert behaviors in a way that not only explains the given data but also generalizes to unseen scenarios. This ensures robustness against reward ambiguity where multiple reward functions can equally expl...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	400,886
2408.08910	Why Do Experts Favor Solar and Wind as Renewable Energies Despite their Intermittency?	As humanity accelerates its shift to renewable energy generation, people who are not experts in renewable energy are learning about energy technologies and the energy market, which are complex. The answers to some questions will be obvious to expert practitioners but not to non-experts. One such question is Why solar a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,204
2502.00013	Behavioural Analytics: Mathematics of the Mind	Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaign...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	529,160
2205.05343	Learning Multitask Gaussian Bayesian Networks	Major depressive disorder (MDD) requires study of brain functional connectivity alterations for patients, which can be uncovered by resting-state functional magnetic resonance imaging (rs-fMRI) data. We consider the problem of identifying alterations of brain functional connectivity for a single MDD patient. This is pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,906
1710.07787	Loss Induced Maximum Power Transfer in Distribution Networks	In this paper, the power flow solution of the two bus network is used to analytically characterise maximum power transfer limits of distribution networks, when subject to both thermal and voltage constraints. Traditional analytic methods are shown to reach contradictory conclusions on the suitability of reactive power ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,990
1703.01393	Understanding and Predicting Delay in Reciprocal Relations	Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	69,353
1511.08855	Semantic Folding Theory And its Application in Semantic Fingerprinting	Human language is recognized as a very complex domain since decades. No computer system has been able to reach human levels of performance so far. The only known computational system capable of proper language processing is the human brain. While we gather more and more data about the brain, its fundamental computation...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	49,584
2206.07011	Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention	Video instance segmentation aims at predicting object segmentation masks for each frame, as well as associating the instances across multiple frames. Recent end-to-end video instance segmentation methods are capable of performing object segmentation and instance association together in a direct parallel sequence decodi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,570
2203.08507	Personal Knowledge Graphs: Use Cases in e-learning Platforms	Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia. Inspired by the widely recent usage of PKGs...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	285,818
2406.02917	A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks	Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving differential equations for forward and inverse problems. In particular, we comp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,003
2404.02088	LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Task	Conversation is the most natural form of human communication, where each utterance can range over a variety of possible emotions. While significant work has been done towards the detection of emotions in text, relatively little work has been done towards finding the cause of the said emotions, especially in multimodal ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,716
2405.17839	PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale	This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a critical gap in existing simulation tools, enhancing the overall utilit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	458,146
2209.12054	From Local to Global: Spectral-Inspired Graph Neural Networks	Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a local graph neighborhood. However, shallow MPNNs tend to miss long-range signals and perform poorly on some heterophilous graphs, while deep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,399
2401.10586	PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks	Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the target model's architecture and parameters. Traditional defense mechanisms, such as adversarial training, gradient masking, and input transform...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	422,698
1903.07806	Estimation of crowd density applying wavelet transform and machine learning	We conducted a simple experiment in which one pedestrian passed through a crowded area and measured the body-rotational angular velocity with commercial tablets. Then, we developed a new method for predicting crowd density by applying the continuous wavelet transform and machine learning to the data obtained in the exp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,703
1401.3464	Learning Bayesian Network Equivalence Classes with Ant Colony Optimization	Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	29,870
1605.00211	On Optimal Offline Time Sharing Policy for Energy Harvesting Underlay Cognitive Radio	RF energy harvesting can be used to power communication devices so that perpetual operation of such devices can be ensured. We consider a RF energy harvesting underlay cognitive radio system operating in slotted fashion. The primary user (PU) is equipped with a reliable power source and transmits with a constant power ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,313
2107.01081	Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning	We present a new framework to measure the intrinsic properties of (deep) neural networks. While we focus on convolutional networks, our framework can be extrapolated to any network architecture. In particular, we evaluate two network properties, namely, capacity, which is related to expressivity, and compression, which...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,367
2407.17823	Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization	Bilevel optimization is widely applied in many machine learning tasks such as hyper-parameter learning, meta learning and reinforcement learning. Although many algorithms recently have been developed to solve the bilevel optimization problems, they generally rely on the (strongly) convex lower-level problems. More rece...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,131
2208.12294	DPAUC: Differentially Private AUC Computation in Federated Learning	Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to potential leaka...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	314,684
2104.14034	Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations	Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract spatio-temporal coherent structures that dictate a given dynamical system. The method consists of stacking collected temporal snapshots into a matrix and mapping the nonlinear dynamics using a linear operator. The standard procedure consi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	232,686
1905.07457	Overfitting in Synthesis: Theory and Practice (Extended Version)	In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specification. We investigate a common limitation across state-of-the-art SyGuS tools that perform counterexample-guided inductive synthesis (CEGIS). We...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	131,235
2408.06618	Generalized knowledge-enhanced framework for biomedical entity and relation extraction	In recent years, there has been an increasing number of frameworks developed for biomedical entity and relation extraction. This research effort aims to address the accelerating growth in biomedical publications and the intricate nature of biomedical texts, which are written for mainly domain experts. To handle these c...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	480,265
2409.01348	PatternPaint: Generating Layout Patterns Using Generative AI and Inpainting Techniques	Generation of diverse VLSI layout patterns is crucial for various downstream tasks in design for manufacturing (DFM) studies. However, the lengthy design cycles often hinder the creation of a comprehensive layout pattern library, and new detrimental patterns may be discovered late in the product development process. Ex...	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	485,303
1509.00948	Exploiting Heterogeneous Robotic Systems in Cooperative Missions	In this paper we consider the problem of coordinating robotic systems with different kinematics, sensing and vision capabilities to achieve certain mission goals. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or large search areas need to be ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	46,546
2208.14660	Unifying Evaluation of Machine Learning Safety Monitors	With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime monitors have been developed to detect prediction errors and keep the system in a safe state during operations. Monitors have been proposed for different applications involving diverse perception tasks and ML models, and specific e...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	true	315,386
2401.09235	A Characterization Theorem for Equivariant Networks with Point-wise Activations	Equivariant neural networks have shown improved performance, expressiveness and sample complexity on symmetrical domains. But for some specific symmetries, representations, and choice of coordinates, the most common point-wise activations, such as ReLU, are not equivariant, hence they cannot be employed in the design o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	422,191
1811.04272	Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning	Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning. Prior work has developed different kinds of shaping methods that enable agents to learn efficiently in complex environments. All these methods, however, tailor human guidance to agents...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	113,043
1801.02363	Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices	Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e., quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q -aware, low-depth quantum circuits that generate Greenberger-Horne-Zeilinger (GHZ) entangled states. The resulti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	87,919
2410.08674	Guidelines for Fine-grained Sentence-level Arabic Readability Annotation	This paper presents the foundational framework and initial findings of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, designed to address the need for comprehensive Arabic language resources aligned with diverse readability levels. Inspired by the Taha/Arabi21 readability reference, BAREC aims to pr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,229
1407.2217	N\'egociation de spectre dans les r\'eseaux de radio cognitive	In this report, we propose a technique using negotiation based on multi-agent system (MAS) in the context of cognitive radio network (CRN). The agents are particularly suited to provide responsive solutions to complex problems such as the negotiation of the spectrum in CRN. We have implemented our proposed solution wit...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	34,510
2406.00720	Age-Gain-Dependent Random Access for Event-Driven Periodic Updating	This paper considers utilizing the knowledge of age gains to reduce the network average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	459,993
2205.05476	Contrastive Supervised Distillation for Continual Representation Learning	In this paper, we propose a novel training procedure for the continual representation learning problem in which a neural network model is sequentially learned to alleviate catastrophic forgetting in visual search tasks. Our method, called Contrastive Supervised Distillation (CSD), reduces feature forgetting while learn...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	295,943
2010.09662	Attention Augmented ConvLSTM for Environment Prediction	Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techniques to bird's-eye view environment representations, such as occupancy grids. ConvLSTM-based frameworks used previously often result in sig...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	201,632
2404.10579	The application of Augmented Reality (AR) in Remote Work and Education	With the rapid advancement of technology, Augmented Reality (AR) technology, known for its ability to deeply integrate virtual information with the real world, is gradually transforming traditional work modes and teaching methods. Particularly in the realms of remote work and online education, AR technology demonstrate...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	447,158
1801.08170	Multiple Antenna Assisted Non-Orthogonal Multiple Access	Non-orthogonal multiple access (NOMA) is potentially capable of circumventing the limitations of the classic orthogonal multiple access schemes, hence it has recently received significant research attention both in industry and academia. This article is focused on exploiting multiple antenna techniques in NOMA networks...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	88,909
1901.11478	An Optimization Framework for Task Sequencing in Curriculum Learning	Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	120,270
1909.01758	Value Iteration Algorithm for Mean-field Games	In the literature, existence of mean-field equilibria has been established for discrete-time mean field games under both the discounted cost and the average cost optimality criteria. In this paper, we provide a value iteration algorithm to compute mean-field equilibrium for both the discounted cost and the average cost...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	143,992
2403.00873	Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions	Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate sec...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	434,166
2112.06909	Hallucinating Pose-Compatible Scenes	What does human pose tell us about a scene? We propose a task to answer this question: given human pose as input, hallucinate a compatible scene. Subtle cues captured by human pose -- action semantics, environment affordances, object interactions -- provide surprising insight into which scenes are compatible. We presen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,326
1906.01629	Exact Combinatorial Optimization with Graph Convolutional Neural Networks	Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,783
2502.02504	Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction	Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedestrians and temporal dependencies of individuals. They overlook the direct impacts of interactions among different pedestrians across various ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	530,352
2305.13527	Aligning the Norwegian UD Treebank with Entity and Coreference Information	This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokm{\aa}l and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,540
2004.09188	Evolving Diverse Sets of Tours for the Travelling Salesperson Problem	Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	173,277
2306.11943	Towards Understanding What Code Language Models Learned	Pre-trained language models are effective in a variety of natural language tasks, but it has been argued their capabilities fall short of fully learning meaning or understanding language. To understand the extent to which language models can learn some form of meaning, we investigate their ability to capture semantics ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	374,770
2311.10963	Learning Deterministic Finite Automata from Confidence Oracles	We discuss the problem of learning a deterministic finite automaton (DFA) from a confidence oracle. That is, we are given access to an oracle $Q$ with incomplete knowledge of some target language $L$ over an alphabet $\Sigma$; the oracle maps a string $x\in\Sigma^*$ to a score in the interval $[-1,1]$ indicating its co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	408,739
2010.00482	Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks	Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smartwatches have made it possible to support individuals in their everyday lives to deve...	false	false	false	false	true	true	true	false	false	true	false	true	false	false	false	false	false	false	198,300
2408.13068	On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning	Recent advances in audio-text cross-modal contrastive learning have shown its potential towards zero-shot learning. One possibility for this is by projecting item embeddings from pre-trained backbone neural networks into a cross-modal space in which item similarity can be calculated in either domain. This process relie...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,996
1509.07035	Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks	This study explores the design and control of the behaviour of agents and robots using simple circuits of spiking neurons and Spike Timing Dependent Plasticity (STDP) as a mechanism of associative and unsupervised learning. Based on a "reward and punishment" classical conditioning, it is demonstrated that these robots ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	47,219
cmp-lg/9705015	Translation Methodology in the Spoken Language Translator: An Evaluation	In this paper we describe how the translation methodology adopted for the Spoken Language Translator (SLT) addresses the characteristics of the speech translation task in a context where it is essential to achieve easy customization to new languages and new domains. We then discuss the issues that arise in any attempt ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,737
2210.02447	Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models	Machine learning based traffic forecasting models leverage sophisticated spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states. However, existing methods assume a reliable and unbiased forecasting environment, which is not always available in the wild. In this work, we investigate...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	321,651
1606.09176	Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal-Noise Interaction	In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,955
2111.08161	Sparse Graph Learning Under Laplacian-Related Constraints	We consider the problem of learning a sparse undirected graph underlying a given set of multivariate data. We focus on graph Laplacian-related constraints on the sparse precision matrix that encodes conditional dependence between the random variables associated with the graph nodes. Under these constraints the off-diag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,579
1905.01739	HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings	We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	129,802
2311.09210	Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models	Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed. The retrieval of irrelevant da...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,039
2007.05952	Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm	Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality (VR), Internet of things (IoT), etc., becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	186,845
2207.07577	Unification and Extension of Classic Information Principles	To formulate a universal framework of information theory is beneficial. This study proves that the sextuple model of the objective information theory (OIT) is a sufficient and necessary condition for discussing information with four basic postulations. It is demonstrated for each metric defined in the OIT, there is a c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	308,241
2107.10669	Accuracy analysis of Educational Data Mining using Feature Selection Algorithm	Gathering relevant information to predict student academic progress is a tedious task. Due to the large amount of irrelevant data present in databases which provides inaccurate results. Currently, it is not possible to accurately measure and analyze student data because there are too many irrelevant attributes and feat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	247,368
1403.1218	Cyclic Orbit Codes and Stabilizer Subfields	Cyclic orbit codes are constant dimension subspace codes that arise as the orbit of a cyclic subgroup of the general linear group acting on subspaces in the given ambient space. With the aid of the largest subfield over which the given subspace is a vector space, the cardinality of the orbit code can be determined, and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,368
1010.1763	Algorithms for nonnegative matrix factorization with the beta-divergence	This paper describes algorithms for nonnegative matrix factorization (NMF) with the beta-divergence (beta-NMF). The beta-divergence is a family of cost functions parametrized by a single shape parameter beta that takes the Euclidean distance, the Kullback-Leibler divergence and the Itakura-Saito divergence as special c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	7,838
2402.15911	PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails	Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks that induce them to generate harmful content. More recent LLMs often incorporate an additional layer of defense, a Guard Model, which is a sec...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	432,329
1102.0676	Architecture of A Scalable Dynamic Parallel WebCrawler with High Speed Downloadable Capability for a Web Search Engine	Today World Wide Web (WWW) has become a huge ocean of information and it is growing in size everyday. Downloading even a fraction of this mammoth data is like sailing through a huge ocean and it is a challenging task indeed. In order to download a large portion of data from WWW, it has become absolutely essential to ma...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	9,013
2410.21415	Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding	Lifelong Multi-Agent Path Finding (LMAPF) is a variant of MAPF where agents are continually assigned new goals, necessitating frequent re-planning to accommodate these dynamic changes. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local obser...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	503,239

Subsets and Splits

No community queries yet

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