Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2410.09681	LoRD: Adapting Differentiable Driving Policies to Distribution Shifts	Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focusing on the motion prediction task. In this work, we explore novel adap...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	497,717
2409.07703	DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?	Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	487,619
1807.09236	Improving pairwise comparison models using Empirical Bayes shrinkage	Comparison data arises in many important contexts, e.g. shopping, web clicks, or sports competitions. Typically we are given a dataset of comparisons and wish to train a model to make predictions about the outcome of unseen comparisons. In many cases available datasets have relatively few comparisons (e.g. there are on...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,686
2104.03783	A Scalable Distributed Collision Avoidance Scheme for Multi-agent UAV systems	In this article we propose a distributed collision avoidance scheme for multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive control (NMPC),where other agents in the system are considered as dynamic obstacles with respect to the ego agent. Our control scheme operates at a low level and command...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	229,177
2304.01340	A Scale-Invariant Trajectory Simplification Method for Efficient Data Collection in Videos	Training data is a critical requirement for machine learning tasks, and labeled training data can be expensive to acquire, often requiring manual or semi-automated data collection pipelines. For tracking applications, the data collection involves drawing bounding boxes around the classes of interest on each frame, and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,030
1802.09747	Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation	Asynchronous algorithms have attracted much attention recently due to the crucial demands on solving large-scale optimization problems. However, the accelerated versions of asynchronous algorithms are rarely studied. In this paper, we propose the "momentum compensation" technique to accelerate asynchronous algorithms f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,383
2402.09197	Implementing local-explainability in Gradient Boosting Trees: Feature Contribution	Gradient Boost Decision Trees (GBDT) is a powerful additive model based on tree ensembles. Its nature makes GBDT a black-box model even though there are multiple explainable artificial intelligence (XAI) models obtaining information by reinterpreting the model globally and locally. Each tree of the ensemble is a transp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	429,410
2105.02083	AdaBoost and robust one-bit compressed sensing	This paper studies binary classification in robust one-bit compressed sensing with adversarial errors. It is assumed that the model is overparameterized and that the parameter of interest is effectively sparse. AdaBoost is considered, and, through its relation to the max-$\ell_1$-margin-classifier, prediction error bou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	233,719
2406.05165	Statistical AoI, Delay, and Error-Rate Bounded QoS Provisioning for Satellite-Terrestrial Integrated Networks	Massive ultra-reliable and low latency communications (mURLLC) has emerged to support wireless time/error-sensitive services, which has attracted significant research attention while imposing several unprecedented challenges not encountered before. By leveraging the significant improvements in space-aerial-terrestrial ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	462,015
2102.12095	Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting	The capability of image semantic segmentation may be deteriorated due to noisy input image, where image denoising prior to segmentation helps. Both image denoising and semantic segmentation have been developed significantly with the advance of deep learning. Thus, we are interested in the synergy between them by using ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,614
2312.06587	QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection	Quick and automated earthquake-damaged building detection from post-event satellite imagery is crucial, yet it is challenging due to the scarcity of training data required to develop robust algorithms. This letter presents the first dataset dedicated to detecting earthquake-damaged buildings from post-event very high r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,584
2112.08532	Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis	We present the first parsing results on the Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.9 million word treebank that is an important resource for research in syntactic change. We describe key features of PPCEME that make it challenging for parsing, including a larger and more varied set of functio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,820
2308.00304	Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models	We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical reasoning ability akin to human intelligence. However, even the most advanced LLMs currently struggle ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	382,895
1906.06563	An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks	Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	135,335
1902.02013	Finding the Transitive Closure of Functional Dependencies using Strategic Port Graph Rewriting	We present a new approach to the logical design of relational databases, based on strategic port graph rewriting. We show how to model relational schemata as attributed port graphs and provide port graph rewriting rules to perform computations on functional dependencies. Using these rules we present a strategic graph p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	120,790
2501.15630	Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approach	Transformer-based models have achieved remarkable results in natural language processing (NLP) tasks such as text classification and machine translation. However, their computational complexity and resource demands pose challenges for scalability and accessibility. This research proposes a hybrid quantum-classical tran...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	527,641
2305.17373	Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning	With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training. To address the data scarcity problem in event detection, we propose MetaEvent, a meta learning...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	368,556
2411.18229	SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation	We propose SharpDepth, a novel approach to monocular metric depth estimation that combines the metric accuracy of discriminative depth estimation methods (e.g., Metric3D, UniDepth) with the fine-grained boundary sharpness typically achieved by generative methods (e.g., Marigold, Lotus). Traditional discriminative model...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,781
2410.22987	V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario	This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	503,861
2312.13476	Fortify Your Defenses: Strategic Budget Allocation to Enhance Power Grid Cybersecurity	The abundance of cyber-physical components in modern day power grid with their diverse hardware and software vulnerabilities has made it difficult to protect them from advanced persistent threats (APTs). An attack graph depicting the propagation of potential cyber-attack sequences from the initial access point to the e...	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	417,318
2205.09726	RankGen: Improving Text Generation with Large Ranking Models	Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues we present RankGen, a 1.2B parameter encoder model for English...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	297,394
2205.10456	PSO-Convolutional Neural Networks with Heterogeneous Learning Rate	Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensive to train them. A myriad of architectures and training strategies ha...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	297,712
2303.05998	Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification	Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no fa\c{c}ade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models' fa\c{c}ades using dense, street-level, terrestrial point clouds seems a promising s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,657
1509.01874	Geoweb 2.0 for Participatory Urban Design: Affordances and Critical Success Factors	In this paper, we discuss the affordances of open-source Geoweb 2.0 platforms to support the participatory design of urban projects in real-world practices.We first introduce the two open-source platforms used in our study for testing purposes. Then, based on evidence from five different field studies we identify five ...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	46,666
2401.03149	CaMML: Context-Aware Multimodal Learner for Large Models	In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate multimodal contextual samples into large models, thereby empowering the model to derive knowledge from analogous, domain-specific, up-to-date in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,984
2102.12773	A New Neuromorphic Computing Approach for Epileptic Seizure Prediction	Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hungry. These inconveniences make CNN-based methods hard to be implemented on wearable devices. Motivated by the energy-efficient spiking neur...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	221,844
2012.12558	Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction	Human motion prediction is an essential part for human-robot collaboration. Unlike most of the existing methods mainly focusing on improving the effectiveness of spatiotemporal modeling for accurate prediction, we take effectiveness and efficiency into consideration, aiming at the prediction quality, computational effi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,981
1908.08616	Quadratic Surface Support Vector Machine with L1 Norm Regularization	We propose $\ell_1$ norm regularized quadratic surface support vector machine models for binary classification in supervised learning. We establish their desired theoretical properties, including the existence and uniqueness of the optimal solution, reduction to the standard SVMs over (almost) linearly separable data s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,601
1812.09537	Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction	Motivation: Traditional computational cluster schedulers are based on user inputs and run time needs request for memory and CPU, not IO. Heavily IO bound task run times, like ones seen in many big data and bioinformatics problems, are dependent on the IO subsystems scheduling and are problematic for cluster resource sc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	117,175
2502.09500	Eidetic Learning: an Efficient and Provable Solution to Catastrophic Forgetting	Catastrophic forgetting -- the phenomenon of a neural network learning a task t1 and losing the ability to perform it after being trained on some other task t2 -- is a long-standing problem for neural networks [McCloskey and Cohen, 1989]. We present a method, Eidetic Learning, that provably solves catastrophic forgetti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,468
1909.11879	Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels	Aspect and opinion term extraction is a critical step in Aspect-Based Sentiment Analysis (ABSA). Our study focuses on evaluating transfer learning using pre-trained BERT (Devlin et al., 2018) to classify tokens from hotel reviews in bahasa Indonesia. The primary challenge is the language informality of the review texts...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	146,953
1712.03935	On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification	Identifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news article as fake is still an open question as it is contingent on many factors which the current state-of-the-art models fail to incorporate. In this paper, we explore a subta...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	86,523
2209.01841	Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of academic articles	Purpose The purpose of this paper is to explore which structures of academic articles referees would pay more attention to, what specific content referees focus on, and whether the distribution of PRC is related to the citations. Design/methodology/approach Firstly, utilizing the feature words of section title and ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	316,026
2111.08171	Solving Linear Algebra by Program Synthesis	We solve MIT's Linear Algebra 18.06 course and Columbia University's Computational Linear Algebra COMS3251 courses with perfect accuracy by interactive program synthesis. This surprisingly strong result is achieved by turning the course questions into programming tasks and then running the programs to produce the corre...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	266,586
2310.19685	DGFN: Double Generative Flow Networks	Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) are a recently introduced method recognized for the ability to generate diverse candidates, in particular in small molecule generation tasks. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,089
2309.15123	Uncovering Neural Scaling Laws in Molecular Representation Learning	Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular represe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,856
1802.08405	Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance	We present \emph{Local Moment Matching (LMM)}, a unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. We construct an efficiently computable estimator that achieves the minimax rates in estimating the distribution up to permutation, and show that the plug-in ap...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	91,101
1812.09764	Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology	While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,226
1803.03670	IcoRating: A Deep-Learning System for Scam ICO Identification	Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	92,293
2403.16215	Systematic construction of continuous-time neural networks for linear dynamical systems	Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	440,932
1904.09302	Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics	This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control met...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	128,337
2211.04924	Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data	Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	329,386
2205.06160	Localized Vision-Language Matching for Open-vocabulary Object Detection	In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a location-guided image-caption matching technique to learn class labels for both nov...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	296,155
1911.07301	Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO	In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the difference lies in whether the antennas are co-located at the base station or spread over...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	153,797
1905.06179	Differentiable Linearized ADMM	Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,916
1605.08396	Robust Downbeat Tracking Using an Ensemble of Convolutional Networks	In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolu...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	56,429
1703.10152	Automatic Argumentative-Zoning Using Word2vec	In comparison with document summarization on the articles from social media and newswire, argumentative zoning (AZ) is an important task in scientific paper analysis. Traditional methodology to carry on this task relies on feature engineering from different levels. In this paper, three models of generating sentence vec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	70,871
2307.05314	Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering	Medical visual question answering (VQA) is a challenging task that requires answering clinical questions of a given medical image, by taking consider of both visual and language information. However, due to the small scale of training data for medical VQA, pre-training fine-tuning paradigms have been a commonly used so...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	378,688
2303.10165	Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs	We study reward-free reinforcement learning (RL) with linear function approximation, where the agent works in two phases: (1) in the exploration phase, the agent interacts with the environment but cannot access the reward; and (2) in the planning phase, the agent is given a reward function and is expected to find a nea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,336
2102.05740	Self-supervised learning for fast and scalable time series hyper-parameter tuning	Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a given model, and therefore, selecting good hyper-parameter values is indispensable. Most of the existing generic hyper-parameter tuning method...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,528
2109.03655	On Event-Driven Knowledge Graph Completion in Digital Factories	Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	254,132
2408.05056	Multi-dimensional Parameter Space Exploration for Streamline-specific Tractography	One of the unspoken challenges of tractography is choosing the right parameters for a given dataset or bundle. In order to tackle this challenge, we explore the multi-dimensional parameter space of tractography using streamline-specific parameters (SSP). We 1) validate a state-of-the-art probabilistic tracking method u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,632
2008.03949	Unsupervised Deep-Learning Based Deformable Image Registration: A Bayesian Framework	Unsupervised deep-learning (DL) models were recently proposed for deformable image registration tasks. In such models, a neural-network is trained to predict the best deformation field by minimizing some dissimilarity function between the moving and the target images. After training on a dataset without reference defor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,080
2208.06636	Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments	This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	312,778
2105.05699	Automating Data Science: Prospects and Challenges	Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	234,892
1604.07751	Compressive phase-only filtering at extreme compression rates	We introduce an efficient method for the reconstruction of the correlation between a compressively measured image and a phase-only filter. The proposed method is based on two properties of phase-only filtering: such filtering is a unitary circulant transform, and the correlation plane it produces is usually sparse. Tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,126
2403.19027	Should I Help a Delivery Robot? Cultivating Prosocial Norms through Observations	We propose leveraging prosocial observations to cultivate new social norms to encourage prosocial behaviors toward delivery robots. With an online experiment, we quantitatively assess updates in norm beliefs regarding human-robot prosocial behaviors through observational learning. Results demonstrate the initially perc...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	442,171
2402.13647	Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions	Unsupervised Text Style Transfer (UTST) has emerged as a critical task within the domain of Natural Language Processing (NLP), aiming to transfer one stylistic aspect of a sentence into another style without changing its semantics, syntax, or other attributes. This task is especially challenging given the intrinsic lac...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	431,357
2501.14543	Reducing Action Space for Deep Reinforcement Learning via Causal Effect Estimation	Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant acti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,163
1909.01040	A Geometry-Sensitive Approach for Photographic Style Classification	Photographs are characterized by different compositional attributes like the Rule of Thirds, depth of field, vanishing-lines etc. The presence or absence of one or more of these attributes contributes to the overall artistic value of an image. In this work, we analyze the ability of deep learning based methods to learn...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,789
1911.11098	StructEdit: Learning Structural Shape Variations	Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation. The common approach of encoding shapes a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	155,023
2502.09660	Towards Fine-grained Interactive Segmentation in Images and Videos	The recent Segment Anything Models (SAMs) have emerged as foundational visual models for general interactive segmentation. Despite demonstrating robust generalization abilities, they still suffer performance degradations in scenarios demanding accurate masks. Existing methods for high-precision interactive segmentation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	533,546
1208.1880	Stereo Acoustic Perception based on Real Time Video Acquisition for Navigational Assistance	A smart navigation system (an Electronic Travel Aid) based on an object detection mechanism has been designed to detect the presence of obstacles that immediately impede the path, by means of real time video processing. The algorithm can be used for any general purpose navigational aid. This paper is discussed, keeping...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	17,995
2004.08116	Triplet Loss for Knowledge Distillation	In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes larger. Various techniques for compressing the size of the models have been proposed to improve performance while reducing computational costs...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	172,973
1602.08194	Scalable and Sustainable Deep Learning via Randomized Hashing	Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, ass...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	52,612
2008.05088	An ocular biomechanics environment for reinforcement learning	Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics. In this paper, we extend the use of reinforcement learning into controlling an ocular biom...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	191,407
1705.00132	Online Learning with Automata-based Expert Sequences	We consider a general framework of online learning with expert advice where regret is defined with respect to sequences of experts accepted by a weighted automaton. Our framework covers several problems previously studied, including competing against k-shifting experts. We give a series of algorithms for this problem, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,631
1706.07119	"Parallel Training Considered Harmful?": Comparing series-parallel and parallel feedforward network training	Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations. Influenced by early arguments, several papers justify the choice of series-parallel rather than parallel configuration claiming it has a lower computational cost, better stability properties during training...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	75,795
2401.13695	Inverse analysis of granular flows using differentiable graph neural network simulator	Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity simulators for these inverse problems are computationally demanding, restricting the number of simulations possible. Additional...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	423,818
2405.20502	Reach-Avoid Control Synthesis for a Quadrotor UAV with Formal Safety Guarantees	Reach-avoid specifications are one of the most common tasks in autonomous aerial vehicle (UAV) applications. Despite the intensive research and development associated with control of aerial vehicles, generating feasible trajectories though complex environments and tracking them with formal safety guarantees remain chal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	459,377
2210.00006	A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps	Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspir...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	320,690
2107.02268	Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition	Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded characters, these systems are in principal open vocabulary systems. In practice, however, they often fail to recognize words not seen during tra...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	244,744
2107.01807	Q-SpiNN: A Framework for Quantizing Spiking Neural Networks	A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs) without decreasing the accuracy significantly is quantization. However, the state-of-the-art only focus on employing the weight quantization directly from a specific quantization scheme, i.e., either the post-training quantization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	244,600
2010.07266	Spatial-Slepian Transform on the Sphere	We present spatial-Slepian transform~(SST) for the representation of signals on the sphere to support localized signal analysis. We use well-optimally concentrated Slepian functions, obtained by solving the Slepian spatial-spectral concentration problem of finding bandlimited and spatially optimally concentrated functi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	200,758
1211.0749	Student Modeling using Case-Based Reasoning in Conventional Learning System	Conventional face-to-face classrooms are still the main learning system applied in Indonesia. In assisting such conventional learning towards an optimal learning, formative evaluations are needed to monitor the progress of the class. This task can be very hard when the size of the class is large. Hence, this research a...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	19,550
1009.0679	Optimal Uncertainty Quantification	We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information abo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,473
1610.05045	Validation of community robustness	The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. In this paper we present a methodology able to clearly detect if the community structure found by some algorithms is statistically significant or is a result of chan...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	62,472
2209.10285	AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization	WiFi-based smart human sensing technology enabled by Channel State Information (CSI) has received great attention in recent years. However, CSI-based sensing systems suffer from performance degradation when deployed in different environments. Existing works solve this problem by domain adaptation using massive unlabele...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,811
1305.7458	Validation of a Microsimulation of the Port of Dover	Modelling and simulating the traffic of heavily used but secure environments such as seaports and airports is of increasing importance. Errors made when simulating these environments can have long standing economic, social and environmental implications. This paper discusses issues and problems that may arise when desi...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	24,913
2006.13108	Distilling Object Detectors with Task Adaptive Regularization	Current state-of-the-art object detectors are at the expense of high computational costs and are hard to deploy to low-end devices. Knowledge distillation, which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the promising solutions for model miniaturization....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	183,796
2404.17725	Boltzmann State-Dependent Rationality	This paper expands on existing learned models of human behavior via a measured step in structured irrationality. Specifically, by replacing the suboptimality constant $\beta$ in a Boltzmann rationality model with a function over states $\beta(s)$, we gain natural expressivity in a computationally tractable manner. This...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	449,965
2205.01992	Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning	The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	294,798
2201.03698	Verified Probabilistic Policies for Deep Reinforcement Learning	Deep reinforcement learning is an increasingly popular technique for synthesising policies to control an agent's interaction with its environment. There is also growing interest in formally verifying that such policies are correct and execute safely. Progress has been made in this area by building on existing work for ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	274,910
2305.16358	Differentiable Clustering with Perturbed Spanning Forests	We introduce a differentiable clustering method based on stochastic perturbations of minimum-weight spanning forests. This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients. We show that our method performs well even in difficult settings, such as data sets with high noise and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	368,049
2202.00048	Single Time-scale Actor-critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees	We propose a single time-scale actor-critic algorithm to solve the linear quadratic regulator (LQR) problem. A least squares temporal difference (LSTD) method is applied to the critic and a natural policy gradient method is used for the actor. We give a proof of convergence with sample complexity $\mathcal{O}(\varepsil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,000
1307.1073	Modelling Reactive and Proactive Behaviour in Simulation: A Case Study in a University Organisation	Simulation is a well established what-if scenario analysis tool in Operational Research (OR). While traditionally Discrete Event Simulation (DES) and System Dynamics Simulation (SDS) are the predominant simulation techniques in OR, a new simulation technique, namely Agent-Based Simulation (ABS), has emerged and is gain...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	25,600
2310.05387	Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels	Discovering governing equations from data is important to many scientific and engineering applications. Despite promising successes, existing methods are still challenged by data sparsity and noise issues, both of which are ubiquitous in practice. Moreover, state-of-the-art methods lack uncertainty quantification and/o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,129
2005.01646	A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing	We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into underlying templates in the presence of multiple confounding sources of variance. Our approach introduces a neural editor model that first gene...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,642
2004.14162	Conversations with Search Engines: SERP-based Conversational Response Generation	In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	174,793
2407.14530	FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network	In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation. Conventional metrics for assessing SQL code generation, such as matching-based and execution-based methods (e.g., exact set match and execution accuracy), are subject to two primary limitations. Firstly, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	474,816
2402.09117	Deterministic identification over channels with finite output: a dimensional perspective on superlinear rates	Following initial work by JaJa, Ahlswede and Cai, and inspired by a recent renewed surge in interest in deterministic identification (DI) via noisy channels, we consider the problem in its generality for memoryless channels with finite output, but arbitrary input alphabets. Such a channel is essentially given by its ou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	429,375
2404.12530	TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents	Reinforcement learning (RL) trains an agent from experiences interacting with the environment. In scenarios where online interactions are impractical, offline RL, which trains the agent using pre-collected datasets, has become popular. While this new paradigm presents remarkable effectiveness across various real-world ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	447,923
2211.13606	Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels	Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are lab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	332,532
2103.08457	RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs	Although 3D Convolutional Neural Networks are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D CNNs is largely unexpl...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	224,907
2008.03230	ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data	Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy ...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	true	false	190,843
0811.4630	Channel State Prediction, Feedback and Scheduling for a Multiuser MIMO-OFDM Downlink	We consider the downlink of a MIMO-OFDM wireless systems where the base-station (BS) has M antennas and serves K single-antenna user terminals (UT) with K larger than or equal to M. Users estimate their channel vectors from common downlink pilot symbols and feed back a prediction, which is used by the BS to compute the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,716
2212.02483	TIDE: Time Derivative Diffusion for Deep Learning on Graphs	A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. The challenge of approaches that use this paradigm is to ensure efficient and accurate long-distance communication between nodes, as deep con...	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,800
2006.14197	Distributed multi-view multi-target tracking based on CPHD filtering	This paper addresses distributed multi-target tracking (DMTT) over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinality probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has a limited FoV, the commonly adopted fusion method...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	184,147
2311.17281	Lower Bounds on Adaptive Sensing for Matrix Recovery	We study lower bounds on adaptive sensing algorithms for recovering low rank matrices using linear measurements. Given an $n \times n$ matrix $A$, a general linear measurement $S(A)$, for an $n \times n$ matrix $S$, is just the inner product of $S$ and $A$, each treated as $n^2$-dimensional vectors. By performing as fe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	411,235
2401.08844	Wind tunnel actuation movement system	In this dissertation project, an actuation system was designed for the supersonic wind tunnel at the University of Manchester. The aim of this project is to build a remote control actuation system which could adjust the angle of attack for the aerodynamic shape to save researchers' time and improve the experimental eff...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	422,042

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