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
2207.09833	AI Fairness: from Principles to Practice	This paper summarizes and evaluates various approaches, methods, and techniques for pursuing fairness in artificial intelligence (AI) systems. It examines the merits and shortcomings of these measures and proposes practical guidelines for defining, measuring, and preventing bias in AI. In particular, it cautions agains...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	309,044
q-bio/0505050	HLA and HIV Infection Progression: Application of the Minimum Description Length Principle to Statistical Genetics	The minimum description length (MDL) principle states that the best model to account for some data minimizes the sum of the lengths, in bits, of the descriptions of the model and the residual error. The description length is thus a criterion for model selection. Description-length analysis of HLA alleles from the Chica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,848
2303.03942	Learning Position From Vehicle Vibration Using an Inertial Measurement Unit	This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS). Traditional GNSS approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low recept...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	349,903
2207.08147	Multi-Task and Transfer Learning for Federated Learning Applications	Federated learning enables many applications benefiting distributed and private datasets of a large number of potential data-holding clients. However, different clients usually have their own particular objectives in terms of the tasks to be learned from the data. So, supporting federated learning with meta-learning to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	308,474
2011.04950	Model-based Reinforcement Learning from Signal Temporal Logic Specifications	Techniques based on Reinforcement Learning (RL) are increasingly being used to design control policies for robotic systems. RL fundamentally relies on state-based reward functions to encode desired behavior of the robot and bad reward functions are prone to exploitation by the learning agent, leading to behavior that i...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	205,753
2304.11134	Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference	This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based on split Gibbs sampling (SGS) draws inspiration from the alternating direction method of multipliers (ADMM). It divides the challenging tas...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	359,698
1301.5582	Multi-Class Detection and Segmentation of Objects in Depth	The quality of life of many people could be improved by autonomous humanoid robots in the home. To function in the human world, a humanoid household robot must be able to locate itself and perceive the environment like a human; scene perception, object detection and segmentation, and object spatial localization in 3D a...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	21,341
2304.04137	RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples	In some practical learning tasks, such as traffic video analysis, the number of available training samples is restricted by different factors, such as limited communication bandwidth and computation power. Determinantal Point Process (DPP) is a common method for selecting the most diverse samples to enhance learning qu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,092
2410.10017	REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding	The paper presents REPeat, a Real2Sim2Real framework designed to enhance bite acquisition in robot-assisted feeding for soft foods. It uses `pre-acquisition actions' such as pushing, cutting, and flipping to improve the success rate of bite acquisition actions such as skewering, scooping, and twirling. If the data-driv...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	497,873
1912.12125	Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking	In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, an...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	158,761
2012.12012	Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet	The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,803
2412.02649	Communicate or Sense? AP Mode Selection in mmWave Cell-Free Massive MIMO-ISAC	Integrated sensing and communication (ISAC) is a promising technology for future mobile networks, enabling sensing applications to be performed by existing communication networks, consequently improving the system efficiency. Millimeter wave (mmWave) signals provide high sensing resolution and high data rate but suffer...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	513,624
1810.12478	Generating new pictures in complex datasets with a simple neural network	We introduce a version of a variational auto-encoder (VAE), which can generate good perturbations of images, when trained on a complex dataset (in our experiments, CIFAR-10). The net is using only two latent generative dimensions per class, with uni-modal probability density. The price one has to pay for good generatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,776
2302.11747	Amos-SLAM: An Anti-Dynamics Two-stage SLAM Approach	The traditional Simultaneous Localization And Mapping (SLAM) systems rely on the assumption of a static environment and fail to accurately estimate the system's location when dynamic objects are present in the background. While learning-based dynamic SLAM systems have difficulties in handling unknown moving objects, ge...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	347,299
2404.06836	O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation	Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit representation has provided a promising direction for online interactive mapping. However, implementing open-vocabulary scene understanding ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,625
2403.01306	ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation	Web-scale training on paired text-image data is becoming increasingly central to multimodal learning, but is challenged by the highly noisy nature of datasets in the wild. Standard data filtering approaches succeed in removing mismatched text-image pairs, but permit semantically related but highly abstract or subjectiv...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	434,352
2402.00570	CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography	Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,643
2006.07429	Manifold GPLVMs for discovering non-Euclidean latent structure in neural data	A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming movements, or mental spatial transformations. Often, these latent variables are internal constructs not directly accessible to the experimenter. H...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,789
1907.03199	What graph neural networks cannot learn: depth vs width	This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown to be Turing universal under sufficient conditions on their depth, width, node attributes, and layer expressiveness. Second, it is discovered that GNNm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,799
2404.04069	Bidirectional Human Interactive AI Framework for Social Robot Navigation	Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	444,503
2403.00259	Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO	Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom-atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics. This perspective gives a brief overview of the traditional approaches employed over the past s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	433,917
2405.14455	TIGER: Text-Instructed 3D Gaussian Retrieval and Coherent Editing	Editing objects within a scene is a critical functionality required across a broad spectrum of applications in computer vision and graphics. As 3D Gaussian Splatting (3DGS) emerges as a frontier in scene representation, the effective modification of 3D Gaussian scenes has become increasingly vital. This process entails...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,437
1304.1515	When Should a Decision Maker Ignore the Advice of a Decision Aid?	This paper argues that the principal difference between decision aids and most other types of information systems is the greater reliance of decision aids on fallible algorithms--algorithms that sometimes generate incorrect advice. It is shown that interactive problem solving with a decision aid that is based on a fall...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,548
2404.08443	Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph	Search engines these days can serve datasets as search results. Datasets get picked up by search technologies based on structured descriptions on their official web pages, informed by metadata ontologies such as the Dataset content type of schema.org. Despite this promotion of the content type dataset as a first-class ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	446,242
1606.09022	Decision making via semi-supervised machine learning techniques	Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules. Decision support systems (D...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,939
2303.07141	An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop	This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panoramic images. Our method is built based on Faster R-CNN for human detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,136
2303.04183	Robustness-preserving Lifelong Learning via Dataset Condensation	Lifelong learning (LL) aims to improve a predictive model as the data source evolves continuously. Most work in this learning paradigm has focused on resolving the problem of 'catastrophic forgetting,' which refers to a notorious dilemma between improving model accuracy over new data and retaining accuracy over previou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	349,982
2103.11311	Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System	Accurate localization and 3D maps are increasingly needed for various artificial intelligence based IoT applications such as augmented reality, intelligent transportation, crowd monitoring, robotics, etc. This article proposes a novel semantic 3D map change detection and update based on a smartphone visual positioning ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	225,756
2206.09136	Provable Generalization of Overparameterized Meta-learning Trained with SGD	Despite the superior empirical success of deep meta-learning, theoretical understanding of overparameterized meta-learning is still limited. This paper studies the generalization of a widely used meta-learning approach, Model-Agnostic Meta-Learning (MAML), which aims to find a good initialization for fast adaptation to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,446
1503.04256	Radar Precoder Design for Spectral Coexistence with Coordinated Multi-point (CoMP) System	This paper details the design of precoders for a MIMO radar spectrally coexistent with a MIMO cellular network. We focus on a coordinated multi-point (CoMP) system where a cluster of base stations (BSs) coordinate their transmissions to the intended user. The radar operates in two modes, interference-mitigation mode wh...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,139
2305.00041	ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields	Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by encoding neural representations of a scene. However, NeRFs require hundreds of images per scene to synthesize photo-realistic novel views. Training them on sparse input views leads to overfitting and incorrect scene depth estimatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	361,185
2205.15737	A Compensation Mechanism for EV Flexibility Services using Discrete Utility Functions	Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e.g., power quality and reliability. This paper proposes a compensation mechanism using EV flexibility m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	299,850
2201.08613	Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization	Localizing keypoints of an object is a basic visual problem. However, supervised learning of a keypoint localization network often requires a large amount of data, which is expensive and time-consuming to obtain. To remedy this, there is an ever-growing interest in semi-supervised learning (SSL), which leverages a smal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,393
2311.15716	Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications	In this work, I discuss how Large Language Models can be applied in the legal domain, circumventing their current drawbacks. Despite their large success and acceptance, their lack of explainability hinders legal experts to trust in their output, and this happens rightfully so. However, in this paper, I argue in favor o...	true	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	410,616
1903.00277	Adversarial Generation of Handwritten Text Images Conditioned on Sequences	State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten wo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,989
2209.05869	Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching	Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks. However, the student model needs to be large in this operation. Otherwise, its performance will drop sharply, thus making it impractical to ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	317,231
2206.03789	Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation	Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos. Previous methods either depend on 3D ConvNets or incorporate additional 2D ConvNets as encoders to extract mixed spatial-temporal features. However, these methods suffer from spatial mis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	301,407
1905.09771	Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories	Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual serv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	131,827
2005.07026	Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors	Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise. However, one of the significant drawbacks of these pretrained generative priors is their limited representation capabilities. ...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	177,169
2007.13740	SER Analysis for SWIPT-Enabled Differential Decode-and-Forward Relay Networks	In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation elimi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	189,216
2402.05757	When is Mean-Field Reinforcement Learning Tractable and Relevant?	Mean-field reinforcement learning has become a popular theoretical framework for efficiently approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting symmetry. However, questions remain regarding the applicability of mean-field approximations: in particular, their approximation accuracy o...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	427,991
2106.04252	Meta-Learning to Compositionally Generalize	Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts. This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience. Neural networks have been shown to struggle with this kind of generalization, in particular p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,654
2001.07871	M^2 Deep-ID: A Novel Model for Multi-View Face Identification Using Convolutional Deep Neural Networks	Despite significant advances in Deep Face Recognition (DFR) systems, introducing new DFRs under specific constraints such as varying pose still remains a big challenge. Most particularly, due to the 3D nature of a human head, facial appearance of the same subject introduces a high intra-class variability when projected...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	161,149
2402.15546	HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding	Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in in...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	432,184
2406.15723	Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment	In automated pronunciation assessment, recent emphasis progressively lies on evaluating multiple aspects to provide enriched feedback. However, acquiring multi-aspect-score labeled data for non-native language learners' speech poses challenges; moreover, it often leads to score-imbalanced distributions. In this paper, ...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	466,836
2012.08096	FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems	Deep neural networks (DNNs) significantly improved the accuracy of optical character recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Ad...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	211,655
2411.06575	Adaptive Kinematic Modeling for Improved Hand Posture Estimates Using a Haptic Glove	Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as poss...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	507,173
2302.07469	Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions	Robots deployed in unstructured, real-world environments operate under considerable uncertainty due to imperfect state estimates, model error, and disturbances. Given this real-world context, the goal of this paper is to develop controllers that are provably safe under uncertainties. To this end, we leverage Control Ba...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	345,744
2402.18747	Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains	We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-generated MT quality judgements are robust to domain shifts between traini...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	433,541
2004.06069	A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0	The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a configurable, scalable and easy to use version available in two open sour...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	172,409
2301.04410	GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis	Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are dominated by noise-contrastive estimation (NCE, also known as contrastive learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	340,053
2309.14146	Examining Temporal Bias in Abusive Language Detection	The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death. Machine learning models have been developed to automatically detect abusive language, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	394,485
2207.08619	CACTUSS: Common Anatomical CT-US Space for US examinations	Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,645
2306.07775	iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios	Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models. While many real-world applications require dynamic models that constantly adapt over...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	373,145
1112.2755	Using Proximity to Predict Activity in Social Networks	The structure of a social network contains information useful for predicting its evolution. Nodes that are "close" in some sense are more likely to become linked in the future than more distant nodes. We show that structural information can also help predict node activity. We use proximity to capture the degree to whic...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,440
2002.01612	Generating Interpretable Poverty Maps using Object Detection in Satellite Images	Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,696
2001.11192	Automatic marker-free registration of tree point-cloud data based on rotating projection	Point-cloud data acquired using a terrestrial laser scanner (TLS) play an important role in digital forestry research. Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information. However, it is time-consuming and difficult to place artificial reflectors in a forest w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,007
2311.00067	Adaptive Control of Euler-Lagrange Systems under Time-varying State Constraints without a Priori Bounded Uncertainty	In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verif...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	404,499
1309.6840	Constrained Bayesian Inference for Low Rank Multitask Learning	We present a novel approach for constrained Bayesian inference. Unlike current methods, our approach does not require convexity of the constraint set. We reduce the constrained variational inference to a parametric optimization over the feasible set of densities and propose a general recipe for such problems. We apply ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,302
2212.09713	A Probabilistic Framework for Lifelong Test-Time Adaptation	Test-time adaptation (TTA) is the problem of updating a pre-trained source model at inference time given test input(s) from a different target domain. Most existing TTA approaches assume the setting in which the target domain is stationary, i.e., all the test inputs come from a single target domain. However, in many pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	337,201
2202.12491	Monogenic Wavelet Scattering Network for Texture Image Classification	The scattering transform network (STN), which has a similar structure as that of a popular convolutional neural network except its use of predefined convolution filters and a small number of layers, can generates a robust representation of an input signal relative to small deformations. We propose a novel Monogenic Wav...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,260
2007.13542	Evaluating the reliability of acoustic speech embeddings	Speech embeddings are fixed-size acoustic representations of variable-length speech sequences. They are increasingly used for a variety of tasks ranging from information retrieval to unsupervised term discovery and speech segmentation. However, there is currently no clear methodology to compare or optimise the quality ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	189,167
2404.12744	Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches	Recent advancements in Large Language Models (LLMs) have revolutionized the AI field but also pose potential safety and ethical risks. Deciphering LLMs' embedded values becomes crucial for assessing and mitigating their risks. Despite extensive investigation into LLMs' values, previous studies heavily rely on human-ori...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	448,013
2310.18804	Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting	Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	403,708
2106.01257	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize	This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks and is used to obtain approximate solutions of a linear system $\bar{A}\theta = \bar{b}$ for which $\bar{A}$ and $\bar{b}$ can only be acces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,441
1303.4375	On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods	The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,004
1303.7225	Evolution of emotions on networks leads to the evolution of cooperation in social dilemmas	We show that the resolution of social dilemmas on random graphs and scale-free networks is facilitated by imitating not the strategy of better performing players but rather their emotions. We assume sympathy and envy as the two emotions that determine the strategy of each player by any given interaction, and we define ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	23,331
2009.14822	Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT	How can we efficiently compress a model while maintaining its performance? Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,153
1810.03756	SPIGAN: Privileged Adversarial Learning from Simulation	Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance. We propose anew unsupervised domain adaptation algorithm, called SPIGAN, relying on Sim-ulat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	109,876
2311.12439	Harnessing FPGA Technology for Enhanced Biomedical Computation	This research delves into sophisticated neural network frameworks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for improved analysis of ECG signals via Field Programmable Gate Arrays (FPGAs). The MIT-BIH Arrhythmia Da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	409,337
2310.00010	Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales	From the last decade, researchers in the field of machine learning (ML) and assistive developmental robotics (ADR) have taken an interest in artificial empathy (AE) as a possible future paradigm for human-robot interaction (HRI). Humans learn empathy since birth, therefore, it is challenging to instill this sense in ro...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	395,783
2004.01136	Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation	Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. When it comes to real-world scenarios such as recommendation system and online advertising, however, it is essential to consider the resource consumption of exploration. In practice, there is typically non-zero cost associat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,824
2004.00433	Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art	Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,635
2405.17886	Graphomotor and Handwriting Disabilities Rating Scale (GHDRS):towards complex and objective assessment	Graphomotor and handwriting disabilities (GD and HD, respectively) could significantly reduce children's quality of life. Effective remediation depends on proper diagnosis; however, current approaches to diagnosis and assessment of GD and HD have several limitations and knowledge gaps, e.g. they are subjective, they do...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,168
2307.09915	Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning	Cross-lingual image captioning is a challenging task that requires addressing both cross-lingual and cross-modal obstacles in multimedia analysis. The crucial issue in this task is to model the global and the local matching between the image and different languages. Existing cross-modal embedding methods based on the t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	380,350
1703.06692	QMDP-Net: Deep Learning for Planning under Partial Observability	This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a plan...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	70,268
1301.7178	Spatial degrees of freedom of MIMO systems in Line-of-Sight Environment	While the efficiency of MIMO transmissions in a rich scattering environment has been demonstrated, less is known about the situation where the fading matrix coefficients come from a line-of-sight model. In this paper, we study in detail how this line-of-sight assumption affects the performance of distributed MIMO trans...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,584
2211.08760	SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition	Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods. However, the most disadvantage of PINNs is that one neural network corresponds to one PD...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	330,763
2304.13135	MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis	Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learn...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	360,466
2309.16700	Framework and Model Analysis on Bengali Document Layout Analysis Dataset: BaDLAD	This study focuses on understanding Bengali Document Layouts using advanced computer programs: Detectron2, YOLOv8, and SAM. We looked at lots of different Bengali documents in our study. Detectron2 is great at finding and separating different parts of documents, like text boxes and paragraphs. YOLOv8 is good at figurin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	395,460
2310.01423	An Empirical Study of AI Generated Text Detection Tools	Since ChatGPT has emerged as a major AIGC model, providing high-quality responses across a wide range of applications (including software development and maintenance), it has attracted much interest from many individuals. ChatGPT has great promise, but there are serious problems that might arise from its misuse, especi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	396,419
1312.1752	Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation	Bio-inspired optimization algorithms have been gaining more popularity recently. One of the most important of these algorithms is particle swarm optimization (PSO). PSO is based on the collective intelligence of a swam of particles. Each particle explores a part of the search space looking for the optimal position and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	28,885
2406.09465	Optimal Kernel Orchestration for Tensor Programs with Korch	Kernel orchestration is the task of mapping the computation defined in different operators of a deep neural network (DNN) to the execution of GPU kernels on modern hardware platforms. Prior approaches optimize kernel orchestration by greedily applying operator fusion, which fuses the computation of multiple operators i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	463,956
2401.14297	PWM strategy with harmonics injection and modulated frequency triangular carrier. A review	A new, programmed pulse width modulation (PWM) technique to control power inverters, which uses a harmonic injection modulator and a frequency modulated triangular carrier, synchronized with the modulating signal is presented in this paper. The instantaneous carrier frequency is adjusted according to a periodic functio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	424,041
1904.00374	Clique pooling for graph classification	We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and entirely nonparametric. The operation is implemented within graph con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,865
2312.03799	Low-power, Continuous Remote Behavioral Localization with Event Cameras	Researchers in natural science need reliable methods for quantifying animal behavior. Recently, numerous computer vision methods emerged to automate the process. However, observing wild species at remote locations remains a challenging task due to difficult lighting conditions and constraints on power supply and data s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	413,440
0711.1466	Predicting relevant empty spots in social interaction	An empty spot refers to an empty hard-to-fill space which can be found in the records of the social interaction, and is the clue to the persons in the underlying social network who do not appear in the records. This contribution addresses a problem to predict relevant empty spots in social interaction. Homogeneous and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	882
2011.13861	A matrix-free isogeometric Galerkin method for Karhunen-Lo\`eve approximation of random fields using tensor product splines, tensor contraction and interpolation based quadrature	The Karhunen-Lo\`eve series expansion (KLE) decomposes a stochastic process into an infinite series of pairwise uncorrelated random variables and pairwise $L^2$-orthogonal functions. For any given truncation order of the infinite series the basis is optimal in the sense that the total mean squared error is minimized. T...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	208,606
2004.14875	Polygonal Building Segmentation by Frame Field Learning	While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	175,037
2111.00064	Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction	Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take numerical node features and graph structure as inputs, have been shown to achieve state-of-the-art performance on various graph-related learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,080
1508.00603	Efficiently list-decodable punctured Reed-Muller codes	The Reed-Muller (RM) code encoding $n$-variate degree-$d$ polynomials over ${\mathbb F}_q$ for $d < q$, with its evaluation on ${\mathbb F}_q^n$, has relative distance $1-d/q$ and can be list decoded from a $1-O(\sqrt{d/q})$ fraction of errors. In this work, for $d \ll q$, we give a length-efficient puncturing of such ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	45,689
2406.12449	Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine	Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	465,417
2310.01520	Bridging the Gap between Structural and Semantic Similarity in Diverse Planning	Diverse planning is the problem of finding multiple plans for a given problem specification, which is at the core of many real-world applications. For example, diverse planning is a critical piece for the efficiency of plan recognition systems when dealing with noisy and missing observations. Providing diverse solution...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	396,456
2410.03529	No Need to Talk: Asynchronous Mixture of Language Models	We introduce SmallTalk LM, an innovative method for training a mixture of language models in an almost asynchronous manner. Each model of the mixture specializes in distinct parts of the data distribution, without the need of high-bandwidth communication between the nodes training each model. At inference, a lightweigh...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	494,829
0707.0181	Location and Spectral Estimation of Weak Wave Packets on Noise Background	The method of location and spectral estimation of weak signals on a noise background is being considered. The method is based on the optimized on order and noise dispersion autoregressive model of a sought signal. A new approach of model order determination is being offered. Available estimation of the noise dispersion...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	363
1401.2018	On the Real-time Prediction Problems of Bursting Hashtags in Twitter	Hundreds of thousands of hashtags are generated every day on Twitter. Only a few become bursting topics. Among the few, only some can be predicted in real-time. In this paper, we take the initiative to conduct a systematic study of a series of challenging real-time prediction problems of bursting hashtags. Which hashta...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	29,709
2410.06526	KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks	In this paper, we introduce Knowledge-Orthogonal Reasoning (KOR), which minimizes the impact of domain-specific knowledge for a more accurate evaluation of models' reasoning abilities in out-of-distribution scenarios. Based on this concept, we propose the Knowledge-Orthogonal Reasoning Benchmark (KOR-Bench), encompassi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	496,246
2310.14642	Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field	In this paper, we address the problem of simultaneous relighting and novel view synthesis of a complex scene from multi-view images with a limited number of light sources. We propose an analysis-synthesis approach called Relit-NeuLF. Following the recent neural 4D light field network (NeuLF), Relit-NeuLF first leverage...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,954
1808.10011	Fast and accessible first-principles calculations of vibrational properties of materials	We present example applications of an approach to first-principles calculations of vibrational properties of materials implemented within the Exabyte.io platform. We deploy models based on the Density Functional Perturbation Theory to extract the phonon dispersion relations and densities of states for an example set of...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	106,311
2405.01660	Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts	Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	451,448

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