Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2412.06441	BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation	In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models. Weight-Decomposed Low-Rank Adaptation (DoRA) improves upon LoRA by separating the magnitude and direction components of the weight matrix, leadi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	515,247
2110.11166	Driving the Herd: Search Engines as Content Influencers	In competitive search settings such as the Web, many documents' authors (publishers) opt to have their documents highly ranked for some queries. To this end, they modify the documents - specifically, their content - in response to induced rankings. Thus, the search engine affects the content in the corpus via its ranki...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	262,382
2201.11871	Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey	Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies could provide satisfactory object de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,431
2404.03324	A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off	The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the $\textit{word-level}$...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,211
1709.06161	PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training	Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	81,040
2403.19324	Rapid nonlinear convex guidance using a monomial method	This paper addresses the challenge of accommodating nonlinear dynamics and constraints in rapid trajectory optimization, envisioned for use in the context of onboard guidance. We present a novel framework that uniquely employs overparameterized monomial coordinates and pre-computed fundamental solution expansions to fa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	442,298
2404.07556	Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding	This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,885
2211.09945	VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch	AI's widespread integration has led to neural networks (NNs) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that aut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,155
1805.01365	Optimal Resource Allocation in Full-Duplex Ambient Backscatter Communication Networks for Green IoT	Ambient backscatter communication (AmBC) enables wireless-powered backscatter devices (BDs) to transmit information over ambient radio-frequency (RF) carriers without using an RF transmitter, and thus has emerged as a promising technology for green Internet-of-Things. This paper considers an AmBC network in which a ful...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	96,644
2007.05060	Program Synthesis with Pragmatic Communication	Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-users, leave the synthesis problem radically ill-posed, because many programs may simultaneously satisfy the specification. Prior work resolve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	186,549
2406.16982	Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology	The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a great demand for high-quality labeling information, and labeling noise in medical big data poses a great challenge to efficient disease risk w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	467,379
2003.12563	DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search	Efficient search is a core issue in Neural Architecture Search (NAS). It is difficult for conventional NAS algorithms to directly search the architectures on large-scale tasks like ImageNet. In general, the cost of GPU hours for NAS grows with regard to training dataset size and candidate set size. One common way is se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,944
2410.14685	Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking	In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that offer significant advantages in speed and dynamic range. The proposed tracking controller is designed to respond to visual feedback from th...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	500,145
2412.00797	Online Poisoning Attack Against Reinforcement Learning under Black-box Environments	This paper proposes an online environment poisoning algorithm tailored for reinforcement learning agents operating in a black-box setting, where an adversary deliberately manipulates training data to lead the agent toward a mischievous policy. In contrast to prior studies that primarily investigate white-box settings, ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	512,821
2312.17050	KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching	Dual-lens super-resolution (SR) is a practical scenario for reference (Ref) based SR by utilizing the telephoto image (Ref) to assist the super-resolution of the low-resolution wide-angle image (LR input). Different from general RefSR, the Ref in dual-lens SR only covers the overlapped field of view (FoV) area. However...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,607
1501.03879	A new ADMM algorithm for the Euclidean median and its application to robust patch regression	The Euclidean Median (EM) of a set of points $\Omega$ in an Euclidean space is the point x minimizing the (weighted) sum of the Euclidean distances of x to the points in $\Omega$. While there exits no closed-form expression for the EM, it can nevertheless be computed using iterative methods such as the Wieszfeld algori...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,303
2101.01137	Gauss-Legendre Features for Gaussian Process Regression	Gaussian processes provide a powerful probabilistic kernel learning framework, which allows learning high quality nonparametric regression models via methods such as Gaussian process regression. Nevertheless, the learning phase of Gaussian process regression requires massive computations which are not realistic for lar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	214,291
2406.13267	The Kinetics Observer: A Tightly Coupled Estimator for Legged Robots	In this paper, we propose the "Kinetics Observer", a novel estimator addressing the challenge of state estimation for legged robots using proprioceptive sensors (encoders, IMU and force/torque sensors). Based on a Multiplicative Extended Kalman Filter, the Kinetics Observer allows the real-time simultaneous estimation ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	465,779
2312.03707	Multi-label Text Classification using GloVe and Neural Network Models	This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditional machine learning and deep neural networks for predictions. However, both approaches have their lim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	413,366
2306.12356	Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP	In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,912
2408.15946	Sigma Flows for Image and Data Labeling and Learning Structured Prediction	This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifolds, including Euclidean image domains as special case. The approach combines the Laplace-Beltrami framework for image denoising and enhancement, introduced by Sochen, Kimmel and Malladi about 25 y...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	484,132
2312.15263	Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency	Depth completion, aiming to predict dense depth maps from sparse depth measurements, plays a crucial role in many computer vision related applications. Deep learning approaches have demonstrated overwhelming success in this task. However, high-precision depth completion without relying on the ground-truth data, which a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,945
1612.00137	RMPE: Regional Multi-person Pose Estimation	Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on huma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,830
2005.03834	Streamline-Based Control of Underwater Gliders in 3D Environments	Autonomous underwater gliders use buoyancy control to achieve forward propulsion via a sawtooth-like, rise-and-fall trajectory. Because gliders are slow-moving relative to ocean currents, glider control must consider the effect of oceanic flows. In previous work, we proposed a method to control underwater vehicles in t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	176,273
1211.3384	An Efficient Soft Decoder of Block Codes Based on Compact Genetic Algorithm	Soft-decision decoding is NP-hard problem of great interest to developers of communication system. We present an efficient soft-decision decoding of linear block codes based on compact genetic algorithm (cGA) and compare its performance with various other decoding algorithms including Shakeel algorithms. The proposed a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	19,738
1706.09288	On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence	In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,113
2402.06014	Trustful Coopetitive Infrastructures for the New Space Exploration Era	In the new space economy, space agencies, large enterprises, and start-ups aim to launch space multi-robot systems (MRS) for various in-situ resource utilization (ISRU) purposes, such as mapping, soil evaluation, and utility provisioning. However, these stakeholders' competing economic interests may hinder effective co...	false	true	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	true	428,118
2010.13766	Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills	Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (RL) setting, especially for learning with physical robots. In this paper we propose a no...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	203,246
2410.04751	Intriguing Properties of Large Language and Vision Models	Recently, large language and vision models (LLVMs) have received significant attention and development efforts due to their remarkable generalization performance across a wide range of tasks requiring perception and cognitive abilities. A key factor behind their success is their simple architecture, which consists of a...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	495,424
2402.00920	Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey	The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	425,785
2211.08583	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization	Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution. While much promising work has been accomplished to address this fragility, a systematic study of the role of optimizers and their out-of-distribution generalization performance ha...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	330,685
2409.01909	LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models	Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PLMs (such as BERT) and LLMs (like GPT-4) have become the current mainst...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	485,506
1903.08589	DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection	Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the underutilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for amel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,863
2305.02260	Standardized Benchmark Dataset for Localized Exposure to a Realistic Source at 10$-$90 GHz	The lack of freely available standardized datasets represents an aggravating factor during the development and testing the performance of novel computational techniques in exposure assessment and dosimetry research. This hinders progress as researchers are required to generate numerical data (field, power and temperatu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,969
2206.07886	Generalization Bounds for Data-Driven Numerical Linear Algebra	Data-driven algorithms can adapt their internal structure or parameters to inputs from unknown application-specific distributions, by learning from a training sample of inputs. Several recent works have applied this approach to problems in numerical linear algebra, obtaining significant empirical gains in performance. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	302,924
2106.15023	Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent	Evading adversarial example detection defenses requires finding adversarial examples that must simultaneously (a) be misclassified by the model and (b) be detected as non-adversarial. We find that existing attacks that attempt to satisfy multiple simultaneous constraints often over-optimize against one constraint at th...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	243,587
2202.06493	FLHub: a Federated Learning model sharing service	As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated learning in the distributed computing framework is attracting attention. The central server does no...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,243
2209.03789	Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance	In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal charact...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,600
2305.17289	Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness	Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,503
2005.09170	On Intrinsic Dataset Properties for Adversarial Machine Learning	Deep neural networks (DNNs) have played a key role in a wide range of machine learning applications. However, DNN classifiers are vulnerable to human-imperceptible adversarial perturbations, which can cause them to misclassify inputs with high confidence. Thus, creating robust DNNs which can defend against malicious ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,843
2208.02606	TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads	Reservoir simulations for petroleum fields and seismic imaging are known as the most demanding workloads for high-performance computing (HPC) in the oil and gas (O&G) industry. The optimization of the simulator numerical parameters plays a vital role as it could save considerable computational efforts. State-of-the-art...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	311,515
2403.04502	Matched-filter Precoded Rate Splitting Multiple Access: A Simple and Energy-efficient Design	We introduce an energy-efficient downlink rate splitting multiple access (RSMA) scheme, employing a simple matched filter (MF) for precoding. We consider a transmitter equipped with multiple antennas, serving several single-antenna users at the same frequency-time resource, each with distinct message requests. Within t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	435,624
2009.10444	Self-Adapting Variable Impedance Actuator Control for Precision and Dynamic Tasks	Variable impedance actuators (VIAs) as tool devices for teleoperation could extend the range of tasks that humans can perform through a teleoperated robot by mimicking the change of upper limb stiffness that humans perform for different tasks, increasing the dynamic range of the robot. This requires appropriate impedan...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	196,894
2410.21698	On the Role of Depth and Looping for In-Context Learning with Task Diversity	The intriguing in-context learning (ICL) abilities of deep Transformer models have lately garnered significant attention. By studying in-context linear regression on unimodal Gaussian data, recent empirical and theoretical works have argued that ICL emerges from Transformers' abilities to simulate learning algorithms l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,357
2109.04460	Protein Folding Neural Networks Are Not Robust	Deep neural networks such as AlphaFold and RoseTTAFold predict remarkably accurate structures of proteins compared to other algorithmic approaches. It is known that biologically small perturbations in the protein sequence do not lead to drastic changes in the protein structure. In this paper, we demonstrate that RoseTT...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,415
2407.14185	Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models	In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the uncertainty inherent in these neural network predictions provides valuable information ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,682
1706.02462	Regular Boardgames	We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages. The objective of RBG is to join key properties as expressiveness, efficiency, and naturalness of the description in one GGP formalism, compensating certain drawbacks of the existing l...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	74,982
2404.08549	Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy	Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope op...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	446,285
2210.16440	ODNet: A Convolutional Neural Network for Asteroid Occultation Detection	We propose to design and build an algorithm that will use a Convolutional Neural Network (CNN) and observations from the Unistellar network to reliably detect asteroid occultations. The Unistellar Network, made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,331
1706.03317	Fault Tolerant Consensus Agreement Algorithm	Recently a new fault tolerant and simple mechanism was designed for solving commit consensus problem. It is based on replicated validation of messages sent between transaction participants and a special dispatcher validator manager node. This paper presents a correctness, safety proofs and performance analysis of this ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	75,145
2006.13827	Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret	We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility. We propose two provably efficient model-free algorithms, Risk-Sensitive Value Iteration (RSVI) and Risk-Sens...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,039
quant-ph/0511175	A Proof of the Security of Quantum Key Distribution	We prove the security of theoretical quantum key distribution against the most general attacks which can be performed on the channel, by an eavesdropper who has unlimited computation abilities, and the full power allowed by the rules of classical and quantum physics. A key created that way can then be used to transmit ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	540,890
1906.09450	Semantically Driven Auto-completion	The Bloomberg Terminal has been a leading source of financial data and analytics for over 30 years. Through its thousands of functions, the Terminal allows its users to query and run analytics over a large array of data sources, including structured, semi-structured, and unstructured data; as well as plot charts, set u...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	136,163
2302.05738	Cross-Modal Fine-Tuning: Align then Refine	Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,149
2412.20321	Hypergraph-Based Dynamic Graph Node Classification	Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels change over time has not been well addressed. Existing methods based on RNNs and self-attention only aggregate features of the same node across ...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	521,171
2303.03207	Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation	The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robotic FEs results in non-intuitive and challenging manoeuvres, constraining their application in clinical settings. While previous studies have...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	349,647
2104.02581	WhONet: Wheel Odometry Neural Network for Vehicular Localisation in GNSS-Deprived Environments	In this paper, a deep learning approach is proposed to accurately position wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived environments. In the absence of GNSS signals, information on the speed of the wheels of a vehicle (or other robots alike), recorded from the wheel encoder, can be used to pr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	228,774
2009.04067	Noise Reduction Technique for Raman Spectrum using Deep Learning Network	In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy. The proposed DL network is developed with several training and test sets of noisy ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,954
1006.1666	On the Proximity Factors of Lattice Reduction-Aided Decoding	Lattice reduction-aided decoding features reduced decoding complexity and near-optimum performance in multi-input multi-output communications. In this paper, a quantitative analysis of lattice reduction-aided decoding is presented. To this aim, the proximity factors are defined to measure the worst-case losses in dista...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,723
1403.2837	HPS: a hierarchical Persian stemming method	In this paper, a novel hierarchical Persian stemming approach based on the Part-Of-Speech of the word in a sentence is presented. The implemented stemmer includes hash tables and several deterministic finite automata in its different levels of hierarchy for removing the prefixes and suffixes of the words. We had two in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	31,512
2404.19615	SemiPL: A Semi-supervised Method for Event Sound Source Localization	In recent years, Event Sound Source Localization has been widely applied in various fields. Recent works typically relying on the contrastive learning framework show impressive performance. However, all work is based on large relatively simple datasets. It's also crucial to understand and analyze human behaviors (actio...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	450,711
2201.12023	Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning	Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	277,498
2207.04375	An Input-Output Feedback Linearization based Exponentially Stable Controller for Multi-UAV Payload Transport	In this paper, an exponentially stable trajectory tracking controller is proposed for multi-UAV payload transport. The multi-UAV payload system has a 2-DOF magnetic spherical joint between the UAVs and the vertical rigid links of the payload frame, so the UAVs can roll or pitch freely. These vertical links are rigidly ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	307,179
2008.08977	Generating Adjacency Matrix for Video Relocalization	In this paper, we continue our work on video relocalization task. Based on using graph convolution to extract intra-video and inter-video frame features, we improve the method by using similarity-metric based graph convolution, whose weighted adjacency matrix is achieved by calculating similarity metric between feature...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	192,573
2501.00632	Different thresholding methods on Nearest Shrunken Centroid algorithm	This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification acc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,736
2010.02122	Quadratic approximate dynamic programming for scheduling water resources: a case study	We address the problem of scheduling water resources in a power system via approximate dynamic programming.To this goal, we model a finite horizon economic dispatch problemwith convex stage cost and affine dynamics, and consider aquadratic approximation of the value functions. Evaluating theachieved policy entails solv...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	198,909
1804.10752	Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese	Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and language model into a single neural network. In these models, the Transformer, a new sequence-to-sequence attention-based model relying entir...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	96,220
2408.07318	A systematic dataset generation technique applied to data-driven automotive aerodynamics	A novel strategy for generating datasets is developed within the context of drag prediction for automotive geometries using neural networks. A primary challenge in this space is constructing a training databse of sufficient size and diversity. Our method relies on a small number of starting data points, and provides a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,539
2006.07459	Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations	Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the mixture components to be nonparametric and have substantial (or even total) overlap. This work proposes an algorithm that consistently estimates any identifiable mixtu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,800
2302.13286	Benchmarking of Cancelable Biometrics for Deep Templates	In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing (i.e., IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we int...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	347,892
2010.08265	Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation	The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatl...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	201,131
2011.01938	Power of data in quantum machine learning	The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically har...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,768
2410.04541	On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective	Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks. However, the reasons behind this success remain unclear. In this work, we propose a new evaluation framework to comprehensively assess LLMs' function modeling abilities. By adopting a Bayesian perspective of function modelin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	495,335
2411.01765	Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education	This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	505,212
2407.00509	Leveraging Ontologies to Document Bias in Data	Machine Learning (ML) systems are capable of reproducing and often amplifying undesired biases. This puts emphasis on the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines, prompting the emergence of documentation frameworks with the idea th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	468,895
2111.05784	Maximum-distance Race Strategies for a Fully Electric Endurance Race Car	This paper presents a bi-level optimization framework to compute the maximum-distance stint and charging strategies for a fully electric endurance race car. Thereby, the lower level computes the minimum-stint-time Powertrain Operation (PO) for a given battery energy budget and stint length, whilst the upper level lever...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	265,875
2402.07723	Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation	Understanding the generalization properties of heavy-tailed stochastic optimization algorithms has attracted increasing attention over the past years. While illuminating interesting aspects of stochastic optimizers by using heavy-tailed stochastic differential equations as proxies, prior works either provided expected ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,823
2110.00665	Online Distribution System State Estimation via Stochastic Gradient Algorithm	Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	258,477
2308.04009	Safe Control Synthesis for Multicopter via Control Barrier Function Backstepping	A safe controller for multicopter is proposed using control barrier function. Multicopter dynamics are reformulated to deal with mixed-relative-degree and non-strict-feedback-form dynamics, and a time-varying safe backstepping controller is designed. Despite the time-varying variation, it is proven that the control inp...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	384,250
2311.09781	Online Reachability Analysis and Space Convexification for Autonomous Racing	This paper presents an optimisation-based approach for an obstacle avoidance problem within an autonomous vehicle racing context. Our control regime leverages online reachability analysis and sensor data to compute the maximal safe traversable region that an agent can traverse within the environment. The idea is to fir...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	408,292
1306.1650	OPS-QFTs: A new type of quaternion Fourier transforms based on the orthogonal planes split with one or two general pure quaternions	We explain the orthogonal planes split (OPS) of quaternions based on the arbitrary choice of one or two linearly independent pure unit quaternions $f,g$. Next we systematically generalize the quaternionic Fourier transform (QFT) applied to quaternion fields to conform with the OPS determined by $f,g$, or by only one pu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,064
2003.04973	Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning	Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an eff...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	167,715
1711.00355	Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach	Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, low latency, and high spectrum efficiency. In the NOMA uplink, successive interference cancellation (SIC) based detec...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	83,704
2410.21296	The Trap of Presumed Equivalence: Artificial General Intelligence Should Not Be Assessed on the Scale of Human Intelligence	A traditional approach to assessing emerging intelligence in the theory of intelligent systems is based on the similarity, "imitation" of human-like actions and behaviors, benchmarking the performance of intelligent systems on the scale of human cognitive skills. In this work we attempt to outline the shortcomings of t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	503,180
2405.14088	High-dimensional Learning with Noisy Labels	This paper provides theoretical insights into high-dimensional binary classification with class-conditional noisy labels. Specifically, we study the behavior of a linear classifier with a label noisiness aware loss function, when both the dimension of data $p$ and the sample size $n$ are large and comparable. Relying o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,238
2103.03180	A Critical Note on Social Cloud	The idea of a social cloud has emerged as a resource sharing paradigm in a social network context. Undoubtedly, state-of-the-art social cloud systems demonstrate the potential of the social cloud acting as complementary to other computing paradigms such as the cloud, grid, peer-to-peer and volunteer computing. However,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	223,196
2201.04572	Transmission Scheme, Detection and Power Allocation for Uplink User Cooperation with NOMA and RSMA	In this paper, we propose two novel cooperative-non-orthogonal-multiple-access (C-NOMA) and cooperative-rate-splitting-multiple-access (C-RSMA) schemes for uplink user cooperation. At the first mini-slot of these schemes, each user transmits its signal and receives the transmitted signal of the other user in full-duple...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	275,137
1006.4925	Simulating information creation in social Semantic Web applications	Appropriate ranking algorithms and incentive mechanisms are essential to the creation of high-quality information by users of a social network. However, evaluating such mechanisms in a quantifiable way is a difficult problem. Studies of live social networks of limited utility, due to the subjective nature of ranking an...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	6,883
2501.19195	Rethinking Early Stopping: Refine, Then Calibrate	Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these predictions is generally evaluated with proper losses like cross-entropy, which decompose into two components: calibration error assesses general...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	529,052
2404.00487	Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App	MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will l...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	442,951
2311.03810	Rethinking and Improving Multi-task Learning for End-to-end Speech Translation	Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning. However, the extent to which auxiliary tasks are highly consistent with the ST task, and how much this approach truly helps, have not been thoroughly studied. In this paper, we investigate t...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	406,005
2208.12339	LinCQA: Faster Consistent Query Answering with Linear Time Guarantees	Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	314,692
1607.05523	Dendritic Spine Shape Analysis: A Clustering Perspective	Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,767
2007.05871	Lifted Codes and Lattices from Codes Over Finite Chain Rings	In this paper we give the generalization of lifted codes over any finite chain ring. This has been done by using the construction of finite chain rings from $p$-adic fields. Further we propose a lattice construction from linear codes over finite chain rings using lifted codes.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	186,810
1912.12223	Bitopological Duality for Algebras of Fittings logic and Natural Duality extension	In this paper, we investigate a bitopological duality for algebras of Fitting's multi-valued logic. We also extend the natural duality theory for $\mathbb{ISP_I}(\mathcal{L})$ by developing a duality for $\mathbb{ISP}(\mathcal{L})$, where $\mathcal{L}$ is a finite algebra in which underlying lattice is bounded distribu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	158,802
2501.12824	Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks	Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,448
2010.10239	Complete Multilingual Neural Machine Translation	Multilingual Neural Machine Translation (MNMT) models are commonly trained on a joint set of bilingual corpora which is acutely English-centric (i.e. English either as the source or target language). While direct data between two languages that are non-English is explicitly available at times, its use is not common. In...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	201,827
2404.09051	Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective	Recently, iteration-based stereo matching has shown great potential. However, these models optimize the disparity map using RNN variants. The discrete optimization process poses a challenge of information loss, which restricts the level of detail that can be expressed in the generated disparity map. In order to address...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	446,521
2005.10777	Manifold Alignment for Semantically Aligned Style Transfer	Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	178,281
2405.08019	AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting	Knowledge distillation, a widely used model compression technique, works on the basis of transferring knowledge from a cumbersome teacher model to a lightweight student model. The technique involves jointly optimizing the task specific and knowledge distillation losses with a weight assigned to them. Despite these weig...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,955

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