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
1508.02788	The Effects of Hyperparameters on SGD Training of Neural Networks	The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	45,938
2207.13807	Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields	We present Pose-NDF, a continuous model for plausible human poses based on neural distance fields (NDFs). Pose or motion priors are important for generating realistic new poses and for reconstructing accurate poses from noisy or partial observations. Pose-NDF learns a manifold of plausible poses as the zero level set o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,404
2006.11376	StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction	Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physics without prior knowledge of underlying equations. However...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	183,195
2003.13676	Deep reinforcement learning for large-scale epidemic control	Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes. For this reason, we investigate a deep reinforcement learning approach to automatically learn pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	170,275
0911.4874	Non-photorealistic image processing: an Impressionist rendering	The paper describes an image processing for a non-photorealistic rendering. The algorithm is based on a random choice of a set of pixels from those ot the original image and substitution of them with colour spots. An iterative procedure is applied to cover, at a desired level, the canvas. The resulting effect mimics th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	5,017
2010.05696	Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance	Domain adaptation refers to the learning scenario that a model learned from the source data is applied on the target data which have the same categories but different distribution. While it has been widely applied, the distribution discrepancy between source data and target data can substantially affect the adaptation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,229
1401.5341	Domain Views for Constraint Programming	Views are a standard abstraction in constraint programming: They make it possible to implement a single version of each constraint, while avoiding to create new variables and constraints that would slow down propagation. Traditional constraint-programming systems provide the concept of {\em variable views} which implem...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	30,192
2009.08205	Generating Label Cohesive and Well-Formed Adversarial Claims	Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fa...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	196,166
2406.00282	Adversarial 3D Virtual Patches using Integrated Gradients	LiDAR sensors are widely used in autonomous vehicles to better perceive the environment. However, prior works have shown that LiDAR signals can be spoofed to hide real objects from 3D object detectors. This study explores the feasibility of reducing the required spoofing area through a novel object-hiding strategy base...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	459,777
2411.07320	Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations	While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographica...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	507,483
2410.03058	DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images	The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce Di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	494,593
2309.05681	Knowledge-based Refinement of Scientific Publication Knowledge Graphs	We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic logic model in the presence of human guidance (knowledge-based learning). Specifically, we learn relational regression trees using functional...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	391,170
2306.00212	Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning	We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on expected total utilities. Our focus is confined to an episodic two-player zero-sum constrained Markov game with independent transition functio...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	369,915
2302.11298	Approximate spectral clustering density-based similarity for noisy datasets	Approximate spectral clustering (ASC) was developed to overcome heavy computational demands of spectral clustering (SC). It maintains SC ability in predicting non-convex clusters. Since it involves a preprocessing step, ASC defines new similarity measures to assign weights on graph edges. Connectivity matrix (CONN) is ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	true	false	false	347,161
2009.07578	Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model	This paper addresses the problem of unsupervised approach of credit card fraud detection in unbalanced dataset using the ARIMA model. The ARIMA model is fitted on the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card da...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	195,984
2207.14513	Uncertainty-Driven Action Quality Assessment	Automatic action quality assessment (AQA) has attracted increasing attention due to its wide applications. However, most existing AQA methods employ deterministic models to predict the final score for each action, while overlooking the subjectivity and diversity among expert judges during the scoring process. In this p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,613
1905.08920	Domain adaptation for part-of-speech tagging of noisy user-generated text	The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data available. This work addresses the problem of POS tagging noisy user-generated text using a neural network. We propose an architecture that trains a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	131,607
2312.00036	Privacy-Preserving Load Forecasting via Personalized Model Obfuscation	The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance ch...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	411,864
2401.08049	EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model	In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. However, existing methods face challenges related to limited generalization, particularly when dealing with challenging identit...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,744
1706.05870	Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images	Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in the last few years, offering numerous advantages over alternative methods of nerve localization (neurostimulation or paraesthesia). However, nerve detection is one of the most tasks that anaesthetists can encounter in the UGRA procedure. Comput...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,589
1809.02768	Generating Distractors for Reading Comprehension Questions from Real Examinations	We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,127
2310.18291	Addressing GAN Training Instabilities via Tunable Classification Losses	Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	403,460
2410.04801	Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering	The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or fine-tuning. As model size increases, high-norm artifacts anomaly appears in the patches of multi-head attention. We observe that this anomal...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	495,446
2409.02885	CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently	The rapidly evolving field of digital oncopathology faces significant challenges, including the need to address diverse and complex clinical questions, often involving rare conditions, with limited availability of labeled data. These limitations hinder the development of robust AI-driven tools in the biomedical space, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,864
1407.0516	Spatially Coupled Turbo Codes: Principles and Finite Length Performance	In this paper, we give an overview of spatially coupled turbo codes (SC-TCs), the spatial coupling of parallel and serially concatenated convolutional codes, recently introduced by the authors. For presentation purposes, we focus on spatially coupled serially concatenated codes (SC-SCCs). We review the main principles ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,341
2407.05689	Ten Years of Teaching Empirical Software Engineering in the context of Energy-efficient Software	In this chapter we share our experience in running ten editions of the Green Lab course at the Vrije Universiteit Amsterdam, the Netherlands. The course is given in the Software Engineering and Green IT track of the Computer Science Master program of the VU. The course takes place every year over a 2-month period and t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	471,091
math/0208155	Toric codes over finite fields	In this note, a class of error-correcting codes is associated to a toric variety associated to a fan defined over a finite field $\fff_q$, analogous to the class of Goppa codes associated to a curve. For such a ``toric code'' satisfying certain additional conditions, we present an efficient decoding algorithm for the d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,634
1811.01774	SCAV'18: Report of the 2nd International Workshop on Safe Control of Autonomous Vehicles	This report summarizes the discussions, open issues, take-away messages, and conclusions of the 2nd SCAV workshop.	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	112,446
2311.17002	Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following	Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts, especially those with quantity, object-attribute binding, and multi-subject descriptions. In this work, we introduce a semantic panel as the middleware in decoding texts to images, supporting the generator to better follow i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,115
1709.02445	Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It	Teams dominate the production of high-impact science and technology. Analyzing teamwork from more than 50 million papers, patents, and software products, 1954-2014, we demonstrate across this period that larger teams developed recent, popular ideas, while small teams disrupted the system by drawing on older and less pr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	80,263
2205.10117	DDDM: a Brain-Inspired Framework for Robust Classification	Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to the one-shot inference performed by most deep neural net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	297,567
1202.5618	An equation-free approach to coarse-graining the dynamics of networks	We propose and illustrate an approach to coarse-graining the dynamics of evolving networks (networks whose connectivity changes dynamically). The approach is based on the equation-free framework: short bursts of detailed network evolution simulations are coupled with lifting and restriction operators that translate bet...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	14,570
2405.06702	Malayalam Sign Language Identification using Finetuned YOLOv8 and Computer Vision Techniques	Technological advancements and innovations are advancing our daily life in all the ways possible but there is a larger section of society who are deprived of accessing the benefits due to their physical inabilities. To reap the real benefits and make it accessible to society, these talented and gifted people should als...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	453,413
1509.01168	Semi-described and semi-supervised learning with Gaussian processes	Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as "semi-described learning". We then introduce a GP framework that solves both, the semi-described and...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	46,573
2210.04676	Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER	As the categories of named entities rapidly increase, the deployed NER models are required to keep updating toward recognizing more entity types, creating a demand for class-incremental learning for NER. Considering the privacy concerns and storage constraints, the standard paradigm for class-incremental NER updates th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	322,547
1307.1482	Towards Combining HTN Planning and Geometric Task Planning	In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities---tasks. We believe that this approach facilitates a more principled interface to symbolic pla...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,634
2310.15846	Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation	Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	402,473
2502.02406	LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models	Cross-attention is commonly adopted in multimodal large language models (MLLMs) for integrating visual information into the language backbone. However, in applications with large visual inputs, such as video understanding, processing a large number of visual tokens in cross-attention layers leads to high memory demands...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	530,299
2202.02673	PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized Environments with Adjustable Fading	Programmable radio environments parametrized by reconfigurable intelligent surfaces (RISs) are emerging as a new wireless communications paradigm, but currently used channel models for the design and analysis of signal-processing algorithms cannot include fading in a manner that is faithful to the underlying wave physi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	278,904
2106.14564	Two-point AG codes from the Beelen-Montanucci maximal curve	In this paper we investigate two-point algebraic-geometry codes (AG codes) coming from the Beelen-Montanucci (BM) maximal curve. We study properties of certain two-point Weierstrass semigroups of the curve and use them for determining a lower bound on the minimum distance of such codes. AG codes with better parameters ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	243,445
2412.10438	Automatic Image Annotation for Mapped Features Detection	Detecting road features is a key enabler for autonomous driving and localization. For instance, a reliable detection of poles which are widespread in road environments can improve localization. Modern deep learning-based perception systems need a significant amount of annotated data. Automatic annotation avoids time-co...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	516,929
2309.17160	Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display	ITM(inverse tone-mapping) converts SDR (standard dynamic range) footage to HDR/WCG (high dynamic range /wide color gamut) for media production. It happens not only when remastering legacy SDR footage in front-end content provider, but also adapting on-theair SDR service on user-end HDR display. The latter requires more...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	395,654
1812.01192	Learning to Fuse Things and Stuff	We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain thes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,455
0911.5553	Randomized vs. orthogonal spectrum allocation in decentralized networks: Outage Analysis	We address a decentralized wireless communication network with a fixed number $u$ of frequency sub-bands to be shared among $N$ transmitter-receiver pairs. It is assumed that the number of users $N$ is a random variable with a given distribution and the channel gains are quasi-static Rayleigh fading. The transmitters a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,050
1512.04052	Big Data Scaling through Metric Mapping: Exploiting the Remarkable Simplicity of Very High Dimensional Spaces using Correspondence Analysis	We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are foun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,093
1402.5360	Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity	In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	31,050
cs/0405050	Traffic Accident Analysis Using Decision Trees and Neural Networks	The costs of fatalities and injuries due to traffic accident have a great impact on society. This paper presents our research to model the severity of injury resulting from traffic accidents using artificial neural networks and decision trees. We have applied them to an actual data set obtained from the National Automo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	538,200
1109.6638	The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)	Sparse coding is a proven principle for learning compact representations of images. However, sparse coding by itself often leads to very redundant dictionaries. With images, this often takes the form of similar edge detectors which are replicated many times at various positions, scales and orientations. An immediate co...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	12,402
1406.0022	Error Decay of (almost) Consistent Signal Estimations from Quantized Gaussian Random Projections	This paper provides new error bounds on "consistent" reconstruction methods for signals observed from quantized random projections. Those signal estimation techniques guarantee a perfect matching between the available quantized data and a new observation of the estimated signal under the same sensing model. Focusing on...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,512
2001.10190	Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet Transform	We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep neural networks, Wave-U-Net, which successively down-samples and up-samples feature maps. We find tha...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	161,764
2502.01943	DAMA: Data- and Model-aware Alignment of Multi-modal LLMs	Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences. However, existing methods exhibit an imbalanced responsiveness to the data of varying hardness, tending to overfit on the easy-to-distinguish data while underfitting on the hard-to-d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,102
2501.06879	Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11	This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifying six common defect types: missing hole, rat bite, open circuit, short circuit, burr, and virtual w...	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	524,168
2210.02494	Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller	Data-driven controls using Gaussian process regression have recently gained much attention. In such approaches, system identification by Gaussian process regression is mostly followed by model-based controller designs. However, the outcomes of Gaussian process regression are often too complicated to apply conventional ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	321,660
2210.13497	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise	Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	326,186
2304.10241	A geometry-aware deep network for depth estimation in monocular endoscopy	Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the important geometric structural consistency, which inevitably leads to performance degradation and distortion of 3D reconstruction. To address this i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,336
2409.12926	MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs	Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	489,774
2201.05272	Toward Fully Automated Robotic Platform for Remote Auscultation	Since most developed countries are facing an increase in the number of patients per healthcare worker due to a declining birth rate and an aging population, relatively simple and safe diagnosis tasks may need to be performed using robotics and automation technologies, without specialists and hospitals. This study prese...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	275,337
2409.19691	CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays	Existing rhetorical understanding and generation datasets or corpora primarily focus on single coarse-grained categories or fine-grained categories, neglecting the common interrelations between different rhetorical devices by treating them as independent sub-tasks. In this paper, we propose the Chinese Essay Rhetoric D...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,807
2406.14653	LLM Granularity for On-the-Fly Robot Control	Assistive robots have attracted significant attention due to their potential to enhance the quality of life for vulnerable individuals like the elderly. The convergence of computer vision, large language models, and robotics has introduced the `visuolinguomotor' mode for assistive robots, where visuals and linguistics ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	466,401
1211.0056	Iterative Hard Thresholding Methods for $l_0$ Regularized Convex Cone Programming	In this paper we consider $l_0$ regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving $l_0$ regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	19,507
2305.02261	End-to-end Training and Decoding for Pivot-based Cascaded Translation Model	Utilizing pivot language effectively can significantly improve low-resource machine translation. Usually, the two translation models, source-pivot and pivot-target, are trained individually and do not utilize the limited (source, target) parallel data. This work proposes an end-to-end training method for the cascaded t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	361,970
2312.09323	Perspectives on the State and Future of Deep Learning - 2023	The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey periodically until the AI singularity paperclip-frenzy-driven doomsday, keeping an updated list of topical questions and interviewing new communi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	415,673
cs/0607098	List decoding of noisy Reed-Muller-like codes	First- and second-order Reed-Muller (RM(1) and RM(2), respectively) codes are two fundamental error-correcting codes which arise in communication as well as in probabilistically-checkable proofs and learning. In this paper, we take the first steps toward extending the quick randomized decoding tools of RM(1) into the r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	539,603
1910.00517	Learning Multi-Stage Sparsification for Maximum Clique Enumeration	We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network analysis tasks. In each stage, our approach learns the characteristics of vertices in terms of various neighborhood features and leverage them...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	147,683
1605.04806	Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization	This paper proposes a new image thresholding segmentation approach using the heuristic method, Convergent Heterogeneous Particle Swarm Optimization algorithm. The proposed algorithm incorporates a new strategy of searching the problem space by dividing the swarm into subswarms. Each subswarm particles search for better...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,919
2208.11948	Learning to Construct 3D Building Wireframes from 3D Line Clouds	Line clouds, though under-investigated in the previous work, potentially encode more compact structural information of buildings than point clouds extracted from multi-view images. In this work, we propose the first network to process line clouds for building wireframe abstraction. The network takes a line cloud as inp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	314,587
1308.3177	Normalized Google Distance of Multisets with Applications	Normalized Google distance (NGD) is a relative semantic distance based on the World Wide Web (or any other large electronic database, for instance Wikipedia) and a search engine that returns aggregate page counts. The earlier NGD between pairs of search terms (including phrases) is not sufficient for all applications. ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	26,440
2210.03929	EgoTaskQA: Understanding Human Tasks in Egocentric Videos	Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	322,233
2305.07376	DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference	DNNs are widely used but face significant computational costs due to matrix multiplications, especially from data movement between the memory and processing units. One promising approach is therefore Processing-in-Memory as it greatly reduces this overhead. However, most PIM solutions rely either on novel memory techno...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	363,877
2406.09601	Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos	The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat Deepfakes videos have developed detectors that are highly accurate at identifying GAN-generated samples. However, the robustness of these dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,000
2311.05486	Disease Gene Prioritization With Quantum Walks	Disease gene prioritization assigns scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. Here, we describe a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interact...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,599
1711.03599	Traffic Models of Periodic Event-Triggered Control Systems	Periodic event-triggered control (PETC) is a version of event-triggered control (ETC) that only requires to measure the plant output periodically instead of continuously. In this work, we present a construction of timing models for these PETC implementations to capture the dynamics of the traffic they generate. In the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	84,244
2407.19832	ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2	Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity. To address this issue, we introduce ML-Mamba, a multimodal language model, which utilizes the la...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	476,948
2206.02211	Variable-rate hierarchical CPC leads to acoustic unit discovery in speech	The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hierarchical representations of speech by applying multiple levels of Contrastive Predictive C...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	300,796
2208.11231	Exact Penalty Method for Federated Learning	Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to dea...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	314,348
1504.07682	Shotgun assembly of labeled graphs	We consider the problem of reconstructing graphs or labeled graphs from neighborhoods of a given radius r. Special instances of this problem include the well known: DNA shotgun assembly; the lesser-known: neural network reconstruction; and a new problem: assembling random jigsaw puzzles. We provide some necessary and s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,568
1806.03368	An Exploration of H-1B Visa Applications in the United States	The H-1B visa program is a very important tool for US-based businesses and educational institutes to recruit foreign talent. While the ultimate decision to certify an application lies with the United States Department of Labor, there are signals that can be used to determine whether an application is likely to be certi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	99,978
cs/0412049	Neural Networks in Mobile Robot Motion	This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning p...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	538,430
1709.08325	Pose-driven Deep Convolutional Model for Person Re-identification	Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,451
2406.04721	End-to-End Design of Polar Coded Integrated Data and Energy Networking	In order to transmit data and transfer energy to the low-power Internet of Things (IoT) devices, integrated data and energy networking (IDEN) system may be harnessed. In this context, we propose a bitwise end-to-end design for polar coded IDEN systems, where the conventional encoding/decoding, modulation/demodulation, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	461,811
2012.15695	EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting	Keyword spotting is a process of finding some specific words or phrases in recorded speeches by computers. Deep neural network algorithms, as a powerful engine, can handle this problem if they are trained over an appropriate dataset. To this end, the football keyword dataset (FKD), as a new keyword spotting dataset in ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	213,869
1705.02073	Cross-lingual Distillation for Text Classification	Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft pro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	72,920
2402.16790	Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance	Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizer...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	432,695
2205.11948	SHARP: Shape-Aware Reconstruction of People in Loose Clothing	Recent advancements in deep learning have enabled 3D human body reconstruction from a monocular image, which has broad applications in multiple domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People in loose clothing), a novel end-to-end trainable network that accurately recovers the 3D geometry...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	298,345
1804.08766	Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management	This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka model predictive control) framework, are rather general and can be applied to ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	95,830
2105.01052	Applied Language Technology: NLP for the Humanities	This contribution describes a two-course module that seeks to provide humanities majors with a basic understanding of language technology and its applications using Python. The learning materials consist of interactive Jupyter Notebooks and accompanying YouTube videos, which are openly available with a Creative Commons...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	233,420
2010.04637	Recurrent babbling: evaluating the acquisition of grammar from limited input data	Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a child would be exposed to. This paper remedies this state of affairs by tr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,813
2411.01477	DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning	Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions,...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	505,092
2408.00004	Handling Numeric Expressions in Automatic Speech Recognition	This paper addresses the problem of correctly formatting numeric expressions in automatic speech recognition (ASR) transcripts. This is challenging since the expected transcript format depends on the context, e.g., 1945 (year) vs. 19:45 (timestamp). We compare cascaded and end-to-end approaches to recognize and format ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	477,678
1908.11360	Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics	This work provides proof-search algorithms and automated counter-model extraction for a class of STIT logics. With this, we answer an open problem concerning syntactic decision procedures and cut-free calculi for STIT logics. A new class of cut-free complete labelled sequent calculi G3LdmL^m_n, for multi-agent STIT wit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	143,363
2412.19110	A Selective Secure Precoding Framework for MU-MIMO Rate-Splitting Multiple Access Networks Under Limited CSIT	In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral effici...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	520,715
2108.08911	Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task	Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users. An efficient alternative of creating explanations is to use an introspection-based method that transforms Q-values into probabilities of success used as the base to explain the ag...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,428
2104.12622	Towards Knowledge Graphs Validation through Weighted Knowledge Sources	The performance of applications, such as personal assistants and search engines, relies on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is knowledge validation, which measures the degree to which statements or triples of KGs are semantically correct. KGs inevit...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	232,273
1905.03577	Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification	In recent years, deep learning has presented a great advance in hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. Howeve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,229
2502.00138	JustAct+: Justified and Accountable Actions in Policy-Regulated, Multi-Domain Data Processing	Inter-organisational data exchange is regulated by norms originating from sources ranging from (inter)national laws, to processing agreements, and individual consent. Verifying norm compliance is complex because laws (e.g., GDPR) distribute responsibility and require accountability. Moreover, in some application domain...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	529,226
1010.0771	Genetic Algorithm for Mulicriteria Optimization of a Multi-Pickup and Delivery Problem with Time Windows	In This paper we present a genetic algorithm for mulicriteria optimization of a multipickup and delivery problem with time windows (m-PDPTW). The m-PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers satisfying precedence, capacity and time constraint...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	7,784
1809.00798	Plastic Waste is Exponentially Filling our Oceans, but where are the Robots?	Plastic waste is filling our oceans at an exponential rate. The situation is catastrophic and has now garnered worldwide attention. Despite the catastrophic conditions, little to no robotics research is conducted in the identification, collection, sorting, and removal of plastic waste from oceans and rivers and at the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	106,657
2410.24104	Clustering to Minimize Cluster-Aware Norm Objectives	We initiate the study of the following general clustering problem. We seek to partition a given set $P$ of data points into $k$ clusters by finding a set $X$ of $k$ centers and assigning each data point to one of the centers. The cost of a cluster, represented by a center $x\in X$, is a monotone, symmetric norm $f$ (in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	504,330
2412.05438	Granular Ball K-Class Twin Support Vector Classifier	This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,824
1401.6500	Holographic Transformation for Quantum Factor Graphs	Recently, a general tool called a holographic transformation, which transforms an expression of the partition function to another form, has been used for polynomial-time algorithms and for improvement and understanding of the belief propagation. In this work, the holographic transformation is generalized to quantum fac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,365

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