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
2201.00377	Parkour Spot ID: Feature Matching in Satellite and Street view images using Deep Learning	How to find places that are not indexed by Google Maps? We propose an intuitive method and framework to locate places based on their distinctive spatial features. The method uses satellite and street view images in machine vision approaches to classify locations. If we can classify locations, we just need to repeat for...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	273,945
2305.03877	Semantically Optimized End-to-End Learning for Positional Telemetry in Vehicular Scenarios	End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver st...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	362,544
2110.04955	BuildingNet: Learning to Label 3D Buildings	We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives. To create our dataset, we used crowdsourcing combined with expert guidan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	260,102
1312.2139	Optimal rates for zero-order convex optimization: the power of two function evaluations	We consider derivative-free algorithms for stochastic and non-stochastic convex optimization problems that use only function values rather than gradients. Focusing on non-asymptotic bounds on convergence rates, we show that if pairs of function values are available, algorithms for $d$-dimensional optimization that use ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,930
2005.07093	Bayesian Bits: Unifying Quantization and Pruning	We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition of the quantization operation, which sequentially considers doubling the bit width. At each new bit width, the residual error between the ful...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,190
1811.06837	A Grammar-Based Structural CNN Decoder for Code Generation	Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	113,606
1809.09495	A family of neighborhood contingency logics	This article proposes the axiomatizations of contingency logics of various natural classes of neighborhood frames. In particular, by defining a suitable canonical neighborhood function, we give sound and complete axiomatizations of monotone contingency logic and regular contingency logic, thereby answering two open que...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	108,721
2106.06777	Model-free Reinforcement Learning for Branching Markov Decision Processes	We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various types that, while spawning other entities, generate a payoff. In compariso...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	240,617
2008.00152	Transactive Energy System Deployment over Insecure Communication Links	In this paper, the privacy and security issues associated with the transactive energy system (TES) deployment over insecure communication links are addressed. In particular, it is ensured that (1) individual agents' bidding information is kept private throughout hierarchical market-based interactions; and (2) any extra...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	189,921
1604.01683	Fusing Face and Periocular biometrics using Canonical correlation analysis	This paper presents a novel face and periocular biometric fusion at feature level using canonical correlation analysis. Face recognition itself has limitations such as illumination, pose, expression, occlusion etc. Also, periocular biometrics has spectacles, head angle, hair and expression as its limitations. Unimodal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,225
1911.05932	GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs	Finding local correspondences between images with different viewpoints requires local descriptors that are robust against geometric transformations. An approach for transformation invariance is to integrate out the transformations by pooling the features extracted from transformed versions of an image. However, the fea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,412
1601.01502	The Expurgation-Augmentation Method for Constructing Good Plane Subspace Codes	As shown in [28], one of the five isomorphism types of optimal binary subspace codes of size 77 for packet length v=6, constant dimension k=3 and minimum subspace distance d=4 can be constructed by first expurgating and then augmenting the corresponding lifted Gabidulin code in a fairly simple way. The method was refin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,755
2301.13089	Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education	There is a lack of enough qualified teachers across Africa which hampers efforts to provide adequate learning support such as educational question answering (EQA) to students. An AI system that can enable students to ask questions via text or voice and get instant answers will make high-quality education accessible. De...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	342,776
1804.05651	IterGANs: Iterative GANs to Learn and Control 3D Object Transformation	We are interested in learning visual representations which allow for 3D manipulations of visual objects based on a single 2D image. We cast this into an image-to-image transformation task, and propose Iterative Generative Adversarial Networks (IterGANs) which iteratively transform an input image into an output image. O...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,119
2107.02791	Depth-supervised NeRF: Fewer Views and Faster Training for Free	A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We for...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	244,942
2108.02652	Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles using Approximate Dynamic Programming	Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	249,402
2401.08376	KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation	Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practic...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	421,863
2409.20181	Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models	Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by set...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	493,027
1601.01100	Memory Matters: Convolutional Recurrent Neural Network for Scene Text Recognition	Text recognition in natural scene is a challenging problem due to the many factors affecting text appearance. In this paper, we presents a method that directly transcribes scene text images to text without needing of sophisticated character segmentation. We leverage recent advances of deep neural networks to model the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,711
1002.0179	B\'{e}zout Identities Associated to a Finite Sequence	We consider finite sequences $s\in D^n$ where $D$ is a commutative, unital, integral domain. We prove three sets of identities (possibly with repetitions), each involving $2n$ polynomials associated to $s$. The right-hand side of these identities is a recursively-defined (non-zero) 'product-of-discrepancies'. There are...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	5,577
2008.03464	Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning	Automatic Speaker Verification systems are gaining popularity these days; spoofing attacks are of prime concern as they make these systems vulnerable. Some spoofing attacks like Replay attacks are easier to implement but are very hard to detect thus creating the need for suitable countermeasures. In this paper, we prop...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,912
2004.04602	Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network	Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also conside...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	171,929
1906.09417	Keyword Spotting for Hearing Assistive Devices Robust to External Speakers	Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness of small electronic devices that allow interaction with them via speech. Often, KWS systems are speaker-independent, which means that any person --user or not-- might trigger them. For applications like KWS for hearing assistive devices this is ...	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	136,149
2406.19092	Adaptive Stochastic Weight Averaging	Ensemble models often improve generalization performances in challenging tasks. Yet, traditional techniques based on prediction averaging incur three well-known disadvantages: the computational overhead of training multiple models, increased latency, and memory requirements at test time. To address these issues, the St...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,298
2111.07129	Visual Understanding of Complex Table Structures from Document Images	Table structure recognition is necessary for a comprehensive understanding of documents. Tables in unstructured business documents are tough to parse due to the high diversity of layouts, varying alignments of contents, and the presence of empty cells. The problem is particularly difficult because of challenges in iden...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	266,285
2406.15305	PID: Prompt-Independent Data Protection Against Latent Diffusion Models	The few-shot fine-tuning of Latent Diffusion Models (LDMs) has enabled them to grasp new concepts from a limited number of images. However, given the vast amount of personal images accessible online, this capability raises critical concerns about civil privacy. While several previous defense methods have been developed...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	466,690
2301.06695	Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks	Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vul...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	340,716
2103.16076	Face Forensics in the Wild	On existing public benchmarks, face forgery detection techniques have achieved great success. However, when used in multi-person videos, which often contain many people active in the scene with only a small subset having been manipulated, their performance remains far from being satisfactory. To take face forgery detec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,444
2404.15879	Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection	LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D. However, object detectors face a critical challenge when dealing with unknown foreground objects, particularly those that were not present in their original training dat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	449,274
2203.06865	Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective	One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, theoretical and empirical analysis to find models that achieve exact or approximate fits. Our contribution i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	285,248
1103.2447	Mini-step Strategy for Transient Analysis	Domain decomposition methods are widely used to solve sparse linear systems from scientific problems, but they are not suited to solve sparse linear systems extracted from integrated circuits. The reason is that the sparse linear system of integrated circuits may be non-diagonal-dominant, and domain decomposition metho...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	9,583
2408.05406	Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test	In the context of Noisy Intermediate-Scale Quantum (NISQ) computing, parameterized quantum circuits (PQCs) represent a promising paradigm for tackling challenges in quantum sensing, optimal control, optimization, and machine learning on near-term quantum hardware. Gradient-based methods are crucial for understanding th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,767
1805.05132	Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection	Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color salienc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,382
2111.00928	Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification	Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve into semi-supervised learning for object detection, where labeled data are more labor-intensive to collect. Current methods are easily dist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,395
2408.16500	CogVLM2: Visual Language Models for Image and Video Understanding	Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new generation of visual language models for image and video understanding inclu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,353
2502.13171	Web Phishing Net (WPN): A scalable machine learning approach for real-time phishing campaign detection	Phishing is the most prevalent type of cyber-attack today and is recognized as the leading source of data breaches with significant consequences for both individuals and corporations. Web-based phishing attacks are the most frequent with vectors such as social media posts and emails containing links to phishing URLs th...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	535,241
2412.16387	Information Limits of Joint Community Detection and Finite Group Synchronization	The emerging problem of joint community detection and group synchronization, with applications in signal processing and machine learning, has been extensively studied in recent years. Previous research has predominantly focused on a statistical model that extends the stochastic block model~(SBM) by incorporating additi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	519,493
2109.04041	Keeping an Eye on Things: Deep Learned Features for Long-Term Visual Localization	In this paper, we learn visual features that we use to first build a map and then localize a robot driving autonomously across a full day of lighting change, including in the dark. We train a neural network to predict sparse keypoints with associated descriptors and scores that can be used together with a classical pos...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,265
2405.19269	Rich-Observation Reinforcement Learning with Continuous Latent Dynamics	Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs. Toward addressing these challenges, we introduce a new theoretical framework, RichCLD (Rich-Observation RL with Continuous Latent Dynamics)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,818
2003.07999	Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images	With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images can enable multiple liver preoperative surgical planning applications. A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,600
1710.07025	Second Order Asymptotics for Communication under Strong Asynchronism	The capacity under strong asynchronism was recently shown to be essentially unaffected by the imposed output sampling rate $\rho$ and decoding delay $d$---the elapsed time between when information is available at the transmitter and when it is decoded. This paper examines this result in the finite blocklength regime an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,867
1709.08535	Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net	The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets. However, there has been limited success in deriving estimates for the full poste...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	81,494
2305.20049	A Unified Conditional Framework for Diffusion-based Image Restoration	Diffusion Probabilistic Models (DPMs) have recently shown remarkable performance in image generation tasks, which are capable of generating highly realistic images. When adopting DPMs for image restoration tasks, the crucial aspect lies in how to integrate the conditional information to guide the DPMs to generate accur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	369,792
2411.16073	Soft-TransFormers for Continual Learning	Inspired by Well-initialized Lottery Ticket Hypothesis (WLTH), which provides suboptimal fine-tuning solutions, we propose a novel fully fine-tuned continual learning (CL) method referred to as Soft-TransFormers (Soft-TF). Soft-TF sequentially learns and selects an optimal soft-network or subnetwork for each task. Duri...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	510,874
1701.02911	Quantum Stabilizer Codes Can Realize Access Structures Impossible by Classical Secret Sharing	We show a simple example of a secret sharing scheme encoding classical secret to quantum shares that can realize an access structure impossible by classical information processing with limitation on the size of each share. The example is based on quantum stabilizer codes.	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	66,617
2405.16739	Oracle-Efficient Reinforcement Learning for Max Value Ensembles	Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	457,576
2207.03386	Egocentric Visual Self-Modeling for Autonomous Robot Dynamics Prediction and Adaptation	The ability of robots to model their own dynamics is key to autonomous planning and learning, as well as for autonomous damage detection and recovery. Traditionally, dynamic models are pre-programmed or learned from external observations. Here, we demonstrate for the first time how a task-agnostic dynamic self-model ca...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	306,822
2010.13983	A Neuro-Symbolic Humanlike Arm Controller for Sophia the Robot	We outline the design and construction of novel robotic arms using machine perception, convolutional neural networks, and symbolic AI for logical control and affordance indexing. We describe our robotic arms built with a humanlike mechanical configuration and aesthetic, with 28 degrees of freedom, touch sensors, and se...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	203,305
2008.04711	Towards a more realistic citation model: The key role of research team sizes	We propose a new citation model which builds on the existing models that explicitly or implicitly include "direct" and "indirect" (learning about a cited paper's existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct cit...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	191,303
1801.09108	Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata	We propose a novel method for predicting image labels by fusing image content descriptors with the social media context of each image. An image uploaded to a social media site such as Flickr often has meaningful, associated information, such as comments and other images the user has uploaded, that is complementary to p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,051
2412.17797	Observation Interference in Partially Observable Assistance Games	We study partially observable assistance games (POAGs), a model of the human-AI value alignment problem which allows the human and the AI assistant to have partial observations. Motivated by concerns of AI deception, we study a qualitatively new phenomenon made possible by partial observability: would an AI assistant e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	520,114
2202.06091	TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding	Watermarking of deep neural networks (DNNs) has gained significant traction in recent years, with numerous (watermarking) strategies being proposed as mechanisms that can help verify the ownership of a DNN in scenarios where these models are obtained without the permission of the owner. However, a growing body of work ...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	280,092
1308.4846	POMDPs under Probabilistic Semantics	We consider partially observable Markov decision processes (POMDPs) with limit-average payoff, where a reward value in the interval [0,1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards. We consider two types of path constraints: (i) quantitative constraint d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	26,572
2006.15517	Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis	Convolutional neural network (CNN)-based image denoising methods have been widely studied recently, because of their high-speed processing capability and good visual quality. However, most of the existing CNN-based denoisers learn the image prior from the spatial domain, and suffer from the problem of spatially variant...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,535
2501.15556	Commute Your Domains: Trajectory Optimality Criterion for Multi-Domain Learning	In multi-domain learning, a single model is trained on diverse data domains to leverage shared knowledge and improve generalization. The order in which the data from these domains is used for training can significantly affect the model's performance on each domain. However, this dependence is under-studied. In this pap...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	527,608
1301.7015	Mining Frequent Graph Patterns with Differential Privacy	Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of indi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	21,576
2403.18258	Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach	This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data. GCIL is one of the hot topics in the field of computer vision, and this is considered one of the crucial ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	441,848
2212.12808	A Comprehensive Review on Autonomous Navigation	The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the chall...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	338,144
1901.07152	Sensitivity Analysis of Deep Neural Networks	Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. We introduce a novel perturbation manifold ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	119,156
1703.06975	Learning to Generate Samples from Noise through Infusion Training	In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,309
2410.10476	Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?	The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown promising performance in temporal reasoning tasks such as temporal question answering. Nevertheless, recent studies have tested the LLMs' perf...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	498,095
2105.03236	Towards Accurate Text-based Image Captioning with Content Diversity Exploration	Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,085
2409.14692	Dynamic Realms: 4D Content Analysis, Recovery and Generation with Geometric, Topological and Physical Priors	My research focuses on the analysis, recovery, and generation of 4D content, where 4D includes three spatial dimensions (x, y, z) and a temporal dimension t, such as shape and motion. This focus goes beyond static objects to include dynamic changes over time, providing a comprehensive understanding of both spatial and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	490,588
1703.01383	Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction	Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection. Inspired by this success of deep learning in computer vision applications, we recently proposed a deep convolutional neural network (CNN) for low-d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,351
2405.13843	Hyperspectral Image Reconstruction for Predicting Chick Embryo Mortality Towards Advancing Egg and Hatchery Industry	As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the context of the broader agricultural landscape outlined, the application of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,104
2006.05440	On Coresets For Regularized Regression	We study the effect of norm based regularization on the size of coresets for regression problems. Specifically, given a matrix $ \mathbf{A} \in {\mathbb{R}}^{n \times d}$ with $n\gg d$ and a vector $\mathbf{b} \in \mathbb{R} ^ n $ and $\lambda > 0$, we analyze the size of coresets for regularized versions of regression...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	181,062
2411.01663	Unlocking the Theory Behind Scaling 1-Bit Neural Networks	Recently, 1-bit Large Language Models (LLMs) have emerged, showcasing an impressive combination of efficiency and performance that rivals traditional LLMs. Research by Wang et al. (2023); Ma et al. (2024) indicates that the performance of these 1-bit LLMs progressively improves as the number of parameters increases, hi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	505,173
1809.02397	Detecting Potential Local Adversarial Examples for Human-Interpretable Defense	Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval. Some people may be tempted to manipulate specific variables, such as the age or the salary, in order to get better chances of approval. In this ongoing work, we propose to discuss, with a first proposition...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	107,044
2203.09777	Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images	GAN-generated deepfakes as a genre of digital images are gaining ground as both catalysts of artistic expression and malicious forms of deception, therefore demanding systems to enforce and accredit their ethical use. Existing techniques for the source attribution of synthetic images identify subtle intrinsic fingerpri...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	286,287
2105.06567	Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric	In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goal of the robot is to safely collect observations of the disturbance and construct an accurate estimate of the underlying disturbance functio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	235,161
2008.06274	Graph-based Modeling of Online Communities for Fake News Detection	Over the past few years, there has been a substantial effort towards automated detection of fake news on social media platforms. Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them. However, no atte...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	191,758
1609.05772	Stochastic Matrix Factorization	This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This restriction includes topic models, a popular method for analyzing unstructured data....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,192
2308.06037	Deep Context Interest Network for Click-Through Rate Prediction	Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from u...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	385,007
2407.08800	Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique	In this work, we propose to use a local clustering approach based on the sparse solution technique to study the medical image, especially the lung cancer image classification task. We view images as the vertices in a weighted graph and the similarity between a pair of images as the edges in the graph. The vertices with...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	472,312
2008.00192	PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding	We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation. Our method, called PanoNet, incorporates a clean and natural structure design that tackles the problem purely as a segmentation task without the time-consuming detection process. We also i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,937
2106.04228	Decentralized Learning in Online Queuing Systems	Motivated by packet routing in computer networks, online queuing systems are composed of queues receiving packets at different rates. Repeatedly, they send packets to servers, each of them treating only at most one packet at a time. In the centralized case, the number of accumulated packets remains bounded (i.e., the s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	239,646
2005.00850	ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation	We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressive translation system as an inference network (Tu and Gimpel, 2018) trained to minimize the autoregressive teacher energy. This contrasts wit...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,405
2406.03046	When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron	Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. Moreover, they often overlook the role of adaptive threshold in spiking neurons, which can enhance their...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	461,063
1804.02668	Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks	Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,455
1809.06131	Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition	We study in this paper how to initialize the parameters of multinomial logistic regression (a fully connected layer followed with softmax and cross entropy loss), which is widely used in deep neural network (DNN) models for classification problems. As logistic regression is widely known not having a closed-form solutio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	107,967
2303.06942	Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images	Interactive segmentation reduces the annotation time of medical images and allows annotators to iteratively refine labels with corrective interactions, such as clicks. While existing interactive models transform clicks into user guidance signals, which are combined with images to form (image, guidance) pairs, the quest...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,060
2406.16477	DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution	Image super-resolution pursuits reconstructing high-fidelity high-resolution counterpart for low-resolution image. In recent years, diffusion-based models have garnered significant attention due to their capabilities with rich prior knowledge. The success of diffusion models based on general text prompts has validated ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	467,143
2207.11776	Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis	Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	309,780
2407.12417	Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions	An ordinal classification problem is one in which the target variable takes values on an ordinal scale. Nowadays, there are many of these problems associated with real-world tasks where it is crucial to accurately classify the extreme classes of the ordinal structure. In this work, we propose a unimodal regularisation ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	473,919
1904.01484	Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?	Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	126,150
1909.01754	An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector	This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The sys...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,988
2404.12948	Next Generation Loss Function for Image Classification	Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been propos...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	448,096
1702.05153	Convolutional encoding of 60,64,68,72-bit self-dual codes	In this paper we obtain the [60,30,12], [64,32,12], [68,34,12], [72,36,12] self-dual codes as tailbitting convolutional codes with the smallest constraint length K=9. In this construction one information bit is modulo two added to the one of the encoder outputs and the first row in the quasi-cyclic generator matrix is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,359
2501.18753	INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation	Task-generic promptable image segmentation aims to achieve segmentation of diverse samples under a single task description by utilizing only one task-generic prompt. Current methods leverage the generalization capabilities of Vision-Language Models (VLMs) to infer instance-specific prompts from these task-generic promp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	528,839
1712.02956	Compact Hash Code Learning with Binary Deep Neural Network	Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning bin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,373
1311.4211	Network communities within and across borders	We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technologic...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	28,477
1811.01254	A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots	Legged robots are becoming popular not only in research, but also in industry, where they can demonstrate their superiority over wheeled machines in a variety of applications. Either when acting as mobile manipulators or just as all-terrain ground vehicles, these machines need to precisely track the desired base and en...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	112,313
2112.14337	Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently	Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions. However, the transferability has not been understood in terms of to which class target model's predictions were misled (i.e., cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	273,497
1505.04211	Discontinuous Piecewise Polynomial Neural Networks	An artificial neural network is presented based on the idea of connections between units that are only active for a specific range of input values and zero outside that range (and so are not evaluated outside the active range). The connection function is represented by a polynomial with compact support. The finite rang...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	43,162
2407.16361	Virtue Ethics For Ethically Tunable Robotic Assistants	The common consensus is that robots designed to work alongside or serve humans must adhere to the ethical standards of their operational environment. To achieve this, several methods based on established ethical theories have been suggested. Nonetheless, numerous empirical studies show that the ethical requirements of ...	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	475,573
2202.02006	5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul	Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	278,676
2303.16949	Concise QBF Encodings for Games on a Grid (extended version)	Encoding 2-player games in QBF correctly and efficiently is challenging and error-prone. To enable concise specifications and uniform encodings of games played on grid boards, like Tic-Tac-Toe, Connect-4, Domineering, Pursuer-Evader and Breakthrough, we introduce Board-game Domain Definition Language (BDDL), inspired b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	355,050
2210.05480	T5 for Hate Speech, Augmented Data and Ensemble	We conduct relatively extensive investigations of automatic hate speech (HS) detection using different state-of-the-art (SoTA) baselines over 11 subtasks of 6 different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods like da...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	322,870
2112.06560	HiClass: a Python library for local hierarchical classification compatible with scikit-learn	HiClass is an open-source Python library for local hierarchical classification entirely compatible with scikit-learn. It contains implementations of the most common design patterns for hierarchical machine learning models found in the literature, that is, the local classifiers per node, per parent node and per level. A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,223
2203.13532	Frequency response of diffusion-based molecular communication channels in bounded environment	Recently, molecular communication (MC) has been studied as a micro-scale communication between cells or molecular robots. In previous works, the MC channels in unbounded environment was analyzed. However, many of the experimentally implemented MC channels are surrounded by walls, thus the boundary condition should be e...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	287,667

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