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
2106.04914	Exploiting Learned Symmetries in Group Equivariant Convolutions	Group Equivariant Convolutions (GConvs) enable convolutional neural networks to be equivariant to various transformation groups, but at an additional parameter and compute cost. We investigate the filter parameters learned by GConvs and find certain conditions under which they become highly redundant. We show that GCon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,901
2009.13055	Rotated Binary Neural Network	Binary Neural Network (BNN) shows its predominance in reducing the complexity of deep neural networks. However, it suffers severe performance degradation. One of the major impediments is the large quantization error between the full-precision weight vector and its binary vector. Previous works focus on compensating for...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	197,623
2402.02313	CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization	This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space. This work has three innovations. First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing. This representation includes a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	426,484
2110.03427	Is Attention always needed? A Case Study on Language Identification from Speech	Language Identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	259,495
2410.09247	Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts	The training data for many Large Language Models (LLMs) is contaminated with test data. This means that public benchmarks used to assess LLMs are compromised, suggesting a performance gap between benchmark scores and actual capabilities. Ideally, a private holdout set could be used to accurately verify scores. Unfortun...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	497,502
2303.16894	ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance	Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view cues embedded in the text modality and fail to weigh the relative importance of different views. In this paper, we propose ViewRefer, a multi...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	355,031
cmp-lg/9806019	An Empirical Investigation of Proposals in Collaborative Dialogues	We describe a corpus-based investigation of proposals in dialogue. First, we describe our DRI compliant coding scheme and report our inter-coder reliability results. Next, we test several hypotheses about what constitutes a well-formed proposal.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,893
2405.14591	Base of RoPE Bounds Context Length	Position embedding is a core component of current Large Language Models (LLMs). Rotary position embedding (RoPE), a technique that encodes the position information with a rotation matrix, has been the de facto choice for position embedding in many LLMs, such as the Llama series. RoPE has been further utilized to extend...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	456,504
1212.1603	Model Reduction using a Frequency-Limited H2-Cost	We propose a method for model reduction on a given frequency range, without the use of input and output filter weights. The method uses a nonlinear optimization approach to minimize a frequency limited H2 like cost function. An important contribution in the paper is the derivation of the gradient of the proposed cost...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	20,180
2104.05207	Online Machine Learning Techniques for Coq: A Comparison	We present a comparison of several online machine learning techniques for tactical learning and proving in the Coq proof assistant. This work builds on top of Tactician, a plugin for Coq that learns from proofs written by the user to synthesize new proofs. Learning happens in an online manner, meaning that Tactician's ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	229,636
2008.08879	A comparative study of similarity-based and GNN-based link prediction approaches	The task of inferring the missing links in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature. They show good prediction performance in many real-world graphs though they are heuri...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,528
2207.05224	Cluster-Based Control of Transition-Independent MDPs	This work studies efficient solution methods for cluster-based control policies of transition-independent Markov decision processes (TI-MDPs). We focus on control of multi-agent systems, whereby a central planner (CP) influences agents to select desirable group behavior. The agents are partitioned into disjoint cluster...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	307,453
2502.09846	Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization	Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consensus through well-calibrated latent variables obtained from neighboring agents. Ne...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	533,627
2101.12490	Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems	In this paper, we address the problem of uncertainty propagation through nonlinear stochastic dynamical systems. More precisely, given a discrete-time continuous-state probabilistic nonlinear dynamical system, we aim at finding the sequence of the moments of the probability distributions of the system states up to any ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	217,578
1912.06354	Bonn Activity Maps: Dataset Description	The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest. Previous datasets are focused on either 3D scene representations with semantic information, tracking of multiple persons and recognition of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,333
2404.10267	OneActor: Consistent Character Generation via Cluster-Conditioned Guidance	Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	447,020
1903.08671	Gradient based sample selection for online continual learning	A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the p...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	124,878
1905.13530	Taming Combinatorial Challenges in Optimal Clutter Removal Tasks	We examine an important combinatorial challenge in clearing clutter using a mobile robot equipped with a manipulator, seeking to compute an optimal object removal sequence for minimizing the task completion time, assuming that each object is grasped once and then subsequently removed. On the structural side, we establi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	133,154
1807.10695	FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software	A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into parallel FPG...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	104,012
2210.13647	Temporally Disentangled Representation Learning	Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by exploiting certain side information, such as class labels, in addition to independence. However, most existing work is constrained by functional...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	326,243
2311.01469	Leveraging Language Models to Detect Greenwashing	In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of stringent regulations in review of such reports allows potential greenwashing. In ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	405,058
2401.01756	Fuzzy Logic Controller Design for Mobile Robot Outdoor Navigation	Many researchers around the world are researching to get control solutions that enhance robots' ability to navigate in dynamic environments autonomously. However, until these days robots have limited capability and many navigation tasks on Earth and other planets have been difficult so far. This paperwork presents the ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	419,488
1411.4738	Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation	The cross-media retrieval problem has received much attention in recent years due to the rapid increasing of multimedia data on the Internet. A new approach to the problem has been raised which intends to match features of different modalities directly. In this research, there are two critical issues: how to get rid of...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	37,669
2411.06055	Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data	Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods red...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,945
2405.13994	Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint	Non-monotone constrained submodular maximization plays a crucial role in various machine learning applications. However, existing algorithms often struggle with a trade-off between approximation guarantees and practical efficiency. The current state-of-the-art is a recent $0.401$-approximation algorithm, but its comput...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	456,186
2301.05604	A LiDAR-Inertial-Visual SLAM System with Loop Detection	We have proposed, to the best of our knowledge, the first-of-its-kind LiDAR-Inertial-Visual-Fused simultaneous localization and mapping (SLAM) system with a strong place recognition capacity. Our proposed SLAM system is consist of visual-inertial odometry (VIO) and LiDAR inertial odometry (LIO) subsystems. We propose t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	340,397
2101.06479	Wearable Sensors for Spatio-Temporal Grip Force Profiling	Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature, biological or biochemical markers, and individual grip forces, which are studied in this paper. A four step pick and drop image guided and r...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	215,732
2006.12363	Greedy Adversarial Equilibrium: An Efficient Alternative to Nonconvex-Nonconcave Min-Max Optimization	Min-max optimization of an objective function $f: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}$ is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics, and deep learning. In many applications $f$ may be nonconvex-nonconcave, and findi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	183,557
2207.14268	MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud	We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene. Our primary contribution is a discrete optimization algorithm that, from a dense set of initially detected cuboids, is able to efficiently filter good boxes from the noisy ones. Inspired by recent applications of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,528
2408.03696	Bridging the Gap between ROS~2 and Classical Real-Time Scheduling for Periodic Tasks	The Robot Operating System 2 (ROS~2) is a widely used middleware that provides software libraries and tools for developing robotic systems. In these systems, tasks are scheduled by ROS~2 executors. Since the scheduling behavior of the default ROS~2 executor is inherently different from classical real-time scheduling th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	479,123
2207.02027	CNN-based Local Vision Transformer for COVID-19 Diagnosis	Deep learning technology can be used as an assistive technology to help doctors quickly and accurately identify COVID-19 infections. Recently, Vision Transformer (ViT) has shown great potential towards image classification due to its global receptive field. However, due to the lack of inductive biases inherent to CNNs,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,377
2303.12512	Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition	A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information to attackers. While recent research took an important step towards attacking black-box FR models through leveraging transferability, their p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,289
2410.15394	A Semi-decentralized and Variational-Equilibrium-Based Trajectory Planner for Connected and Autonomous Vehicles	This paper designs a novel trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated methods by exploiting vehicle-to-everything (V2X) technology. The trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	500,518
1803.07917	The Hbot : A Holonomic Spherical Haptic Interface Driven by Non-Holonomic Wheels	We present the Hbot, a holonomic, singularity-free spherical robot designed for haptic simulations. The Hbot is made up of a caged sphere actuated by steered and driven non-holonomic wheels to produce continuous and unlimited spherical motions. We analyse the kinematic interface between a sphere and $n$ arbitrarily pos...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	93,153
2202.10923	MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate	The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms.Using this fluctuation effect, combined with the stratified sampling strategy, this paper designs a novel \underline{M}em...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,709
2403.17546	Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining	The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job description...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	441,514
2109.04134	Tiny CNN for feature point description for document analysis: approach and dataset	In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that the specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,290
2205.03892	ConvMAE: Masked Convolution Meets Masked Autoencoders	Vision Transformers (ViT) become widely-adopted architectures for various vision tasks. Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer architectures can further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and sem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,458
2410.14479	Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models	Large Language Models (LLMs) have demonstrated remarkable capabilities in generating coherent text but remain limited by the static nature of their training data. Retrieval Augmented Generation (RAG) addresses this issue by combining LLMs with up-to-date information retrieval, but also expand the attack surface of the ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	500,040
1101.2421	Decentralized Formation Control Part II: Algebraic aspects of information flow and singularities	Given an ensemble of autonomous agents and a task to achieve cooperatively, how much do the agents need to know about the state of the ensemble and about the task in order to achieve it? We introduce new methods to understand these aspects of decentralized control. Precisely, we introduce a framework to capture what ag...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	8,802
1808.09732	Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes	In the last several years, the field of computer assisted language learning has increasingly focused on computer aided question generation. However, this approach often provides test takers with an exhaustive amount of questions that are not designed for any specific testing purpose. In this work, we present a personal...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,255
2108.10165	ODAM: Object Detection, Association, and Mapping using Posed RGB Video	Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics. We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos. The proposed system relies on a deep lea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,818
2406.15241	Retrieval Augmented Zero-Shot Text Classification	Zero-shot text learning enables text classifiers to handle unseen classes efficiently, alleviating the need for task-specific training data. A simple approach often relies on comparing embeddings of query (text) to those of potential classes. However, the embeddings of a simple query sometimes lack rich contextual info...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	466,666
math/0302154	Twisted Klein curves modulo 2	We give an explicit description of all 168 quartic curves over the field of two elements that are isomorphic to the Klein curve over an algebraic extension. Some of the curves have been known for their small class number, others for attaining the maximal number of rational points.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,648
1105.4880	Pareto Characterization of the Multicell MIMO Performance Region With Simple Receivers	We study the performance region of a general multicell downlink scenario with multiantenna transmitters, hardware impairments, and low-complexity receivers that treat interference as noise. The Pareto boundary of this region describes all efficient resource allocations, but is generally hard to compute. We propose a no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,485
2104.14795	Mitigating Political Bias in Language Models Through Reinforced Calibration	Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) fra...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	232,949
2305.08466	Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives	This paper addresses the problem of nearly optimal Vapnik--Chervonenkis dimension (VC-dimension) and pseudo-dimension estimations of the derivative functions of deep neural networks (DNNs). Two important applications of these estimations include: 1) Establishing a nearly tight approximation result of DNNs in the Sobole...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	364,292
2407.04534	Introducing 'Inside' Out of Distribution	Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies, in general, and in the context of ML, in particular, primarily focus on extrapolatory OOD (outside), neglecting potential cases of interpolatory OOD (inside). This...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,597
2211.02799	Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation	The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	328,700
1904.05419	FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning	The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demogra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,310
2312.10679	Bengali Intent Classification with Generative Adversarial BERT	Intent classification is a fundamental task in natural language understanding, aiming to categorize user queries or sentences into predefined classes to understand user intent. The most challenging aspect of this particular task lies in effectively incorporating all possible classes of intent into a dataset while ensur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,276
2011.03863	Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering	Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that models overfit to specific tasks, without learning to utilize external knowledge or perform general semantic reasoning. In contrast, zero-shot...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	205,377
1908.10907	DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides	Automatic recognition and segmentation methods now become the essential requirement in identifying co-seismic landslides, which are fundamental for disaster assessment and mitigation in large-scale earthquakes. This approach used to be carried out through pixel-based or object-oriented methods. However, due to the mass...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,239
2303.06253	Predicting risk of delirium from ambient noise and light information in the ICU	Existing Intensive Care Unit (ICU) delirium prediction models do not consider environmental factors despite strong evidence of their influence on delirium. This study reports the first deep-learning based delirium prediction model for ICU patients using only ambient noise and light information. Ambient light and noise ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,761
2212.11376	Artistic Arbitrary Style Transfer	Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,774
1809.05511	Dynamic Model of Planar Sliding	In this paper, we present a principled method to model general planar sliding motion with distributed convex contact patch. The effect of contact patch with indeterminate pressure distribution can be equivalently modeled as the contact wrench at one point contact. We call this point equivalent contact point. Our dynami...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	107,809
2310.20077	Partial Tensorized Transformers for Natural Language Processing	The transformer architecture has revolutionized Natural Language Processing (NLP) and other machine-learning tasks, due to its unprecedented accuracy. However, their extensive memory and parameter requirements often hinder their practical applications. In this work, we study the effect of tensor-train decomposition to ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	404,243
2201.11511	Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification	Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications. One of the most challenging problems for graph-based semi-supervised node classification is how to use...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	277,319
cs/0702072	Logic Programming with Satisfiability	This paper presents a Prolog interface to the MiniSat satisfiability solver. Logic program- ming with satisfiability combines the strengths of the two paradigms: logic programming for encoding search problems into satisfiability on the one hand and efficient SAT solving on the other. This synergy between these two expo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	540,156
2312.07331	Coupled Confusion Correction: Learning from Crowds with Sparse Annotations	As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sourcing has been widely adopted to alleviate the cost of collecting labels, which also inevitably introduces label noise and eventually degrad...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,879
2409.06884	Safe and Stable Connected Cruise Control for Connected Automated Vehicles with Response Lag	Controlling connected automated vehicles (CAVs) via vehicle-to-everything (V2X) connectivity holds significant promise for improving fuel economy and traffic efficiency. However, to deploy CAVs and reap their benefits, their controllers must guarantee their safety. In this paper, we apply control barrier function (CBF)...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	487,306
2401.00639	Geometry Depth Consistency in RGBD Relative Pose Estimation	Relative pose estimation for RGBD cameras is crucial in a number of applications. Previous approaches either rely on the RGB aspect of the images to estimate pose thus not fully making use of depth in the estimation process or estimate pose from the 3D cloud of points that each image produces, thus not making full use ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,052
2211.09672	Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approach	Computing tasks are ubiquitous in space missions. Conventionally, these tasks are offloaded to ground servers for computation, where the transmission of raw data on satellite-to-ground links severely constrains the performance. To overcome this limitation, recent works offload tasks to visible low-earth-orbit (LEO) sat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	331,045
1510.07380	SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version	Simultaneous localization and Planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous POMDP (partially-observable Markov decision process), which needs to be repeatedly solved online. This paper addresses this problem and proposes a d...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	48,196
2403.15409	Coupled generator decomposition for fusion of electro- and magnetoencephalography data	Data fusion modeling can identify common features across diverse data sources while accounting for source-specific variability. Here we introduce the concept of a \textit{coupled generator decomposition} and demonstrate how it generalizes sparse principal component analysis (SPCA) for data fusion. Leveraging data from ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	440,540
2004.03588	Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots	In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model aware of the speaker change information, which is an important and intrinsic property of mul...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,614
1807.08315	Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors	We investigate an energy-harvesting wireless sensor transmitting latency-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides whet...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	103,512
2104.11138	NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy	Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,831
2308.08887	Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification	This paper aims to learn a domain-generalizable (DG) person re-identification (ReID) representation from large-scale videos \textbf{without any annotation}. Prior DG ReID methods employ limited labeled data for training due to the high cost of annotation, which restricts further advances. To overcome the barriers of da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,080
1401.5197	A user-friendly nano-CT image alignment and 3D reconstruction platform based on LabVIEW	X-ray computed tomography at the nanometer scale (nano-CT) offers a wide range of applications in scientific and industrial areas. Here we describe a reliable, user-friendly and fast software package based on LabVIEW that may allow to perform all procedures after the acquisition of raw projection images in order to obt...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	30,172
2310.13849	A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains	The human visual system uses two parallel pathways for spatial processing and object recognition. In contrast, computer vision systems tend to use a single feedforward pathway, rendering them less robust, adaptive, or efficient than human vision. To bridge this gap, we developed a dual-stream vision model inspired by t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,606
2312.14423	Efficacy of Machine-Generated Instructions	Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the ge...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	417,625
2311.06543	Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study	Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to generate high-quality hu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	406,980
2007.15475	Connecting actuarial judgment to probabilistic learning techniques with graph theory	Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	189,674
2411.15702	Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing	Real-time computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented characteristics of conventional communications often do not align with the special needs of real-time CV tasks. To alleviate this issue,...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	true	510,728
2303.12865	NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions	Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors. Recently, the integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs), has transfor...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	353,439
2110.13041	Applications and Techniques for Fast Machine Learning in Science	In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast M...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	263,051
2005.09120	Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation	In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains). Our method is inspired by the fact that the spatial relationshi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,817
2301.10908	Distilling Cognitive Backdoor Patterns within an Image	This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the "minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can le...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	341,948
1511.04601	Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification	Sparse coding with dictionary learning (DL) has shown excellent classification performance. Despite the considerable number of existing works, how to obtain features on top of which dictionaries can be better learned remains an open and interesting question. Many current prevailing DL methods directly adopt well-perfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,917
1608.03047	Computational Limitations of First-Order Repressor Systems	Almost all current approaches for engineering modular logic components in synthetic biology use first-order regulators, including most CRISPR/CAS, TAL, zinc finger, and RNA interference systems. Many practitioners understand intuitively that second and higher order binding is necessary for scalability, and this is easy...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	59,633
2406.01823	Causal Discovery with Fewer Conditional Independence Tests	Many questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the well-celebrated PC algorithm, often incur an exponential number of conditional independence (CI) tests, posing limitations in various applicati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	460,478
2412.15077	Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers	Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep neural networks are often overparameterized. The usage of these large models consumes a lot of computation resources. In this paper, we introd...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	518,934
2207.12878	Safe Model Predictive Control Approach for Non-holonomic Mobile Robots	We design an model predictive control (MPC) approach for planning and control of non-holonomic mobile robots. Linearizing the system dynamics around the pre-computed reference trajectory gives a time-varying LQ MPC problem. We analytically show that by specially designing the MPC controller, the time-varying, linearize...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	310,145
1808.07993	Deep Feature Pyramid Reconfiguration for Object Detection	State-of-the-art object detectors usually learn multi-scale representations to get better results by employing feature pyramids. However, the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,850
1911.09389	Classification-driven Single Image Dehazing	Most existing dehazing algorithms often use hand-crafted features or Convolutional Neural Networks (CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,506
2202.05469	Privacy-preserving Generative Framework Against Membership Inference Attacks	Artificial intelligence and machine learning have been integrated into all aspects of our lives and the privacy of personal data has attracted more and more attention. Since the generation of the model needs to extract the effective information of the training data, the model has the risk of leaking the privacy of the ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	279,888
2210.12565	A Visual Tour Of Current Challenges In Multimodal Language Models	Transformer models trained on massive text corpora have become the de facto models for a wide range of natural language processing tasks. However, learning effective word representations for function words remains challenging. Multimodal learning, which visually grounds transformer models in imagery, can overcome the c...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	325,798
2308.09643	biquality-learn: a Python library for Biquality Learning	The democratization of Data Mining has been widely successful thanks in part to powerful and easy-to-use Machine Learning libraries. These libraries have been particularly tailored to tackle Supervised Learning. However, strong supervision signals are scarce in practice, and practitioners must resort to weak supervisio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	386,371
1806.00360	Towards a new system for drowsiness detection based on eye blinking and head posture estimation	Driver drowsiness problem is considered as one of the most important reasons that increases road accidents number. We propose in this paper a new approach for realtime driver drowsiness in order to prevent road accidents. The system uses a smart video camera that takes drivers faces images and supervises the eye blink ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,291
2205.11098	PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection	The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have an urgent requirement for not only accurate but also efficient 3D object detection. Recently, knowledge distilla...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	297,992
1908.03983	Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning	In the process of exploring the world, the curiosity constantly drives humans to cognize new things. Supposing you are a zoologist, for a presented animal image, you can recognize it immediately if you know its class. Otherwise, you would more likely attempt to cognize it by exploiting the side-information (e.g., seman...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	141,370
2204.02083	The number of extended irreducible binary Goppa codes	Goppa, in the 1970s, discovered the relation between algebraic geometry and codes, which led to the family of Goppa codes. As one of the most interesting subclasses of linear codes, the family of Goppa codes is often chosen as a key in the McEliece cryptosystem. Knowledge of the number of inequivalent binary Goppa code...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	289,821
1303.5929	DLOLIS-A: Description Logic based Text Ontology Learning	Ontology Learning has been the subject of intensive study for the past decade. Researchers in this field have been motivated by the possibility of automatically building a knowledge base on top of text documents so as to support reasoning based knowledge extraction. While most works in this field have been primarily st...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,226
1604.07363	Efficient estimation of probability of conflict between air traffic using Subset Simulation	This paper presents an efficient method for estimating the probability of conflict between air traffic within a block of airspace. Autonomous Sense-and-Avoid is an essential safety feature to enable Unmanned Air Systems to operate alongside other (manned or unmanned) air traffic. The ability to estimate probability of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	55,082
2001.03329	Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification	Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,947
2407.08948	Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis	The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreover, the limited availability of well-annotated datasets constrains the development of robust analysis models. Against this backdrop, this s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,374
2405.00199	Field Report on a Wearable and Versatile Solution for Field Acquisition and Exploration	This report presents a wearable plug-and-play platform for data acquisition in the field. The platform, extending a waterproof Pelican Case into a 20 kg backpack offers 5.5 hours of power autonomy, while recording data with two cameras, a lidar, an Inertial Measurement Unit (IMU), and a Global Navigation Satellite Syst...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	450,823
1209.3916	Qualitative Modelling via Constraint Programming: Past, Present and Future	Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications ...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	18,610
2303.10334	Extracting Class Activation Maps from Non-Discriminative Features as well	Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the "leg" of "sheep") mistakenly as background. The crux behind is that the weight of the classifi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,401

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