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
2009.11963	Toward a Thermodynamics of Meaning	As language models such as GPT-3 become increasingly successful at generating realistic text, questions about what purely text-based modeling can learn about the world have become more urgent. Is text purely syntactic, as skeptics argue? Or does it in fact contain some semantic information that a sufficiently sophistic...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	197,292
2407.08683	SEED-Story: Multimodal Long Story Generation with Large Language Model	With the remarkable advancements in image generation and open-form text generation, the creation of interleaved image-text content has become an increasingly intriguing field. Multimodal story generation, characterized by producing narrative texts and vivid images in an interleaved manner, has emerged as a valuable and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,264
2303.01695	Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits	Evolutionary multi-objective algorithms have been widely shown to be successful when utilized for a variety of stochastic combinatorial optimization problems. Chance constrained optimization plays an important role in complex real-world scenarios, as it allows decision makers to take into account the uncertainty of the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	349,068
1411.7895	Influence of sociodemographic characteristics on human mobility	Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geol...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	37,972
2501.14285	Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search	The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component call...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	527,058
2207.03901	Reproducing sensory induced hallucinations via neural fields	Understanding sensory-induced cortical patterns in the primary visual cortex V1 is an important challenge both for physiological motivations and for improving our understanding of human perception and visual organisation. In this work, we focus on pattern formation in the visual cortex when the cortical activity is dri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,011
2005.00343	The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines	Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, their attention, and even intention. In this paper, we detail how this large-scale dataset was captured by 32 participants in their native ki...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,205
2404.04824	Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions	Remaining Useful Life (RUL) predictions play vital role for asset planning and maintenance leading to many benefits to industries such as reduced downtime, low maintenance costs, etc. Although various efforts have been devoted to study this topic, most existing works are restricted for i.i.d conditions assuming the sam...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	444,807
2407.13210	Improved Esophageal Varices Assessment from Non-Contrast CT Scans	Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures. Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-invasive imaging modality, it has yet to gain full acceptance as a primary cli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,285
2412.12324	F-RBA: A Federated Learning-based Framework for Risk-based Authentication	The proliferation of Internet services has led to an increasing need to protect private data. User authentication serves as a crucial mechanism to ensure data security. Although robust authentication forms the cornerstone of remote service security, it can still leave users vulnerable to credential disclosure, device-t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	517,825
1312.5276	Integration by parts and representation of information functionals	We introduce a new formalism for computing expectations of functionals of arbitrary random vectors, by using generalised integration by parts formulae. In doing so we extend recent representation formulae for the score function introduced in Nourdin, Peccati and Swan (JFA, to appear) and also provide a new proof of a c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,218
1910.11560	Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization	Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,821
2004.09821	Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints	Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,469
2001.08785	Semi-Autoregressive Training Improves Mask-Predict Decoding	The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mas...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	161,383
2410.20911	Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks	Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs' susceptibility to...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	503,015
2208.06340	Real numbers equally compressible in every base	This work solves an open question in finite-state compressibility posed by Lutz and Mayordomo about compressibility of real numbers in different bases. Finite-state compressibility, or equivalently, finite-state dimension, quantifies the asymptotic lower density of information in an infinite sequence. Absolutely norm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	312,683
2302.09606	LapGym -- An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery	Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However, progress in algorithms and methods depends on the availability of standardized learning environments that represent skills relevant to RALS...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	346,495
1210.4184	The Kernel Pitman-Yor Process	In this work, we propose the kernel Pitman-Yor process (KPYP) for nonparametric clustering of data with general spatial or temporal interdependencies. The KPYP is constructed by first introducing an infinite sequence of random locations. Then, based on the stick-breaking construction of the Pitman-Yor process, we defin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	19,121
2202.12932	Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations	End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data without prescribing a mathematical model for the dynamics. Unfortunately, this flexibility comes at the cost of understanding the dynamical...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,408
2410.11579	Machine Learning via rough mereology	Rough sets (RS)proved a thriving realm with successes inn many fields of ML and AI. In this note, we expand RS to RM - rough mereology which provides a measurable degree of uncertainty to those areas.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	498,630
2203.17272	MyStyle: A Personalized Generative Prior	We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	289,095
2209.12172	Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition	Identity-invariant facial expression recognition (FER) has been one of the challenging computer vision tasks. Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity. This paper proposes to quanti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,444
1803.10769	Network Traffic Anomaly Detection Using Recurrent Neural Networks	We show that a recurrent neural network is able to learn a model to represent sequences of communications between computers on a network and can be used to identify outlier network traffic. Defending computer networks is a challenging problem and is typically addressed by manually identifying known malicious actor beha...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	93,758
2406.10017	Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration	After the revelation that neural networks tend to produce overconfident predictions, the problem of calibration, which aims to align confidence with accuracy to enhance the reliability of predictions, has gained significant importance. Several solutions based on calibration maps have been proposed to address the proble...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	464,192
2406.18568	A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods	Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow and peripheral blood. Manual diagnosis of this disease is a tedious and time-consuming operation, making it difficult for professionals to accurately examine blast cell ch...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	468,077
1812.09793	Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery	We present a novel approach to perform ground-based estimation and prediction of the surface solar irradiance with the view to predicting photovoltaic energy production. We propose the use of mini-batch k-means clustering to extract features, referred to as per cluster number of pixels (PCNP), from sky images taken by ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	117,230
1903.04253	A Unified Formulation for Visual Odometry	Monocular Odometry systems can be broadly categorized as being either Direct, Indirect, or a hybrid of both. While Indirect systems process an alternative image representation to compute geometric residuals, Direct methods process the image pixels directly to generate photometric residuals. Both paradigms have distinct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,938
2011.15079	Forecasting Characteristic 3D Poses of Human Actions	We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, characteristic pose -- for instance, from observing a person picking up an apple, predict the pose of the person eating the apple. Prior work on...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	208,974
2207.00253	Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations	Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algor...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	305,696
2312.15478	A Group Fairness Lens for Large Language Models	The rapid advancement of large language models has revolutionized various applications but also raised crucial concerns about their potential to perpetuate biases and unfairness when deployed in social media contexts. Evaluating LLMs' potential biases and fairness has become crucial, as existing methods rely on limited...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	418,023
2211.07719	(When) Are Contrastive Explanations of Reinforcement Learning Helpful?	Global explanations of a reinforcement learning (RL) agent's expected behavior can make it safer to deploy. However, such explanations are often difficult to understand because of the complicated nature of many RL policies. Effective human explanations are often contrastive, referencing a known contrast (policy) to red...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,345
2010.04992	A Recursive Markov Boundary-Based Approach to Causal Structure Learning	Constraint-based methods are one of the main approaches for causal structure learning that are particularly valued as they are asymptotically guaranteed to find a structure that is Markov equivalent to the causal graph of the system. On the other hand, they may require an exponentially large number of conditional indep...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	199,953
2208.08781	Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions	The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	313,476
2209.01173	Optimal bump functions for shallow ReLU networks: Weight decay, depth separation and the curse of dimensionality	In this note, we study how neural networks with a single hidden layer and ReLU activation interpolate data drawn from a radially symmetric distribution with target labels 1 at the origin and 0 outside the unit ball, if no labels are known inside the unit ball. With weight decay regularization and in the infinite neuron...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,795
2212.12921	Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization	The extensive need for computational resources poses a significant obstacle to deploying large-scale Deep Neural Networks (DNN) on devices with constrained resources. At the same time, studies have demonstrated that a significant number of these DNN parameters are redundant and extraneous. In this paper, we introduce a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,172
2211.08013	Drone-based Volume Estimation in Indoor Environments	Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incor...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	330,449
2403.09437	Improving Real-Time Omnidirectional 3D Multi-Person Human Pose Estimation with People Matching and Unsupervised 2D-3D Lifting	Current human pose estimation systems focus on retrieving an accurate 3D global estimate of a single person. Therefore, this paper presents one of the first 3D multi-person human pose estimation systems that is able to work in real-time and is also able to handle basic forms of occlusion. First, we adjust an off-the-sh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	437,766
1609.09449	A Cross Entropy based Stochastic Approximation Algorithm for Reinforcement Learning with Linear Function Approximation	In this paper, we provide a new algorithm for the problem of prediction in Reinforcement Learning, \emph{i.e.}, estimating the Value Function of a Markov Reward Process (MRP) using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	61,720
1501.06216	S-AMP for Non-linear Observation Models	Recently we extended Approximate message passing (AMP) algorithm to be able to handle general invariant matrix ensembles. In this contribution we extend our S-AMP approach to non-linear observation models. We obtain generalized AMP (GAMP) algorithm as the special case when the measurement matrix has zero-mean iid Gauss...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,587
1107.1851	Task swapping networks in distributed systems	In this paper we propose task swapping networks for task reassignments by using task swappings in distributed systems. Some classes of task reassignments are achieved by using iterative local task swappings between software agents in distributed systems. We use group-theoretic methods to find a minimum-length sequence ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	11,227
2502.02046	Contextual Memory Reweaving in Large Language Models Using Layered Latent State Reconstruction	Memory retention challenges in deep neural architectures have ongoing limitations in the ability to process and recall extended contextual information. Token dependencies degrade as sequence length increases, leading to a decline in coherence and factual consistency across longer outputs. A structured approach is intro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	530,156
2205.11634	TransforMatcher: Match-to-Match Attention for Semantic Correspondence	Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic image matching learner, dubbed TransforMatcher, which builds on the success of transformer networks in vis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	298,213
2109.12640	An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces	Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,366
1503.00591	Deep Transfer Network: Unsupervised Domain Adaptation	Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given featur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	40,721
1109.1276	Application of the Modified 2-opt and Jumping Gene Operators in Multi-Objective Genetic Algorithm to solve MOTSP	Evolutionary Multi-Objective Optimization is becoming a hot research area and quite a few papers regarding these algorithms have been published. However the role of local search techniques has not been expanded adequately. This paper studies the role of a local search technique called 2-opt for the Multi-Objective Trav...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	12,011
2112.04368	Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems	In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent inf...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	270,504
2405.14903	NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation	We present a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries. Our system features a fast differentiable Navier-Stokes solver with solid-fluid interface handling, a low-dimensional differentiable parametric geometry representation, a control-shape co-design a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	456,660
2310.14423	A Quadratic Synchronization Rule for Distributed Deep Learning	In distributed deep learning with data parallelism, synchronizing gradients at each training step can cause a huge communication overhead, especially when many nodes work together to train large models. Local gradient methods, such as Local SGD, address this issue by allowing workers to compute locally for $H$ steps wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,845
2303.11950	Learning A Sparse Transformer Network for Effective Image Deraining	Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,079
2307.00926	Reduced-Complexity Cross-Domain Iterative Detection for OTFS Modulation via Delay-Doppler Decoupling	In this paper, a reduced-complexity cross-domain iterative detection for orthogonal time frequency space (OTFS) modulation is proposed, which exploits channel properties in both time and delay-Doppler domains. Specifically, we first show that in the time domain effective channel, the path delay only introduces interfer...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	377,181
1812.06934	Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations	The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a mor...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	116,711
2201.05946	Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs	Understanding political polarization on social platforms is important as public opinions may become increasingly extreme when they are circulated in homogeneous communities, thus potentially causing damage in the real world. Automatically detecting the political ideology of social media users can help better understand...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	275,560
2312.10701	Bengali License Plate Recognition: Unveiling Clarity with CNN and GFP-GAN	Automated License Plate Recognition(ALPR) is a system that automatically reads and extracts data from vehicle license plates using image processing and computer vision techniques. The Goal of LPR is to identify and read the license plate number accurately and quickly, even under challenging, conditions such as poor lig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,286
2207.02200	Offline RL Policies Should be Trained to be Adaptive	Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge is to employ pessimistic or conservative methods, which avoid behaviors that are too dissimilar from those in the training dataset. However,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	306,437
2407.11590	Rethinking Learned Image Compression: Context is All You Need	Since LIC has made rapid progress recently compared to traditional methods, this paper attempts to discuss the question about 'Where is the boundary of Learned Image Compression(LIC)?'. Thus this paper splits the above problem into two sub-problems:1)Where is the boundary of rate-distortion performance of PSNR? 2)How t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	473,530
2403.06952	SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data	Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fall short of generating images that precisely match the details of the text inputs, such as incorrect spatial relationship or missing objects. In ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	436,677
1907.02862	Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox -- user guide version 2.0	The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectiv...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	137,702
2305.04417	Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts	We evaluated the capability of a state-of-the-art generative pre-trained transformer (GPT) model to perform semantic annotation of short text snippets (one to few sentences) coming from legal documents of various types. Discussions of potential uses (e.g., document drafting, summarization) of this emerging technology i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	362,757
1709.09840	PSA: A novel optimization algorithm based on survival rules of porcellio scaber	Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we present an algorithm calle...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	81,690
1912.11000	Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks	Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) framework based on 2D U-net and a densely connected network structure with tailored design in dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,459
1401.6787	On the capacity of the dither-quantized Gaussian channel	This paper studies the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $\Delta$. It is shown that the capacity of this channel tends to that of the unquantized Gaussian channel when $\Delta$ tends to zero, and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,401
2311.00729	ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection	Temporal action detection (TAD) involves the localization and classification of action instances within untrimmed videos. While standard TAD follows fully supervised learning with closed-set setting on large training data, recent zero-shot TAD methods showcase the promising open-set setting by leveraging large-scale co...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	404,753
2502.14486	How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation	Jailbreak attacks, where harmful prompts bypass generative models' built-in safety, raise serious concerns about model vulnerability. While many defense methods have been proposed, the trade-offs between safety and helpfulness, and their application to Large Vision-Language Models (LVLMs), are not well understood. This...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	535,850
2109.13291	Nonlinear modeling and feedback control of boom barrier automation	We address modeling and control of a gate access automation system. A model of the mechatronic system is derived and identified. Then an approximate explicit feedback linearization scheme is proposed, which ensures almost linear response between the external input and the delivered torque. A nonlinear optimization prob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	257,583
2502.07500	Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems	Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. However, such generalized frameworks are not available for solving graph problems. Graph structures are u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,635
1504.00191	Automated Document Indexing via Intelligent Hierarchical Clustering: A Novel Approach	With the rising quantity of textual data available in electronic format, the need to organize it become a highly challenging task. In the present paper, we explore a document organization framework that exploits an intelligent hierarchical clustering algorithm to generate an index over a set of documents. The framework...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	41,682
2402.00341	Recasting Regional Lighting for Shadow Removal	Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow reg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,569
1412.6806	Striving for Simplicity: The All Convolutional Net	Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	38,718
1503.05702	The Open Access Advantage Considering Citation, Article Usage and Social Media Attention	In this study, we compare the difference in the impact between open access (OA) and non-open access (non-OA) articles. 1761 Nature Communications articles published from 1 Jan. 2012 to 31 Aug. 2013 are selected as our research objects, including 587 OA articles and 1174 non-OA articles. Citation data and daily updated ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	41,274
2310.02997	Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing	Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,084
2404.05043	Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization	We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	444,912
1912.00528	The intriguing role of module criticality in the generalization of deep networks	We study the phenomenon that some modules of deep neural networks (DNNs) are more critical than others. Meaning that rewinding their parameter values back to initialization, while keeping other modules fixed at the trained parameters, results in a large drop in the network's performance. Our analysis reveals interestin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,801
2305.09425	When is an SHM problem a Multi-Task-Learning problem?	Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for the context of structural health monitoring (SHM): (i) the natural occurrence of multiple tasks; (ii) using outputs as inputs (both linked to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	364,630
1911.10657	Reducing the Human Effort in Developing PET-CT Registration	We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspondences that are created manually by medical experts with 3D manipulation, or assisted alignments done by utilizing mutual information across ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,898
1811.10180	Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera	Event-based cameras can measure intensity changes (called `{\it events}') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relativel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,432
2311.13541	Linear Log-Normal Attention with Unbiased Concentration	Transformer models have achieved remarkable results in a wide range of applications. However, their scalability is hampered by the quadratic time and memory complexity of the self-attention mechanism concerning the sequence length. This limitation poses a substantial obstacle when dealing with long documents or high-re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	409,767
2204.00138	Distributionally Robust Decision Making Leveraging Conditional Distributions	Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,147
1806.05521	SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment	Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SEMAXIS, a simple yet powerful framework to characterize word semantics using many semantic axes in word- vector spaces beyond sentiment. We demonstrate t...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	100,497
2304.07238	Robustness of community structure under edge addition	Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiri...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	358,276
2302.07944	Effective Data Augmentation With Diffusion Models	Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from e...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	345,875
1911.03678	Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning	Recent work has highlighted the advantage of jointly learning grounded sentence representations from multiple languages. However, the data used in these studies has been limited to an aligned scenario: the same images annotated with sentences in multiple languages. We focus on the more realistic disjoint scenario in wh...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	152,721
2407.07710	An in-depth study of the power function $x^{q+2}$ over the finite field $\mathbb{F}_{q^2}$: the differential, boomerang, and Walsh spectra, with an application to coding theory	Let $q = p^m$, where $p$ is an odd prime number and $m$ is a positive integer. In this paper, we examine the finite field $\mathbb{F}_{q^2}$, which consists of $q^2$ elements. We first present an alternative method to determine the differential spectrum of the power function $f(x) = x^{q+2}$ on $\mathbb{F}_{q^2}$, inco...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	471,867
2411.12789	Automated 3D Physical Simulation of Open-world Scene with Gaussian Splatting	Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,548
2401.15305	A Practical Probabilistic Benchmark for AI Weather Models	Since the weather is chaotic, forecasts aim to predict the distribution of future states rather than make a single prediction. Recently, multiple data driven weather models have emerged claiming breakthroughs in skill. However, these have mostly been benchmarked using deterministic skill scores, and little is known abo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,398
2104.07481	Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions	Virtual development and prototyping has already become an integral part in the field of automated driving systems (ADS). There are plenty of software tools that are used for the virtual development of ADS. One such tool is CarMaker from IPG Automotive, which is widely used in the scientific community and in the automot...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	230,435
2308.12156	Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals	This paper discusses the benefits of incorporating multimodal data for improving latent emotion recognition accuracy, focusing on micro-expression (ME) and physiological signals (PS). The proposed approach presents a novel multimodal learning framework that combines ME and PS, including a 1D separable and mixable depth...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	387,438
2112.15199	Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling	In this paper we study the convex-concave saddle-point problem $\min_x \max_y f(x) + y^T \mathbf{A} x - g(y)$, where $f(x)$ and $g(y)$ are smooth and convex functions. We propose an Accelerated Primal-Dual Gradient Method (APDG) for solving this problem, achieving (i) an optimal linear convergence rate in the strongly-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,713
2501.06942	Comparison of Autoencoders for tokenization of ASL datasets	Generative AI, powered by large language models (LLMs), has revolutionized applications across text, audio, images, and video. This study focuses on developing and evaluating encoder-decoder architectures for the American Sign Language (ASL) image dataset, consisting of 87,000 images across 29 hand sign classes. Three ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	524,198
2301.03767	Metric Compatible Training for Online Backfilling in Large-Scale Retrieval	Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computational cost and even entails the downtime of the service. Although backward-compatible learning sidesteps this challenge by tackling query-sid...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	339,879
1901.11420	Is Image Memorability Prediction Solved?	This paper deals with the prediction of the memorability of a given image. We start by proposing an algorithm that reaches human-level performance on the LaMem dataset - the only large scale benchmark for memorability prediction. The suggested algorithm is based on three observations we make regarding convolutional neu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,250
2003.04092	Searching Central Difference Convolutional Networks for Face Anti-Spoofing	Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak in describing detailed fine-grained information and easily being ineffective when the environment varies (e.g., different illumination), a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,452
2207.02726	Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users	When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low quality, the medical exam that produced the image often needs to be redone. In telemedicine, a common problem is that the quality issue is o...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	306,600
2104.10496	Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model	There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error metrics, for example average distance between model-generated and observed trajectories. Such metrics permit relative comparison of models, bu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,602
2406.01006	SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning	Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data and the need for semantic understanding for complex tasks like debugging and ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	460,120
2107.00324	Robotic Template Library	Robotic Template Library (RTL) is a set of tools for dealing with geometry and point cloud processing, especially in robotic applications. The software package covers basic objects such as vectors, line segments, quaternions, rigid transformations, etc., however, its main contribution lies in the more advanced modules:...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	244,119
1911.00764	Single-Shot Panoptic Segmentation	We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate. Current state-of-the-art methods are far from reaching video frame rate and mostly rely on merging instance se...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	151,908
2007.03797	Personalized Cross-Silo Federated Learning on Non-IID Data	Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	186,164
2403.19270	sDPO: Don't Use Your Data All at Once	As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important. We propose stepwise DPO (sDPO), an extension of the recently popularized direct preference optimization (DPO) for alignment tuning. This approach involves dividing the available preference d...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	442,276
2105.13580	MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors	We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has success...	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	237,344
2012.00893	Evaluating Explanations: How much do explanations from the teacher aid students?	While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	209,263

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.