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
2409.15518	Eagle: Efficient Training-Free Router for Multi-LLM Inference	The proliferation of Large Language Models (LLMs) with varying capabilities and costs has created a need for efficient model selection in AI systems. LLM routers address this need by dynamically choosing the most suitable model for a given query based on task requirements and budget constraints. However, existing route...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	490,943
2012.04514	Human Motion Tracking by Registering an Articulated Surface to 3-D Points and Normals	We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of a kinematic human-body representation, as well as probabilities that the data are assigned eit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,478
1705.08982	Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks	Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time inte...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	74,121
2407.07038	Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics	This work introduces the ClimateSent-GAT Model, an innovative method that integrates Graph Attention Networks (GATs) with techniques from natural language processing to accurately identify and predict disagreements within Reddit comment-reply pairs. Our model classifies disagreements into three categories: agree, disag...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	471,618
2212.02710	Beyond Object Recognition: A New Benchmark towards Object Concept Learning	Understanding objects is a central building block of artificial intelligence, especially for embodied AI. Even though object recognition excels with deep learning, current machines still struggle to learn higher-level knowledge, e.g., what attributes an object has, and what can we do with an object. In this work, we pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	334,854
2203.06901	Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations	We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,263
2111.03237	Towards Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems	We consider an inverse problem $\mathbf{y}= f(\mathbf{Ax})$, where $\mathbf{x}\in\mathbb{R}^n$ is the signal of interest, $\mathbf{A}$ is the sensing matrix, $f$ is a nonlinear function and $\mathbf{y} \in \mathbb{R}^m$ is the measurement vector. In many applications, we have some level of freedom to design the sensing...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	265,099
2412.05533	Can large language models be privacy preserving and fair medical coders?	Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy in such settings and, in this work, we examine two key trade-offs in applying DP to the NLP task of medical coding (ICD classification). Reg...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	514,857
1804.00370	Differentially Private Hierarchical Count-of-Counts Histograms	We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input table into groups (e.g., group of people in the same household), and for every integer j report the number of groups of size j. Hierarchical c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	94,007
1511.09013	An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems	We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal tha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,611
2002.04290	Task-Based Quantization with Application to MIMO Receivers	Multiple-input multiple-output (MIMO) systems are required to communicate reliably at high spectral bands using a large number of antennas, while operating under strict power and cost constraints. In order to meet these constraints, future MIMO receivers are expected to operate with low resolution quantizers, namely, u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	163,569
2007.07320	Learning Syllogism with Euler Neural-Networks	Traditional neural networks represent everything as a vector, and are able to approximate a subset of logical reasoning to a certain degree. As basic logic relations are better represented by topological relations between regions, we propose a novel neural network that represents everything as a ball and is able to lea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	187,293
1901.04585	Agent-Based Modelling Approach for Distributed Decision Support in an IoT Network	An increasing number of emerging applications, e.g., internet of things, vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	118,619
2208.10967	The Value of Out-of-Distribution Data	We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples. As the ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	314,266
2003.06731	A model of figure ground organization incorporating local and global cues	Figure Ground Organization (FGO) -- inferring spatial depth ordering of objects in a visual scene -- involves determining which side of an occlusion boundary is figure (closer to the observer) and which is ground (further away from the observer). A combination of global cues, like convexity, and local cues, like T-junc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	168,207
2201.10355	Neural Architecture Search for Spiking Neural Networks	Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal pe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	276,965
1812.05271	TextBugger: Generating Adversarial Text Against Real-world Applications	Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increas...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	116,383
2108.07253	Who's Waldo? Linking People Across Text and Images	We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to en...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	250,868
2403.05249	On Representing Electronic Wave Functions with Sign Equivariant Neural Networks	Recent neural networks demonstrated impressively accurate approximations of electronic ground-state wave functions. Such neural networks typically consist of a permutation-equivariant neural network followed by a permutation-antisymmetric operation to enforce the electronic exchange symmetry. While accurate, such neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,934
2404.06621	What is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models	Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based Masked Language Models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is needed more than ever. While many studies have evaluated gender bias in English MLMs, on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	445,525
2110.14131	Temporal Knowledge Distillation for On-device Audio Classification	Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment. Many studies leverage knowledge distillation to boost predictive performance by transferring the knowledge from large models to on-device models. However, most lack a mechanis...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,424
1702.03767	Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?	Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	68,180
1211.2150	NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR	In this paper we propose a robust approach for text extraction and recognition from video clips which is called Neuro-Fuzzy system for Arabic Video OCR. In Arabic video text recognition, a number of noise components provide the text relatively more complicated to separate from the background. Further, the characters ca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,653
1903.09927	Using RGB Image as Visual Input for Mapless Robot Navigation	Robot navigation in mapless environment is one of the essential problems and challenges in mobile robots. Deep reinforcement learning is a promising technique to tackle the task of mapless navigation. Since reinforcement learning requires a lot of explorations, it is usually necessary to train the agent in the simulato...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	125,167
2401.15607	Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems	Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	424,514
2405.17534	SMR: State Memory Replay for Long Sequence Modeling	Despite the promising performance of state space models (SSMs) in long sequence modeling, limitations still exist. Advanced SSMs like S5 and S6 (Mamba) in addressing non-uniform sampling, their recursive structures impede efficient SSM computation via convolution. To overcome compatibility limitations in parallel convo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,994
2401.06309	Cyberattacks on Adaptive Cruise Control Vehicles: An Analytical Characterization	While automated vehicles (AVs) are expected to revolutionize future transportation systems, emerging AV technologies open a door for malicious actors to compromise intelligent vehicles. As the first generation of AVs, adaptive cruise control (ACC) vehicles are vulnerable to cyberattacks. While recent effort has been ma...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	421,109
2302.08175	A numerical approximation method for the Fisher-Rao distance between multivariate normal distributions	We present a simple method to approximate Rao's distance between multivariate normal distributions based on discretizing curves joining normal distributions and approximating Rao's distances between successive nearby normal distributions on the curves by the square root of Jeffreys divergence, the symmetrized Kullback-...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	345,969
2012.11014	KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA	One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	212,506
2109.06180	Deep Generative Models to Extend Active Directory Graphs with Honeypot Users	Active Directory (AD) is a crucial element of large organizations, given its central role in managing access to resources. Since AD is used by all users in the organization, it is hard to detect attackers. We propose to generate and place fake users (honeyusers) in AD structures to help detect attacks. However, not any...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	255,077
2404.00011	A novel interface for adversarial trivia question-writing	A critical component when developing question-answering AIs is an adversarial dataset that challenges models to adapt to the complex syntax and reasoning underlying our natural language. Present techniques for procedurally generating adversarial texts are not robust enough for training on complex tasks such as answerin...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	442,716
2103.12866	PAC-Bayesian theory for stochastic LTI systems	In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models. The motivation for deriving such error bounds is that they will allow deriving similar error bounds for more general dynamical systems, including recurrent neural networks. In turn, PACBayesian error bounds are known to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	226,303
2106.03873	Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions	In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers' uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers' upt...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,490
2207.05750	FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR	In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, whe...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	307,653
2407.20198	SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking	In this paper, we introduce SpaER, a pioneering method for fetal motion tracking that leverages equivariant filters and self-attention mechanisms to effectively learn spatio-temporal representations. Different from conventional approaches that statically estimate fetal brain motions from pairs of images, our method dyn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,082
1410.0117	Coupling Top-down and Bottom-up Methods for 3D Human Pose and Shape Estimation from Monocular Image Sequences	Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused on acquiring behavioral information of the targets and their activities. Continuous evolution of intelligence being gathered of the human centric activities has put increased focus on the humans, especially inferring their innate characteristic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	36,438
1706.02690	Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks	We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,017
2412.20158	Stronger together? The homophily trap in networks	While homophily -- the tendency to link with similar others -- may nurture a sense of belonging and shared values, it can also hinder diversity and widen inequalities. Here, we unravel this trade-off analytically, revealing homophily traps for minority groups: scenarios where increased homophilic interaction among mino...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	false	521,113
2402.09649	ProtChatGPT: Towards Understanding Proteins with Large Language Models	Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialize...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,612
2306.04228	Data Mining for Faster, Interpretable Solutions to Inverse Problems: A Case Study Using Additive Manufacturing	Solving inverse problems, where we find the input values that result in desired values of outputs, can be challenging. The solution process is often computationally expensive and it can be difficult to interpret the solution in high-dimensional input spaces. In this paper, we use a problem from additive manufacturing t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	371,658
2312.03805	SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios	Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks. However, the prompt-based methods that are fine-tuned solely with base classes may struggle to generalize to novel classes in open-vocabulary scenarios, especially when data are limited. To address t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,443
2201.08954	Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network	Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality sa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,497
2311.00660	TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain	Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce artifacts and distortions and struggle to control the amount of rain generated due to a lack of proper constraints. In this paper, we propose a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	404,727
2501.11508	See In Detail: Enhancing Sparse-view 3D Gaussian Splatting with Local Depth and Semantic Regularization	3D Gaussian Splatting (3DGS) has shown remarkable performance in novel view synthesis. However, its rendering quality deteriorates with sparse inphut views, leading to distorted content and reduced details. This limitation hinders its practical application. To address this issue, we propose a sparse-view 3DGS method. G...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,948
1704.07744	Secure Transmission with Large Numbers of Antennas and Finite Alphabet Inputs	In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we investigate the case where instantaneous channel state information (CSI) of the eavesdropper is known at the transmitter. We show analytically that a generalized singular value ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,413
2408.00772	Hybrid Deep Learning Framework for Enhanced Melanoma Detection	Cancer is a leading cause of death worldwide, necessitating advancements in early detection and treatment technologies. In this paper, we present a novel and highly efficient melanoma detection framework that synergistically combines the strengths of U-Net for segmentation and EfficientNet for the classification of ski...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,972
2307.10172	DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI	Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, u...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	380,440
2011.00226	GTOC X: Karmarkar's Gang's Approach and Results	This paper describes the methods used and the results obtained by team Karmarkar's Gang for the 10th edition of the Global Trajectory Optimization Competition. The methods used by our team are described. These methods involve: mothership targeting flyby to target high-value stars using a single impulse, fast ships targ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	204,139
2407.10445	Backdoor Attacks against Image-to-Image Networks	Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkable performance, the backdoor vulnerability of I2I networks has not been explored. To fill this research gap, we conduct a comprehensive investi...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	472,989
1505.02385	Energy Efficiency in Secure Multi-Antenna Systems	The problem of resource allocation in multiple-antenna wiretap channels is investigated, wherein a malicious user tries to eavesdrop the communication between two legitimate users. Both multiple input single output single-antenna eavesdropper (MISO-SE) and multiple input multiple output multiple-antenna eavesdropper (M...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,955
2310.16065	The Hyperdimensional Transform: a Holographic Representation of Functions	Integral transforms are invaluable mathematical tools to map functions into spaces where they are easier to characterize. We introduce the hyperdimensional transform as a new kind of integral transform. It converts square-integrable functions into noise-robust, holographic, high-dimensional representations called hyper...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	402,559
2110.14960	An AI-based Approach for Tracing Content Requirements in Financial Documents	The completeness (in terms of content) of financial documents is a fundamental requirement for investment funds. To ensure completeness, financial regulators spend a huge amount of time for carefully checking every financial document based on the relevant content requirements, which prescribe the information types to b...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	263,711
2309.06113	Inspection planning under execution uncertainty	Autonomous inspection tasks necessitate path-planning algorithms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty, posing challenges to successfully completing such tasks. Unfortunately, existing...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,316
1305.4610	On the Optimality of Treating Interference as Noise	It is shown that in the K-user interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in dB scale), then the simple scheme of using point to point Gaussian codebooks ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,705
2405.08331	Are Generics and Negativity about Social Groups Common on Social Media? A Comparative Analysis of Twitter (X) Data	Generics (unquantified generalizations) are thought to be pervasive in communication and when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, h...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	454,063
2308.03519	Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings	In this paper, we propose Vocab-Expander at https://vocab-expander.com, an online tool that enables end-users (e.g., technology scouts) to create and expand a vocabulary of their domain of interest. It utilizes an ensemble of state-of-the-art word embedding techniques based on web text and ConceptNet, a common-sense kn...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	384,065
2407.15680	HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning	Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal with not just textual but also visual inputs. Despite rapid progress in VLMs, resources for evaluating and addressing multimodal hallucinat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	475,280
2106.09232	Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction	Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,589
2203.04129	YouTube-GDD: A challenging gun detection dataset with rich contextual information	An automatic gun detection system can detect potential gun-related violence at an early stage that is of paramount importance for citizens security. In the whole system, object detection algorithm is the key to perceive the environment so that the system can detect dangerous objects such as pistols and rifles. However,...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	284,359
2108.10360	Interpreting Face Inference Models using Hierarchical Network Dissection	This paper presents Hierarchical Network Dissection, a general pipeline to interpret the internal representation of face-centric inference models. Using a probabilistic formulation, our pipeline pairs units of the model with concepts in our "Face Dictionary", a collection of facial concepts with corresponding sample im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,872
cs/0605028	The Gaussian Multiple Access Wire-Tap Channel	We consider the Gaussian Multiple Access Wire-Tap Channel (GMAC-WT). In this scenario, multiple users communicate with an intended receiver in the presence of an intelligent and informed wire-tapper who receives a degraded version of the signal at the receiver. We define suitable security measures for this multi-access...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	539,432
2405.04333	A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI	Since late 2022, generative AI has taken the world by storm, with widespread use of tools including ChatGPT, Gemini, and Claude. Generative AI and large language model (LLM) applications are transforming how individuals find and access data and knowledge. However, the intricate relationship between open data and genera...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,535
2107.10004	Deep Iterative 2D/3D Registration	Deep Learning-based 2D/3D registration methods are highly robust but often lack the necessary registration accuracy for clinical application. A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy. H...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	247,191
1702.07935	Image Stitching by Line-guided Local Warping with Global Similarity Constraint	Low-textured image stitching remains a challenging problem. It is difficult to achieve good alignment and it is easy to break image structures due to insufficient and unreliable point correspondences. Moreover, because of the viewpoint variations between multiple images, the stitched images suffer from projective disto...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,869
2204.00618	ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion	We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems in low/medium-resource scenarios. Through extensive experiments, we show that our approach permits the application of speech synthesis and voice conversi...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	289,323
1909.00523	Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering	Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,655
2410.01677	Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia	Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cognitive abilities, including planning, reasoning, and reflection. In thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	493,885
2106.03760	DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning	The Mixture-of-Experts (MoE) architecture is showing promising results in improving parameter sharing in multi-task learning (MTL) and in scaling high-capacity neural networks. State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example. While conceptually appealing,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,436
1910.10031	Zero-Crossing Precoding With Maximum Distance to the Decision Threshold for Channels With 1-Bit Quantization and Oversampling	Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receivers. The proposed method implies that the information is conveyed w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	150,369
1805.06924	Towards a more flexible Language of Thought: Bayesian grammar updates after each concept exposure	Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples. The aim of most of these studies is to reveal the underlying language structuring these represe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	97,700
1912.02628	Fundamental Limitations in Sequential Prediction and Recursive Algorithms: $\mathcal{L}_{p}$ Bounds via an Entropic Analysis	In this paper, we obtain fundamental $\mathcal{L}_{p}$ bounds in sequential prediction and recursive algorithms via an entropic analysis. Both classes of problems are examined by investigating the underlying entropic relationships of the data and/or noises involved, and the derived lower bounds may all be quantified in...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	156,397
1803.04371	Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces	We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of norms, under a capacity assumption on the hypothesis space and a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,446
2403.03397	Explaining Genetic Programming Trees using Large Language Models	Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensiona...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	435,172
2211.17091	Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models	The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models. The approach introduces a discriminator that gives explicit supervision to a denoising sample path whether it is realistic or not. Unlike GANs, our approach does not require joint training of score and discri...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	333,856
1904.06491	Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)	A brain can detect outlier just by using only normal samples. Similarly, one-class classification (OCC) also uses only normal samples to train the model and trained model can be used for outlier detection. In this paper, a multi-layer architecture for OCC is proposed by stacking various Graph-Embedded Kernel Ridge Regr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,563
2502.03123	Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences	In this study, Disentanglement in Difference(DiD) is proposed to address the inherent inconsistency between the statistical independence of latent variables and the goal of semantic disentanglement in disentanglement representation learning. Conventional disentanglement methods achieve disentanglement representation by...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,603
1310.0932	Event-triggered transmission for linear control over communication channels	We consider an exponentially stable closed loop interconnection of a continuous linear plant and a continuous linear controller, and we study the problem of interconnecting the plant output to the controller input through a digital channel. We propose a family of "transmission-lazy" sensors whose goal is to transmit th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	27,530
2111.13179	A Sphere Packing Bound for Vector Gaussian Fading Channels under Peak Amplitude Constraints	An upper bound on the capacity of multiple-input multiple-output (MIMO) Gaussian fading channels is derived under peak amplitude constraints. The upper bound is obtained borrowing concepts from convex geometry and it extends to MIMO channels notable results from the geometric analysis on the capacity of scalar Gaussian...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	268,214
1906.07601	Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability	We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	135,641
2402.03175	Beyond the Black Box: A Statistical Model for LLM Reasoning and Inference	This paper introduces a novel Bayesian learning model to explain the behavior of Large Language Models (LLMs), focusing on their core optimization metric of next token prediction. We develop a theoretical framework based on an ideal generative text model represented by a multinomial transition probability matrix with a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	426,891
2304.13799	Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines	This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a "mean value" model, and anticipate maintenance requirements. The PINN model is applied to diesel engines with a variable-geometr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,707
2105.10439	Covariance-Free Sparse Bayesian Learning	Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,399
2207.08631	Latent Partition Implicit with Surface Codes for 3D Representation	Deep implicit functions have shown remarkable shape modeling ability in various 3D computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as multiple parts. Current solutions learn various primitives and blend the primitives directly in the spatial space, which still struggle to approxi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,649
1809.11047	Cross-situational learning of large lexicons with finite memory	Cross-situational word learning, wherein a learner combines information about possible meanings of a word across multiple exposures, has previously been shown to be a very powerful strategy to acquire a large lexicon in a short time. However, this success may derive from idealizations that are made when modeling the wo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	109,039
2410.04316	Data-driven Under Frequency Load Shedding Using Reinforcement Learning	Underfrequency load shedding (UFLS) is a critical control strategy in power systems aimed at maintaining system stability and preventing blackouts during severe frequency drops. Traditional UFLS schemes often rely on predefined rules and thresholds, which may not adapt effectively to the dynamic and complex nature of m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	495,233
2411.01423	Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design	The rapid discovery of new chemical compounds is essential for advancing global health and developing treatments. While generative models show promise in creating novel molecules, challenges remain in ensuring the real-world applicability of these molecules and finding such molecules efficiently. To address this, we in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	505,066
2405.07510	PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator	We present Piecewise Rectified Flow (PeRFlow), a flow-based method for accelerating diffusion models. PeRFlow divides the sampling process of generative flows into several time windows and straightens the trajectories in each interval via the reflow operation, thereby approaching piecewise linear flows. PeRFlow achieve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,744
2003.03143	Triple Memory Networks: a Brain-Inspired Method for Continual Learning	Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. By cont...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,141
2203.11276	Model Comparison in Approximate Bayesian Computation	A common problem in natural sciences is the comparison of competing models in the light of observed data. Bayesian model comparison provides a statistically sound framework for this comparison based on the evidence each model provides for the data. However, this framework relies on the calculation of likelihood functio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,856
1309.3660	(Failure of the) Wisdom of the crowds in an endogenous opinion dynamics model with multiply biased agents	We study an endogenous opinion (or, belief) dynamics model where we endogenize the social network that models the link (`trust') weights between agents. Our network adjustment mechanism is simple: an agent increases her weight for another agent if that agent has been close to truth (whence, our adjustment criterion is ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	27,032
1807.06517	Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing	Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with dom...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	103,133
2303.06548	CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution	As a method of image restoration, image super-resolution has been extensively studied at first. How to transform a low-resolution image to restore its high-resolution image information is a problem that researchers have been exploring. In the early physical transformation methods, the high-resolution pictures generated...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,890
2410.07160	TextToon: Real-Time Text Toonify Head Avatar from Single Video	We propose TextToon, a method to generate a drivable toonified avatar. Given a short monocular video sequence and a written instruction about the avatar style, our model can generate a high-fidelity toonified avatar that can be driven in real-time by another video with arbitrary identities. Existing related works heavi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	496,511
1905.08332	Behavior Identification and Prediction for a Probabilistic Risk Framework	Operation in a real world traffic requires autonomous vehicles to be able to plan their motion in complex environments (multiple moving participants). Planning through such environment requires the right search space to be provided for the trajectory or maneuver planners so that the safest motion for the ego vehicle ca...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	131,449
2012.10235	AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text	Adversarial examples are vital to expose the vulnerability of machine learning models. Despite the success of the most popular substitution-based methods which substitutes some characters or words in the original examples, only substitution is insufficient to uncover all robustness issues of models. In this paper, we p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	212,292
1810.03417	POLO: a POLicy-based Optimization library	We present POLO --- a C++ library for large-scale parallel optimization research that emphasizes ease-of-use, flexibility and efficiency in algorithm design. It uses multiple inheritance and template programming to decompose algorithms into essential policies and facilitate code reuse. With its clear separation between...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	109,803
2409.19069	Localizing Memorization in SSL Vision Encoders	Recent work on studying memorization in self-supervised learning (SSL) suggests that even though SSL encoders are trained on millions of images, they still memorize individual data points. While effort has been put into characterizing the memorized data and linking encoder memorization to downstream utility, little is ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	492,526
2501.10072	Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions	Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions using part-of-speech (POS) tagging and bigram analysis. By leveraging deep neural networks, we classify literary authors based on POS tag v...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	525,377
1904.01971	Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval	Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-domain image retrieval from a natural image gallery with free-hand sketch under a zero-shot scenario. Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding na...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,295
2501.10273	SEANN: A Domain-Informed Neural Network for Epidemiological Insights	In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with ex...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	525,459

Subsets and Splits

No community queries yet

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