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
2409.02571	iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search	Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a numeric range as parameters returns the data object whose numeric v...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	true	485,754
2001.08618	Compositional properties of emergent languages in deep learning	Recent findings in multi-agent deep learning systems point towards the emergence of compositional languages. These claims are often made without exact analysis or testing of the language. In this work, we analyze the emergent language resulting from two different cooperative multi-agent game with more exact measures fo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	161,339
2502.07183	Space-Aware Instruction Tuning: Dataset and Benchmark for Guide Dog Robots Assisting the Visually Impaired	Guide dog robots offer promising solutions to enhance mobility and safety for visually impaired individuals, addressing the limitations of traditional guide dogs, particularly in perceptual intelligence and communication. With the emergence of Vision-Language Models (VLMs), robots are now capable of generating natural ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	532,467
2407.17956	SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images	The advancement of deep learning in object detection has predominantly focused on megapixel images, leaving a critical gap in the efficient processing of gigapixel images. These super high-resolution images present unique challenges due to their immense size and computational demands. To address this, we introduce 'Sac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,192
1903.00840	Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data	Learning a generative model from partial data (data with missingness) is a challenging area of machine learning research. We study a specific implementation of the Auto-Encoding Variational Bayes (AEVB) algorithm, named in this paper as a Variational Auto-Decoder (VAD). VAD is a generic framework which uses Variational...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	123,116
2203.16335	Rapid Scalable Distributed Power Flow with Open-Source Implementation	This paper introduces a new method for solving the distributed AC power flow (PF) problem by further exploiting the problem formulation. We propose a new variant of the ALADIN algorithm devised specifically for this type of problem. This new variant is characterized by using a reduced modelling method of the distribute...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	288,750
2403.18125	For those who don't know (how) to ask: Building a dataset of technology questions for digital newcomers	While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many ef...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	441,772
2109.02852	Defending a Perimeter from a Ground Intruder Using an Aerial Defender: Theory and Practice	The perimeter defense game has received interest in recent years as a variant of the pursuit-evasion game. A number of previous works have solved this game to obtain the optimal strategies for defender and intruder, but the derived theory considers the players as point particles with first-order assumptions. In this wo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	253,874
2409.06029	SongCreator: Lyrics-based Universal Song Generation	Music is an integral part of human culture, embodying human intelligence and creativity, of which songs compose an essential part. While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	486,974
2109.10009	An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States	We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment. Through linking Google's multi-dimensional mobility index to economic activities, public health status, and mitigati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	256,477
2501.03420	Designing Telepresence Robots to Support Place Attachment	People feel attached to places that are meaningful to them, which psychological research calls "place attachment." Place attachment is associated with self-identity, self-continuity, and psychological well-being. Even small cues, including videos, images, sounds, and scents, can facilitate feelings of connection and be...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	522,865
1909.07285	Fast transcription of speech in low-resource languages	We present software that, in only a few hours, transcribes forty hours of recorded speech in a surprise language, using only a few tens of megabytes of noisy text in that language, and a zero-resource grapheme to phoneme (G2P) table. A pretrained acoustic model maps acoustic features to phonemes; a reversed G2P maps th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,639
2112.09151	TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations	Face manipulation methods can be misused to affect an individual's privacy or to spread disinformation. To this end, we introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by ca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,047
1907.01831	GeoPrune: Efficiently Finding Shareable Vehicles Based on Geometric Properties	On-demand ride-sharing is rapidly growing.Matching trip requests to vehicles efficiently is critical for the service quality of ride-sharing. To match trip requests with vehicles, a prune-and-select scheme is commonly used. The pruning stage identifies feasible vehicles that can satisfy the trip constraints (e.g., trip...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	137,449
1904.12739	Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images	We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for the analysis of WSIs, divide the image (bag) into patches (instances), which are used to predict a single bag-level label. These approaches perfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,213
1805.02356	Multimodal Machine Translation with Reinforcement Learning	Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement lear...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	true	false	false	true	96,849
2405.07769	$\alpha$VIL: Learning to Leverage Auxiliary Tasks for Multitask Learning	Multitask Learning is a Machine Learning paradigm that aims to train a range of (usually related) tasks with the help of a shared model. While the goal is often to improve the joint performance of all training tasks, another approach is to focus on the performance of a specific target task, while treating the remaining...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,850
2208.04549	Disentangled Representation Learning Using ($\beta$-)VAE and GAN	Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provid...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	312,148
2111.08282	Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image	Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term 're-rende...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	266,634
2201.12716	You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration	Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. Additionally, coarse keypoint predictions and igno...	false	false	false	false	true	false	false	true	false	false	true	true	false	false	false	false	false	false	277,755
2105.00843	Performance and Energy-Aware Bi-objective Tasks Scheduling for Cloud Data Centers	Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, ope...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	233,354
2212.14749	Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications	Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images in...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,731
2409.12622	Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions	This study introduces a novel theoretical framework for analyzing heteroscedastic Gaussian processes (HGPs) that identify unknown systems in a data-driven manner. Although HGPs effectively address the heteroscedasticity of noise in complex training datasets, calculating the exact posterior distributions of the HGPs is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	489,660
2306.05304	Bayesian Optimisation of Functions on Graphs	The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on the other hand, Bay...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	372,141
1505.00326	Combining Existential Rules and Description Logics (Extended Version)	Query answering under existential rules -- implications with existential quantifiers in the head -- is known to be decidable when imposing restrictions on the rule bodies such as frontier-guardedness [BLM10, BLMS11]. Query answering is also decidable for description logics [Baa03], which further allow disjunction and f...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	42,706
1810.10551	Fast and accurate object detection in high resolution 4K and 8K video using GPUs	Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images. The resolution of recording devices is gradually increasing and there is a rising need for new methods of processing high resolution data...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	111,316
2112.12316	Signed and Unsigned Partial Information Decompositions of Continuous Network Interactions	We investigate the partial information decomposition (PID) framework as a tool for edge nomination. We consider both the $I_{\cap}^{\text{min}}$ and $I_{\cap}^{\text{PM}}$ PIDs, from arXiv:1004.2515 and arXiv:1801.09010 respectively, and we both numerically and analytically investigate the utility of these frameworks f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	272,930
2403.11369	What Makes Math Word Problems Challenging for LLMs?	This paper investigates the question of what makes math word problems (MWPs) in English challenging for large language models (LLMs). We conduct an in-depth analysis of the key linguistic and mathematical characteristics of MWPs. In addition, we train feature-based classifiers to better understand the impact of each fe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	438,663
1703.04446	A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration	We present an efficient solver for diffeomorphic image registration problems in the framework of Large Deformations Diffeomorphic Metric Mappings (LDDMM). We use an optimal control formulation, in which the velocity field of a hyperbolic PDE needs to be found such that the distance between the final state of the system...	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,903
1605.01189	A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents	The contribution of this paper is fourfold. The first contribution is a novel, generic method for automatic ground truth generation of camera-captured document images (books, magazines, articles, invoices, etc.). It enables us to build large-scale (i.e., millions of images) labeled camera-captured/scanned documents dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,448
2411.12100	Stability and Performance Analysis on Self-dual Cones	In this paper, we consider nonsymmetric solutions to certain Lyapunov and Riccati equations and inequalities with coefficient matrices corresponding to cone-preserving dynamical systems. Most results presented here appear to be novel even in the special case of positive systems. First, we provide a simple eigenvalue cr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	509,292
2303.03278	Faithfulness-Aware Decoding Strategies for Abstractive Summarization	Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	349,665
2012.04222	Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery	Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,386
2203.12092	Toward Physically Realizable Quantum Neural Networks	There has been significant recent interest in quantum neural networks (QNNs), along with their applications in diverse domains. Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quantum mechanics are satisfied and that the networks are physically realiz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	287,136
2312.10041	Digital Twin Technology Enabled Proactive Safety Application for Vulnerable Road Users: A Real-World Case Study	While measures, such as traffic calming and advance driver assistance systems, can improve safety for Vulnerable Road Users (VRUs), their effectiveness ultimately relies on the responsible behavior of drivers and pedestrians who must adhere to traffic rules or take appropriate actions. However, these measures offer no ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	415,977
1503.02216	Higher order Matching Pursuit for Low Rank Tensor Learning	Low rank tensor learning, such as tensor completion and multilinear multitask learning, has received much attention in recent years. In this paper, we propose higher order matching pursuit for low rank tensor learning problems with a convex or a nonconvex cost function, which is a generalization of the matching pursuit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	40,910
2309.09021	Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning	Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resulting in a lack of explainability and explicit constraints enforced on ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	392,438
1705.00294	Tales of Emotion and Stock in China: Volatility, Causality and Prediction	How the online social media, like Twitter or its variant Weibo, interacts with the stock market and whether it can be a convincing proxy to predict the stock market have been debated for years, especially for China. As the traditional theory in behavioral finance states, the individual emotions can influence decision-m...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	72,649
2106.03128	MOC-GAN: Mixing Objects and Captions to Generate Realistic Images	Generating images with conditional descriptions gains increasing interests in recent years. However, existing conditional inputs are suffering from either unstructured forms (captions) or limited information and expensive labeling (scene graphs). For a targeted scene, the core items, objects, are usually definite while...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,185
2105.12549	KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes	Synthetic data has been applied in many deep learning based computer vision tasks. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework. We demonstrate how adversarial training alone can introd...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,039
1606.07953	Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records	Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	57,805
1606.04171	A Primer on 3GPP Narrowband Internet of Things (NB-IoT)	Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced in 3GPP Release 13 for providing wide-area coverage for the Internet of Things (IoT). This article provides an overview of the air interface of NB-IoT. We describe how NB-IoT addresses key IoT requirements such as deployment flexibility, low...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	57,203
2004.14797	STARC: Structured Annotations for Reading Comprehension	We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,010
2012.02529	Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals	Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual int...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,803
2101.12506	Learning User Preferences in Non-Stationary Environments	Recommendation systems often use online collaborative filtering (CF) algorithms to identify items a given user likes over time, based on ratings that this user and a large number of other users have provided in the past. This problem has been studied extensively when users' preferences do not change over time (static c...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	217,585
2501.16466	On the Feasibility of Using LLMs to Execute Multistage Network Attacks	LLMs have shown preliminary promise in some security tasks and CTF challenges. However, it is unclear whether LLMs are able to realize multistage network attacks, which involve executing a wide variety of actions across multiple hosts such as conducting reconnaissance, exploiting vulnerabilities to gain initial access,...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	527,974
2406.01077	Hybrid Quadratic Programming -- Pullback Bundle Dynamical Systems Control	Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handle arbitrary geometry curved spaces, namely manifolds, beyond the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	460,160
1707.02749	Improving speaker turn embedding by crossmodal transfer learning from face embedding	Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,752
2211.11127	Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training	The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability analysis of these systems faces the same challenge. Existing approaches rely on over-approximating DNNs using simpler polynomial models. Howev...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	331,591
1408.5666	Compressing Encrypted Data and Permutation Cipher	In a system that performs both encryption and lossy compression, the conventional way is to compress first and then encrypt the compressed data. This separation approach proves to be optimal. In certain applications where sensitive information should be protected as early as possible, it is preferable to perform encryp...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	35,571
2104.10548	A note on some information-theoretic divergences between Zeta distributions	We consider the zeta distributions which are discrete power law distributions that can be interpreted as the counterparts of the continuous Pareto distributions with unit scale. The family of zeta distributions forms a discrete exponential family with normalizing constants expressed using the Riemann zeta function. We ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	231,620
1903.02791	Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network	Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	123,574
2209.01006	Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise	This paper investigates theoretical properties of subsampling and hashing as tools for approximate Euclidean norm-preserving embeddings for vectors with (unknown) additive Gaussian noises. Such embeddings are sometimes called Johnson-lindenstrauss embeddings due to their celebrated lemma. Previous work shows that as sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	315,750
1407.7027	Motor Learning Mechanism on the Neuron Scale	Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implicati...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	34,902
2211.02638	A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data	Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,644
2001.06246	Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning	Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high co...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	160,756
1709.02055	Event-Triggered Stabilization of Nonlinear Systems with Time-Varying Sensing and Actuation Delay	This paper studies the problem of stabilization of a nonlinear system with time-varying delays in both sensing and actuation using event-triggered control. Our proposed strategy seeks to opportunistically minimize the number of control updates while guaranteeing stabilization and builds on predictor feedback to compens...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	80,195
2101.00041	Optimizing Optimizers: Regret-optimal gradient descent algorithms	The need for fast and robust optimization algorithms are of critical importance in all areas of machine learning. This paper treats the task of designing optimization algorithms as an optimal control problem. Using regret as a metric for an algorithm's performance, we study the existence, uniqueness and consistency of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,943
2409.00120	ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings	This paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance. There exists a noticeable need for research on the CS between English and Korean. We highlight that the current Equivalence Constraint (EC) theory for CS in other languages may only partially capture English...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	484,789
2303.00055	Learning time-scales in two-layers neural networks	Gradient-based learning in multi-layer neural networks displays a number of striking features. In particular, the decrease rate of empirical risk is non-monotone even after averaging over large batches. Long plateaus in which one observes barely any progress alternate with intervals of rapid decrease. These successive ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,472
2202.08969	Private Quantiles Estimation in the Presence of Atoms	We consider the differentially private estimation of multiple quantiles (MQ) of a distribution from a dataset, a key building block in modern data analysis. We apply the recent non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem. We establish that the resulting method is closely related to the rece...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	281,048
2410.19272	Coordinated Reply Attacks in Influence Operations: Characterization and Detection	Coordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers. Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic. ...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	502,240
1907.00148	Improved ICH classification using task-dependent learning	Head CT is one of the most commonly performed imaging studied in the Emergency Department setting and Intracranial hemorrhage (ICH) is among the most critical and timesensitive findings to be detected on Head CT. We present BloodNet, a deep learning architecture designed for optimal triaging of Head CTs, with the goal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,949
1812.09320	The Cost of Delay in Status Updates and their Value: Non-linear Ageing	We consider a status update communication system consisting of a source-destination link. A stochastic process is observed at the source, where samples are extracted at random time instances, and delivered to the destination, thus, providing status updates for the source. In this paper, we expand the concept of informa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	117,129
1712.06414	The Wisdom of Polarized Crowds	As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated with ...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	true	86,884
2304.05869	LMR: Lane Distance-Based Metric for Trajectory Prediction	The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, which means that errors are weighted equally in all directions. Euclidean metrics are insufficient for structured environments like roads, si...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	357,769
1702.00458	Convergence Results for Neural Networks via Electrodynamics	We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradient descent converges to the target function is equivalent to the following question in electrodynamics: Given $k$ fixed protons in $\mathbb{R}...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	67,656
2006.05838	To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs	Regularized Auto-Encoders (RAEs) form a rich class of neural generative models. They effectively model the joint-distribution between the data and the latent space using an Encoder-Decoder combination, with regularization imposed in terms of a prior over the latent space. Despite their advantages, such as stability in ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	181,214
2310.14607	Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications	Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in ma...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	401,938
1403.5877	Non-uniform Feature Sampling for Decision Tree Ensembles	We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: $(i)$ \emph{leverage scores-based} and $(ii)$ \emph{norm-based} feature selection. Experimenta...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	31,772
2211.11369	Enterprise Model Library for Business-IT-Alignment	The knowledge of the world is passed on through libraries. Accordingly, domain expertise and experiences should also be transferred within an enterprise by a knowledge base. Therefore, models are an established medium to describe good practices for complex systems, processes, and interconnections. However, there is no ...	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	true	331,704
1902.07118	Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation	The availability and accuracy of Channel State Information (CSI) play a crucial role for coherent detection in almost every communication system. Particularly in the recently proposed cell-free massive MIMO system, in which a large number of distributed Access Points (APs) is connected to a Central processing Unit (CPU...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,915
2405.18190	Mutation-Bias Learning in Games	We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type, allowing us to pres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	458,311
1706.09087	Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing	We study the problem of recovering an $s$-sparse signal $\mathbf{x}^{\star}\in\mathbb{C}^n$ from corrupted measurements $\mathbf{y} = \mathbf{A}\mathbf{x}^{\star}+\mathbf{z}^{\star}+\mathbf{w}$, where $\mathbf{z}^{\star}\in\mathbb{C}^m$ is a $k$-sparse corruption vector whose nonzero entries may be arbitrarily large an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,086
1603.03925	Image Captioning with Semantic Attention	Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,168
2403.11532	Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)	Research on Out-Of-Distribution (OOD) detection focuses mainly on building scores that efficiently distinguish OOD data from In Distribution (ID) data. On the other hand, Conformal Prediction (CP) uses non-conformity scores to construct prediction sets with probabilistic coverage guarantees. In this work, we propose to...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	438,746
2501.12067	EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition	Parameter-efficient fine-tuning methods, such as LoRA, reduces the number of trainable parameters. However, they often suffer from scalability issues and differences between their learning pattern and full fine-tuning. To overcome these limitations, we propose Efficient Weight-Decomposed Low-Rank Adaptation (EDoRA): a ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	526,153
2411.02309	Grid-Based Projection of Spatial Data into Knowledge Graphs	The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	505,436
2302.04265	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models	We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generative trajectories for $N$ dimensional data by embedding paths in $N{+}D$ dimensional space while still controlling the progression with a simp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	344,638
2103.06595	Bump Hunting in Latent Space	Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning C...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	224,356
1911.12037	Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking	Multi-target multi-camera tracking (MTMCT) systems track targets across cameras. Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood. In single camera tracking, local neighborhood refers to consecutive frames; in multi-camera tracking, it re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,299
2105.08143	On exploration requirements for learning safety constraints	Enforcing safety for dynamical systems is challenging, since it requires constraint satisfaction along trajectory predictions. Equivalent control constraints can be computed in the form of sets that enforce positive invariance, and can thus guarantee safety in feedback controllers without predictions. However, these co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	235,662
2502.05271	RobotMover: Learning to Move Large Objects by Imitating the Dynamic Chain	Moving large objects, such as furniture, is a critical capability for robots operating in human environments. This task presents significant challenges due to two key factors: the need to synchronize whole-body movements to prevent collisions between the robot and the object, and the under-actuated dynamics arising fro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	531,530
2108.12643	Master memory function for delay-based reservoir computers with single-variable dynamics	We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	252,565
2306.16103	1M parameters are enough? A lightweight CNN-based model for medical image segmentation	Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low comput...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,279
2501.15971	REINFORCE-ING Chemical Language Models in Drug Design	Chemical language models, combined with reinforcement learning, have shown significant promise to efficiently traverse large chemical spaces in drug design. However, the performance of various RL algorithms and their best practices for practical drug design are still unclear. Here, starting from the principles of the R...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,777
1208.1004	Social Trust as a solution to address sparsity-inherent problems of Recommender systems	Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately so far with regard t...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	17,945
2309.05102	Is Learning in Biological Neural Networks based on Stochastic Gradient Descent? An analysis using stochastic processes	In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	390,958
2312.15300	Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models	Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks. However, the exploration of MLLM potential in visual quality assessment, a vital aspect of low-level vision, remains limited. To address this gap, we introduce Q-Boost, a nov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,960
1901.06958	Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks	Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approxima...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,123
2310.06278	BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models	In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by ChatGPT has made great progress. Such large language models (LLMs) serve people in ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	398,505
2012.03034	A Transactive Retail Market Mechanism for Active Distribution Network Integrated with Large-scale Distributed Energy Resources	The burgeoning integration of distributed energy resources (DER) poses new challenges for the economic and safe operation of the electricity system. The current distribution-side policy is largely based on mandatory regulations and incentives, rather than the design of a competitive market mechanism to arouse DERs to f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	209,962
2402.12451	The Revolution of Multimodal Large Language Models: A Survey	Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research efforts are being devoted to the development of Multimodal Large Language Models (MLLMs). These models can seamlessly integrate visual and tex...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	true	430,855
2411.11197	Stealing Training Graphs from Graph Neural Networks	Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands extensive expert annotations, which are expensive and usually contain sensitive information of data providers. The trained GNN models are o...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	508,951
2006.11692	Semi-Supervised Object Detection with Sparsely Annotated Dataset	In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance. When using t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	183,334
2011.12450	Sparse R-CNN: End-to-End Object Detection with Learnable Proposals	We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as $k$ anchor boxes pre-defined on all grids of image feature map of size $H\times W$. In our method, however, a fixed sparse set of learned object proposals, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,162
1601.00199	A Unified Framework for Compositional Fitting of Active Appearance Models	Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using Compositional Gradient Descent (CGD) algorithms. We present a unified and complete view of these algorithms and classi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,615
2007.05312	Preventing active re-identification attacks on social graphs via sybil subgraph obfuscation	This paper addresses active re-identification attacks in the context of privacy-preserving social graph publication. Active attacks are those where the adversary can leverage fake accounts, a.k.a. sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. In this pap...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	186,638
2203.09566	Leveraging Adversarial Examples to Quantify Membership Information Leakage	The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today. Identifying training data based on a trained model is a standard way of measuring the privacy risks induced by the model. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,195
2102.12311	Essentially Decentralized Conjugate Gradients	Solving structured systems of linear equations in a non-centralized fashion is an important step in many distributed optimization and control algorithms. Fast convergence is required in manifold applications. Known decentralized algorithms, however, typically exhibit asymptotic convergence at a linear rate. This note p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	221,692

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