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
1301.3857	Gaussian Process Networks	In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the {em marginal likelihood/} of the data given a candidate struct...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	21,169
1708.03297	Outage Performance of Two-Hop OFDM Systems with Spatially Random Decode-and-Forward Relays	In this paper, we analyze the outage performance of different multicarrier relay selection schemes for two-hop orthogonal frequency-division multiplexing (OFDM) systems in a Poisson field of relays. In particular, special emphasis is placed on decode-and-forward (DF) relay systems, equipped with bulk and per-subcarrier...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,747
1310.0395	Protein Threading Based on Nonlinear Integer Programming	Protein threading is a method of computational protein structure prediction used for protein sequences which have the same fold as proteins of known structures but do not have homologous proteins with known structure. The most popular algorithm is based on linear integer programming. In this paper, we consider methods ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	27,488
1606.05809	Degrees of Freedom of Spatial Self-Interference Suppression for In-Band Full-Duplex with Inter-node Interference	We study a three-node network with a full-duplex base-station communicating with one uplink and one downlink half-duplex node. In this network, both self-interference and inter-node interference are present. We use an antenna-theory-based channel model to study the spatial degrees of freedom of such network, and study ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,477
2308.11080	Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen	Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	386,995
1703.02156	On the Limits of Learning Representations with Label-Based Supervision	Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. Since the emergence of AlexNet every winning submission of the ImageNet challenge has employed end-to-end representation learning, and due to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	69,503
1910.04149	Unaligned Image-to-Sequence Transformation with Loop Consistency	We tackle the problem of modeling sequential visual phenomena. Given examples of a phenomena that can be divided into discrete time steps, we aim to take an input from any such time and realize this input at all other time steps in the sequence. Furthermore, we aim to do this without ground-truth aligned sequences -- a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,691
2404.03916	Estimating mixed memberships in multi-layer networks	Community detection in multi-layer networks has emerged as a crucial area of modern network analysis. However, conventional approaches often assume that nodes belong exclusively to a single community, which fails to capture the complex structure of real-world networks where nodes may belong to multiple communities simu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	444,451
1509.07715	Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach	Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore ne...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	47,285
1710.10230	Not-So-Random Features	We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpret...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,335
2311.03520	Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data	Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel mode...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	405,879
1206.6852	Structured Priors for Structure Learning	Traditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in. Her...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	17,077
2210.05060	AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization	An audio-visual event (AVE) is denoted by the correspondence of the visual and auditory signals in a video segment. Precise localization of the AVEs is very challenging since it demands effective multi-modal feature correspondence to ground the short and long range temporal interactions. Existing approaches struggle in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,683
2308.01835	Distribution-Free Inference for the Regression Function of Binary Classification	One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes the corresponding misclassification probabilities. The paper presents a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	383,382
1308.5304	Enhancing Secrecy with Multi-Antenna Transmission in Wireless Ad Hoc Networks	We study physical-layer security in wireless ad hoc networks and investigate two types of multi-antenna transmission schemes for providing secrecy enhancements. To establish secure transmission against malicious eavesdroppers, we consider the generation of artificial noise with either sectoring or beamforming. For both...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,620
1511.07386	Pushing the Boundaries of Boundary Detection using Deep Learning	In this work we show that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	49,421
2306.05140	A Two-dimensional Spatial Optimization Framework for Vehicle Powertrain Systems	This paper presents a modeling framework to optimize the two-dimensional placement of powertrain elements inside the vehicle, explicitly accounting for the rotation, relative placement and alignment. Specifically, we first capture the multi-level nature of the system mathematically, and construct a model that captures ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	372,072
2304.14371	Neural Field Conditioning Strategies for 2D Semantic Segmentation	Neural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, and not memorize only one, it needs to be conditioned on a latent code which describes the signal at hand. Despite being an important aspect, there has been little research on conditio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	360,921
2303.08893	A Multifidelity deep operator network approach to closure for multiscale systems	Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables. Despite their success, PROMs can be susceptible to inaccuracies, even instabilities, due to the improper accounting of the interaction between the r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	351,810
2412.02831	FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management	The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature estimates, a valuable improvement over non-radiometric data that requires irradiance...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	513,696
2308.06596	On the Performance Trade-off of Distributed Integrated Sensing and Communication Networks	In this letter, we analyze the performance trade-off in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantita...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	385,198
2403.17293	Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques	Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell's inter...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,396
2206.12195	Bioinspired composite learning control under discontinuous friction for industrial robots	Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems wit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	304,512
2501.06241	Predicting House Rental Prices in Ghana Using Machine Learning	This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,901
2104.08753	One-shot quantum state redistribution and quantum Markov chains	We revisit the task of quantum state redistribution in the one-shot setting, and design a protocol for this task with communication cost in terms of a measure of distance from quantum Markov chains. More precisely, the distance is defined in terms of quantum max-relative entropy and quantum hypothesis testing entropy. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	230,956
1403.6381	An efficiency dependency parser using hybrid approach for tamil language	Natural language processing is a prompt research area across the country. Parsing is one of the very crucial tool in language analysis system which aims to forecast the structural relationship among the words in a given sentence. Many researchers have already developed so many language tools but the accuracy is not mee...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	31,818
2011.00587	Incremental Model Building Homotopy Approach for Solving Exact AC-Constrained Optimal Power Flow	Alternating-Current Optimal Power Flow (AC-OPF) is framed as a NP-hard non-convex optimization problem that solves for the most economical dispatch of grid generation given the AC-network and device constraints. Although there are no standard methodologies for obtaining the global optimum for the problem, there is cons...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	204,284
1711.03674	Breast density classification with deep convolutional neural networks	Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	84,256
2306.01699	Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy	Group imbalance, resulting from inadequate or unrepresentative data collection methods, is a primary cause of representation bias in datasets. Representation bias can exist with respect to different groups of one or more protected attributes and might lead to prejudicial and discriminatory outcomes toward certain group...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,551
0906.2547	Superactivation of the Asymptotic Zero-Error Classical Capacity of a Quantum Channel	The zero-error classical capacity of a quantum channel is the asymptotic rate at which it can be used to send classical bits perfectly, so that they can be decoded with zero probability of error. We show that there exist pairs of quantum channels, neither of which individually have any zero-error capacity whatsoever (e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,877
2409.16999	WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks	Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	491,611
2210.07002	Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy	In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings. This typically comes with a privacy-utility trade-off between protection of individuals and usability of the data for downstream applications. One of the challenges...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,540
2102.03011	Sampling Based Scene-Space Video Processing	Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly dependent on the accuracy of this "scene-space" information. We present a novel, sampling-based framework for processing video that enables high-qu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	218,598
2212.06295	Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety	Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and text summarization. We provide a simple new prompting strategy that leads to yet another supposedly "super-human" result, this time outperformi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	336,060
cs/0509032	A Simple Model to Generate Hard Satisfiable Instances	In this paper, we try to further demonstrate that the models of random CSP instances proposed by [Xu and Li, 2000; 2003] are of theoretical and practical interest. Indeed, these models, called RB and RD, present several nice features. First, it is quite easy to generate random instances of any arity since no particular...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	538,950
2212.12645	HandsOff: Labeled Dataset Generation With No Additional Human Annotations	Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	338,084
2501.13051	Column-Oriented Datalog on the GPU	Datalog is a logic programming language widely used in knowledge representation and reasoning (KRR), program analysis, and social media mining due to its expressiveness and high performance. Traditionally, Datalog engines use either row-oriented or column-oriented storage. Engines like VLog and Nemo favor column-orient...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	526,537
1911.08335	Generative Audio Synthesis with a Parametric Model	Use a parametric representation of audio to train a generative model in the interest of obtaining more flexible control over the generated sound.	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,161
2012.01837	Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training	This paper describes an end-to-end adversarial singing voice conversion (EA-SVC) approach. It can directly generate arbitrary singing waveform by given phonetic posteriorgram (PPG) representing content, F0 representing pitch, and speaker embedding representing timbre, respectively. Proposed system is composed of three ...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	209,545
2301.00732	Improved NP-Hardness of Approximation for Orthogonality Dimension and Minrank	The orthogonality dimension of a graph $G$ over $\mathbb{R}$ is the smallest integer $k$ for which one can assign a nonzero $k$-dimensional real vector to each vertex of $G$, such that every two adjacent vertices receive orthogonal vectors. We prove that for every sufficiently large integer $k$, it is $\mathsf{NP}$-har...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	339,003
2210.09603	Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs	As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	324,600
2212.12869	A general construction of regular complete permutation polynomials	Let $r\geq 3$ be a positive integer and $\mathbb{F}_q$ the finite field with $q$ elements. In this paper, we consider the $r$-regular complete permutation property of maps with the form $f=\tau\circ\sigma_M\circ\tau^{-1}$ where $\tau$ is a PP over an extension field $\mathbb{F}_{q^d}$ and $\sigma_M$ is an invertible li...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	338,159
1610.09908	Joint Large-Scale Motion Estimation and Image Reconstruction	This article describes the implementation of the joint motion estimation and image reconstruction framework presented by Burger, Dirks and Sch\"onlieb and extends this framework to large-scale motion between consecutive image frames. The variational framework uses displacements between consecutive frames based on the o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,131
1206.4654	A Generalized Loop Correction Method for Approximate Inference in Graphical Models	Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but can be incorrect or non-convergent for loopy graphical models. Recently, several new approximate inference algorithms based on cavity distrib...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	16,705
2403.14344	Tell Me What You Want (What You Really, Really Want): Addressing the Expectation Gap for Goal Conveyance from Humans to Robots	Conveying human goals to autonomous systems (AS) occurs both when the system is being designed and when it is being operated. The design-step conveyance is typically mediated by robotics and AI engineers, who must appropriately capture end-user requirements and concepts of operations, while the operation-step conveyanc...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	440,025
1710.00453	Visual Reasoning with Natural Language	Natural language provides a widely accessible and expressive interface for robotic agents. To understand language in complex environments, agents must reason about the full range of language inputs and their correspondence to the world. Such reasoning over language and vision is an open problem that is receiving increa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,863
2408.06620	Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection	Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improvements in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,266
2009.07838	FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition	This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,062
1707.04638	Predicting multicellular function through multi-layer tissue networks	Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,080
1804.04419	Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification	Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,852
2410.09701	Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models	The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding has been obtained for ICL in reinforcement learning (RL), the previous results are largely confined to the single-agent setting. This work p...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	true	497,730
2004.12062	Probabilistic Serial Mechanism for Multi-Type Resource Allocation	In multi-type resource allocation (MTRA) problems, there are p $\ge$ 2 types of items, and n agents, who each demand one unit of items of each type, and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	174,115
2411.19301	Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising	In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured, type-constrained facts, or lon...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	512,195
2103.13192	On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison	User preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, o...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	226,419
2407.01795	Honor Among Bandits: No-Regret Learning for Online Fair Division	We consider the problem of online fair division of indivisible goods to players when there are a finite number of types of goods and player values are drawn from distributions with unknown means. Our goal is to maximize social welfare subject to allocating the goods fairly in expectation. When a player's value for an i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	469,453
2406.18582	CanFields: Consolidating 4D Dynamic Shapes from Raw Scans	We introduce Canonical Consolidation Fields (CanFields), a new method for reconstructing a time series of independently captured 3D scans into a single, coherent deforming shape. This 4D representation enables continuous refinement across both space and time. Unlike prior methods that often over-smooth the geometry or ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	468,089
2311.06440	Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text	Data quality is a problem that perpetually resurfaces throughout the field of NLP, regardless of task, domain, or architecture, and remains especially severe for lower-resource languages. A typical and insidious issue, affecting both training data and model output, is data that is repetitive and dominated by linguistic...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	406,941
2107.06010	Zero-shot Speech Translation	Speech Translation (ST) is the task of translating speech in one language into text in another language. Traditional cascaded approaches for ST, using Automatic Speech Recognition (ASR) and Machine Translation (MT) systems, are prone to error propagation. End-to-end approaches use only one system to avoid propagating e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	245,954
2402.13292	A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot Systems	The fundamental goal assignment problem for a multi-robot application aims to assign a unique goal to each robot while ensuring collision-free paths, minimizing the total movement cost. A plausible algorithmic solution to this NP-hard problem involves an iterative process that integrates a task planner to compute the g...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	431,192
2410.19867	Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets	The quest for simplification in physics drives the exploration of concise mathematical representations for complex systems. This Dissertation focuses on the concept of dimensionality reduction as a means to obtain low-dimensional descriptions from high-dimensional data, facilitating comprehension and analysis. We addre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,542
2501.14439	Optimizing Human Pose Estimation Through Focused Human and Joint Regions	Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an open challenge. One aspect that has been overlooked so far is that existing methods learn motion clues...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	527,117
2011.04064	AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk	Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment to facilitate informed decisions that may sustain the economic vi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	205,446
1801.10447	Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks	Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, $l_1$-norm, average percentage of zeros, etc) and retain only the top ranked filters. Once the low s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,294
2104.04123	Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers	Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal yield. Autonomous navigation of an agricultural vehicle involves the control of different dynamic subsystems, such as the yaw angle dynamics...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	229,297
2312.09310	Neural Time-Reversed Generalized Riccati Equation	Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to solving these problems are Dynamic Programming and the Pontryagin Maximum Principle. In both approaches, Hamiltonian equations offer an interpr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	415,669
2212.11603	Sequential Decision Problems with Weak Feedback	This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback. A major part of this thesis focuses on the unsupervised sequential selection problem, where one can not infer the loss incurred for selecting an action from observed feed...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	337,836
2403.18274	DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment	Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remains challenging to explore effective visual-LiDAR fusion, mainly due to the intrinsic data structure inconsistency between two modalities: Ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,856
2205.09826	DPER: Dynamic Programming for Exist-Random Stochastic SAT	In Bayesian inference, the maximum a posteriori (MAP) problem combines the most probable explanation (MPE) and marginalization (MAR) problems. The counterpart in propositional logic is the exist-random stochastic satisfiability (ER-SSAT) problem, which combines the satisfiability (SAT) and weighted model counting (WMC)...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	297,428
2411.17218	GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network	Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subseq...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	511,357
2412.18886	Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization	Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phases. While adversarial training has demonstrated remarkable effectiveness in image classification tasks, its suitability for GNN models has be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,622
1805.05135	A Note on Reverse Pinsker Inequalities	A simple method is shown to provide optimal variational bounds on $f$-divergences with possible constraints on relative information extremums. Known results are refined or proved to be optimal as particular cases.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,383
2211.12206	Twitter has a Binary Privacy Setting, are Users Aware of How It Works?	Twitter accounts are public by default, but Twitter gives the option to create protected accounts, where only approved followers can see their tweets. The publicly visible information changes based on the account type and the visibility of tweets also depends solely on the poster's account type which can cause unintend...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	332,034
2210.08203	Unit Selection: Learning Benefit Function from Finite Population Data	The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if incentivized and a different way if not. The unit selection problem consists of evaluation and search subproblems. Li and Pearl defi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	324,039
2109.14180	Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy	In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy. Feature selection is one of the most important technologies in data prepossessing, aimi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	257,882
2405.02067	Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data	Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Models (TBMs) such as XGBoost have historically performed better on tabular data. It has been shown that subsampling of training data when bui...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,614
1802.07303	MoNet: Moments Embedding Network	Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks. Different from conventional global average pooling or fully connected layer, bilinear pooling gathers 2nd order information in a t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,868
1807.08518	Implementing Neural Turing Machines	Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,558
1608.00310	Video Summarization in a Multi-View Camera Network	While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space. We learn the embedding by minimizing an objective function ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,269
2308.01415	An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model	At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks. Thus, this paper presents a carefully designed data creation pipeline for this purpose. Particularly, we initiate a dialogue between an AI inve...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	383,241
1903.03759	Machine Learning Based Prediction and Classification of Computational Jobs in Cloud Computing Centers	With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center. In order to have a better perception ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	123,811
2104.00704	Remote Sensing Image Classification with the SEN12MS Dataset	Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,088
2309.03244	EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation	We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. EGIC is based on two novel building blocks: i) OASIS-C, a conditional pre-trained semantic segmentation-guided discriminator, which provides both spatially and seman...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	390,329
2010.12623	Unsupervised Multi-hop Question Answering by Question Generation	Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised frame...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	202,763
2206.14740	Saturating systems and the rank covering radius	We introduce the concept of a rank saturating system and outline its correspondence to a rank-metric code with a given covering radius. We consider the problem of finding the value of $s_{q^m/q}(k,\rho)$, which is the minimum $\mathbb{F}_q$-dimension of a $q$-system in $\mathbb{F}_{q^m}^k$ which is rank $\rho$-saturati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	305,383
2310.05657	A Closer Look into Automatic Evaluation Using Large Language Models	Using large language models (LLMs) to evaluate text quality has recently gained popularity. Some prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process. In this paper, we analyze LLM evaluation (Chiang and Lee, 2023) and G-Eval (Liu et al., 2023), and we d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	398,238
2312.11822	Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation	Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using tradi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,727
1805.07874	GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization	Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	97,972
2301.11553	Robust Transformer with Locality Inductive Bias and Feature Normalization	Vision transformers have been demonstrated to yield state-of-the-art results on a variety of computer vision tasks using attention-based networks. However, research works in transformers mostly do not investigate robustness/accuracy trade-off, and they still struggle to handle adversarial perturbations. In this paper, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	342,192
2306.14257	A Self-Encoder for Learning Nearest Neighbors	We present the self-encoder, a neural network trained to guess the identity of each data sample. Despite its simplicity, it learns a very useful representation of data, in a self-supervised way. Specifically, the self-encoder learns to distribute the data samples in the embedding space so that they are linearly separab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,610
2012.06238	Query Understanding for Natural Language Enterprise Search	Natural Language Search (NLS) extends the capabilities of search engines that perform keyword search allowing users to issue queries in a more "natural" language. The engine tries to understand the meaning of the queries and to map the query words to the symbols it supports like Persons, Organizations, Time Expressions...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	211,045
2111.04295	The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle	Thompson sampling (TS) has attracted a lot of interest in the bandit area. It was introduced in the 1930s but has not been theoretically proven until recent years. All of its analysis in the combinatorial multi-armed bandit (CMAB) setting requires an exact oracle to provide optimal solutions with any input. However, su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	265,446
2203.02072	X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback	We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that captu...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,615
2502.14351	SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images	Positron Emission Tomography (PET) imaging plays a crucial role in modern medical diagnostics by revealing the metabolic processes within a patient's body, which is essential for quantification of therapy response and monitoring treatment progress. However, the segmentation of PET images presents unique challenges due ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,786
2208.00207	LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction	In the practical applications of computed tomography imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scanning conditions. The noisy incomplete projection data results in the ill-posedness of the inverse problems. In this work, we theoretically v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,773
1812.05551	Exploration Conscious Reinforcement Learning Revisited	The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as $\epsilon$-gr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,435
1209.0999	Visual Exploration of Simulated and Measured Blood Flow	Morphology of cardiovascular tissue is influenced by the unsteady behavior of the blood flow and vice versa. Therefore, the pathogenesis of several cardiovascular diseases is directly affected by the blood-flow dynamics. Understanding flow behavior is of vital importance to understand the cardiovascular system and pote...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	18,405
2309.02556	Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images	In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the performance of models. Accordingly, in this paper, we propose a novel met...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	390,078
2202.02959	A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data	Understanding the structure and mineralogical composition of a region is an essential step in mining, both during exploration (before mining) and in the mining process. During exploration, sparse but high-quality data are gathered to assess the overall orebody. During the mining process, boundary positions and material...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,021
1607.02678	Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework	In order to create an "in-the-wild" dataset of facial emotions with large number of balanced samples, this paper proposes a game-based data collection framework. The framework mainly include three components: a game engine, a game interface, and a data collection and evaluation module. We use a deep learning approach t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,396
1611.00138	MusicMood: Predicting the mood of music from song lyrics using machine learning	Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommend...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	63,177

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