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
2002.00718	Modeling the Background for Incremental Learning in Semantic Segmentation	Despite their effectiveness in a wide range of tasks, deep architectures suffer from some important limitations. In particular, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to update their model as new classes are available but the original training set is not retained...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,434
2205.08833	Speckle Image Restoration without Clean Data	Speckle noise is an inherent disturbance in coherent imaging systems such as digital holography, synthetic aperture radar, optical coherence tomography, or ultrasound systems. These systems usually produce only single observation per view angle of the same interest object, imposing the difficulty to leverage the statis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	297,073
2107.07071	A Combinatorial Interpretation for the Shor-Laflamme Weight Enumerators of CWS Codes	We show that one of the Shor-Laflamme weight enumerators of a codeword stabilized quantum code may be interpreted as the distance enumerator of an associated classical code.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	246,301
1301.2959	New elements for a network (including brain) general theory during learning period	This study deals with the evolution of the so called 'intelligent' networks (insect society without leader, cells of an organism, brain,...) during their learning period. First we summarize briefly the Version 2 (published in French), whose the main characteristics are: 1) A network connected to its environment is cons...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	21,058
2405.18305	Volt-PF Control Mode for Distribution Feeder Voltage Management Under High Penetration of Distributed Energy Resources	Volt-VAr control is a popular method for mitigating overvoltage violations caused by high penetration of distributed energy resources (DERs) in distribution feeders. An inherent limitation of volt-VAr control is that the reactive power (Q) absorbed/injected by the DER is determined based only on the terminal voltage, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	458,365
2205.14987	A Continuous Time Framework for Discrete Denoising Models	We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time ve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,564
1204.6583	A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems	In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is applied to major learning algorithms such as support vector machine (SVM) and boosting methods. The loss function represents the penalty of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,733
2011.07960	Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle	Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an in...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	206,724
1008.2186	RDFViewS: A Storage Tuning Wizard for RDF Applications	In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPAR...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	7,265
2006.14032	Compositional Explanations of Neurons	We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts that closely approximate neuron behavior. Compared to prior work that uses atomic labels as explanations, analyzing neurons compositionally allows us to more precisely and expressively characterize their...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	184,100
2410.15986	A quantitative Robbins-Siegmund theorem	The Robbins-Siegmund theorem is one of the most important results in stochastic optimization, where it is widely used to prove the convergence of stochastic algorithms. We provide a quantitative version of the theorem, establishing a bound on how far one needs to look in order to locate a region of metastability in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,827
1707.05587	Graph learning under sparsity priors	Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred f...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,261
1909.08994	Scalable Deep Unsupervised Clustering with Concrete GMVAEs	Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such methods requires marginalizing over the discrete latent variables, causing training time complexity to be linear in the number clusters. By appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,115
1304.1492	Map Learning with Indistinguishable Locations	Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional uncertainty. Uncertainty also arises in combining spatial information when one location is mistakenly identified...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,525
2210.15470	DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing	In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the applicatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	326,968
2303.17619	Gaze-based Attention Recognition for Human-Robot Collaboration	Attention (and distraction) recognition is a key factor in improving human-robot collaboration. We present an assembly scenario where a human operator and a cobot collaborate equally to piece together a gearbox. The setup provides multiple opportunities for the cobot to adapt its behavior depending on the operator's at...	true	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	355,288
2312.10787	Learning Discrete-Time Major-Minor Mean Field Games	Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to homogeneous players that weakly influence each other, and cannot model major players that strongly influence other players, severely limiting ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	416,313
2405.03252	A Universal List Decoding Algorithm with Application to Decoding of Polar Codes	This paper is concerned with a guessing codeword decoding (GCD) of linear block codes. Compared with the guessing noise decoding (GND), which is only efficient for high-rate codes, the GCD is efficient for not only high-rate codes but also low-rate codes. We prove that the GCD typically requires a fewer number of queri...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	452,115
2401.14688	Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support	Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support. To address this need, we present Taiyi-Diffusion-XL, a new Chinese and English bilingual text-to-image model which is develope...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	424,190
1403.4735	On the Automorphisms of Order 15 for a Binary Self-Dual [96, 48, 20] Code	The structure of binary self-dual codes invariant under the action of a cyclic group of order $pq$ for odd primes $p\neq q$ is considered. As an application we prove the nonexistence of an extremal self-dual $[96, 48, 20]$ code with an automorphism of order $15$ which closes a gap in `"On extremal self-dual codes of le...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,673
1401.0282	Design of a GIS-based Assistant Software Agent for the Incident Commander to Coordinate Emergency Response Operations	Problem: This paper addresses the design of an intelligent software system for the IC (incident commander) of a team in order to coordinate actions of agents (field units or robots) in the domain of emergency/crisis response operations. Objective: This paper proposes GICoordinator. It is a GIS-based assistant software ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	29,545
2203.14912	Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning	In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedious reward function tuning to achieve the desired motion style. Imitation learning approaches such as adversarial motion priors aim to reduce...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	288,172
2104.10307	Analyzing the Effect of Persistent Asset Switches on a Class of Hybrid-Inspired Optimization Algorithms	Convex optimization challenges are currently pervasive in many science and engineering domains. In many applications of convex optimization, such as those involving multi-agent systems and resource allocation, the objective function can persistently switch during the execution of an optimization algorithm. Motivated by...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	231,525
1903.01148	Asymmetric Single Magnitude Four Error Correcting Codes	Limited magnitude asymmetric error model is well suited for flash memory. In this paper, we consider the construction of asymmetric codes correcting single error over $\mathbb{Z}_{2^{k}r}$ and which are based on so called $B_{1}[4](2^{k}r)$ set. In fact, we reduce the construction of a maximal size $B_{1}[4](2^{k}r)$ s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	123,199
2312.04609	Short-term prediction of construction waste transport activities using AI-Truck	Construction waste hauling trucks (or `slag trucks') are among the most commonly seen heavy-duty diesel vehicles in urban streets, which not only produce significant carbon, NO$_{\textbf{x}}$ and PM$_{\textbf{2.5}}$ emissions but are also a major source of on-road and on-site fugitive dust. Slag trucks are subject to a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	413,757
2203.16069	A unified analysis framework for iterative parallel-in-time algorithms	Parallel-in-time integration has been the focus of intensive research efforts over the past two decades due to the advent of massively parallel computer architectures and the scaling limits of purely spatial parallelization. Various iterative parallel-in-time (PinT) algorithms have been proposed, like Parareal, PFASST,...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	288,642
2501.07334	Anonymization of Documents for Law Enforcement with Machine Learning	The steadily increasing utilization of data-driven methods and approaches in areas that handle sensitive personal information such as in law enforcement mandates an ever increasing effort in these institutions to comply with data protection guidelines. In this work, we present a system for automatically anonymizing ima...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	524,362
1304.2714	Higher Order Probabilities	A number of writers have supposed that for the full specification of belief, higher order probabilities are required. Some have even supposed that there may be an unending sequence of higher order probabilities of probabilities of probabilities.... In the present paper we show that higher order probabilities can always...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,723
2402.10517	Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs	Recently, considerable efforts have been directed towards compressing Large Language Models (LLMs), which showcase groundbreaking capabilities across diverse applications but entail significant deployment costs due to their large sizes. Meanwhile, much less attention has been given to mitigating the costs associated wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	430,000
2402.14144	Extending identifiability results from isolated networks to embedded networks	This paper deals with the design of Excitation and Measurement Patterns (EMPs) for the identification of dynamical networks, when the objective is to identify only a subnetwork embedded in a larger network. Recent results have shown how to construct EMPs that guarantee identifiability for a range of networks with speci...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	431,544
2008.02186	A Novel Method For Designing Transferable Soft Sensors And Its Application	In this paper, a new approach is proposed for designing transferable soft sensors. Soft sensing is one of the significant applications of data-driven methods in the condition monitoring of plants. While hard sensors can be easily used in various plants, soft sensors are confined to the specific plant they are designed ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	190,545
2211.11174	Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning	In AI, generalization refers to a model's ability to perform well on out-of-distribution data related to the given task, beyond the data it was trained on. For an AI agent to excel, it must also possess the continual learning capability, whereby an agent incrementally learns to perform a sequence of tasks without forge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,612
2009.02997	Predicting Requests in Large-Scale Online P2P Ridesharing	Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced polluti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	194,716
2404.00658	KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation	This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping. We pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,049
2010.13471	Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms	Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not fea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,141
2207.13835	Impactful Robots: Evaluating Visual and Audio Warnings to Help Users Brace for Impact in Human Robot Interaction	Wearable robotic devices have potential to assist and protect their users. Toward design of a Smart Helmet, this article examines the effectiveness of audio and visual warnings to help participants brace for impacts. A user study examines different warnings and impacts applied to users while running. Perturbation force...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	310,414
2406.03486	BIPED: Pedagogically Informed Tutoring System for ESL Education	Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learn...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	461,261
2501.06843	Leveraging the Global Research Infrastructure to Characterize the Impact of National Science Foundation Research	The Global Research infrastructure (GRI) is made up of the repositories and organizations that provide persistent identifiers (PIDs) and metadata for many kinds of research objects and connect these objects to funders, research institutions, researchers, and one another using PIDs. The INFORMATE Project has combined th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	524,156
2004.08526	Effect of Text Color on Word Embeddings	In natural scenes and documents, we can find the correlation between a text and its color. For instance, the word, "hot", is often printed in red, while "cold" is often in blue. This correlation can be thought of as a feature that represents the semantic difference between the words. Based on this observation, we propo...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	173,084
2401.15889	Sliced Wasserstein with Random-Path Projecting Directions	Slicing distribution selection has been used as an effective technique to improve the performance of parameter estimators based on minimizing sliced Wasserstein distance in applications. Previous works either utilize expensive optimization to select the slicing distribution or use slicing distributions that require exp...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	424,632
2308.09909	Never Explore Repeatedly in Multi-Agent Reinforcement Learning	In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on estimating variational posteriors using neural network approximators, a notable challenge has surfaced due to the limited expressive capabil...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	386,480
2209.13325	Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models	Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited devices. Therefore, transformer quantization attracts wide resear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,849
2204.02488	Discovering and forecasting extreme events via active learning in neural operators	Extreme events in society and nature, such as pandemic spikes, rogue waves, or structural failures, can have catastrophic consequences. Characterizing extremes is difficult as they occur rarely, arise from seemingly benign conditions, and belong to complex and often unknown infinite-dimensional systems. Such challenges...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	289,962
2211.08971	Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods	Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processes. TAIGA-IACT enables us to select gamma quanta from the total cosmic radiation flux and recover their primary parameters, such as energy an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,825
2106.04229	BIGDML: Towards Exact Machine Learning Force Fields for Materials	Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for traini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,647
2204.10206	Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics	Question answering-based summarization evaluation metrics must automatically determine whether the QA model's prediction is correct or not, a task known as answer verification. In this work, we benchmark the lexical answer verification methods which have been used by current QA-based metrics as well as two more sophist...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	292,706
1802.02840	Neural Network Renormalization Group	We present a variational renormalization group (RG) approach using a deep generative model based on normalizing flows. The model performs hierarchical change-of-variables transformations from the physical space to a latent space with reduced mutual information. Conversely, the neural net directly maps independent Gauss...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,846
1712.01694	Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering	The materialist dialectical method is a philosophical investigative method to analyze aspects of reality. These aspects are viewed as complex processes composed by basic units named poles, which interact with each other. Dialectics has experienced considerable progress in the 19th century, with Hegel's dialectics and, ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	true	86,155
2404.12400	Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning	In the landscape of spatio-temporal data analytics, effective trajectory representation learning is paramount. To bridge the gap of learning accurate representations with efficient and flexible mechanisms, we introduce Efflex, a comprehensive pipeline for transformative graph modeling and representation learning of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	447,875
1802.02254	Trajectory-driven Influential Billboard Placement	In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards $U$ (each with a location and a cost), a database of trajectories $\mathcal{T}$ and a budget $L$, find a set of billboards within the budget to influence the largest number of trajectories. One...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	89,743
2004.02458	Optimal Correlators for Detection and Estimation in Optical Receivers	Motivated by modern applications of light detection and ranging (LIDAR), we study the model of an optical receiver based on an avalanche photo-diode (APD), followed by electronic circuitry for detection of reflected optical signals and estimation of their delay.This model is known to be quite complicated as it consists...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	171,236
2311.15030	A Learning Quasi-stiffness Control Framework of a Powered Trans-femoral Prosthesis for Adaptive Speed and Incline Walking	Impedance-based control represents a prevalent strategy in the powered trans femoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walkin...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	410,354
2501.11196	Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning	Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResUNet architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an Atrous S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,825
cs/0606118	Adapting a general parser to a sublanguage	In this paper, we propose a method to adapt a general parser (Link Parser) to sublanguages, focusing on the parsing of texts in biology. Our main proposal is the use of terminology (identication and analysis of terms) in order to reduce the complexity of the text to be parsed. Several other strategies are explored and ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	539,546
1301.7368	Irrelevance and Independence Relations in Quasi-Bayesian Networks	This paper analyzes irrelevance and independence relations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How can irrelevance/independence relations in Quasi-Bayesian networks be detected, enforced and expl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,602
2404.18820	Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior	Image compression at extremely low bitrates (below 0.1 bits per pixel (bpp)) is a significant challenge due to substantial information loss. In this work, we propose a novel two-stage extreme image compression framework that exploits the powerful generative capability of pre-trained diffusion models to achieve realisti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,400
1212.6592	Social Teaching: Being Informative vs. Being Right in Sequential Decision Making	We show that it can be suboptimal for Bayesian decision-making agents employing social learning to use correct prior probabilities as their initial beliefs. We consider sequential Bayesian binary hypothesis testing where each individual agent makes a binary decision based on an initial belief, a private signal, and the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,655
2209.07947	Omni-Dimensional Dynamic Convolution	Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of $n$ convolutional kernels weighted with their input-dependent attentions c...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	317,953
2006.03481	Providing reliability in Recommender Systems through Bernoulli Matrix Factorization	Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative filtering. This paper proposes Bernoulli Matrix Factorization (BeMF), which is a matrix factorization model, to provide both prediction valu...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	180,324
2410.07611	Parallel Digital Twin-driven Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks	Optimization of user association in a densely deployed heterogeneous cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hinder...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	496,712
1804.06958	A-CCNN: adaptive ccnn for density estimation and crowd counting	Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,419
2404.17484	Sparse Reconstruction of Optical Doppler Tomography Based on State Space Model	Optical Doppler Tomography (ODT) is a blood flow imaging technique popularly used in bioengineering applications. The fundamental unit of ODT is the 1D frequency response along the A-line (depth), named raw A-scan. A 2D ODT image (B-scan) is obtained by first sensing raw A-scans along the B-line (width), and then const...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,871
2210.08971	APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing	Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the qu...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	324,368
0804.3599	Respect My Authority! HITS Without Hyperlinks, Utilizing Cluster-Based Language Models	We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that thes...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	1,620
2212.07635	Let's consider more general nonlinear approaches to study teleconnections of climate variables	The recent work by (Rieger et al 2021) is concerned with the problem of extracting features from spatio-temporal geophysical signals. The authors introduce the complex rotated MCA (xMCA) to deal with lagged effects and non-orthogonality of the feature representation. This method essentially (1) transforms the signals t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,473
2309.14957	Context-Aware Generative Models for Prediction of Aircraft Ground Tracks	Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based, with parameters that are calibrated using aircraft surveillance data harvested across the world. These models are, therefore, agnostic to t...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	394,798
2207.09217	Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking	Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	308,838
1305.0606	Results from a Practical Deployment of the MyZone Decentralized P2P Social Network	This paper presents MyZone, a private online social network for relatively small, closely-knit communities. MyZone has three important distinguishing features. First, users keep the ownership of their data and have complete control over maintaining their privacy. Second, MyZone is free from any possibility of content c...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	true	24,363
2104.11067	K\"unstliche Intelligenz, quo vadis?	This paper outlines the state of the art in AI. It then describes basic machine learning and knowledge processing techniques. Based on this, some possibilities and limitations of future AI developments are discussed.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	231,807
2005.08898	Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent	Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to factorize the matrix into two compact low-rank factors, and then optimize these factors directly via simple iterative methods such as gradie...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	177,763
2501.09948	AI Explainability for Power Electronics: From a Lipschitz Continuity Perspective	Lifecycle management of power converters continues to thrive with emerging artificial intelligence (AI) solutions, yet AI mathematical explainability remains unexplored in power electronics (PE) community. The lack of theoretical rigor challenges adoption in mission-critical applications. Therefore, this letter propose...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	525,332
2109.02022	Recommending Researchers in Machine Learning based on Author-Topic Model	The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top r...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	253,608
2404.02438	From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives	In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the decedent's COD. Turning VAs into actionable insights for researchers and policyma...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	443,855
2110.06192	Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes	We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive pol...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	260,529
1908.06381	Long-Duration Fully Autonomous Operation of Rotorcraft Unmanned Aerial Systems for Remote-Sensing Data Acquisition	Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, e.g. days or weeks. This has so far been impractical due to the l...	false	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	142,003
2409.01691	When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels	Tooth point cloud segmentation is a fundamental task in many orthodontic applications. Current research mainly focuses on fully supervised learning which demands expensive and tedious manual point-wise annotation. Although recent weakly-supervised alternatives are proposed to use weak labels for 3D segmentation and ach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,443
2402.14658	OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement	The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter. To address this, we introduce OpenCodeInterpreter, a family of open-source code systems de...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	431,779
2110.03262	Situated Dialogue Learning through Procedural Environment Generation	We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al. 2019) -- a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	259,427
0801.1060	On the Period of a Periodic-Finite-Type Shift	Periodic-finite-type shifts (PFT's) form a class of sofic shifts that strictly contains the class of shifts of finite type (SFT's). In this paper, we investigate how the notion of "period" inherent in the definition of a PFT causes it to differ from an SFT, and how the period influences the properties of a PFT.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,132
2411.10156	Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Intervention	To address the sycophancy problem caused by reinforcement learning from human feedback in large language models, this research applies synthetic data intervention technology to the decoder-only transformer architecture. Based on the research gaps in the existing literature, the researcher designed an experimental proce...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,522
1811.01057	Semidefinite relaxations for certifying robustness to adversarial examples	Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs---imperceptibly but adversarially perturbed versions of natural inputs. We have witnessed an arms race between defenders who attempt to train robust networks and attackers who try to constr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	112,255
2008.06464	Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network	In this paper, we investigate the computational resource allocation problem in a distributed Ad-Hoc vehicular network with no centralized infrastructure support. To support the ever increasing computational needs in such a vehicular network, the distributed virtual cloud network (VCN) is formed, based on which a comput...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	191,812
2211.07647	An Interpretable Neuron Embedding for Static Knowledge Distillation	Although deep neural networks have shown well-performance in various tasks, the poor interpretability of the models is always criticized. In the paper, we propose a new interpretable neural network method, by embedding neurons into the semantic space to extract their intrinsic global semantics. In contrast to previous ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	330,322
2309.15670	MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection	In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	395,065
2411.19943	Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability	Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we introduce the concept of critical tokens -- elements within reasoning trajectories that significantly influence incorrect outcomes. We present ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	512,438
2205.08467	Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer	The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	296,952
1807.08415	Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories	Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	103,535
2206.03003	Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records	In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based not o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	301,111
2202.11170	Multi-fidelity reinforcement learning framework for shape optimization	Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized for several scientific applications, specifically in cases where classical optimization or control methods are limited. One key limitation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,787
2208.11283	A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis	Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	314,368
2111.10342	GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation	In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking GNN-based recommendation models in an efficient and unified way. GRecX consists of core libraries for building GNN-based recommendation benchmarks, as well as the implementations of popular GNN-based recommendation models. The core li...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	267,291
2006.08040	Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs	We develop a new approach to obtaining high probability regret bounds for online learning with bandit feedback against an adaptive adversary. While existing approaches all require carefully constructing optimistic and biased loss estimators, our approach uses standard unbiased estimators and relies on a simple increasi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,047
1106.1017	MMSE of "Bad" Codes	We examine codes, over the additive Gaussian noise channel, designed for reliable communication at some specific signal-to-noise ratio (SNR) and constrained by the permitted minimum mean-square error (MMSE) at lower SNRs. The maximum possible rate is below point-to-point capacity, and hence these are non-optimal codes ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,737
1609.09430	CNN Architectures for Large-Scale Audio Classification	Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs)...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,715
2202.06369	Incremental user embedding modeling for personalized text classification	Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has become increasingly challenging due to eve...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	280,195
2011.09776	Improving Bayesian Network Structure Learning in the Presence of Measurement Error	Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the re...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	207,302
1112.0371	Zigzag Codes: MDS Array Codes with Optimal Rebuilding	MDS array codes are widely used in storage systems to protect data against erasures. We address the \emph{rebuilding ratio} problem, namely, in the case of erasures, what is the fraction of the remaining information that needs to be accessed in order to rebuild \emph{exactly} the lost information? It is clear that when...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,288
2105.10922	OntoED: Low-resource Event Detection with Ontology Embedding	Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	236,545
2205.01089	ComPhy: Compositional Physical Reasoning of Objects and Events from Videos	Objects' motions in nature are governed by complex interactions and their properties. While some properties, such as shape and material, can be identified via the object's visual appearances, others like mass and electric charge are not directly visible. The compositionality between the visible and hidden properties po...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	294,478
1910.00294	When and Why is Document-level Context Useful in Neural Machine Translation?	Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensiv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,631

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