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
1806.04836	Partial Replanning for Decentralized Dynamic Task Allocation	In time-sensitive and dynamic missions, multi-UAV teams must respond quickly to new information and objectives. This paper presents a dynamic decentralized task allocation algorithm for allocating new tasks that appear online during the solving of the task allocation problem. Our algorithm extends the Consensus-Based B...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	100,330
2407.19154	RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometry	3D sensing is a fundamental task for Autonomous Vehicles. Its deployment often relies on aligned RGB cameras and LiDAR. Despite meticulous synchronization and calibration, systematic misalignment persists in LiDAR projected depthmap. This is due to the physical baseline distance between the two sensors. The artifact is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,663
2209.05286	DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text	Models that accurately detect depression from text are important tools for addressing the post-pandemic mental health crisis. BERT-based classifiers' promising performance and the off-the-shelf availability make them great candidates for this task. However, these models are known to suffer from performance inconsistenc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	317,055
2007.09465	PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation	We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,956
1908.03569	Concepts and Applications of Conformal Prediction in Computational Drug Discovery	Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings. Among the large number of algorithms developed over the last decades...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,263
2210.03841	Breaking BERT: Evaluating and Optimizing Sparsified Attention	Transformers allow attention between all pairs of tokens, but there is reason to believe that most of these connections - and their quadratic time and memory - may not be necessary. But which ones? We evaluate the impact of sparsification patterns with a series of ablation experiments. First, we compare masks based on ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	322,191
2301.00201	Exploring Singularities in point clouds with the graph Laplacian: An explicit approach	We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of datasets. Our theory provides theoretical guarantees and explicit bounds on the functional forms of the graph Laplacian when it acts on functions defined close to singularities of the underlying manifold. We...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,837
2307.07231	Long Short-term Memory with Two-Compartment Spiking Neuron	The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	379,326
2501.04914	From Mesh Completion to AI Designed Crown	Designing a dental crown is a time-consuming and labor intensive process. Our goal is to simplify crown design and minimize the tediousness of making manual adjustments while still ensuring the highest level of accuracy and consistency. To this end, we present a new end- to-end deep learning approach, coined Dental Mes...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	523,392
2107.05748	Evaluation of an Inflated Beam Model Applied to Everted Tubes	Everted tubes have often been modeled as inflated beams to determine transverse and axial buckling conditions. This paper seeks to validate the assumption that an everted tube can be modeled in this way. The tip deflections of everted and uneverted beams under transverse cantilever loads are compared with a tip deflect...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	245,869
2303.12479	Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation	Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different timescale...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	353,277
2002.04988	Saliency Driven Perceptual Image Compression	This paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a hierarchical auto-regressive model. This paper demonstrates that the popularly used evaluations ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,754
2207.00321	Safe Controlled Invariance for Linear Systems Using Sum-of-Squares Programming	Safety is closely related to set invariance for dynamical systems. However, synthesizing a safe invariant set and at the same time synthesizing the associated safe controller still remains challenging. In this note we introduce a simple invariance-based method for linear systems with safety guarantee. The proposed meth...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	305,718
1905.12914	Meta Dropout: Learning to Perturb Features for Generalization	A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test examples, we may achieve better generalization performance. However, obtaining such perturbation is not possible in standard machine learning f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,933
1606.06357	Complex Embeddings for Simple Link Prediction	In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	57,564
2204.05783	Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets	Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,144
2008.02231	Can You Read Me Now? Content Aware Rectification using Angle Supervision	The ubiquity of smartphone cameras has led to more and more documents being captured by cameras rather than scanned. Unlike flatbed scanners, photographed documents are often folded and crumpled, resulting in large local variance in text structure. The problem of document rectification is fundamental to the Optical Cha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,563
2410.03788	Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning	Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the reliance on trajectory data, which often fails to capture the semantic interdependencies of activities, and the inherent incompleteness of real-w...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	494,970
1908.08676	Hierarchically-Refined Label Attention Network for Sequence Labeling	CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain ove...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	142,624
2103.14071	Accelerating Big-Data Sorting Through Programmable Switches	Sorting is a fundamental and well studied problem that has been studied extensively. Sorting plays an important role in the area of databases, as many queries can be served much faster if the relations are first sorted. One of the most popular sorting algorithm in databases is merge sort. In modern data-centers, data...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	226,713
2204.10747	A New Polar Code Design Based on Reciprocal Channel Approximation	This paper revisits polar code design for a binary-input additive white Gaussian noise (BI-AWGN) channel when successive cancellation (SC) decoding is applied at the receiver. We focus on the reciprocal channel approximation (RCA), which is often adopted in the design of low-density parity-check (LDPC) codes. In order ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	292,899
1702.05764	Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm	What is the best way to describe a user in a social network with just a few numbers? Mathematically, this is equivalent to assigning a vector representation to each node in a graph, a process called graph embedding. We propose a novel framework, GEM-D that unifies most of the past algorithms such as LapEigs, DeepWalk a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	68,466
2109.00217	Multi-Sample based Contrastive Loss for Top-k Recommendation	The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a p...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	253,048
1909.04493	Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba	Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex quer...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	144,818
2304.10535	Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion	We present Farm3D, a method for learning category-specific 3D reconstructors for articulated objects, relying solely on "free" virtual supervision from a pre-trained 2D diffusion-based image generator. Recent approaches can learn a monocular network that predicts the 3D shape, albedo, illumination, and viewpoint of any...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,449
1606.03968	Visual-Inertial-Semantic Scene Representation for 3-D Object Detection	We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A min...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	57,172
2312.13208	LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces	Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces with the state-of-the-art performance of recent large language models ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	417,236
2408.12781	The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction	The utilization of AI in an increasing number of fields is the latest iteration of a long process, where machines and systems have been replacing humans, or changing the roles that they play, in various tasks. Although humans are often resistant to technological innovation, especially in workplaces, there is a general ...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	482,877
1411.0264	On the read-once property of branching programs and CNFs of bounded treewidth	In this paper we prove a space lower bound of $n^{\Omega(k)}$ for non-deterministic (syntactic) read-once branching programs ({\sc nrobp}s) on functions expressible as {\sc cnf}s with treewidth at most $k$ of their primal graphs. This lower bound rules out the possibility of fixed-parameter space complexity of {\sc nro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	37,232
1802.03274	Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas	Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas. The process involves applying a high voltage electric field to the tissue containing the mass using needle electrodes, leaving cancerous cells irreversibly damaged and vulnerable to apopt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,938
2103.03466	Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change	A multiplicative constant scaling factor is often applied to the model output to adjust the dynamics of neural network parameters. This has been used as one of the key interventions in an empirical study of lazy and active behavior. However, we show that the combination of such scaling and a commonly used adaptive lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,293
2005.12513	DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning	Whether it be in a man-made machine or a biological system, form and function are often directly related. In the latter, however, this particular relationship is often unclear due to the intricate nature of biology. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cort...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	178,751
2410.17933	Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning	One of the biggest challenges of building artificial intelligence (AI) model in healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausted, costly, and sometimes impossible. In this paper, we propose a framework for global h...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	501,670
1804.07068	Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning	In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize a similar syntactic st...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,446
1907.12336	Goal-Driven Sequential Data Abstraction	Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	140,089
2207.06646	DropNet: Reducing Neural Network Complexity via Iterative Pruning	Modern deep neural networks require a significant amount of computing time and power to train and deploy, which limits their usage on edge devices. Inspired by the iterative weight pruning in the Lottery Ticket Hypothesis, we propose DropNet, an iterative pruning method which prunes nodes/filters to reduce network comp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,942
2410.23643	SceneComplete: Open-World 3D Scene Completion in Complex Real World Environments for Robot Manipulation	Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid mistakenly colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited inpu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	504,121
2206.12520	Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks	We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning hav...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	304,629
2001.11905	Verifying Tree Ensembles by Reasoning about Potential Instances	Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's prediction?" or "What kind of predictions could possibly be made for a partially described example?" This last question is particularly important...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	162,198
2101.01568	Adversarially trained LSTMs on reduced order models of urban air pollution simulations	This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Components Analysis (PCA) and adversarial training, is a way to improve the forecast skill of reduced order models obtained from the original model s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,396
1409.8202	Short-Term Predictability of Photovoltaic Production over Italy	Photovoltaic (PV) power production increased drastically in Europe throughout the last years. About the 6% of electricity in Italy comes from PV and for an efficient management of the power grid an accurate and reliable forecasting of production would be needed. Starting from a dataset of electricity production of 65 I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,392
2212.14334	Constant Approximation for Normalized Modularity and Associations Clustering	We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster. We show that our definition is closely related to popular clustering measures, namely no...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	338,582
2409.03763	A Dataset for Mechanical Mechanisms	This study introduces a dataset consisting of approximately 9,000 images of mechanical mechanisms and their corresponding descriptions, aimed at supporting research in mechanism design. The dataset consists of a diverse collection of 2D and 3D sketches, meticulously curated to ensure relevance and quality. We demonstra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,163
2404.06954	Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy	Recently, dynamic computation methods have shown notable acceleration for Large Language Models (LLMs) by skipping several layers of computations through elaborate heuristics or additional predictors. However, in the decoding process of existing approaches, different samples are assigned different computational budgets...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	445,661
2309.17382	Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency	Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unclear how to complete a given task provably within a minimum number of interactions with the external environment, e.g., through an internal ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	395,750
2409.06211	STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning	Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in Large language models (LLMs). Despite this reduction, the massive number of experts in MoEs still makes them expensive to serve. In this paper, we study how to address this, by pruning MoEs. Among pruning methodol...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	487,043
2209.05672	Human-Guided Planning for Complex Manipulation Tasks Using the Screw Geometry of Motion	In this paper, we present a novel method of motion planning for performing complex manipulation tasks by using human demonstration and exploiting the screw geometry of motion. We consider complex manipulation tasks where there are constraints on the motion of the end effector of the robot. Examples of such tasks includ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	317,164
2301.03358	Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks	In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks for reducing network slicing cost while satisfying the quality of service requirements. We propose a two-stage network slicing framework, wh...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	339,771
2112.03546	Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media	Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly-connected ``hub" individuals...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	270,244
2012.10890	PPGN: Phrase-Guided Proposal Generation Network For Referring Expression Comprehension	Reference expression comprehension (REC) aims to find the location that the phrase refer to in a given image. Proposal generation and proposal representation are two effective techniques in many two-stage REC methods. However, most of the existing works only focus on proposal representation and neglect the importance o...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	212,471
2501.08538	Homophily-aware Heterogeneous Graph Contrastive Learning	Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS,...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,804
1705.09980	Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations	We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-to-sequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1 (F-score on AMR-tripl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	74,314
1907.06846	Coherency and Online Signal Selection Based Wide Area Control of Wind Integrated Power Grid	This paper introduces a novel method of designing wide area control (WAC) based on a discrete linear quadratic regulator and Kalman filtering based state-estimation that can be applied for real-time damping of interarea oscillations of wind integrated power grid. The main advantages of the proposed method are that the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	138,721
2408.00214	Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control	Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. To showcase the potential of LLM technologies, we consider the base station (BS) power control as a case study, a fundamental but crucial tec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	477,744
2010.03633	Simplicial Neural Networks	We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are natural multi-dimensional extensions of graphs that encode not only pairwise relationships but also higher-order interactions between vertices...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,460
2205.05428	An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity	The leaky ReLU network with a group sparse regularization term has been widely used in the recent years. However, training such a network yields a nonsmooth nonconvex optimization problem and there exists a lack of approaches to compute a stationary point deterministically. In this paper, we first resolve the multi-lay...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,926
1301.2272	Enumerating Markov Equivalence Classes of Acyclic Digraph Models	Graphical Markov models determined by acyclic digraphs (ADGs), also called directed acyclic graphs (DAGs), are widely studied in statistics, computer science (as Bayesian networks), operations research (as influence diagrams), and many related fields. Because different ADGs may determine the same Markov equivalence cla...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,948
1902.11088	Scaling Matters in Deep Structured-Prediction Models	Deep structured-prediction energy-based models combine the expressive power of learned representations and the ability of embedding knowledge about the task at hand into the system. A common way to learn parameters of such models consists in a multistage procedure where different combinations of components are trained ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,850
1903.01651	On the Global Synchronization of Pulse-coupled Oscillators Interacting on Chain and Directed Tree Graphs	Driven by increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators (PCOs) has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synch...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,307
2006.02014	Norm-Based Curriculum Learning for Neural Machine Translation	A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	179,933
2004.11839	Detecting Driver's Distraction using Long-term Recurrent Convolutional Network	In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that generates EEG signals from the brain. We collected real EEG signals from participants who undertook a 40-minute driving simulation and were r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	174,039
2103.06993	Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots	Long-term deployment of a fleet of mobile robots requires reliable and secure two-way communication channels between individual robots and remote human operators for supervision and tasking. Existing open-source solutions to this problem degrade in performance in challenging real-world situations such as intermittent a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	224,458
2008.11849	SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference	In recent years, there has been a flurry of research in deep neural network pruning and compression. Early approaches prune weights individually. However, it is difficult to take advantage of the resulting unstructured sparsity patterns on modern hardware like GPUs. As a result, pruning strategies which impose sparsity...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,399
1208.0080	The Complexity of Social Coordination	Coordination is a challenging everyday task; just think of the last time you organized a party or a meeting involving several people. As a growing part of our social and professional life goes online, an opportunity for an improved coordination process arises. Recently, Gupta et al. proposed entangled queries as a decl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	17,857
1806.10055	Twisted Gabidulin Codes in the GPT Cryptosystem	In this paper, we investigate twisted Gabidulin codes in the GPT code-based public-key cryptosystem. We show that Overbeck's attack is not feasible for a subfamily of twisted Gabidulin codes. The resulting key sizes are significantly lower than in the original McEliece system and also slightly smaller than in Loidreau'...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	101,477
2103.16364	ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification	Unsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increasing attention for its effectiveness in unsupervised representation learning. The main idea of instance contrastive learning is to match a s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,557
2303.11272	Agent-based Simulation for Online Mental Health Matching	Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdevelope...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	352,780
2401.15059	Fully Independent Communication in Multi-Agent Reinforcement Learning	Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of communication approaches in MARL. While multiple communication methods have been proposed, these might still be too complex and not easily ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	424,306
1912.07913	Learning high-dimensional probability distributions using tree tensor networks	We consider the problem of the estimation of a high-dimensional probability distribution from i.i.d. samples of the distribution using model classes of functions in tree-based tensor formats, a particular case of tensor networks associated with a dimension partition tree. The distribution is assumed to admit a density ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	157,720
1911.00617	Explicit Explore-Exploit Algorithms in Continuous State Spaces	We present a new model-based algorithm for reinforcement learning (RL) which consists of explicit exploration and exploitation phases, and is applicable in large or infinite state spaces. The algorithm maintains a set of dynamics models consistent with current experience and explores by finding policies which induce hi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	151,863
2412.05078	An Experimental Framework for Implementing Decentralized Autonomous Database Systems in Rust	This paper presents an experimental framework for implementing Decentralized Autonomous Database Systems (DADBS) using the Rust programming language. As traditional centralized databases face challenges in scalability, security, and autonomy, DADBS emerge as a promising solution, using blockchain principles to create d...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	514,681
2208.00116	Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds	Cooperative perception allows a Connected Autonomous Vehicle (CAV) to interact with the other CAVs in the vicinity to enhance perception of surrounding objects to increase safety and reliability. It can compensate for the limitations of the conventional vehicular perception such as blind spots, low resolution, and weat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,745
2306.13926	Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective	Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs. However, understanding the feature learning aspect of GNNs is still in its initial stage. This study aims to brid...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,460
2408.15461	Hand1000: Generating Realistic Hands from Text with Only 1,000 Images	Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	483,943
0912.2425	Consensus and synchronization in discrete-time networks of multi-agents with stochastically switching topologies and time delays	We analyze stability of consensus algorithms in networks of multi-agents with time-varying topologies and delays. The topology and delays are modeled as induced by an adapted process and are rather general, including i.i.d.\ topology processes, asynchronous consensus algorithms, and Markovian jumping switching. In case...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	5,149
1608.05167	AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification	Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in remote sensing area and numerous algorithms have been proposed for this ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,938
2006.11078	Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers	An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of the art frameworks focus on adversarial attacks for images and other structured model inputs, but not for categorical sequences models. Su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,097
2312.08537	Object-Centric Conformance Alignments with Synchronization (Extended Version)	Real-world processes operate on objects that are inter-dependent. To accurately reflect the nature of such processes, object-centric process mining techniques are needed, notably conformance checking. However, while the object-centric perspective has recently gained traction, few concrete process mining techniques have...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	415,334
1804.06620	Visualizing the Feature Importance for Black Box Models	In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	95,343
2011.04125	Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra	We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues. De-quantizing such algorithms has received a flurry of attention in recent years; we obtain sharper bounds for these problems. More significan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	205,471
1811.04775	Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach	In this paper, we studied the problem of beam alignment for millimeter wave (mmWave) communications, in which we assume a hybrid analog and digital beamforming structure is employed at the transmitter (i.e. base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e. user). By exploi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	113,174
2404.09371	The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation	Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	446,642
2012.11646	Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health	Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning algorithms have been found to be effective for learning the optimal context under which to provide suggestions. However, these algorithms ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	212,690
2106.06351	Part-aware Panoptic Segmentation	In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Citysc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,456
2011.03783	AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations	In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	205,351
2110.10275	Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach	Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to ma...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,084
2006.10734	Forward Prediction for Physical Reasoning	Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state. We study the performance of state-of-the-art forward-prediction models in the complex physical-reasoning tasks of the PHYRE benchmark. We do so by incorporating models that operate on object or ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	182,992
1102.5586	Covert channel detection using Information Theory	This paper presents an information theory based detection framework for covert channels. We first show that the usual notion of interference does not characterize the notion of deliberate information flow of covert channels. We then show that even an enhanced notion of "iterated multivalued interference" can not captur...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	9,402
2501.00332	MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation	Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses. Howeve...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	521,627
2107.07356	DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections	The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate att...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	246,393
1803.06555	Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages	We address the problem of finding descriptive explanations of facts stored in a knowledge graph. This is important in high-risk domains such as healthcare, intelligence, etc. where users need additional information for decision making and is especially crucial for applications that rely on automatically constructed kno...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	92,862
2304.13181	Sample-Specific Debiasing for Better Image-Text Models	Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) and dissimilar (negative) pairs of data points. Drawing negative sample...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	360,492
2211.08141	SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss	In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA). We train a deep encoder to learn features such that the Self-Similarity-Matrix (SSM) resulting from those approximates a ground-truth SSM. This is done by minimizing a loss between both SSMs. Since this loss is differen...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,492
1909.00230	Collaborative Policy Learning for Open Knowledge Graph Reasoning	In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse and incomplete graphs, due to the lack of evidential paths that can reach target entities. Here we study open knowledge graph reasoning---a ta...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	143,573
2308.07072	Teeth And Root Canals Segmentation Using ZXYFormer With Uncertainty Guidance And Weight Transfer	This study attempts to segment teeth and root-canals simultaneously from CBCT images, but there are very challenging problems in this process. First, the clinical CBCT image data is very large (e.g., 672 688 688), and the use of downsampling operation will lose useful information about teeth and root canals. Second,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	385,383
2201.09748	A Critical Review of Baseband Architectures for CubeSats Communication Systems	Small satellite communications recently entered a period of massive interest driven by the uprising space applications. CubeSats are particularly attractive due to their low development costs which makes them very promising in playing a central role in the global wireless communication sector with numerous applications...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	276,772
2003.07490	Recent Advances and Challenges in Task-oriented Dialog System	Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in task-oriented dialog systems. We also discuss three...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	168,444
2409.06590	Lightweight single-image super-resolution network based on dual paths	The single image super-resolution(SISR) algorithms under deep learning currently have two main models, one based on convolutional neural networks and the other based on Transformer. The former uses the stacking of convolutional layers with different convolutional kernel sizes to design the model, which enables the mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,182
1711.08762	DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem	This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	85,265
2007.13034	Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve	Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	189,001

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