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
1808.07747	On the Diversity of Uncoded OTFS Modulation in Doubly-Dispersive Channels	Orthogonal time frequency space (OTFS) is a 2-dimensional (2D) modulation technique designed in the delay-Doppler domain. A key premise behind OTFS is the transformation of a time varying multipath channel into an almost non-fading 2D channel in delay-Doppler domain such that all symbols in a transmission frame experie...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,806
1305.5796	Efficient methods for computing observation impact in 4D-Var data assimilation	This paper presents a practical computational approach to quantify the effect of individual observations in estimating the state of a system. Such an analysis can be used for pruning redundant measurements, and for designing future sensor networks. The mathematical approach is based on computing the sensitivity of the ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,791
2405.03637	Collage: Light-Weight Low-Precision Strategy for LLM Training	Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical accuracy and unstable training rendering the model less useful. We argue that low-precision floating points can perform well provided the er...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,251
2112.08024	Visually Guided UGV for Autonomous Mobile Manipulation in Dynamic and Unstructured GPS Denied Environments	A robotic solution for the unmanned ground vehicles (UGVs) to execute the highly complex task of object manipulation in an autonomous mode is presented. This paper primarily focuses on developing an autonomous robotic system capable of assembling elementary blocks to build the large 3D structures in GPS-denied environm...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	271,674
2010.10640	Private Weighted Sum Aggregation	As large amounts of data are circulated both from users to a cloud server and between users, there is a critical need for privately aggregating the shared data. This paper considers the problem of private weighted sum aggregation with secret weights, where an aggregator wants to compute the weighted sum of the local da...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	201,943
2311.05889	Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models	Wireless capsule endoscopy (WCE) is a non-invasive method for visualizing the gastrointestinal (GI) tract, crucial for diagnosing GI tract diseases. However, interpreting WCE results can be time-consuming and tiring. Existing studies have employed deep neural networks (DNNs) for automatic GI tract lesion detection, but...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	406,747
2408.02029	Mining Path Association Rules in Large Property Graphs (with Appendix)	How can we mine frequent path regularities from a graph with edge labels and vertex attributes? The task of association rule mining successfully discovers regular patterns in item sets and substructures. Still, to our best knowledge, this concept has not yet been extended to path patterns in large property graphs. In t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	478,472
2409.16310	A Survey on Codes from Simplicial Complexes	In the field of mathematics, a purely combinatorial equivalent to a simplicial complex, or more generally, a down-set, is an abstract structure known as a family of sets. This family is closed under the operation of taking subsets, meaning that every subset of a set within the family is also included in the family. The...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	491,293
1808.06843	Deep Learned Full-3D Object Completion from Single View	3D geometry is a very informative cue when interacting with and navigating an environment. This writing proposes a new approach to 3D reconstruction and scene understanding, which implicitly learns 3D geometry from depth maps pairing a deep convolutional neural network architecture with an auto-encoder. A data set of s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,622
2201.05349	Training Free Graph Neural Networks for Graph Matching	We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free). TFGM provides four widely applicable principles for designing training-free GNNs and is generalizable to supe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,369
2306.10239	Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection	Video Anomaly Detection (VAD) is an essential yet challenging task in signal processing. Since certain anomalies cannot be detected by isolated analysis of either temporal or spatial information, the interaction between these two types of data is considered crucial for VAD. However, current dual-stream architectures ei...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	374,154
2010.03378	Descriptive analysis of computational methods for automating mammograms with practical applications	Mammography is a vital screening technique for early revealing and identification of breast cancer in order to assist to decrease mortality rate. Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	199,387
2403.11418	Variational Sampling of Temporal Trajectories	A deterministic temporal process can be determined by its trajectory, an element in the product space of (a) initial condition $z_0 \in \mathcal{Z}$ and (b) transition function $f: (\mathcal{Z}, \mathcal{T}) \to \mathcal{Z}$ often influenced by the control of the underlying dynamical system. Existing methods often mode...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	438,687
2404.19749	Scale-Robust Timely Asynchronous Decentralized Learning	We consider an asynchronous decentralized learning system, which consists of a network of connected devices trying to learn a machine learning model without any centralized parameter server. The users in the network have their own local training data, which is used for learning across all the nodes in the network. The ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	true	450,757
2107.09579	Semantic Reasoning with Differentiable Graph Transformations	This paper introduces a differentiable semantic reasoner, where rules are presented as a relevant set of graph transformations. These rules can be written manually or inferred by a set of facts and goals presented as a training set. While the internal representation uses embeddings in a latent space, each rule can be e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	247,067
2110.03195	Coresets for Decision Trees of Signals	A $k$-decision tree $t$ (or $k$-tree) is a recursive partition of a matrix (2D-signal) into $k\geq 1$ block matrices (axis-parallel rectangles, leaves) where each rectangle is assigned a real label. Its regression or classification loss to a given matrix $D$ of $N$ entries (labels) is the sum of squared differences ove...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	259,407
2104.10716	Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks	Graph neural networks (GNNs), an emerging deep learning model class, can extract meaningful representations from highly expressive graph-structured data and are therefore gaining popularity for wider ranges of applications. However, current GNNs suffer from the poor performance of their sparse-dense matrix multiplicati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	231,673
2201.10776	DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction	Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities to aid in radiological decision-making. Given the need for lowering the time cost of multiple acquisitions, current deep accelerated MRI reconstruction networks focus on exploiting the redundancy between multiple contrasts. However, existing...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	277,098
2103.09898	Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel	The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	225,279
2112.03421	Virtual Replay Cache	Return caching is a recent strategy that enables efficient minibatch training with multistep estimators (e.g. the {\lambda}-return) for deep reinforcement learning. By precomputing return estimates in sequential batches and then storing the results in an auxiliary data structure for later sampling, the average computat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,192
1907.04461	Model Development Process	Predictive modeling has an increasing number of applications in various fields. High demand for predictive models drives creation of tools that automate and support work of data scientist on the model development. To better understand what can be automated we need first a description of the model life-cycle. In this pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,110
1706.06051	Learning to Schedule Deadline- and Operator-Sensitive Tasks	The use of semi-autonomous and autonomous robotic assistants to aid in care of the elderly is expected to ease the burden on human caretakers, with small-stage testing already occurring in a variety of countries. Yet, it is likely that these robots will need to request human assistance via teleoperation when domain exp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	75,614
2107.08374	Detecting Braess Routes: an Algorithm Accounting for Queuing Delays With an Extended Graph	The Braess paradox is a counter-intuitive phenomenon whereby adding roads to a network results in higher travel time at equilibrium. In this paper we present an algorithm to detect the occurrence of this paradox in real-world networks with the help of an improved graph representation accounting for queues. The addition...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	246,709
1703.09625	Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences	Existing RNN-based approaches for action recognition from depth sequences require either skeleton joints or hand-crafted depth features as inputs. An end-to-end manner, mapping from raw depth maps to action classes, is non-trivial to design due to the fact that: 1) single channel map lacks texture thus weakens the disc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,775
2303.05387	Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches	The ability to automatically identify industry sector coverage in articles on legal developments, or any kind of news articles for that matter, can bring plentiful of benefits both to the readers and the content creators themselves. By having articles tagged based on industry coverage, readers from all around the world...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	350,445
2309.11805	JobRecoGPT -- Explainable job recommendations using LLMs	In today's rapidly evolving job market, finding the right opportunity can be a daunting challenge. With advancements in the field of AI, computers can now recommend suitable jobs to candidates. However, the task of recommending jobs is not same as recommending movies to viewers. Apart from must-have criteria, like skil...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	393,543
2208.02652	A novel robot calibration method with plane constraint based on dial indicator	In pace with the electronic technology development and the production technology improvement, industrial robot Give Scope to the Advantage in social services and industrial production. However, due to long-term mechanical wear and structural deformation, the absolute positioning accuracy is low, which greatly hinders t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	311,527
2010.16031	SMOT: Single-Shot Multi Object Tracking	We present single-shot multi-object tracker (SMOT), a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,940
2103.14811	SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition	Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	226,978
2201.07993	Serializable HTAP with Abort-/Wait-free Snapshot Read	Concurrency Control (CC) ensuring consistency of updated data is an essential element of OLTP systems. Recently, hybrid transactional/analytical processing (HTAP) systems developed for executing OLTP and OLAP have attracted much attention. The OLAP side CC domain has been isolated from OLTP's CC and in many cases has b...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	276,196
2412.07265	Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations	In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,603
2306.04821	AI-based Identification of Most Critical Cyberattacks in Industrial Systems	Modern industrial systems face a growing threat from sophisticated cyberattacks that can cause significant operational disruptions. This work presents a novel methodology for identification of the most critical cyberattacks that may disrupt the operation of such a system. Application of the proposed framework can enabl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	371,912
2110.08390	Non-Isolated Single-Switch Zeta Based High-Step up DC-DC Converter with Coupled Inductor	In this paper, a non-isolated high step-up DC-DC converter has been proposed for renewable energy applications. The proposed structure converter has been derived from the fundamental Zeta converter, in both of which only a single switch is employed. The voltage gain ratio has considerably enhanced in this converter wit...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	261,372
2101.01880	On-Device Document Classification using multimodal features	From small screenshots to large videos, documents take up a bulk of space in a modern smartphone. Documents in a phone can accumulate from various sources, and with the high storage capacity of mobiles, hundreds of documents are accumulated in a short period. However, searching or managing documents remains an onerous ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	214,473
1704.05393	Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews	In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised - due to the fact that it does not rely on pre-labeled reviews - and it is agnostic - since it does not make any assumption about the domain or the language of the review co...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	71,997
2305.01291	Arax: A Runtime Framework for Decoupling Applications from Heterogeneous Accelerators	Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as TensorFlow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available resources elastically during application execution, and (c) reducing the required pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	361,634
2312.16471	A Survey on Super Resolution for video Enhancement Using GAN	This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks. The studies covered in these summaries provide fresh techniques to addressing the issues of improving...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	418,394
2302.05021	ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification	Physiological signals are high-dimensional time series of great practical values in medical and healthcare applications. However, previous works on its classification fail to obtain promising results due to the intractable data characteristics and the severe label sparsity issues. In this paper, we try to address these...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,901
2411.05448	Influencers' Reposts and Viral Diffusion: Prestige Bias in Online Communities	Cultural evolution theory suggests that prestige bias (whereby individuals preferentially learn from prestigious figures) has played a key role in human ecological success. However, its impact within online environments remains unclear, particularly regarding whether reposts by prestigious individuals amplify diffusion...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	506,667
2109.06980	Explainable Identification of Dementia from Transcripts using Transformer Networks	Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of mod...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	255,333
1310.4023	Overlapping community detection in signed networks	Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,785
2407.16463	Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators	Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,608
2107.04192	Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique	The task of predicting affective information in the wild such as seven basic emotions or action units from human faces has gradually become more interesting due to the accessibility and availability of massive annotated datasets. In this study, we propose a method that utilizes the association between seven basic emoti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,377
2310.19798	Gradient-Based Dovetail Joint Shape Optimization for Stiffness	It is common to manufacture an object by decomposing it into parts that can be assembled. This decomposition is often required by size limits of the machine, the complex structure of the shape, etc. To make it possible to easily assemble the final object, it is often desirable to design geometry that enables robust con...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	404,136
2206.02885	Norm Participation Grounds Language	The striking recent advances in eliciting seemingly meaningful language behaviour from language-only machine learning models have only made more apparent, through the surfacing of clear limitations, the need to go beyond the language-only mode and to ground these models "in the world". Proposals for doing so vary in th...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	301,062
1703.10355	From Deep to Shallow: Transformations of Deep Rectifier Networks	In this paper, we introduce transformations of deep rectifier networks, enabling the conversion of deep rectifier networks into shallow rectifier networks. We subsequently prove that any rectifier net of any depth can be represented by a maximum of a number of functions that can be realized by a shallow network with a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,908
2212.05946	Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks	Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,947
1809.00129	Contextual Encoding for Translation Quality Estimation	The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and which are wrong. In this paper, propose a method to effectively encode the local and global contextual information for each target word using ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,500
2212.07287	Achievable Information Rates and Concatenated Codes for the DNA Nanopore Sequencing Channel	The errors occurring in DNA-based storage are correlated in nature, which is a direct consequence of the synthesis and sequencing processes. In this paper, we consider the memory-$k$ nanopore channel model recently introduced by Hamoum et al., which models the inherent memory of the channel. We derive the maximum a pos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	336,361
1902.00194	Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models	We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i.d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,354
2011.05419	Modeling and Passivity Properties of Multi-Producer District Heating Systems	We propose a comprehensive nonlinear ODE-based thermo-hydraulic model of a district heating system featuring several heat producers, consumers and storage devices which are interconnected through a distribution network of meshed topology whose temperature dynamics are explicitly considered. Moreover, we analyze the con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	205,908
1907.05226	Gain with no Pain: Efficient Kernel-PCA by Nystr\"om Sampling	In this paper, we propose and study a Nystr\"om based approach to efficient large scale kernel principal component analysis (PCA). The latter is a natural nonlinear extension of classical PCA based on considering a nonlinear feature map or the corresponding kernel. Like other kernel approaches, kernel PCA enjoys good m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,300
1507.01168	Kernel Based Sequential Data Anomaly Detection in Business Process Event Logs	Business Process Management Systems (BPMS) log events and traces of activities during the execution of a process. Anomalies are defined as deviation or departure from the normal or common order. Anomaly detection in business process logs has several applications such as fraud detection and understanding the causes of p...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	44,828
2307.11403	Channel Estimation for RIS-Aided MIMO Systems: A Partially Decoupled Atomic Norm Minimization Approach	Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (P...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	380,895
1806.05599	A Survey on Open Information Extraction	We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a cr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	100,513
1909.01939	EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks	Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on controlling the contributions of current and previous information. However, the exploration of different importance levels of different eleme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,051
2102.12370	HiPaR: Hierarchical Pattern-aided Regression	We introduce HiPaR, a novel pattern-aided regression method for tabular data containing both categorical and numerical attributes. HiPaR mines hybrid rules of the form $p \Rightarrow y = f(X)$ where $p$ is the characterization of a data region and $f(X)$ is a linear regression model on a variable of interest $y$. HiPaR...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,714
2403.06677	Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction	In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings. Riemannian variance-reduced methods usually involve a double-loop structure, computing a full gradient at the start of each loop. Determinin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	436,562
2012.01991	Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning	In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	209,611
2303.11774	Exact Non-Oblivious Performance of Rademacher Random Embeddings	This paper revisits the performance of Rademacher random projections, establishing novel statistical guarantees that are numerically sharp and non-oblivious with respect to the input data. More specifically, the central result is the Schur-concavity property of Rademacher random projections with respect to the inputs. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,004
1208.0221	Measuring Two-Event Structural Correlations on Graphs	Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal import...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	17,882
1609.03659	DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images	Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extract...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,914
2309.01907	SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection	Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This comple...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	389,849
2001.09219	Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience	The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to the role of people who inject domain knowledge into ML models. One promising learning paradigm is Active Learning (AL), by which the model int...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	161,503
2410.19878	Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies	The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	502,550
2501.13904	Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models	Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,868
2211.16368	DBA: Efficient Transformer with Dynamic Bilinear Low-Rank Attention	Many studies have been conducted to improve the efficiency of Transformer from quadric to linear. Among them, the low-rank-based methods aim to learn the projection matrices to compress the sequence length. However, the projection matrices are fixed once they have been learned, which compress sequence length with dedic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	333,617
2406.11817	Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level	Direct Preference Optimization (DPO), a standard method for aligning language models with human preferences, is traditionally applied to offline preferences. Recent studies show that DPO benefits from iterative training with online preferences labeled by a trained reward model. In this work, we identify a pitfall of va...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	465,075
1811.02689	Training Domain Specific Models for Energy-Efficient Object Detection	We propose an end-to-end framework for training domain specific models (DSMs) to obtain both high accuracy and computational efficiency for object detection tasks. DSMs are trained with distillation \cite{hinton2015distilling} and focus on achieving high accuracy at a limited domain (e.g. fixed view of an intersection)...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	112,661
1207.4589	Minimum-Length Scheduling with Finite Queues: Solution Characterization and Algorithmic Framework	We consider a set of transmitter-receiver pairs, or links, that share a common channel and address the problem of emptying backlogged queues at the transmitters in minimum time. The problem amounts to determining activation subsets of links and their time durations to form a minimum-length schedule. The problem of sche...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	17,645
2102.10240	Learning Neural Generative Dynamics for Molecular Conformation Generation	We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,016
2002.03485	Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis	Implementing enterprise process automation often requires significant technical expertise and engineering effort. It would be beneficial for non-technical users to be able to describe a business process in natural language and have an intelligent system generate the workflow that can be automatically executed. A buildi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,277
2409.13302	Distributed Control for 3D Inspection using Multi-UAV Systems	Cooperative control of multi-UAV systems has attracted substantial research attention due to its significance in various application sectors such as emergency response, search and rescue missions, and critical infrastructure inspection. This paper proposes a distributed control algorithm to generate collision-free traj...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	489,933
1705.02152	Shrinking Horizon Model Predictive Control with Signal Temporal Logic Constraints under Stochastic Disturbances	We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with Signal Temporal Logic (STL) specification constraints under stochastic disturbances. The control objective is to maximize an optimization function under the restriction that a given STL specification is satisfied with hi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	72,941
2104.10847	Localization of Ice-Rink for Broadcast Hockey Videos	In this work, an automatic and simple framework for hockey ice-rink localization from broadcast videos is introduced. First, video is broken into video-shots by a hierarchical partitioning of the video frames, and thresholding based on their histograms. To localize the frames on the ice-rink model, a ResNet18-based reg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,735
2403.04884	Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks	Implantable retinal prostheses offer a promising solution to restore partial vision by circumventing damaged photoreceptor cells in the retina and directly stimulating the remaining functional retinal cells. However, the information transmission between the camera and retinal cells is often limited by the low resolutio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	435,775
2401.14066	CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion	Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. Firstly, users struggle to craft textual prompts that meticulously detail v...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	423,959
2406.09680	Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks	Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume that all machine learning models are of the same type, although it becomes more likely that different edge devices adopt different types of AI ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	464,035
2311.14337	TVT: Training-Free Vision Transformer Search on Tiny Datasets	Training-free Vision Transformer (ViT) architecture search is presented to search for a better ViT with zero-cost proxies. While ViTs achieve significant distillation gains from CNN teacher models on small datasets, the current zero-cost proxies in ViTs do not generalize well to the distillation training paradigm accor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,076
1810.00760	Riemannian Adaptive Optimization Methods	Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings. However, some of the most popular of these optimization tools - namely A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,254
2408.01600	Physics-Informed Geometry-Aware Neural Operator	Engineering design problems often involve solving parametric Partial Differential Equations (PDEs) under variable PDE parameters and domain geometry. Recently, neural operators have shown promise in learning PDE operators and quickly predicting the PDE solutions. However, training these neural operators typically requi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	478,293
1506.07608	Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science	With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. Here we identify the 250 most heavily used journals in each of 26 research fields (4,721 journals, 19.4M articles in total) indexed by the Scopu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	44,537
2305.19144	BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation	Although neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in detecting certain phenomena that can be considered as critical errors, such as deviations in entities and numbers. In contrast, traditional eva...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	369,395
2104.04320	Optimal partitioning in distributed state estimation considering a modified convergence criterion	Distributed state estimation (DSE) is considered as a more robust and reliable alternative for centralized state estimation (CSE) in power system. Especially, taking into account the future power grid, so called smart grid in which bi-directional transfer of energy and information happens, and renewable energy sources ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	229,358
2302.13970	Estimating the Convex Hull of the Image of a Set with Smooth Boundary: Error Bounds and Applications	We study the problem of estimating the convex hull of the image $f(X)\subset\mathbb{R}^n$ of a compact set $X\subset\mathbb{R}^m$ with smooth boundary through a smooth function $f:\mathbb{R}^m\to\mathbb{R}^n$. Assuming that $f$ is a submersion, we derive a new bound on the Hausdorff distance between the convex hull of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	348,112
1903.04181	Graph Data on the Web: extend the pivot, don't reinvent the wheel	This article is a collective position paper from the Wimmics research team, expressing our vision of how Web graph data technologies should evolve in the future in order to ensure a high-level of interoperability between the many types of applications that produce and consume graph data. Wimmics stands for Web-Instrume...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	false	123,919
2411.16752	Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy	The Zero-shot Composed Image Retrieval (ZSCIR) requires retrieving images that match the query image and the relative captions. Current methods focus on projecting the query image into the text feature space, subsequently combining them with features of query texts for retrieval. However, retrieving images only with th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,159
1705.02853	Geometric Properties of Isostables and Basins of Attraction of Monotone Systems	In this paper, we study geometric properties of basins of attraction of monotone systems. Our results are based on a combination of monotone systems theory and spectral operator theory. We exploit the framework of the Koopman operator, which provides a linear infinite-dimensional description of nonlinear dynamical syst...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	73,072
2011.03914	Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions	We aim to develop an efficient programming method for equipping service robots with the skill of performing sign language motions. This paper addresses the problem of transferring complex dual-arm sign language motions characterized by the coordination among arms and hands from human to robot, which is seldom considere...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	205,399
1911.00886	Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction	Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models with deep networks remarkably enhance model capacities. In deep CTR models, exploiting users' historical data is essential for learning user...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	151,944
2009.10326	Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management	The task-oriented spoken dialogue system (SDS) aims to assist a human user in accomplishing a specific task (e.g., hotel booking). The dialogue management is a core part of SDS. There are two main missions in dialogue management: dialogue belief state tracking (summarising conversation history) and dialogue decision-ma...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	196,867
2310.19788	Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget	This study investigates the experimental design problem for identifying the arm with the highest expected outcome, referred to as best arm identification (BAI). In our experiments, the number of treatment-allocation rounds is fixed. During each round, a decision-maker allocates an arm and observes a corresponding outco...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,127
2402.17694	Optimal Control Barrier Functions: Maximizing the Action Space Subject to Control Bounds	This letter addresses the constraint compatibility problem of control barrier functions (CBFs), which occurs when a safety-critical CBF requires a system to apply more control effort than it is capable of generating. This inevitably leads to a safety violation, which transitions the system to an unsafe (and possibly da...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	433,099
1709.00310	Detection via simultaneous trajectory estimation and long time integration	In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono-static and bi-static configurations, respectively, as well as multi-s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	79,882
2106.06345	Proximal Optimal Transport Modeling of Population Dynamics	We propose a new approach to model the collective dynamics of a population of particles evolving with time. As is often the case in challenging scientific applications, notably single-cell genomics, measuring features for these particles requires destroying them. As a result, the population can only be monitored with p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,455
2403.14270	Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection	Visual relationship detection aims to identify objects and their relationships in images. Prior methods approach this task by adding separate relationship modules or decoders to existing object detection architectures. This separation increases complexity and hinders end-to-end training, which limits performance. We pr...	false	false	false	false	false	false	true	true	true	false	false	true	false	false	false	false	false	false	439,991
2005.08465	Context-aware and Scale-insensitive Temporal Repetition Counting	Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,628
1502.03851	Discovering Human Interactions in Videos with Limited Data Labeling	We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	40,194
2404.05720	Language-Independent Representations Improve Zero-Shot Summarization	Finetuning pretrained models on downstream generation tasks often leads to catastrophic forgetting in zero-shot conditions. In this work, we focus on summarization and tackle the problem through the lens of language-independent representations. After training on monolingual summarization, we perform zero-shot transfer ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	445,185
2412.20226	Embodiment-Agnostic Navigation Policy Trained with Visual Demonstrations	Learning to navigate in unstructured environments is a challenging task for robots. While reinforcement learning can be effective, it often requires extensive data collection and can pose risk. Learning from expert demonstrations, on the other hand, offers a more efficient approach. However, many existing methods rely ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	521,141

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