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
2312.07066	DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models	Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity. Consequently, Visual Storytelling (VST), a task that involves generating meaningful and coherent narratives from a collection of...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	414,787
1206.3235	Identifying reasoning patterns in games	We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	16,494
2101.05087	Secure Consensus with Distributed Detection via Two-hop Communication	In this paper, we consider a multi-agent resilient consensus problem, where some of the nodes may behave maliciously. The approach is to equip all nodes with a scheme to detect neighboring nodes when they behave in an abnormal fashion. To this end, the nodes exchange not only their own states but also information regar...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	215,330
2011.14514	Optimally Supporting IoT with Cell-Free Massive MIMO	We study internet of things (IoT) systems supported by cell-free (CF) massive MIMO (mMIMO) with optimal linear channel estimation. For the uplink, we consider optimal linear MIMO receiver and obtain an uplink SINR approximation involving only large-scale fading coefficients using random matrix (RM) theory. Using this a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	208,803
2101.07434	Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation	Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,033
1509.01038	Multi-Source Cooperative Communication with Opportunistic Interference Cancelling Relays	In this paper we present a multi-user cooperative protocol for wireless networks. Two sources transmit simultaneously their information blocks and relays employ opportunistically successive interference cancellation (SIC) in an effort to decode them. An adaptive decode/amplify-and-forward scheme is applied at the relay...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	46,557
2303.05300	CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems	Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during these engineered systems' design and operational phases. However, many challenges exist in developing a modeling fram...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	350,412
2206.03796	Adaptive Neural Network-based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft	This paper presents a neural network-based Unscented Kalman Filter (UKF) to estimate and track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the target's orbit and attitude relative to the servicer based on th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	301,410
2110.08203	Shared Visual Representations of Drawing for Communication: How do different biases affect human interpretability and intent?	We present an investigation into how representational losses can affect the drawings produced by artificial agents playing a communication game. Building upon recent advances, we show that a combination of powerful pretrained encoder networks, with appropriate inductive biases, can lead to agents that draw recognisable...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	261,290
2308.08885	Event-Guided Procedure Planning from Instructional Videos with Text Supervision	In this work, we focus on the task of procedure planning from instructional videos with text supervision, where a model aims to predict an action sequence to transform the initial visual state into the goal visual state. A critical challenge of this task is the large semantic gap between observed visual states and unob...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,078
2408.08809	Ziv-Merhav estimation for hidden-Markov processes	We present a proof of strong consistency of a Ziv-Merhav-type estimator of the cross entropy rate for pairs of hidden-Markov processes. Our proof strategy has two novel aspects: the focus on decoupling properties of the laws and the use of tools from the thermodynamic formalism.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	481,161
1305.2006	LabelRankT: Incremental Community Detection in Dynamic Networks via Label Propagation	An increasingly important challenge in network analysis is efficient detection and tracking of communities in dynamic networks for which changes arrive as a stream. There is a need for algorithms that can incrementally update and monitor communities whose evolution generates huge realtime data streams, such as the Inte...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,486
cs/0205063	Distance function wavelets - Part II: Extended results and conjectures	Report II is concerned with the extended results of distance function wavelets (DFW). The fractional DFW transforms are first addressed relating to the fractal geometry and fractional derivative, and then, the discrete Helmholtz-Fourier transform is briefly presented. The Green second identity may be an alternative dev...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	537,585
2212.13520	Challenges in anomaly and change point detection	This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,325
1801.08486	Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation	Image segmentation is a fundamental problem in medical image analysis. In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data. However, expert annotations on big medical datasets are tedious, expensive or som...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	88,958
1312.0790	Test Set Selection using Active Information Acquisition for Predictive Models	In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	28,812
1909.11143	Spontaneous Fruit Fly Optimisation for truss weight minimisation: Performance evaluation based on the no free lunch theorem	Over the past decade, several researchers have presented various optimisation algorithms for use in truss design. The no free lunch theorem implies that no optimisation algorithm fits all problems; therefore, the interest is not only in the accuracy and convergence rate of the algorithm but also the tuning effort and p...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	146,718
2005.11320	Line Failure Localization of Power Networks Part II: Cut Set Outages	Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult. In Part II of this paper, we continue the study of line failure localizability in transmission networks and characterize the impact of cut set outages. We establish a Simple Path Criterion...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	178,441
1905.00737	The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation	We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmentation (VOS). In addition to the original semi-supervised track and the interactive track introduced in the previous edition, a new unsuperv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,549
1909.02487	Ab-Initio Solution of the Many-Electron Schr\"odinger Equation with Deep Neural Networks	Given access to accurate solutions of the many-electron Schr\"odinger equation, nearly all chemistry could be derived from first principles. Exact wavefunctions of interesting chemical systems are out of reach because they are NP-hard to compute in general, but approximations can be found using polynomially-scaling alg...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,210
1707.01591	A Data Science Approach to Understanding Residential Water Contamination in Flint	When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamina...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	76,566
2405.16535	A Complete Inverse Optimality Study for a Tank-Liquid System	This paper presents a complete inverse optimality study for a linearized tank-liquid system where the liquid is described by the viscous Saint-Venant model with surface tension and possible wall friction. We define an appropriate weak solution notion for which we establish existence/uniqueness results with inputs that ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	457,476
cs/0204055	Intelligent Search of Correlated Alarms for GSM Networks with Model-based Constraints	In order to control the process of data mining and focus on the things of interest to us, many kinds of constraints have been added into the algorithms of data mining. However, discovering the correlated alarms in the alarm database needs deep domain constraints. Because the correlated alarms greatly depend on the logi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537,564
2003.00602	Federating Recommendations Using Differentially Private Prototypes	Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users' interaction patterns. However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we lear...	false	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	166,343
2409.18291	Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control	This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suff...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,176
2308.14114	Hybrid Transformer-RNN Architecture for Household Occupancy Detection Using Low-Resolution Smart Meter Data	Residential occupancy detection has become an enabling technology in today's urbanized world for various smart home applications, such as building automation, energy management, and improved security and comfort. Digitalization of the energy system provides smart meter data that can be used for occupancy detection in a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,197
2001.05834	Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks	This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and validation, as well as intervention support during minimally invasive and image-guided surgeries like radiof...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	160,644
1607.08368	Local Feature Detectors, Descriptors, and Image Representations: A Survey	With the advances in both stable interest region detectors and robust and distinctive descriptors, local feature-based image or object retrieval has become a popular research topic. %All of the local feature-based image retrieval system involves two important processes: local feature extraction and image representation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,150
1806.04480	Improving latent variable descriptiveness with AutoGen	Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,244
2409.00837	You-Only-Randomize-Once: Shaping Statistical Properties in Constraint-based PCG	In procedural content generation, modeling the generation task as a constraint satisfaction problem lets us define local and global constraints on the generated output. However, a generator's perceived quality often involves statistics rather than just hard constraints. For example, we may desire that generated outputs...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	485,089
1008.4658	A high speed unsupervised speaker retrieval using vector quantization and second-order statistics	This paper describes an effective unsupervised method for query-by-example speaker retrieval. We suppose that only one speaker is in each audio file or in audio segment. The audio data are modeled using a common universal codebook. The codebook is based on bag-of-frames (BOF). The features corresponding to the audio fr...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	7,386
2110.03735	Adversarial Unlearning of Backdoors via Implicit Hypergradient	We propose a minimax formulation for removing backdoors from a given poisoned model based on a small set of clean data. This formulation encompasses much of prior work on backdoor removal. We propose the Implicit Bacdoor Adversarial Unlearning (I-BAU) algorithm to solve the minimax. Unlike previous work, which breaks d...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	259,605
2205.15434	A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems	In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game theory (GT) assumes that agents are not affected by risk from other agents and only strive to maximise their expected utility. For example, i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	299,719
2501.16393	Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning	Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	527,956
1212.5577	A Structured Construction of Optimal Measurement Matrix for Noiseless Compressed Sensing via Analog Polarization	In this paper, we propose a method of structured construction of the optimal measurement matrix for noiseless compressed sensing (CS), which achieves the minimum number of measurements which only needs to be as large as the sparsity of the signal itself to be recovered to guarantee almost error-free recovery, for suffi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,563
1912.13137	Decentralized Subchannel Scheduling for C-V2X Mode-4: A Non-Standard Configuration for CAM Retransmissions	In Release 14, 3GPP introduced a novel paradigm known as cellular vehicle--to--everything (C-V2X) \emph{mode-4} to specifically support vehicular communications in scenarios without network coverage. Such a scheme has been devised to operate distributedly harnessing a sensing mechanism whereby vehicles can monitor the ...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	159,009
1007.3588	Improved construction of irregular progressive edge-growth Tanner graphs	The progressive edge-growth algorithm is a well-known procedure to construct regular and irregular low-density parity-check codes. In this paper, we propose a modification of the original algorithm that improves the performance of these codes in the waterfall region when constructing codes complying with both, check an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,085
0903.4426	Capacity Scaling Laws for Underwater Networks	The underwater acoustic channel is characterized by a path loss that depends not only on the transmission distance, but also on the signal frequency. Signals transmitted from one user to another over a distance $l$ are subject to a power loss of $l^{-\alpha}{a(f)}^{-l}$. Although a terrestrial radio channel can be mode...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,413
2111.11250	Action Recognition with Domain Invariant Features of Skeleton Image	Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community. The recent Convolutional Neural Network (CNN)-based methods have shown commendable performance in learning spatio-temporal representations for skeleton ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	267,600
2103.03454	Structured Scene Memory for Vision-Language Navigation	Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	223,289
2407.08618	Tamil Language Computing: the Present and the Future	This paper delves into the text processing aspects of Language Computing, which enables computers to understand, interpret, and generate human language. Focusing on tasks such as speech recognition, machine translation, sentiment analysis, text summarization, and language modelling, language computing integrates discip...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	472,234
2403.06159	Cracking the neural code for word recognition in convolutional neural networks	Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate highly similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of sizes and absolute positions. How neural circuits a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,330
2110.01938	Sicilian Translator: A Recipe for Low-Resource NMT	With 17,000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language. Using small subword vocabularies, we trained small Transformer models with high dropout parameters and achieved BLEU scores in the upper 20s. Then we supplemented our dataset ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	258,950
2501.16922	Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models	Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as a lack of integration with planning, incomprehensible internal structure, and inability to learn continually. We present the initial design for an AI system...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,153
2312.05108	Unlocking Energy Flexibility From Thermal Inertia of Buildings: A Robust Optimization Approach	Towards integrating renewable electricity generation sources into the grid, an important facilitator is the energy flexibility provided by buildings' thermal inertia. Most of the existing research follows a single-step price- or incentive-based scheme for unlocking the flexibility potential of buildings. In contrast, t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	413,949
2501.14184	Tight Sample Complexity Bounds for Parameter Estimation Under Quantum Differential Privacy for Qubits	This short note provides tight upper and lower bounds for minimal number of samples (copies of quantum states) required to attain a prescribed accuracy (measured by error variance) for scalar parameters using unbiased estimators under quantum local differential privacy for qubits. In the small privacy budget $\epsilon$...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	527,013
0904.1931	KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis	Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	3,532
2406.10527	Panoptic-FlashOcc: An Efficient Baseline to Marry Semantic Occupancy with Panoptic via Instance Center	Panoptic occupancy poses a novel challenge by aiming to integrate instance occupancy and semantic occupancy within a unified framework. However, there is still a lack of efficient solutions for panoptic occupancy. In this paper, we propose Panoptic-FlashOcc, a straightforward yet robust 2D feature framework that enable...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,443
2005.03003	Fault Tree Analysis: Identifying Maximum Probability Minimal Cut Sets with MaxSAT	In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the appro...	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	true	176,035
1811.06847	Adversarial Unsupervised Representation Learning for Activity Time-Series	Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,610
2410.16711	Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification	The application of deep learning-based architecture has seen a tremendous rise in recent years. For example, medical image classification using deep learning achieved breakthrough results. Convolutional Neural Networks (CNNs) are implemented predominantly in medical image classification and segmentation. On the other h...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	501,156
1710.02035	HANDY: A Hybrid Association Rules Mining Approach for Network Layer Discovery of Services for Mobile Ad hoc Network	Mobile Ad hoc Network (MANET) is an infrastructure-less network formed between a set of mobile nodes. The discovery of services in MANET is a challenging job due to the unique properties of network. In this paper, a novel service discovery framework called Hybrid Association Rules Based Network Layer Discovery of Servi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	82,095
2411.04586	On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data	Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel dete...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	506,330
2003.13541	A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service	Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures. Thanks to its ability to remotely execute and train deep learning models (that typically require high computational loads and memory oc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,236
2004.14564	Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing	We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,933
2411.17835	Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction	We present Arabic-Nougat, a suite of OCR models for converting Arabic book pages into structured Markdown text. Based on Meta's Nougat architecture, Arabic-Nougat includes three specialized models: arabic-small-nougat, arabic-base-nougat, and arabic-large-nougat. These models are fine-tuned on a synthetic dataset, arab...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	511,627
1902.00623	Collaborative Quantization for Cross-Modal Similarity Search	Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,454
2210.12367	Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation	Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied. In this paper, we conduct the first quantitative analysis on the robustness of pre-trained Seq2Seq models. We find that even current SOTA pre-trained Seq2Seq model (BART) is still v...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,709
2403.15444	A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition	Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In this survey we investigate how knowledge can be transferred and utilized...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	440,566
1507.05737	Online Metric-Weighted Linear Representations for Robust Visual Tracking	In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,321
2104.06742	Optimal Downlink Training Sequence for Massive MIMO Secret-Key Generation	In this paper, the secret-key capacity is maximized by optimizing the downlink training sequence in a time division duplexing (TDD) massive multiple-input-multiple-output (MIMO) scenario. Both single-user and multiple user cases are considered. As opposed to previous works, the optimal training sequence and the related...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	230,179
2306.09700	End-to-End Vectorized HD-map Construction with Piecewise Bezier Curve	Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community. Most existing approaches first obtain rasterized map with the segmentation-based pipeline and then conduct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,933
2003.01343	Improving Candidate Generation for Low-resource Cross-lingual Entity Linking	Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	166,624
2409.08397	360PanT: Training-Free Text-Driven 360-Degree Panorama-to-Panorama Translation	Preserving boundary continuity in the translation of 360-degree panoramas remains a significant challenge for existing text-driven image-to-image translation methods. These methods often produce visually jarring discontinuities at the translated panorama's boundaries, disrupting the immersive experience. To address thi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	487,889
2103.13797	On Optimal Power Control for Energy Harvesting Communications with Lookahead	Consider the problem of power control for an energy harvesting communication system, where the transmitter is equipped with a finite-sized rechargeable battery and is able to look ahead to observe a fixed number of future energy arrivals. An implicit characterization of the maximum average throughput over an additive w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	226,608
2008.11586	Weakly Supervised Learning with Side Information for Noisy Labeled Images	In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data. To tackle this problem, some image related side information, such as captions and tags, often reveal underlying relationships across images. In this paper, we present an efficient weakly supe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,320
2305.04457	Real-World Denoising via Diffusion Model	Real-world image denoising is an extremely important image processing problem, which aims to recover clean images from noisy images captured in natural environments. In recent years, diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. However...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,775
cmp-lg/9504024	A Morphographemic Model for Error Correction in Nonconcatenative Strings	This paper introduces a spelling correction system which integrates seamlessly with morphological analysis using a multi-tape formalism. Handling of various Semitic error problems is illustrated, with reference to Arabic and Syriac examples. The model handles errors vocalisation, diacritics, phonetic syncopation and mo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,349
1108.3240	Multi-robot Deployment From LTL Specifications with Reduced Communication	In this paper, we develop a computational framework for fully automatic deployment of a team of unicycles from a global specification given as an LTL formula over some regions of interest. Our hierarchical approach consists of four steps: (i) the construction of finite abstractions for the motions of each robot, (ii) t...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	11,683
2405.06296	Fast Evaluation of DNN for Past Dataset in Incremental Learning	During the operation of a system including a deep neural network (DNN), new input values that were not included in the training dataset are given to the DNN. In such a case, the DNN may be incrementally trained with the new input values; however, that training may reduce the accuracy of the DNN in regard to the dataset...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	453,251
1506.03705	Random Maxout Features	In this paper, we propose and study random maxout features, which are constructed by first projecting the input data onto sets of randomly generated vectors with Gaussian elements, and then outputing the maximum projection value for each set. We show that the resulting random feature map, when used in conjunction with ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,088
2001.00631	On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition	There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primaril...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,282
2412.05731	A Scoping Review of ChatGPT Research in Accounting and Finance	This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common theme...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	514,951
2409.17348	Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication	Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting its applicability in...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	491,750
1405.2363	A sampling-based approach to scalable constraint satisfaction in linear sampled-data systems---Part I: Computation	Sampled-data (SD) systems, which are composed of both discrete- and continuous-time components, are arguably one of the most common classes of cyberphysical systems in practice; most modern controllers are implemented on digital platforms while the plant dynamics that are being controlled evolve continuously in time. A...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	32,970
2101.08661	Regularization via deep generative models: an analysis point of view	This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end models, such approaches seem particularly interesting since the same network can be used for many different problems and experimental conditio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	216,381
2006.02027	Sampling-Based Motion Planning on Sequenced Manifolds	We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion. The problem is formulated as a fixed sequence of intersecting manifolds, which the robot needs to traverse in order to solve the task. We specify a class of sequential motion planning...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	179,936
2008.09887	Semi-Supervised Data Programming with Subset Selection	The paradigm of data programming, which uses weak supervision in the form of rules/labelling functions, and semi-supervised learning, which augments small amounts of labelled data with a large unlabelled dataset, have shown great promise in several text classification scenarios. In this work, we argue that by not using...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,848
1802.01459	An information model for modular robots: the Hardware Robot Information Model (HRIM)	Today's landscape of robotics is dominated by vertical integration where single vendors develop the final product leading to slow progress, expensive products and customer lock-in. Opposite to this, an horizontal integration would result in a rapid development of cost-effective mass-market products with an additional c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	89,610
2009.05283	Fair and accurate age prediction using distribution aware data curation and augmentation	Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial recognition and age prediction systems as a consequence. Age prediction is an especially difficult application with the issue of fairness re...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	195,285
2501.18122	VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints	Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. W...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,582
2410.01618	SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment	LiDAR bundle adjustment (BA) is an effective approach to reduce the drifts in pose estimation from the front-end. Existing works on LiDAR BA usually rely on predefined geometric features for landmark representation. This reliance restricts generalizability, as the system will inevitably deteriorate in environments wher...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	493,854
1909.04031	A Study of Context Dependencies in Multi-page Product Search	In product search, users tend to browse results on multiple search result pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which item to purchase. Users' clicks can be considered as implicit feedback which indicates their preferences and used to re-rank subsequent SERPs. Relevance feedback (RF) t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	144,679
2404.02446	Masked Completion via Structured Diffusion with White-Box Transformers	Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the representations as foundations for downstream tasks. These networks are empirically designed; as such, they are usually not interpretable, their repre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,857
2201.08018	Transfer Learning for Fault Diagnosis of Transmission Lines	Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time sensing and detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made a fault detection, classificat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	276,204
2009.03173	Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable	Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional performance of various architectures in practice, we study a theoretical question: what is the condition for deep neural architectures to preserv...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	194,762
2407.02543	Towards the Next Frontier in Speech Representation Learning Using Disentanglement	The popular frameworks for self-supervised learning of speech representations have largely focused on frame-level masked prediction of speech regions. While this has shown promising downstream task performance for speech recognition and related tasks, this has largely ignored factors of speech that are encoded at coars...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	469,788
2306.16306	Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction	The irregularity and permutation invariance of point cloud data pose challenges for effective learning. Conventional methods for addressing this issue involve converting raw point clouds to intermediate representations such as 3D voxel grids or range images. While such intermediate representations solve the problem of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,334
1810.02607	Spatially-weighted Anomaly Detection	Many types of anomaly detection methods have been proposed recently, and applied to a wide variety of fields including medical screening and production quality checking. Some methods have utilized images, and, in some cases, a part of the anomaly images is known beforehand. However, this kind of information is dismisse...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	109,626
1605.01838	DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM	Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	55,535
1809.04263	Efficiency and detectability of random reactive jamming in carrier sense wireless networks	A natural basis for the detection of a wireless random reactive jammer (RRJ) is the perceived violation by the detector (typically located at the access point (AP)) of the carrier sensing protocol underpinning many wireless random access protocols (e.g., WiFi). Specifically, when the wireless medium is perceived by a s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	107,518
1912.07044	Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons	We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layer model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,499
2501.05680	EXION: Exploiting Inter- and Intra-Iteration Output Sparsity for Diffusion Models	Over the past few years, diffusion models have emerged as novel AI solutions, generating diverse multi-modal outputs from text prompts. Despite their capabilities, they face challenges in computing, such as excessive latency and energy consumption due to their iterative architecture. Although prior works specialized in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	523,681
1805.01597	Pytrec_eval: An Extremely Fast Python Interface to trec_eval	We introduce pytrec_eval, a Python interface to the tree_eval information retrieval evaluation toolkit. pytrec_eval exposes the reference implementations of trec_eval within Python as a native extension. We show that pytrec_eval is around one order of magnitude faster than invoking trec_eval as a sub process from withi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	96,682
2410.16543	Large language models enabled multiagent ensemble method for efficient EHR data labeling	This study introduces a novel multiagent ensemble method powered by LLMs to address a key challenge in ML - data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,078
2408.15172	X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation	Large Language Models (LLMs) and Large Multimodal Models (LMMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or employ basic multimodal strategies that do not fu...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	483,824
2108.08636	Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework	In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The sur...	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	251,326
1611.07718	Deep Convolutional Neural Networks with Merge-and-Run Mappings	A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow. To further reduce the training difficulty, we present a simple network architecture, deep merge-and-run neural networks. The novelty lies in a m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,398
1502.03729	Coherent-Classical Estimation for Linear Quantum Systems	We study a coherent-classical estimation scheme for a class of linear quantum systems, where the estimator is a mixed quantum-classical system that may or may not involve coherent feedback. We show that when the quantum plant or the quantum part of the estimator (coherent controller) is an annihilation operator only sy...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	40,185
2501.13350	DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations	Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate predictions but are computationally expensive, particularly for complex geometries. Several machine learning (ML) models have been proposed in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,644

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