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
2406.16456	Automated Privacy-Preserving Techniques via Meta-Learning	Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aiming to transform the data while ensuring the privacy of individuals. Some of these techniques have been incorporated into tools, whereas oth...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	467,135
1605.04686	GMD-Based Hybrid Precoding For Millimeter-Wave Massive MIMO Systems	Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains and relieve the huge energy consumption in mmWave massive MIMO systems, thus attracting much interests from academic and industry. However, most existing hybrid precoding schemes are based on singular value decomposition (SVD)....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,906
1703.08881	Solvability regions of affinely parameterized quadratic equations	Quadratic systems of equations appear in several applications. The results in this paper are motivated by quadratic systems of equations that describe equilibrium behavior of physical infrastructure networks like the power and gas grids. The quadratic systems in infrastructure networks are parameterized- the parameters...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	70,661
1501.06450	IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering	Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space. However, many dimensionality reduction methods confront the so-called "crowding problem" that clusters tend to overlap with each other i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	39,617
2012.08012	Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision	The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not gener...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	211,627
2402.12398	Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment	Happiness computing based on large-scale online web data and machine learning methods is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced Machine Learning (ML) models with explanations are used to compute the happiness online assessment while maintaini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	430,834
1809.03784	Compressive Massive Random Access for Massive Machine-Type Communications (mMTC)	In future wireless networks, one fundamental challenge for massive machine-type communications (mMTC) lies in the reliable support of massive connectivity with low latency. Against this background, this paper proposes a compressive sensing (CS)-based massive random access scheme for mMTC by leveraging the inherent spor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	107,409
2310.12650	Hibikino-Musashi@Home 2023 Team Description Paper	This paper describes an overview of the techniques of Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team has developed a dataset generator for the training of a robot vision system and an open-source development environment running on a human support robot simulator. ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	401,101
1902.06007	Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning	Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn \textit{tabula rasa} disregards the logical structure of many domains as well as the wealth of readily available knowledge from domain experts that could help "warm start" t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	121,658
2305.19558	Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing	Mobile augmented reality (MAR) blends a real scenario with overlaid virtual content, which has been envisioned as one of the ubiquitous interfaces to the Metaverse. Due to the limited computing power and battery life of MAR devices, it is common to offload the computation tasks to edge or cloud servers in close proximi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	369,581
1401.7374	A Message-Passing Approach to Combating Hidden Terminals in Wireless Networks	Collisions with hidden terminals is a major cause of performance degradation in 802.11 and likewise wireless networks. Carrier sense multiple access with collision avoidance (CSMA/CA) is utilized to avoid collisions at the cost of spatial reuse. This report studies receiver design to mitigate interference from hidden t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	30,448
1704.00325	Structured Parallel Programming for Monte Carlo Tree Search	In this paper, we present a new algorithm for parallel Monte Carlo tree search (MCTS). It is based on the pipeline pattern and allows flexible management of the control flow of the operations in parallel MCTS. The pipeline pattern provides for the first structured parallel programming approach to MCTS. Moreover, we pro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	71,062
2104.08116	Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media	Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream tasks. In this article, we investigate whether temporal adaptation can ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,668
2303.09989	Finding Competence Regions in Domain Generalization	We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distribution. Assuming a mild distribution shift, we wish to accept out-of-distribution (OOD) data from a new domain whenever a model's estimated...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,266
1708.01659	HTM-MAT: An online prediction software toolbox based on cortical machine learning algorithm	HTM-MAT is a MATLAB based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	78,423
2105.00192	Deep Insights of Deepfake Technology : A Review	Under the aegis of computer vision and deep learning technology, a new emerging techniques has introduced that anyone can make highly realistic but fake videos, images even can manipulates the voices. This technology is widely known as Deepfake Technology. Although it seems interesting techniques to make fake videos or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,122
2310.17711	Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term	With advancements in natural language processing (NLP) models, automatic explanation generation has been proposed to mitigate misinformation on social media platforms in addition to adding warning labels to identified fake news. While many researchers have focused on generating good explanations, how these explanations...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,254
2010.13637	Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence	Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap,...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	true	false	203,201
1905.02458	Reachability analysis of linear hybrid systems via block decomposition	Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally expensive for linear hybrid systems. Reachability analysis works by iteratively applying continuous and discrete post operators to compute states reachable according to continuous...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	129,978
2205.10760	CNNs Avoid Curse of Dimensionality by Learning on Patches	Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori analyses thus far. A priori theories explaining the generalization perfo...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	297,858
1311.6556	Double Ramp Loss Based Reject Option Classifier	We consider the problem of learning reject option classifiers. The goodness of a reject option classifier is quantified using $0-d-1$ loss function wherein a loss $d \in (0,.5)$ is assigned for rejection. In this paper, we propose {\em double ramp loss} function which gives a continuous upper bound for $(0-d-1)$ loss. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,665
2412.20742	UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models	The domain gap between remote sensing imagery and natural images has recently received widespread attention and Vision-Language Models (VLMs) have demonstrated excellent generalization performance in remote sensing multimodal tasks. However, current research is still limited in exploring how remote sensing VLMs handle ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,338
2410.13047	LLM Confidence Evaluation Measures in Zero-Shot CSS Classification	Assessing classification confidence is critical for leveraging large language models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computational Social Science (CSS) tasks. In this paper, we make three key contributions: (1) we propose an uncertainty quantification (UQ) performanc...	true	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	499,341
1610.08217	Assessing Percolation Threshold Based on High-Order Non-Backtracking Matrices	Percolation threshold of a network is the critical value such that when nodes or edges are randomly selected with probability below the value, the network is fragmented but when the probability is above the value, a giant component connecting large portion of the network would emerge. Assessing the percolation threshol...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,899
1511.08531	Structured learning of metric ensembles with application to person re-identification	Matching individuals across non-overlapping camera networks, known as person re-identification, is a fundamentally challenging problem due to the large visual appearance changes caused by variations of viewpoints, lighting, and occlusion. Approaches in literature can be categoried into two streams: The first stream is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,552
1709.03307	Pilot Optimization and Power Allocation for OFDM-based Full-duplex Relay Networks with IQ-imbalances	In OFDM relay networks with IQ imbalances and full-duplex relay station (RS), how to optimize pilot pattern and power allocation using the criterion of minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain least-squares channel estimator has a heavy impact on self-interference cancellation. Firstl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	80,442
2406.09414	Depth Anything V2	This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more robust depth predictions through three key practices: 1) replacing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,933
2409.02253	How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?	Large foundation models have revolutionized the field, yet challenges remain in optimizing multi-modal models for specialized visual tasks. We propose a novel, generalizable methodology to identify preferred image distributions for black-box Vision-Language Models (VLMs) by measuring output consistency across varied in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,624
2101.11425	Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task	With the ease of access to information, and its rapid dissemination over the internet (both velocity and volume), it has become challenging to filter out truthful information from fake ones. The research community is now faced with the task of automatic detection of fake news, which carries real-world socio-political i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,266
2311.14412	A Comparison of PDF Projection with Normalizing Flows and SurVAE	Normalizing flows (NF) recently gained attention as a way to construct generative networks with exact likelihood calculation out of composable layers. However, NF is restricted to dimension-preserving transformations. Surjection VAE (SurVAE) has been proposed to extend NF to dimension-altering transformations. Such net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,102
2407.02906	Single Image Rolling Shutter Removal with Diffusion Models	We present RS-Diffusion, the first Diffusion Models-based method for single-frame Rolling Shutter (RS) correction. RS artifacts compromise visual quality of frames due to the row wise exposure of CMOS sensors. Most previous methods have focused on multi-frame approaches, using temporal information from consecutive fram...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,939
1705.02953	Temporal Segment Networks for Action Recognition in Videos	Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,090
1904.08199	Cultivating Online: Question Routing in a Question and Answering Community for Agriculture	Community-based Question and Answering (CQA) platforms are nowadays enlightening over a billion people with crowdsourced knowledge. A key design issue in CQA platforms is how to find the potential answerers and to provide the askers timely and suitable answers, i.e., the so-called \textit{question routing} problem. Sta...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	128,002
2109.01374	Joint Management and Analysis of Textual Documents and Tabular Data within the AUDAL Data Lake	In 2010, the concept of data lake emerged as an alternative to data warehouses for big data management. Data lakes follow a schema-on-read approach to provide rich and flexible analyses. However, although trendy in both the industry and academia, the concept of data lake is still maturing, and there are still few metho...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	253,419
2112.12970	SGTR: End-to-end Scene Graph Generation with Transformer	Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, we propose a novel SGG method to addres...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	273,100
2404.09886	ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation	In this research, we propose an innovative method to boost Knowledge Distillation efficiency without the need for resource-heavy teacher models. Knowledge Distillation trains a smaller ``student'' model with guidance from a larger ``teacher'' model, which is computationally costly. However, the main benefit comes from ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	446,870
2310.14978	LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding	The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a lack of effective solutions for training TTFS-based spiking neural network (SNN). In this paper, we ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	402,108
2307.13816	Uncertainty Quantification in the Road-level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)	Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	381,708
2408.03449	EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures	Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal processing techniques must be fast, accurate, and resource-conscious to deliver low-latency neural a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	479,015
2401.05441	An adaptive network-based approach for advanced forecasting of cryptocurrency values	This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Domina...	false	true	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	420,790
1201.0035	The information path functional approach for solution of a controllable stochastic problem	We study a stochastic control system, described by Ito controllable equation, and evaluate the solutions by an entropy functional (EF), defined by the equation functions of controllable drift and diffusion. Considering a control problem for this functional, we solve the EF control variation problem (VP), which leads to...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	13,627
2410.23396	Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach	Effective governance and steering of behavior in complex multi-agent systems (MAS) are essential for managing system-wide outcomes, particularly in environments where interactions are structured by dynamic networks. In many applications, the goal is to promote pro-social behavior among agents, where network structure p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	504,026
2007.08322	Understanding Implicit Regularization in Over-Parameterized Single Index Model	In this paper, we leverage over-parameterization to design regularization-free algorithms for the high-dimensional single index model and provide theoretical guarantees for the induced implicit regularization phenomenon. Specifically, we study both vector and matrix single index models where the link function is nonlin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,592
2105.03775	NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer	This paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning. In this task, we want to predict the correct answer based on a question given a context. Usually, contexts are very lengthy and require a large receptive field from the model. Thu...	false	false	false	false	false	true	true	false	true	true	false	false	false	false	false	false	false	false	234,259
2407.10714	SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation	The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to search for the most relevant items from the historical sequence. However...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	473,093
2008.03765	Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression	Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,022
2410.10127	MAIR: A Massive Benchmark for Evaluating Instructed Retrieval	Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions. However, existing IR benchmarks focus on a limited scope of tasks, making them insufficient f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	497,924
1909.03433	Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes	We present a distributionally robust formulation of a stochastic optimization problem for non-i.i.d vector autoregressive data. We use the Wasserstein distance to define robustness in the space of distributions and we show, using duality theory, that the problem is equivalent to a finite convex-concave saddle point pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,478
2501.04911	A Machine Learning Model for Crowd Density Classification in Hajj Video Frames	Managing the massive annual gatherings of Hajj and Umrah presents significant challenges, particularly as the Saudi government aims to increase the number of pilgrims. Currently, around two million pilgrims attend Hajj and 26 million attend Umrah making crowd control especially in critical areas like the Grand Mosque d...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	523,391
2205.11197	Feature-Distribution Perturbation and Calibration for Generalized Person ReID	Person Re-identification (ReID) has been advanced remarkably over the last 10 years along with the rapid development of deep learning for visual recognition. However, the i.i.d. (independent and identically distributed) assumption commonly held in most deep learning models is somewhat non-applicable to ReID considering...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	298,045
1403.1353	Collaborative Representation for Classification, Sparse or Non-sparse?	Sparse representation based classification (SRC) has been proved to be a simple, effective and robust solution to face recognition. As it gets popular, doubts on the necessity of enforcing sparsity starts coming up, and primary experimental results showed that simply changing the $l_1$-norm based regularization to the ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	31,387
2009.12469	A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting	Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relational and spatial dependencies. In addition, dynamic contextual information can have a significant impact on the demand values, and therefor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,434
2401.17481	Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge Robotics	This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial intelligence in smart homes. Since tiny drones operate in highly dynamic environment...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	425,211
2406.12258	Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics	This paper presents a novel perspective for enhancing anti-spoofing performance in zero-shot data domain generalization. Unlike traditional image classification tasks, face anti-spoofing datasets display unique generalization characteristics, necessitating novel zero-shot data domain generalization. One step forward to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	465,303
2211.06840	FPT: Improving Prompt Tuning Efficiency via Progressive Training	Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of tunable parameters and achieving satisfying performance, PT is training-inefficient due to its slow convergence. To improve PT's training eff...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	330,045
1708.06128	Revisiting knowledge transfer for training object class detectors	We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations. We present a unified knowledge transfer framework based on training a single neural network multi-class object detector over a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,275
1901.01291	On the Utility of Model Learning in HRI	Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context of HRI, part of the world to be modeled is the human. One option is for the robot to treat the human as a black box and learn a policy for ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	117,944
2103.10609	Boosting Adversarial Transferability through Enhanced Momentum	Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack methods have achieved great white-box attack performance, but exhibit low transferability when attacking other models. Various momentum iterative...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,510
2106.00573	One4all User Representation for Recommender Systems in E-commerce	General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields. For an e-commerce domain, the objective of general-purpose, i.e., one for all, representations would be efficient applications for extensive downstream tasks such as user profilin...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	238,176
2401.06952	Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation	Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions. Currently, this work is still done manually by train dispatchers, which is challenging to maintain performance under various problem instances. To mitigate this issue, this study pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	421,357
2002.04235	Learning Structured Communication for Multi-agent Reinforcement Learning	This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology for communication structures in MARL literature, which are often manually specified. Then we propose a novel framework termed as Learning Stru...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,553
1811.01439	Explaining Explanations in AI	Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and mos...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	112,365
2101.06098	How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study	The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skil...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	215,609
2011.12102	Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging	A healthy lifestyle is the key to better health and happiness and has a considerable effect on quality of life and disease prevention. Current lifelogging/egocentric datasets are not suitable for lifestyle analysis; consequently, there is no research on lifestyle analysis in the field of computer vision. In this work, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,057
2501.19400	Vintix: Action Model via In-Context Reinforcement Learning	In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization. However, the scalability of ICRL beyond toy tas...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	529,150
2304.08054	Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models	Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strategies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	358,573
2212.02231	TMSTC*: A Turn-minimizing Algorithm For Multi-robot Coverage Path Planning	Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, coverage path planning benefits greatly from multiple robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree Coverage Star(TMSTC*), an ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	334,731
2401.02433	FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients	With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land cover classification tasks. While diffusion models have made great progress in generative models and image classification tasks, existing mo...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	419,719
1704.07433	Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples	Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD): the variance in predicted probability of the correct class across iterations of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,350
2112.13593	Multi-modal Attention Network for Stock Movements Prediction	Stock prices move as piece-wise trending fluctuation rather than a purely random walk. Traditionally, the prediction of future stock movements is based on the historical trading record. Nowadays, with the development of social media, many active participants in the market choose to publicize their strategies, which pro...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	273,300
2307.10614	HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems	Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	380,617
2106.13414	The Price of Tolerance in Distribution Testing	We revisit the problem of tolerant distribution testing. That is, given samples from an unknown distribution $p$ over $\{1, \dots, n\}$, is it $\varepsilon_1$-close to or $\varepsilon_2$-far from a reference distribution $q$ (in total variation distance)? Despite significant interest over the past decade, this problem ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	243,068
2302.08893	Active learning for data streams: a survey	Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,220
2502.08363	Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding	The attention mechanism is essential for the impressive capabilities of transformer-based Large Language Models (LLMs). However, calculating attention is computationally intensive due to its quadratic dependency on the sequence length. We introduce a novel approach called Top-Theta Attention, or simply Top-$\theta$, wh...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	532,992
2403.02677	Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters	We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quali...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	434,892
0804.0980	Large MIMO Detection: A Low-Complexity Detector at High Spectral Efficiencies	We consider large MIMO systems, where by `{\em large}' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies possible in such systems. We present a low-complexity detector which achieves uncode...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,540
1104.0230	Separate Source-Channel Coding for Broadcasting Correlated Gaussians	The problem of broadcasting a pair of correlated Gaussian sources using optimal separate source and channel codes is studied. Considerable performance gains over previously known separate source-channel schemes are observed. Although source-channel separation yields suboptimal performance in general, it is shown that t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,841
2307.09964	Towards green AI-based software systems: an architecture-centric approach (GAISSA)	Nowadays, AI-based systems have achieved outstanding results and have outperformed humans in different domains. However, the processes of training AI models and inferring from them require high computational resources, which pose a significant challenge in the current energy efficiency societal demand. To cope with thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	380,365
1703.02721	On Approximation Guarantees for Greedy Low Rank Optimization	We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also uncovers previously unknown connections between the low rank estimation and combinatorial optimization, so much so that our bounds are reminisce...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	69,610
2010.13094	Autoencoding Improves Pre-trained Word Embeddings	Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically, this post-process...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,999
2401.01325	LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning	It is well known that LLMs cannot generalize well to long contexts whose lengths are larger than the training sequence length. This poses challenges when employing LLMs for processing long input sequences during inference. In this work, we argue that LLMs themselves have inherent capabilities to handle long contexts wi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	419,313
2401.12492	Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?	Pre-trained language models consider the context of neighboring words and documents but lack any author context of the human generating the text. However, language depends on the author's states, traits, social, situational, and environmental attributes, collectively referred to as human context (Soni et al., 2024). Hu...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	423,402
1903.07765	Deep Reinforcement Learning with Decorrelation	Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	124,690
1103.4177	On the Capacity of the Noncausal Relay Channel	This paper studies the noncausal relay channel, also known as the relay channel with unlimited lookahead, introduced by El Gamal, Hassanpour, and Mammen. Unlike the standard relay channel model, where the relay encodes its signal based on the previous received output symbols, the relay in the noncausal relay channel en...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,704
2311.07632	ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering	Biomedical information graphs are crucial for interaction discovering of biomedical information in modern age, such as identification of multifarious molecular interactions and drug discovery, which attracts increasing interests in biomedicine, bioinformatics, and human healthcare communities. Nowadays, more and more g...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	407,425
2312.01131	FDM Printing: a Fabrication Method for Fluidic Soft Circuits?	Existing fluidic soft logic gates for the control of soft robots either rely on extensive manual fabrication processes or expensive printing techniques. In our work, we explore Fused Deposition Modeling for creating fully 3D printed fluidic logic gates. We print a soft bistable valve from thermoplastic polyurethane usi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	412,328
2107.04507	Learning-Based Nonlinear $H^\infty$ Control via Game-Theoretic Differential Dynamic Programming	In this work, we present a learning-based nonlinear $H^\infty$ control algorithm that guarantee system performance under learned dynamics and disturbance estimate. The Gaussian Process (GP) regression is utilized to update the nominal dynamics of the system and provide disturbance estimate based on data gathered throug...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,479
2312.01288	Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul	This paper studies task-oriented edge networks where multiple edge internet-of-things nodes execute machine learning tasks with the help of powerful deep neural networks (DNNs) at a network cloud. Separate edge nodes (ENs) result in a partially observable system where they can only get partitioned features of the globa...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	412,392
2002.07454	Distributed Optimization over Block-Cyclic Data	We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains several blocks, and each client's training data follow block-specific and non-i.i.d. distributions. Such a data structure would introduce client ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	164,479
2409.15887	Self-Supervised Graph Embedding Clustering	The K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. However, it combines the K-means clustering and dimensionality reduction processes for optimization, leading to limitations in the clustering effect due to the introduced...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,111
1705.01365	Quantified advantage of discontinuous weight selection in approximations with deep neural networks	We consider approximations of 1D Lipschitz functions by deep ReLU networks of a fixed width. We prove that without the assumption of continuous weight selection the uniform approximation error is lower than with this assumption at least by a factor logarithmic in the size of the network.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	72,834
2404.17651	Hard ASH: Sparsity and the right optimizer make a continual learner	In class incremental learning, neural networks typically suffer from catastrophic forgetting. We show that an MLP featuring a sparse activation function and an adaptive learning rate optimizer can compete with established regularization techniques in the Split-MNIST task. We highlight the effectiveness of the Adaptive ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	449,938
2405.08745	Enhancing Blind Video Quality Assessment with Rich Quality-aware Features	In this paper, we present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. Motivated by previous researches that leverage pre-trained features extracted from various computer vision models as the feature representation for BVQA, we further explore rich quali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	454,201
2207.14133	Learning unseen coexisting attractors	Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system. It can learn the underlying dynamical system using fewer trainable parameters and hence smaller training data sets than competing approaches. Recently, a simpler formulation, known as next-generation reservoir ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,491
2406.12527	FuseGen: PLM Fusion for Data-generation based Zero-shot Learning	Data generation-based zero-shot learning, although effective in training Small Task-specific Models (STMs) via synthetic datasets generated by Pre-trained Language Models (PLMs), is often limited by the low quality of such synthetic datasets. Previous solutions have primarily focused on single PLM settings, where synth...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,447
2502.09642	Krutrim LLM: Multilingual Foundational Model for over a Billion People	India is a diverse society with unique challenges in developing AI systems, including linguistic diversity, oral traditions, data accessibility, and scalability. Existing foundation models are primarily trained on English, limiting their effectiveness for India's population. Indic languages comprise only 1 percent of C...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	533,529
1604.00133	Good Practice in CNN Feature Transfer	The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification. Systematically, we study three facts in CNN transfer. 1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resiz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,982
1311.4040	Enhanced XML Validation using SRML	Data validation is becoming more and more important with the ever-growing amount of data being consumed and transmitted by systems over the Internet. It is important to ensure that the data being sent is valid as it may contain entry errors, which may be consumed by different systems causing further errors. XML has bec...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	28,460
1407.2006	Interevent time distributions of human multi-level activity in a virtual world	Studying human behaviour in virtual environments provides extraordinary opportunities for a quantitative analysis of social phenomena with levels of accuracy that approach those of the natural sciences. In this paper we use records of player activities in the massive multiplayer online game Pardus over 1,238 consecutiv...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,490
2308.13888	Neural Implicit Morphing of Face Images	Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose, lighting, gender, and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	388,095

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.