A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Filters
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
[article]
2023
arXiv
pre-print
Preserved Fulltext
Other Versions
In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. ...
In this paper, we provide a quantitative comparison of different Stateful RNN modeling strategies, and propose two strategies to enforce both intra- and inter-batch temporal dependency. ...
In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. ...
arXiv:2210.08347v2
fatcat:2tytxl2kabaeddyleecraxww54
Digital Twins Temporal Dependencies-Based on Time Series Using Multivariate Long Short-Term Memory
2023
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Long Short-Term Memory (LSTM) networks have been used to represent complex temporal dependencies and identify long-term links in the Industrial Internet of Things (IIoT). ...
This paper proposed a Digital Twin temporal dependency technique using LSTM to capture the long-term dependencies in IIoT time series data, estimate the lag between the input and intended output, and handle ...
Additional studies of LSTNet [32] , designed to capture both long-term and short-term dependencies, employ a fully connected layer for data autoregression. ...
doi:10.3390/electronics12194187
fatcat:vuiylzzoxfh2hpfzd4jonaeq4i
ELDEN: Exploration via Local Dependencies
[article]
2023
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
ELDEN utilizes a novel scheme -- the partial derivative of the learned dynamics to model the local dependencies between entities accurately and computationally efficiently. ...
In this work, we propose a new way of defining interesting states for environments with factored state spaces and complex chained dependencies, where an agent's actions may change the value of one entity ...
The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research. ...
arXiv:2310.08702v1
fatcat:kqvmjjtttvajlp75gxpiwsrixy
Mini-batch sample selection strategies for deep learning based speech recognition
2021
Applied Acoustics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this study, a variant of gradient descent optimization, mini-batch gradient descent is used. ...
For this purpose, gender and accent adjusted strategies are proposed for selecting mini-batch samples. ...
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper ...
doi:10.1016/j.apacoust.2020.107573
fatcat:fml6wyg345a3jh67ze7uwxe26y
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. ...
Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. ...
ACKNOWLEDGMENTS The authors would like to thank Kefan Dong for insightful discussions. This work is supported by the National Science Foundation under Grant CCF-2006526. ...
arXiv:2111.13485v2
fatcat:azie5koihrhxjgbegyxkm7kb3u
Cross-layer classification framework for automatic social behavioural analysis in surveillance scenario
2016
Neural computing & applications (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
environment context, a relational descriptor that emphasises position and attention-based characteristics, and a new classification approach based on mini-batches. ...
However, the characterisation of individual and group behaviours is a topic not so well studied in the video surveillance community due to not only its intrinsic difficulty and large variety of topics ...
Acknowledgments This work was financed by the ERDF-European Regional Development Fund through the Operational Programme for ...
doi:10.1007/s00521-016-2282-z
fatcat:riof7dscyvhlthma4op5r5eqdy
The EVALITA Dependency Parsing Task: From 2007 to 2011
[chapter]
2013
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
establish a reference forum for research on Computational Linguistics of the Italian community with contributions from a wide range of disciplines going from Computational Linguistics, Linguistics and ...
Established in 2007, EVALITA (http://www.evalita.it) is the evaluation campaign of Natural Language Processing and Speech Technologies for the Italian language, organized around shared tasks focusing on ...
performances, very similar to a state-of-the-art multiclass classifier. ...
doi:10.1007/978-3-642-35828-9_1
fatcat:p6dyjaxm4zbitfajtciwclwipu
PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In order to go beyond the proposed solutions, we leverage encoder-decoder Transformer networks for parallel decoding a set of learned object queries. ...
In this paper, we introduce a model called PRediction Transformer (PReTR) that extracts features from the multi-agent scenes by employing a factorized spatio-temporal attention module. ...
ACKNOWLEDGEMENT This work was carried out in the framework of the Open-Lab "Artificial Intelligence" in the context of a partnership between INRIA institute and Stellantis company. ...
arXiv:2203.09293v1
fatcat:ljw4tg6zirfhfm6mix2kvhmfhm
Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks
2022
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. ...
The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. ...
cost, while the second term is the cross-entropy loss for the target mini-batch. ...
doi:10.3390/s22114075
pmid:35684694
pmcid:PMC9185309
fatcat:7wqxafttjrauvj6kgnr3c66tbu
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning
[article]
2023
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
In this study, we propose a factorized 4D CNN architecture with attention (F4D) that is capable of learning more effective, finer-grained, long-term spatiotemporal video representations. ...
Recent studies have shown that video-level representation learning is crucial to the capture and understanding of the long-range temporal structure for video action recognition. ...
CONCLUSION In this paper, we presented an effective yet simple framework for video level representation learning namely F4D, to model both short-range motion and long-range temporal dependency at a large ...
arXiv:2401.08609v1
fatcat:q3atdqj4xjaytimw6gheifqjku
Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine
2020
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance ...
In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. ...
[9] developed a mini batch hybrid deep NN that takes two paths for RUL estimation; the multidimensional feature extraction based on Long Short Term Memory (LSTM) and convolutional NN and the prediction ...
doi:10.3390/app10031062
fatcat:eftjcaum6fc2dedpo33vzdkxxu
A Modified Long Short-Term Memory-Deep Deterministic Policy Gradient-Based Scheduling Method for Active Distribution Networks
2022
Frontiers in Energy Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To improve the decision-making level of active distribution networks (ADNs), this paper proposes a novel framework for coordinated scheduling based on the long short-term memory network (LSTM) with deep ...
To tackle this problem, a LSTM module is employed to perform feature extraction on the ADN environment, which can realize the recognition and learning of massive temporal structure data. ...
Therefore, a LSTM module is employed to extract the temporal characteristics of loads and PVs and further improve the long-term performance of the scheduling model. ...
doi:10.3389/fenrg.2022.913130
fatcat:55pwqai32zbf3p3k3746vpm7hi
Threat-Event Detection for Distributed Networks Based on Spatiotemporal Markov Random Field
2020
IEEE Transactions on Dependable and Secure Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Different from existing victim-centric approaches, in this work we propose a new network-centric approach for the detection of distributed threat-events. ...
A Gaussian mixture model is used to capture the statistical features of the network traffic for each behavior pattern. We derive algorithms for parameter estimation and event detection. ...
ACKNOWLEDGMENT The authors would like to thank all anonymous reviewers for their valuable comments to improve this work. ...
doi:10.1109/tdsc.2020.3036664
fatcat:vk7wcah6fjaxvkhv4u2d5ioole
Lip Reading by Alternating between Spatiotemporal and Spatial Convolutions
2021
Journal of Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this work, for the first time, the benefits of alternating between spatiotemporal and spatial convolutions for learning effective features from the LR sequences are studied. ...
The designed LR system utilizes the ALSOS module in-between ResNet blocks, as well as Temporal Convolutional Networks (TCNs) in the backend for classification. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. ...
doi:10.3390/jimaging7050091
pmid:34460687
fatcat:qvmvnhyeojft7eo4xupyjmbcua
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability ...
In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally exists in reinforcement learning training data, on the probability of information leakage ...
ACKNOWLEDGEMENTS The authors would like to thank Hamidreza Ghafghazi and Spencer Main for their valuable contribution to the design and development of the preliminary version of the codebase. ...
arXiv:2109.03975v3
fatcat:cn2j6leok5dpzg5xrjckovgkra
« Previous
Showing results 1 — 15 out of 2,773 results