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Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning [article]

Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph Lim, Andrew Jaegle
2020 arXiv   pre-print
We do so using a hierarchical Keyframe-Inpainter (KeyIn) model that first generates a video's keyframes and then inpaints the rest by generating the frames at the intervening times.  ...  We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.  ...  ACKNOWLEDGEMENTS We thank the members of the GRASP laboratory at Penn, CLVR laboratory at USC, and RAIL at UC Berkeley for many fruitful discussions.  ... 
arXiv:1904.05869v2 fatcat:sghepdpykfbene2wkcl765r7ki

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Gharibeh, H.F., Fault Diagnosis in Photovoltaic Arrays Using GBSSL Method and Proposing a Fault Correction System; TII Aug. 2020 5300-5308  ...  Sun, D., Motion Planning and Robust Control for the Endovascular Navigation of a Microrobot; TII July 2020 4557-4566 Meng, Q., see Cai, H., TII Jan. 2020 587-594 Meng, Q., see Dong, H., TII Dec. 2020  ...  ., +, TII Aug. 2020 5300-5308 Fault-Attention Generative Probabilistic Adversarial Autoencoder for Machine Anomaly Detection.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

On Neural Differential Equations [article]

Patrick Kidger
2022 arXiv   pre-print
time series); and neural stochastic differential equations (e.g. to produce generative models capable of representing complex stochastic dynamics, or sampling from complex high-dimensional distributions  ...  We anticipate this thesis will be of interest to anyone interested in the marriage of deep learning with dynamical systems, and hope it will provide a useful reference for the current state of the art.  ...  This 'auxiliary' SDE, which depends on samples of the observed data x true , may be used to autoencode the data.  ... 
arXiv:2202.02435v1 fatcat:vglknmvlgfeddoe2cxohubauxm

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, and article number.  ...  Article numbers are based on specified topic areas and corresponding codes associated with the publication.  ...  Drift Compensation of Electronic Nose Data; TIM 2021 2513312 Tian, Y., Xu, Y., Zhu, Q., and He, Y., Novel Virtual Sample Generation Using Target-Relevant Autoencoder for Small Data-Based Soft Sensor;  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

XMD: An Expansive Hardware-telemetry based Malware Detector to enhance Endpoint Detection [article]

Harshit Kumar, Biswadeep Chakraborty, Sudarshan Sharma, Nikhil Chawla, Saibal Mukhopadhyay
2022 arXiv   pre-print
We train and evaluate XMD using hardware telemetries collected from 904 benign applications and 1205 malware samples.  ...  XMD improves over currently used HPC-based detectors by 32.91% for the in-distribution test data and by 67.57% for the concept drift test data.  ...  In somware analysis using time and frequency informed autoencoders.  ... 
arXiv:2206.12447v1 fatcat:efmg2oglzbh55ckae3wpnmqsyy

Engineering, Technology & Applied Science Research (ETASR), Vol. 12, No. 2, pp. 8228-8481

Various
2022 Zenodo  
of science application, technology, and engineering.  ...  ISSN: 1792-8036 and 2241-4487.  ...  ACKNOWLEDGMENT We wish to thank the car owners who allowed us to take photos of their cars, the completion of our study would not have been possible without their support.  ... 
doi:10.5281/zenodo.6509470 fatcat:nn7pi5ec35gzplz6ewln2lbbrm

29th Annual Computational Neuroscience Meeting: CNS*2020

2020 BMC Neuroscience  
This includes (i) how growing axons navigate to their targets by detecting and responding to molecular cues in their environment, (ii) the formation of maps in the visual cortex and how these are influenced  ...  Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification.  ...  Acknowledgements: This research is funded by the National Science Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California Nano-Systems  ... 
doi:10.1186/s12868-020-00593-1 pmid:33342424 fatcat:edosycf35zfifm552a2aogis7a

Poster Abstracts

2017 Integrative and Comparative Biology  
of general eDNA in the water samples.  ...  Hepatopancreas, ovary and hemolymph samples were harvested from each crab and analyzed using an ELISA assay to determine vitellogenin concentrations.  ...  A final possibility is the limited geographic range of the sample hindered detection of any effect.  ... 
doi:10.1093/icb/icx001 fatcat:tnwx27gpsjddjd4qvecg7ip5ma

Advancing the search for gravitational waves using machine learning

Hunter Gabbard
2021
The LVK uses sophisticated techniques such as matched filtering and Bayesian inference in order to both detect and infer source parameters from GW events.  ...  Over 100 years ago Einstein formulated his now famous theory of General Relativity.  ...  Of the dataset generated, we use 90% of these samples for training, 5% for validation, and 5% for testing.  ... 
doi:10.5525/gla.thesis.82605 fatcat:s7i5kffeizgdnkqyguc5oduigi

Training and evaluating adversarial networks: from kernel discrepancies to applications

Mikolaj Binkowski, Eyal Neuman, Engineering And Physical Sciences Research Council
2021
This generic framework introduced by Goodfellow et al. [2014] became a leading paradigm in generative modelling of images, and allowed generation of sharp and convincing samples, often hardly distinguishable  ...  Implicit generative modelling has experienced remarkable transformation with the emergence and expansion of Generative Adversarial Networks.  ...  Both Fréchet and Kernel distance provide scores with respect to a reference real sample and require both the real sample and the generated one to be independent and identically distributed.  ... 
doi:10.25560/89972 fatcat:xyzljrflyncvjnxu647be5kbxa

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

International Conference On Ecological Informatics, Thüringer Universitäts- Und Landesbibliothek Jena, Jitendra Gaikwad
2019
The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration  ...  learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes.  ...  As night flight calls are soft and impulsive sounds, their detection is a very challenging task.  ... 
doi:10.22032/dbt.38375 fatcat:qwx5h42r4zdibdhwjxtpcia6oe

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

International Conference On Ecological Informatics, Thüringer Universitäts- Und Landesbibliothek Jena, Jitendra Gaikwad
2018
The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration  ...  learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes.  ...  Vast amounts of presence data and information about distribution and size of mammals and birds have been generated in a very short time using this approach.  ... 
doi:10.22032/dbt.37846 fatcat:abpbnzmatncp7piugi3udxzcxu

Bayesian learning for nonlinear system identification

Wei Pan, Guy-Bart Stan
2019
Then we move to a more practical and significant yet complicated scenario where heterogeneous datasets are used simultaneously.  ...  The dimensionality, or the complexity, grew with the sample size, and "ultra-high" refers to the case where the dimensionality increased at a non-polynomial rate.  ...  can we detect the occurrence and magnitude of a fault, namely, how can we estimate the magnitude of the errors v ij ≠ w [f ] ij , 'i, j, using the smallest possible number of samples.  ... 
doi:10.25560/68510 fatcat:ko656tegpvgebjnrrqlgostkhu

Robust subspace learning for static and dynamic affect and behaviour modelling

Christos Georgakis, Maja Pantic, Engineering And Physical Sciences Research Council, European Commission
2017
essential for the deployment of next-generation, emotionally- and socially-competent human-centered interfaces.  ...  Endowing machines with the ability to seamlessly detect, analyze, model, predict as well as simulate and synthesize manifestations of internal emotional and behavioral states in real-world data is deemed  ...  ., Brownian dynamics in motion models), it is in general unknown and, hence, should be learned from the available data.  ... 
doi:10.25560/52432 fatcat:5vuiijjh6nc3bj75jlx3jifwlq