LatinX in AI at Neural Information Processing Systems Conference 2022最新文献

Boosting Self-supervised Video-based Human Action Recognition Through Knowledge Distillation 通过知识升华提高基于自监督视频的人类行为识别

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai202211286

Fernando Camarena, M. González-Mendoza, Leonardo Chang, N. Hernández-Gress

{"title":"Boosting Self-supervised Video-based Human Action Recognition Through Knowledge Distillation","authors":"Fernando Camarena, M. González-Mendoza, Leonardo Chang, N. Hernández-Gress","doi":"10.52591/lxai202211286","DOIUrl":"https://doi.org/10.52591/lxai202211286","url":null,"abstract":"Deep learning architectures lead the state-of-the-art in several computer vision, natural language processing, and reinforcement learning tasks due to their ability to extract multi-level representations without human engineering. The model’s performance is affected by the amount of labeled data used in training. Hence, novel approaches like self-supervised learning (SSL) extract the supervisory signal using unlabeled data. Although SSL reduces the dependency on human annotations, there are still two main drawbacks. First, high-computational resources are required to train a large-scale model from scratch. Second, knowledge from an SSL model is commonly finetuning to a target model, which forces them to share the same parameters and architecture and make it task-dependent. This paper explores how SSL benefits from knowledge distillation in constructing an efficient and non-task-dependent training framework. The experimental design compared the training process of an SSL algorithm trained from scratch and boosted by knowledge distillation in a teacher-student paradigm using the video-based human action recognition dataset UCF101. Results show that knowledge distillation accelerates the convergence of a network and removes the reliance on model architectures.","PeriodicalId":266286,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129554714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Machine Learning Prediction of Electronic Stopping Power 电子停止功率的机器学习预测

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai202211281

Felipe Bivort

引用次数: 0

Using Deep Learning and Macroscopic Imaging of Porcine Heart Valve Leaflets to Predict Uniaxial Stress-Strain Responses 利用深度学习和猪心脏瓣膜小叶的宏观成像预测单轴应力-应变响应

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai2022112812

L. H. Victor, C. Barberan, Richard Baraniuk, Jane Grande-Allen

引用次数: 0

Anomaly Detection through Density Matrices and Kernel Density Estimation (AD-DMKDE) 基于密度矩阵和核密度估计的异常检测

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai2022112810

Oscar Bustos-Brinez, Joseph A. Gallego-Mejia, Fabio Gonzalez

引用次数: 0

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai202211282

Marcos Tidball, C. Furlanetto

{"title":"Similarity Search of Low Surface Brightness Galaxies in Large Astronomical Catalogs","authors":"Marcos Tidball, C. Furlanetto","doi":"10.52591/lxai202211282","DOIUrl":"https://doi.org/10.52591/lxai202211282","url":null,"abstract":"Low Surface Brightness Galaxies (LSBGs) constitute an important segment of the galaxy population, however, due to their diffuse nature, their search is challenging. The detection of LSBGs is usually done with a combination of parametric methods and visual inspection, which becomes unfeasible for future astronomical surveys that will collect petabytes of data. Thus, in this work we explore the usage of Locality-Sensitive Hashing for the approximate similarity search of LSBGs in large astronomical catalogs. We use 11670190 objects from the Dark Energy Survey Y3 Gold coadd catalog to create an approximate k nearest neighbors model based on the properties of the objects, developing a tool able to find new LSBG candidates while using only one known LSBG. From just one labeled examplewe are able to find various known LSBGs and many objects visually similar to LSBGs but not yet catalogued. Also, due to the generality of similarity search models, we are able to search for and recover other rare astronomical objects without the need of retraining or generating a large sample. Our code is available on https://github.com/zysymu/lsh-astro.","PeriodicalId":266286,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114768453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Adversarial Examples for Text Classification By Perturbing Latent Representations 基于扰动潜在表征的文本分类对抗实例研究

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai202211284

Korn Sooksatra, Pablo Rivas, Bikram Khanal

引用次数: 0

Classification of fine hand movements of the same limb through EEG signals. 脑电信号对同一肢体精细手部运动的分类。

LatinX in AI at Neural Information Processing Systems Conference 2022 Pub Date : 2022-11-28 DOI: 10.52591/lxai202211285

J. Sánchez

{"title":"Classification of fine hand movements of the same limb through EEG signals.","authors":"J. Sánchez","doi":"10.52591/lxai202211285","DOIUrl":"https://doi.org/10.52591/lxai202211285","url":null,"abstract":"Discriminating fine movements within the same limb using electroencephalography (EEG) signals is a current challenge to non-invasive BCIs systems due to the close spatial representations on the motor cortex area of the brain, the signal-to-noise ratio, and the stochastic nature of this kind of signals. This research presents the performance evaluation of different strategies of classification using Linear Discriminant Analysis (LDA) method and power spectral density (PSD) features for three tasks: make a fist, open the hand, and keep the anatomical position of the hand. For this, EEG signals were collected from 10 healthy subjects and evaluated with different cross-validation methods: Monte Carlo, to implement an Offline Analysis And Leave-one-out for a pseudo-online implementation. The results show that the average accuracy for classifying the start of each task is approximately 76% for offline and Pseudo-online Analysis, classifying just the start of movement is 54% and 62% respectively for same both methods and 45% for and 32% classifying between classes. Based on these results, it can be said that the implementation of a BCI based on PSD features and LDA method could work to detect the start of one of the proposed tasks, but to discriminate the movement it is necessary to implement a different strategy for increase accuracy in the classification problem.","PeriodicalId":266286,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0