Generative adversarial networks
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TLDR
A generative adversarial networks algorithm designed to solve the generative modeling problem and its applications in medicine, education and robotics are studied.Abstract:
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.read more
Citations
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Journal ArticleDOI
A Survey on Contrastive Self-Supervised Learning
TL;DR: In contrastive self-supervised learning as discussed by the authors, augmented versions of the same sample close to each other while trying to push away embeddings from different samples is used to learn representations for several downstream tasks.
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.
TL;DR: In this article, Artificial Neural Networks and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity.
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Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models.
TL;DR: In this paper, a survey of the field of discrete speech emotion recognition is presented, followed by a multi-aspect comparison between practical neural network approaches in speech emotion classification. And then, the authors present a multiscale comparison between machine learning techniques and deep learning techniques for speech emotion detection.
Journal ArticleDOI
Artificial Neural Networks Based Optimization Techniques: A Review
Maher G. M. Abdolrasol,Sajid Hussain,Taha Selim Ustun,Mahidur R. Sarker,Mahammad A. Hannan,Ramizi Mohamed,Jamal Abd Ali,Saad Mekhilef,Abdalrhman Milad +8 more
TL;DR: In this article, an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA), is presented.
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Towards a Robust Deep Neural Network in Texts: A Survey
TL;DR: A taxonomy of adversarial attacks and defenses in texts from the perspective of different natural language processing (NLP) tasks is given, and how to build a robust DNN model via testing and verification is introduced.
References
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Proceedings ArticleDOI
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Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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Proceedings Article
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