bookmarks (hide)621
- Keras-GAN - Keras implementations of Generative Adversarial Networks.
- Educational resources for machine learning
- scipy.misc.logsumexp
- http://urbansed.weebly.com/
- https://upc-mai-dl.github.io/
- In this tutorial, I will first teach you how to build a recurrent neural network (RNN) with a single layer, consisting of one single neuron, with PyTorch and Google Colab. I will also show you how…
- Stochastic Adaptive Neural Architecture Search. Contribute to TomVeniat/SANAS development by creating an account on GitHub.
- Learning Complex Basis Functions for Invariant Signal Representations with the Complex Autoencoder - SonyCSLParis/cae-invar
- Restoring ancient text using deep learning: a case study on Greek epigraphy. - sommerschield/ancient-text-restoration
- Deep learning course CE7454, 2019. Contribute to xbresson/CE7454_2019 development by creating an account on GitHub.
- An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim - shaohua0116/MMAML-Classification
- Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.
- Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
- Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 For more information, please visit: http://deeplearning.cs.cmu.edu/ Con...
- Submanifold sparse convolutional networks
- Virtual Adversarial Training (VAT) implementation for PyTorch - lyakaap/VAT-pytorch
- A PyTorch Implementation of End-to-End Models for Speech-to-Text - awni/speech
- CVXPY creates powerful new PyTorch and TensorFlow layers
- Generative Models
- 10-414/714: Deep Learning Systems
- Structured Text Generation. Contribute to dottxt-ai/outlines development by creating an account on GitHub.
- Basic physics provides a “straight, fast” way to get up to speed with flow-based generative models
- notebook du papier https://arxiv.org/pdf/2004.08531.pdf
- IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz - GitHub - dynamicslab/databook_python: IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
- In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised! VAE's are a very ...
publications
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- (2019)cite arxiv:1906.00910.
- (2016)cite arxiv:1606.04838.
- (2018)cite arxiv:1801.01944.
- CoRR, (2018)cite arxiv:1810.05095Comment: To appear on Nature Reviews Physics. The revised accepted version will be posted 6 months after publication.
- CoRR, (2018)
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
- (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.
- Information, 14 (2): 137 (February 2023)
- (2014)cite arxiv:1406.7872Comment: Notes prepared to accompany a series of tutorial lectures given by the author at the 1st Lake Michigan Workshop on Combinatorics and Graph Theory, Western Michigan University, March 15--16 2014.
- Scientific Reports, 8 (1): 8912-- (2018)
- (2019)cite arxiv:1909.10893.
- (2019)cite arxiv:1911.05722Comment: Technical report.
- (2015)cite arxiv:1508.04306Comment: Originally submitted on June 5, 2015.
- (2019)cite arxiv:1908.04351.
- CoRR, (2018)
- ICML, volume 80 of JMLR Workshop and Conference Proceedings, page 2673-2682. JMLR.org, (2018)
- (2020)cite arxiv:2002.02065Comment: 5 pages.
- (2019)cite arxiv:1910.06711.
- (2018)cite arxiv:1808.07804.
