Computer Science
7,622 items in 38 lists
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SHAP arxiv.org
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IP arxiv.org
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PDA arxiv.org
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Singh, et. al arxiv.org
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MFI arxiv.org
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TreeView arxiv.org
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STA arxiv.org
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RETAIN arxiv.org
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Hara, et. al. arxiv.org
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Lei, et. al. arxiv.org
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X-TREPAN arxiv.org
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Simonynan, et. al arxiv.org
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GPD arxiv.org
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LOCO amstat.tandfonline.com
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POIMs academic.oup.com
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G-REX academia.edu
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SVM+P academia.edu
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ExplainD aaai.org
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JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas youtube.com
JAX's core design, how it's powering new research, and how you can start using it.
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NeurIPS 2020: JAX Ecosystem Meetup youtube.com
JAX, its use at DeepMind, and discussion between engineers, scientists, and JAX core team.
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Bayesian Programming with JAX + NumPyro — Andy Kitchen youtube.com
Introduction to Bayesian modelling using NumPyro.
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Introduction to JAX youtube.com
Simple neural network from scratch in JAX.
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Implementing NeRF in JAX by Soumik Rakshit and Saurav Maheshkar wandb.ai
A tutorial on 3D volumetric rendering of scenes represented by Neural Radiance Fields in JAX.
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Writing a Training Loop in JAX + FLAX by Saurav Maheshkar and Soumik Rakshit wandb.ai
A tutorial on writing a simple end-to-end training and evaluation pipeline in JAX, Flax and Optax.
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Deep Learning tutorials with JAX+Flax by Phillip Lippe uvadlc-notebooks.readthedocs.io
A series of notebooks explaining various deep learning concepts, from basics (e.g. intro to JAX/Flax, activiation functions) to recent advances (e.g., Vision Transformers, SimCLR), with translations to PyTorch.
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Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond | NeurIPS 2020 slideslive.com
Tutorial created by Zico Kolter, David Duvenaud, and Matt Johnson with Colab notebooks avaliable in Deep Implicit Layers.
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JAX: Accelerated machine-learning research via composable function transformations in Python | NeurIPS 2019 | Skye… slideslive.com
JAX intro presentation in Program Transformations for Machine Learning workshop.
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From PyTorch to JAX: towards neural net frameworks that purify stateful code by Sabrina J. Mielke sjmielke.com
Showcases how to go from a PyTorch-like style of coding to a more Functional-style of coding.
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Evolving Neural Networks in JAX by Robert Tjarko Lange roberttlange.github.io
Explores how JAX can power the next generation of scalable neuroevolution algorithms.
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Getting started with JAX (MLPs, CNNs & RNNs) by Robert Lange roberttlange.github.io
Neural network building blocks from scratch with the basic JAX operators.
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Reddit reddit.com
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Understanding Autodiff with JAX by Srihari Radhakrishna radx.in
Understand how autodiff works using JAX.
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Elegy poets-ai.github.io
A High Level API for Deep Learning in JAX. Supports Flax, Haiku, and Optax.
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White paper describing an early version of JAX, detailing how computation is traced and compiled.
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Plugging Into JAX by Nick Doiron medium.com
Compares Flax, Haiku, and Objax on the Kaggle flower classification challenge.
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Solving y=mx+b with Jax on a TPU Pod slice - Mat Kelcey matpalm.com
A four part YouTube tutorial series with Colab notebooks that starts with Jax fundamentals and moves up to training with a data parallel approach on a v3-32 TPU Pod slice.
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Out of distribution (OOD) detection by Mat Kelcey matpalm.com
Implements different methods for OOD detection.
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Evolved channel selection by Mat Kelcey matpalm.com
Trains a classification model robust to different combinations of input channels at different resolutions, then uses a genetic algorithm to decide the best combination for a particular loss.
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Ensemble networks by Mat Kelcey matpalm.com
Ensemble nets are a method of representing an ensemble of models as one single logical model.
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Deterministic ADVI in JAX by Martin Ingram martiningram.github.io
Walk through of implementing automatic differentiation variational inference (ADVI) easily and cleanly with JAX.
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Jax in Action manning.com
A hands-on guide to using JAX for deep learning and other mathematically-intensive applications.
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Exploring hyperparameter meta-loss landscapes with JAX by Luke Metz lukemetz.com
Demonstrates how to use JAX to perform inner-loss optimization with SGD and Momentum, outer-loss optimization with gradients, and outer-loss optimization using evolutionary strategies.
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Simple PDE solver + Constrained Optimization with JAX by Philip Mocz levelup.gitconnected.com
A simple example of solving the advection-diffusion equations with JAX and using it in a constrained optimization problem to find initial conditions that yield desired result.
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Learn JAX: From Linear Regression to Neural Networks by Rito Ghosh kaggle.com
A gentle introduction to JAX and using it to implement Linear and Logistic Regression, and Neural Network models and using them to solve real world problems.
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How to add a progress bar to JAX scans and loops by Jeremie Coullon jeremiecoullon.com
Tutorial on how to add a progress bar to compiled loops in JAX using the host_callback module.
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Writing an MCMC sampler in JAX by Jeremie Coullon jeremiecoullon.com
Tutorial on the different ways to write an MCMC sampler in JAX along with speed benchmarks.
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Oryx github.com
Probabilistic programming language based on program transformations.
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MLP Mixer github.com
Minimal implementation of MLP-Mixer: An all-MLP Architecture for Vision.
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gMLP github.com
Implementation of Pay Attention to MLPs.
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JAX, Flax & Transformers 🤗 github.com
3 days of talks around JAX / Flax, Transformers, large-scale language modeling and other great topics.
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