AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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DeepLIFT ieeexplore-ieee-org.ezproxy.libraries.wright.edu
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The Institute for Ethical AI & Machine Learning ethical.institute
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LIME dl.acm.org
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Lou, et. al. dl.acm.org
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BayLIME arxiv.org
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KL-LIME arxiv.org
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ProfWeight arxiv.org
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RISE arxiv.org
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LORE arxiv.org
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TreeSHAP arxiv.org
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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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STA arxiv.org
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Hara, et. al. arxiv.org
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Lei, et. al. arxiv.org
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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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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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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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Jax in Action manning.com
A hands-on guide to using JAX for deep learning and other mathematically-intensive applications.
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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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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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Reformer github.com
Implementation of the Reformer (efficient transformer) architecture.
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VDVAE github.com
Adaptation of Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images, original code at openai/vdvae.
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Performer github.com
Flax implementation of the Performer (linear transformer via FAVOR+) architecture.
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MUSIQ github.com
Checkpoints and model inference code for the ICCV 2021 paper MUSIQ: Multi-scale Image Quality Transformer
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IPA-GNN github.com
Official implementation of Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
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Dreamfields github.com
Official implementation of the ICLR 2022 paper Progressive Distillation for Fast Sampling of Diffusion Models.
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D3PM github.com
Official implementation of Structured Denoising Diffusion Models in Discrete State-Spaces.
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Combiner github.com
Official implementation of Combiner: Full Attention Transformer with Sparse Computation Cost.
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BNN-HMC github.com
Implementation for the paper What Are Bayesian Neural Network Posteriors Really Like?.
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ARDM github.com
Official implementation of Autoregressive Diffusion Models.
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Accurate Quantized Training github.com
Tools and libraries for running and analyzing neural network quantization experiments in JAX and Flax.
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Gated Linear Networks github.com
GLNs are a family of backpropagation-free neural networks.
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Sampling Path Candidates with Machine Learning differt.eertmans.be
Official tutorial and implementation from the paper Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing.
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Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more.
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