Merge branch 'main' into patch-1

readme.md

@@ -88,6 +88,8 @@ This section contains libraries that are well-made and useful, but have not nece
 - [KFAC-JAX](https://github.com/deepmind/kfac-jax) - Second Order Optimization with Approximate Curvature for NNs. <img src="https://img.shields.io/github/stars/deepmind/kfac-jax?style=social" align="center">
 - [TF2JAX](https://github.com/deepmind/tf2jax) - Convert functions/graphs to JAX functions. <img src="https://img.shields.io/github/stars/deepmind/tf2jax?style=social" align="center">
 - [jwave](https://github.com/ucl-bug/jwave) - A library for differentiable acoustic simulations <img src="https://img.shields.io/github/stars/ucl-bug/jwave?style=social" align="center">
+- [GPJax](https://github.com/thomaspinder/GPJax) - Gaussian processes in JAX.
+- [Jumanji](https://github.com/instadeepai/jumanji) - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX. <img src="https://img.shields.io/github/stars/instadeepai/jumanji?style=social" align="center">
 - [Eqxvision](https://github.com/paganpasta/eqxvision) - Equinox version of Torchvision. <img src="https://img.shields.io/github/stars/paganpasta/eqxvision?style=social" align="center">
 
 <a name="models-and-projects" />
@@ -98,7 +100,6 @@ This section contains libraries that are well-made and useful, but have not nece
 
 - [Fourier Feature Networks](https://github.com/tancik/fourier-feature-networks) - Official implementation of [_Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains_](https://people.eecs.berkeley.edu/~bmild/fourfeat).
 - [kalman-jax](https://github.com/AaltoML/kalman-jax) - Approximate inference for Markov (i.e., temporal) Gaussian processes using iterated Kalman filtering and smoothing.
-- [GPJax](https://github.com/thomaspinder/GPJax) - Gaussian processes in JAX.
 - [jaxns](https://github.com/Joshuaalbert/jaxns) - Nested sampling in JAX.
 - [Amortized Bayesian Optimization](https://github.com/google-research/google-research/tree/master/amortized_bo) - Code related to [_Amortized Bayesian Optimization over Discrete Spaces_](http://www.auai.org/uai2020/proceedings/329_main_paper.pdf).
 - [Accurate Quantized Training](https://github.com/google-research/google-research/tree/master/aqt) - Tools and libraries for running and analyzing neural network quantization experiments in JAX and Flax.
@@ -142,6 +143,7 @@ This section contains libraries that are well-made and useful, but have not nece
 - [Dreamfields](https://github.com/google-research/google-research/tree/master/dreamfields) - Official implementation of the ICLR 2022 paper [_Progressive Distillation for Fast Sampling of Diffusion Models_](https://ajayj.com/dreamfields).
 - [GIFT](https://github.com/google-research/google-research/tree/master/gift) - Official implementation of [_Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent_](https://arxiv.org/abs/2106.06080).
 - [Light Field Neural Rendering](https://github.com/google-research/google-research/tree/master/light_field_neural_rendering) - Official implementation of [_Light Field Neural Rendering_](https://arxiv.org/abs/2112.09687).
+- [Sharpened Cosine Similarity in JAX by Raphael Pisoni](https://colab.research.google.com/drive/1KUKFEMneQMS3OzPYnWZGkEnry3PdzCfn?usp=sharing) -  A JAX/Flax implementation of the Sharpened Cosine Similarity layer.
 
 ### Haiku
 
@@ -214,6 +216,9 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers,
 - [Writing an MCMC sampler in JAX by Jeremie Coullon](https://www.jeremiecoullon.com/2020/11/10/mcmcjax3ways/) - Tutorial on the different ways to write an MCMC sampler in JAX along with speed benchmarks.
 - [How to add a progress bar to JAX scans and loops by Jeremie Coullon](https://www.jeremiecoullon.com/2021/01/29/jax_progress_bar/) - Tutorial on how to add a progress bar to compiled loops in JAX using the `host_callback` module.
 - [Get started with JAX by Aleksa Gordić](https://github.com/gordicaleksa/get-started-with-JAX) - A series of notebooks and videos going from zero JAX knowledge to building neural networks in Haiku.
+- [Writing a Training Loop in JAX + FLAX by Saurav Maheshkar and Soumik Rakshit](https://wandb.ai/jax-series/simple-training-loop/reports/Writing-a-Training-Loop-in-JAX-FLAX--VmlldzoyMzA4ODEy) - A tutorial on writing a simple end-to-end training and evaluation pipeline in JAX, Flax and Optax.
+- [Implementing NeRF in JAX by Soumik Rakshit and Saurav Maheshkar](https://wandb.ai/wandb/nerf-jax/reports/Implementing-NeRF-in-JAX--VmlldzoxODA2NDk2?galleryTag=jax) - A tutorial on 3D volumetric rendering of scenes represented by Neural Radiance Fields in JAX.
+- [Deep Learning tutorials with JAX+Flax by Phillip Lippe](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial2/Introduction_to_JAX.html) - 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.
 
 <a name="community" />