lightonai / double-trouble-in-double-descent
Double Trouble in the Double Descent Curve with Optical Processing Units.
☆12Updated 2 years ago
Alternatives and similar repositories for double-trouble-in-double-descent:
Users that are interested in double-trouble-in-double-descent are comparing it to the libraries listed below
- Conformational exploration SARS-CoV-2 (coronavirus responsible for COVID-19)☆16Updated 2 years ago
- Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.☆20Updated 4 years ago
- ML benchmarks performance featuring LightOn's Optical Processing Unit (OPU) vs CPU and GPU.☆21Updated last year
- Optical Transfer Learning☆27Updated last year
- Fast graph classifier with optical random features☆12Updated 3 years ago
- Double Descent Curve with Optical Random Features☆28Updated 2 years ago
- Code to perform Model-Free Episodic Control using Aurora OPUs☆17Updated 4 years ago
- Code for our paper on best practices to train neural networks with direct feedback alignment (DFA).☆21Updated 5 years ago
- Implementation of NEWMA: a new method for scalable model-free online change-point detection☆46Updated 4 years ago
- Python client for the LightOn Muse API☆14Updated 2 years ago
- Python library for running large-scale computations on LightOn's OPUs☆35Updated 2 years ago
- Public rankings of extreme-scale models☆13Updated 3 years ago
- Study on the applicability of Direct Feedback Alignment to neural view synthesis, recommender systems, geometric learning, and natural la…☆86Updated 2 years ago
- Inference code in Pytorch for GPT-like models, such as PAGnol, a family of models with up to 1.5B parameters, trained on datasets in Fren…☆20Updated 2 years ago
- Experiments with Direct Feedback Alignment training scheme for DNNs☆31Updated 7 years ago
- Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input (NeurIPS 2019)☆12Updated 9 months ago
- Architecture embeddings independent from the parametrization of the search space☆15Updated 3 years ago
- Experiments with Direct Feedback Alignment and comparison to Backpropagation.☆8Updated 7 years ago
- PyTorch-based code for training fully-connected and convolutional networks using backpropagation (BP), feedback alignment (FA), direct fe…☆64Updated 3 years ago
- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions☆20Updated 5 years ago
- Learning to Learn: Gradient-free Optimization framework☆36Updated 3 years ago
- PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020☆15Updated 3 years ago
- ☆10Updated 3 years ago
- Fit an Ising model with neural spike train data using Minimum Probability Flow Learning. Based on code from Jascha Sohl-Dickstein.☆19Updated 7 years ago
- ☆12Updated 3 years ago
- Demo: Slightly More Bio-Plausible Backprop☆21Updated 7 years ago
- Hessian backpropagation (HBP): PyTorch extension of backpropagation for block-diagonal curvature matrix approximations☆20Updated last year
- ☆15Updated 3 years ago
- Fully documented Pytorch implementation of the Equilibrium Propagation algorithm.☆31Updated 5 years ago