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Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
Official Code for Invertible Residual Networks
A PyTorch Implementation of Density Estimation Using Real NVP
An implementation of DIP-VAE from the paper "Variational Inference of Disentangled Latent Concepts from Unlabelled Observations" by Kumar et al. (Published at ICLR 2018) https://arxiv.org/abs/1711.…
Integrate mutual information with conditional GAN (CGAN) to achieve controlled MINST image generation.
Simple and clean implementation of Conditional Variational AutoEncoder (cVAE) using PyTorch
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Using LLMs to generate formal verification programs or mathematical proofs for distributed protocols
This program in Python allows the Kalman-TD Model to fit simulated behavioural data.
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
ND-Adam is a tailored version of Adam for training DNNs.
An open source python library for scalable Bayesian optimisation.
A pytorch library for graph and hypergraph computation.
PyTorch Implementation of the paper "Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures" published in the 3rd Workshop on Mathematical Reasoni…
PyTorch Implementation of the paper "A Neuro-vector-symbolic architecture for Solving Raven's Progressive Matrices" published in Nature Machine Intelligence 2023.
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
The PSL software from the University of Maryland and the University of California Santa Cruz
Various examples to showcase the functionality of PSL.
Direct Feedback Alignment on MNIST dataset implemented in TF2.0/Keras API
「Direct Feedback Aligment를 이용한 신경망 학습 알고리즘 구현」에 대한 내용을 다루고 있습니다.
bioflax provides a JAX implementation of biologically plausible learning algorithms
Code for our paper on best practices to train neural networks with direct feedback alignment (DFA).
