Assignment 1: automatic differentiation

Implementing Multiple Layer Neural Network from Scratch

(Spring 2017) Assignment 2: GPU Executor

Computational graph library for machine learning

Model-based Policy Gradients

Jaxpr Visualisation Tool

A computation graph micro-framework providing seamless lazy and concurrent evaluation

GenCoG: A DSL-Based Approach to Generating Computation Graphs for TVM Testing (ISSTA‘23)

artifax is a Python package to evaluate nodes in a computation graph where the dependencies associated with each node are extracted directly from their function signatures.

Real-time execution and remote monitoring and tuning of BDSim Block-Diagrams for modeling and control of Dynamical Systems

Implementation of automatic differentiation (AD) in forward and backward modes with mathematical explanations

Python library for developing data processing algorithms as computational graphs and their integration with publish-subscribe systems

Assignment 2: GPU Executor

Assignment 1: automatic differentiation

A tiny library to build hierarchical computation graphs that isolate function logic while avoiding duplicate work

This repository contains the code that produces the numeric section in On the Use of TensorFlow Computation Graphs in combination with Distributed Optimization to Solve Large-Scale Convex Problems

Projeto de TCC baseado em processamento de imagens e visão computacional para identificar a evolução de ogras da engenharia civil

The implementation of a deep learning framework from scratch in plain Numpy.