Avik Pal

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Design multi-agent environments and simple reward functions such that social driving behavior emerges

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

PyTorch based framework for easy and efficient training of GANs

Differentiable RayTracing in Julia

Unofficial Julia bindings for logging experiments to wandb.ai

Distributed Data Parallel Training of Deep Neural Networks

Fast Neural Style Transfer in Julia

Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

Benchmarks across Deep Learning Frameworks in Julia and Python

A simple way to specify experiment configurations

Local regularization of Neural Differential Equations

On efficient training and inference of Neural Differential Equations

CNN Visualizations in Flux

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CS335 Compilers IITK Course Project

Implementation of Google's Deep Dream in Julia using Flux

A legal assistant which can summarise any form of a legal document and answer specific queries regarding the document

Machine Learning And Deep Learning Basics covered by working on Commonplace Datasets

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Testing out Nonlinear Solvers that Automatically Switch between Discrete and Continuous Variants

ESC101 IIT Kanpur Lab Assignment Solutions

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

The official registry of general Julia packages

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Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Multi-threaded BLAS-like library that provides pure Julia matrix multiplication

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Please do not feed the models

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A Julia wrapper for the NVIDIA Collective Communications Library.

Benchmarks for lux projects

Test bed for batched non linear solve algorithms (mostly for machine learning applications)

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.

Densenet and Other Models on the MURA (musculoskeletal radiographs) Dataset using Flux

21st Century AD

Python Multi-Agent Reinforcement Learning framework

A library of data interpolation and smoothing functions

An educational resource to help anyone learn deep reinforcement learning.

Julia wrapper for NNPACK

CUDA Tile programming in Julia

A profiling and performance analysis tool for machine learning

GitHub action to downgrade julia compat entries before testing

Differentiable models for tabular data

julia package providing an interface for ODE-solvers written in Fortran

Pirated SLEEF port.

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Package for fitting models according to the deep compartment modeling framework for pharmacometric applications.

Symbolic programming for the next generation of numerical software

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

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Code for the paper "Learning Differential Equations that are Easy to Solve"

CS 771A: Introduction to Machine Learning, IIT Kanpur, 2019-20-autumn offering

Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild

💸 An app created to help users manage a budget and purchases

Parallel test runner

Julia protobuf implementation

Backward compatible ML compute opset inspired by HLO/MHLO

Collection of builder repositories for BinaryBuilder.jl

Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs

Julia API for MLX

Utility package for accessing common Machine Learning datasets in Julia

Optimisers.jl defines many standard optimisers and utilities for learning loops.

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Julia package for tensor contractions and related operations

KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA (+ more DSLs)

Development repository for the Triton language and compiler

Neural ODEs on arbitrary Manifolds

Differentiable, Hardware Accelerated, Molecular Dynamics

Implementation of language-level autograd compiler for Julia

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Julia bindings for the Enzyme automatic differentiator

Forward Mode Automatic Differentiation for Julia

You like pytorch? You like micrograd? You love tinygrad! ❤️

Scoped settings in Julia, building on ScopedValues

🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

Simple chains

Abstract types and methods for Gaussian Processes.

High-performance automatic differentiation of LLVM and MLIR.

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Figure out what implicit exports a Julia module is relying on, in order to make them explicit

Deep Learning for humans

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A machine learning compiler for GPUs, CPUs, and ML accelerators

Simple least squares and curve fitting functions

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Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Fast and simple fluid simulator in Julia

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Fortran-like arrays with arbitrary, zero or negative starting indices.

General fixed point mapping acceleration and optimization in Julia

Flux's ex AD

Macro(s) for vectorizing loops.

Optimization functions for Julia

Run testitems in parallel.

The ImageNet 2012 Classification Dataset (ILSVRC 2012-2017) in Julia

MLIR For Beginners tutorial

The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.

A PyTorch Library for Accelerating 3D Deep Learning Research

CUDA programming in Julia.

Official repository for reproducing the paper "Constants of motion network" (NeurIPS 2022)

Julia package for inference and training of Llama-style language models using Lux and Reactant

A small package to simplify partial function application

Arrays with arbitrarily nested named components.

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Overdub Your Julia Code

Symbolic expressions, rewriting and simplification

Reusable array functionality for Julia's various GPU backends.

KAN for Vision Transformer

OpenCL Julia bindings

A Julia package for regression in the sphere based on Universal Differential Equations

The dispatch doctor prescribes type stability

MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

GraphNetCore.jl is a software package for the Julia programming language that provides an the core functionality of the MeshGraphNets.jl package. Some parts are based on the implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

Repository containing the abstract interface to the emulators used in the CosmologicalEmulators organization

Global glacier model using Universal Differential Equations for climate-glacier interactions

Julia package of loss functions for machine learning.

Heterogeneous programming in Julia

MPI wrappers for Julia

Interface designs for enforcing static computations in array functions with Julia

Parameterise all the things

A Review of Sensitivity Methods for Differential Equations

Code and experiments for the NeurIPS 2023 paper Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints

Fast jacobian computation through sparsity exploitation and matrix coloring

forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

Julia interface for dlpack

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Statically sized arrays for Julia

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

A Julia Basket of Hand-Picked Krylov Methods

Easily benchmark a Julia package over its commit history

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers

A Julia package to represent semiseparable and almost banded matrices

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

A Julia package for representing banded matrices

Functionality for working efficiently with block diagonal matrices.

Wrappers for MINPACK into the SciML Common Interface

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

The Code Tracer

Benchmarks for scientific machine learning (SciML) software and differential equation solvers

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

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Repository for SciML AD backend types

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Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Taylor-mode automatic differentiation for higher-order derivatives

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.

Simpler stacktraces for the Julia Programming Language

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

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A fork of Codespeed that includes the instances run at https://speed.python.org/ and https://speed.pypy.org

Abbreviated Stack Traces specialized for Lux.jl models

Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem

Documenter's Markdown / MkDocs backend

A style guide for stylish Julia developers

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Julia interface to WebGL using Three-js custom elements and Patchwork.jl

A documentation generator for Julia.

Atomic graph models for molecules and crystals in Julia

Join the GitHub Graduation Yearbook and "walk the stage" on June 5.

The Base interface of the SciML ecosystem

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Linear operators for discretizations of differential equations and scientific machine learning (SciML)

No description, website, or topics provided.

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

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Solvers for steady states in scientific machine learning (SciML)

A place to submit conda recipes before they become fully fledged conda-forge feedstocks

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Flux Website

Relax! Flux is the ML library that doesn't make you tensor

Julia Project website

Self-driving car simulator for the Duckietown universe

A Curious Cumulation of CUDA Cuisine

Spaces for Gym.jl packages

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

Differentiable Duckietown

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A robot powered training repository :robot:

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Final project for RecVis 18

Solution for the For Ai Pruning Challenge

Builder for NNPACK. Currently supports only Linux

Predicting whether a given set of people survive on the Titanic