continuation-jax

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

TensorFlow Eager implementation of NEAT and Adaptive HyperNEAT

An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.

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.

Curated list of methods that focuses on improving the efficiency of diffusion models

C++ implementation of neural networks library with Keras-like API. Contains majority of commonly used layers, losses and optimizers. Supports sequential and multi-input-output (flow) models. Supports single CPU, Multi-CPU and GPU tensor operations (using cuDNN and cuBLAS).

[Biomedical Signal Processing and Control] ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer

Run many functions (adaptively) on many cores (>10k-100k) using mpi4py.futures, ipyparallel, loky, or dask-mpi. :tada: