Popular GitHub Repositories for Hidden

Machine learning, in numpy

simple neural network library in ANSI C

State Space Models library in JAX

EdgeChains.js is Full-Stack GenAI library. Front-end, backend, apis, prompt management, distributed computing. All core prompts & chains are managed declaratively in jsonnet (and not hidden in classes)

[ICCV'23] Hidden Biases of End-to-End Driving Models

Powerful and highly functional startpage integrated with chatGPT and hidden under the super minimalistic and animated design (static/hosted/github pages)

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

CSV-AI is the ultimate app powered by LangChain, OpenAI, and Streamlit that allows you to unlock hidden insights in your CSV files. With CSV-AI, you can effortlessly interact with, summarize, and analyze your CSV files in one convenient place.

Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"

Input Output Hidden Markov Model (IOHMM) in Python

Neural HMMs are all you need (for high-quality attention-free TTS)

Python tutorials as Jupyter Notebooks for NLP, ML, AI

a novel architecture that leverages Autoencoders to superimpose the hidden representations of a base model and a fine-tuned model within a shared parameter space. Using B-spline-based blending coefficients and autoencoders that adaptively reconstruct the original hidden states based on the input data distribution.

Deep Markov Models

[CVPR 2023 Highlight ?] Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision

Framework to learn Named Entity Recognition models without labelled data using weak supervision.

Pure Python Library for ES-HyperNEAT. Contains implementations of HyperNEAT and ES-HyperNEAT.

Implementation of Artificial Neural Networks using NumPy

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

Official implementation of the paper The Hidden Language of Diffusion Models