johnhalloran321

johnhalloran321's Stars

• Wang-ML-Lab/bayesian-peft

  [NeurIPS 2024] BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models

  Language:Python234

• gkamradt/LLMTest_NeedleInAHaystack

  Doing simple retrieval from LLM models at various context lengths to measure accuracy

  Language:Jupyter Notebook1.6k177

• aymeric-roucher/LongContext_vs_RAG_NeedleInAHaystack

  Comparing retrieval abilities from GPT4-Turbo and a RAG system on a toy example for various context lengths

  Language:Jupyter Notebook352

• microsoft/prv_accountant

  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.

  Language:Python7310

• vulus98/Rethinking-attention

  My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

  Language:Python424

• facebookresearch/fisher_information_loss

  This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

  Language:Python498

• google/differential-privacy

  Google's differential privacy libraries.

  Language:Go3.1k359

• p-lambda/dsir

  DSIR large-scale data selection framework for language model training

  Language:Python23819

• hmmlearn/hmmlearn

  Hidden Markov Models in Python, with scikit-learn like API

  Language:Python3.1k739

• p-lambda/incontext-learning

  Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

  Language:Python9813

• IST-DASLab/sparsegpt

  Code for the ICML 2023 paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot".

  Language:Python74898

• karpathy/llama2.c

  Inference Llama 2 in one file of pure C

  Language:C17.6k2.1k

• Trusted-AI/adversarial-robustness-toolbox

  Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

  Language:Python5k1.2k

• POSTECH-CVLab/PyTorch-StudioGAN

  StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

  Language:Python3.4k343

• stanford-crfm/BioMedLM

  Language:Python61164

• Michedev/DDPMs-Pytorch

  Implementation of various DDPM papers to understand how they work

  Language:Python829

• ddbourgin/numpy-ml

  Machine learning, in numpy

  Language:Python15.8k3.8k

• ddbourgin/em

  Notes + notebooks on EM + variational EM algorithms for Bayesian methods tutorial

  Language:Jupyter Notebook3714

• exaloop/codon

  A high-performance, zero-overhead, extensible Python compiler using LLVM

  Language:C++15.2k522

• CompVis/stable-diffusion

  A latent text-to-image diffusion model

  Language:Jupyter Notebook68.9k10.2k

• andreasjansson/language-detection.el

  Automatic programming language detection of code snippets, in Emacs Lisp

  Language:Emacs Lisp748

• OlgaChernytska/word2vec-pytorch

  Implementation of the first paper on word2vec

  Language:Python20791

• AntixK/PyTorch-VAE

  A Collection of Variational Autoencoders (VAE) in PyTorch.

  Language:Python6.8k1.1k

• salesforce/genhance

  Language:Jupyter Notebook3211

• bbuck/tab-pinner

  The Chrome extensions Tab Pinner (Keyboard Shortcuts)

  Language:JavaScript397

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook17k4.2k

• yang-song/score_sde_pytorch

  PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

  Language:Jupyter Notebook1.8k324

• chenhongge/RobustTrees

  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

  Language:C++6711

• vgsatorras/hybrid-inference

  Language:Python224

• mruffini/Hierarchical-Methods-of-Moments

  Language:Python41