NYU Deep Learning Spring 2021

[ECCV 2022] Compositional Generation using Diffusion Models

The aim of this repository is to create RBMs, EBMs and DBNs in generalized manner, so as to allow modification and variation in model types.

(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

The official repository of "Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models".

EIT-EBM

Conditional RBM

Measuring Outcome via Evidence-Based Management

Python implementation of "Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit," EMNLP 2018

A package to interact with Sales Data Controllers (SDC) for Rwanda Revenue Authority

Implementation of Stein Variational Gradient Descent to learn neural samplers.

Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

Get started with Diffusion Models, EBMs, SDE

Post-hoc Out-of-Distribution Detection

Simple tool for extracting/repacking text from Ryza 3 (A24) EBM files

This repository, `project_learn_TalkToEBM`, explores and implements Explainable Boosting Machines (EBMs) with a focus on creating interactive explanations and allowing users to "talk" to the model. It likely includes code for training EBMs, generating explanations, and potentially a user interface for interacting with the model's predictions

This is the algorithm part of the literature screening auxiliary system.Only the original data set and some relevant information of the experimental results are given here.

This is my project for the Information Retrieval subject

An AI-powered bridge health classification system that automatically categorizes bridge inspection reports into health levels using machine learning. The system leverages Explainable Boosting Machine (EBM) to achieve high accuracy while maintaining interpretability.

Energy-Based Models with SGLD Sampling is a compact PyTorch playground for learning 2D densities via contrastive divergence and persistent SGLD chains. It ships with a Rich/ Typer CLI, Pydantic configs, and replay-buffered sampling so you can train, checkpoint, and visualize energy landscapes (PNG/GIF) in seconds.