Synthetic data generation for tabular data
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Updated
Jul 16, 2024 - Python
Synthetic data generation for tabular data
Python library for solving reinforcement learning (RL) problems using generative models.
Python library for solving reinforcement learning (RL) problems using generative models (e.g. Diffusion Models).
NeurIPS 2023 - TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models Official Code
Generative Quantum Circuits
PyTorch implementation of PerCo (Towards Image Compression with Perfect Realism at Ultra-Low Bitrates, ICLR 2024)
Official code repository of CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
Bayes Classifier with Gaussian Mixture Models to generate handwritten images
Retrieval-Augmented (RAG) based pretrained GPT model that predicts and analyses the November 2024 US General Elections using news sources (CNN, FoxNews, Politico, and NPR) as context
A collection of digital implementation of historical generative models for music composition.
Machine learning and data analysis package implemented in JavaScript and its online demo.
Code for "Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis"
Conditional Generative Adversarial Networks (CGANs) extend the capabilities of traditional GANs by conditioning both the generator and discriminator models on additional information, typically class labels or other forms of auxiliary information.
This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.
Model Zoo for Generative Models.
IDDM (Industrial, landscape, animate...), support DDPM, DDIM, PLMS, webui and multi-GPU distributed training. Pytorch实现,生成模型,扩散模型,分布式训练
SCEPTER is an open-source framework used for training, fine-tuning, and inference with generative models.
time series forecasting with deep learning
Implementation of papers in 100 lines of code.
This is an open collection of state-of-the-art (SOTA), novel Text to X (X can be everything) methods (papers, codes and datasets).
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