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Sampling with gradient-based Markov Chain Monte Carlo approaches
python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011
A simple pytorch implementation of Langevin Monte Carlo algorithms.
Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
Skew Gaussian Processes by Alessio Benavoli, Dario Azzimonti and Dario Piga
(ICML2023) Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
Library of functions and wrapper scripts for accessing ANSS ComCat server data
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).
Pytorch implementation of Neural Processes for functions and images 🎆
A novel general non-stationary point process model based on neural networks.
Paper lists for Temporal Point Process
calibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution
A general framework for learning spatio-temporal point processes via reinforcement learning
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.
wsshin / jemdoc_mathjax
Forked from jem/jemdocjemdoc with MathJax support and more
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips