eogns282 Goto Github PK

anhp-andtt

Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)

anp

bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

beta-tcvae

code for "Isolating Sources of Disentanglement in Variational Autoencoders".

bnp

Pytorch implementation of neural processes and variants

byol_multi_temp

chronos-forecasting

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

csdi

Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

deep-kernel-gp

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood

deq

[NeurIPS'19] Deep Equilibrium Models

disentanglement-pytorch

Disentanglement library for PyTorch

disentangling-vae

Experiments for understanding disentanglement in VAE latent representations

etdataset

The Electricity Transformer dataset is collected to support the further investigation on the long sequence forecasting problem.

fft-conv-pytorch

Implementation of 1D, 2D, and 3D FFT convolutions in PyTorch. Much faster than direct convolutions for large kernel sizes.

flow_paper

google-research

Google AI Research

gpconvcnp

Code for "GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data"

h3

Language Modeling with the H3 State Space Model

hierarchical-disentanglement

Code for Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement

imode

Neural Ordinary Differential Equations for Intervention Modeling

lm-human-preferences

Code for the paper Fine-Tuning Language Models from Human Preferences

mathematics-roadmap

A Comprehensive Roadmap to Mathematics

minimalrl

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

mlpinit-for-gnns

[ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

neural-process-family

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

neural-processes

Pytorch implementation of Neural Processes for functions and images :fireworks:

neural_stpp

Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

neuralcollapse

Code reproducing Neural Collapse phenomenon on MSE and cross-entropy loss

paper-study

phiflow

Research-oriented differentiable fluid simulation framework