Rose-STL-Lab / AutoSTPP
Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.
☆24Updated 4 months ago
Alternatives and similar repositories for AutoSTPP:
Users that are interested in AutoSTPP are comparing it to the libraries listed below
- ☆11Updated 9 months ago
- Official implementation of E(n)-equivariant Graph Neural Cellular Automata☆26Updated 10 months ago
- Code for "Accelerating Training with Neuron Interaction and Nowcasting Networks" [to appear at ICLR 2025]☆18Updated this week
- Generative cellular automaton-like learning environments for RL.☆19Updated last month
- ☆10Updated 2 years ago
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆19Updated 2 years ago
- Official code for paper: Conservative objective models are a special kind of contrastive divergence-based energy model☆14Updated last year
- Official Implementation of NeurIPS'23 Paper "Cross-Episodic Curriculum for Transformer Agents"☆31Updated last year
- A scalable implementation of diffusion and flow-matching with XGBoost models, applied to calorimeter data.☆17Updated 4 months ago
- ☆46Updated 2 months ago
- A simple example of VAEs with KANs☆12Updated 9 months ago
- Quantification of Uncertainty with Adversarial Models☆28Updated last year
- Repository for the PopulAtion Parameter Averaging (PAPA) paper☆26Updated 11 months ago
- Implementation of Spectral State Space Models☆16Updated last year
- [NeurIPS 2024, spotlight] Multivariate Learned Adaptive Noise for Diffusion Models☆16Updated 3 months ago
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆25Updated 3 years ago
- General Invertible Transformations for Flow-based Generative Models☆17Updated 4 years ago
- PAC-Bayes generalization certificates for ICP☆20Updated last year
- Automatically generate simple meta-learning tasks from a very large space☆15Updated last year
- ☆16Updated 10 months ago
- Repo for paper: Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems, accepted at AISTATS 2024☆12Updated last year
- JAX implementation of "Fine-Tuning Language Models with Just Forward Passes"☆19Updated last year
- Simple, extensible implementations of some meta-learning algorithms in Jax☆9Updated 4 years ago
- Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"☆42Updated last year
- Code for "Continuous-Time Meta-Learning with Forward Mode Differentiation" (ICLR 2022)☆27Updated 3 years ago
- ☆31Updated 11 months ago
- Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.☆20Updated last year
- code associated with paper "Sparse Bayesian Optimization"☆26Updated last year
- Implementation of Gradient Agreement Filtering, from Chaubard et al. of Stanford, but for single machine microbatches, in Pytorch☆23Updated last month