Probabilistic deep learning using JAX
-
Updated
Jul 20, 2024 - Python
Probabilistic deep learning using JAX
A framework for composing Neural Processes in Python
Batch-aware online task creation for meta-learning.
Official code implementation for SIGIR 23 paper Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
Practical Equivariances via Relational Conditional Neural Processes (Huang et al., NeurIPS 2023)
Replication of the "Conditional Neural Processes" (2018) and "Neural Processes" (2018) papers by Garnelo et al.
[ICLR'22] Multi-Task Neural Processes
Tensorflow implementation of Neural Scene Representation and Rendering
Official repo to paper
Implementation of Contrastive Neural Processes in PyTorch
Implementation of Neural Process(NP) and its Varaints
Engineering masters research project on multi-output neural processes
Code for deep learning-based glioma/tumor growth models
Code for "GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data"
[WWW 2021]Task-adaptive Neural Process for User Cold-Start Recommendation
This repository contains PyTorch implementations of Neural Process, Attentive Neural Process, and Recurrent Attentive Neural Process.
Implementation of GQN in PyTorch
This is a reproduction of Garnelo et al., Neural Processes. arXiv:1807.01622 [cs, stat] (2018).
Add a description, image, and links to the neural-processes topic page so that developers can more easily learn about it.
To associate your repository with the neural-processes topic, visit your repo's landing page and select "manage topics."