PyTorch implementation of latent neural processes.
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Updated
Apr 27, 2024 - Jupyter Notebook
PyTorch implementation of latent neural processes.
A framework for composing Neural Processes in Python
Adaptive Conditional Quantile Neural Processes - PyTorch
Probabilistic deep learning using JAX
implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
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)
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Batch-aware online task creation for meta-learning.
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
Neural Time Series Analysis
Neural Process implementations in JAX and PyTorch
Implementation of Neural Process(NP) and its Varaints
Engineering masters research project on multi-output neural processes
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