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cs.AI2023
Bayesian Program Learning by Decompiling Amortized Knowledge
Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth
DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortized b…
cs.LG2023
Autoencoding Conditional Neural Processes for Representation Learning
Victor Prokhorov, Ivan Titov, N. Siddharth
Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data. They have seen particular application in conte…
cs.CL2023
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
Mattia Opper, Victor Prokhorov, N. Siddharth
This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured repr…