105 citations · 261 across the 26 of their papers we have counts for
27 papers
Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation
Patrick Kage, Trevor Hedges, N. Siddharth +1
Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable for processing datasets. Amon…
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…
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…
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…
Drawing out of Distribution with Neuro-Symbolic Generative Models
Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le +1
Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles,…
Adversarial Masking for Self-Supervised Learning
Yuge Shi, N. Siddharth, Philip H. S. Torr +1
We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial object…