56 citations · 243 across the 35 of their papers we have counts for
7 papers · 1 filter
Deep Message Passing on Sets
Yifeng Shi, Junier Oliva, Marc Niethammer
Modern methods for learning over graph input data have shown the fruitfulness of accounting for relationships among elements in a collection. However, most methods that learn over…
Meta-Neighborhoods
Siyuan Shan, Yang Li, Junier Oliva
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence. In this work, we step forward in this direction and propose a semi-p…
Flow Models for Arbitrary Conditional Likelihoods
Yang Li, Shoaib Akbar, Junier B. Oliva
Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative modeling approaches are focused sol…
MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva +1
Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that woul…
Meta-Curvature
Eunbyung Park, Junier B. Oliva
We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation. MC expands on the model-agnostic meta-learner (MAML)…
Exchangeable Generative Models with Flow Scans
Christopher Bender, Kevin O'Connor, Yang Li +3
In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformatio…
A Forest from the Trees: Generation through Neighborhoods
Yang Li, Tianxiang Gao, Junier B. Oliva
In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless u…