15 citations · 30 across the 4 of their papers we have counts for
6 papers
Eigen Memory Trees
Mark Rucker, Jordan T. Ash, John Langford +2
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios. EMTs store data at the leaves of a binary tree and route new sample…
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel +5
Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmenta…
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy +1
Noise contrastive learning is a popular technique for unsupervised representation learning. In this approach, a representation is obtained via reduction to supervised learning, whe…
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan T. Ash, Geoffrey Roeder +2
Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular…
On Warm-Starting Neural Network Training
Jordan T. Ash, Ryan P. Adams
In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural proper…
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy +2
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of…