1.8k citations · 3.6k across the 14 of their papers we have counts for
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Gradient Agreement as an Optimization Objective for Meta-Learning
Amir Erfan Eshratifar, David Eigen, Massoud Pedram
This paper presents a novel optimization method for maximizing generalization over tasks in meta-learning. The goal of meta-learning is to learn a model for an agent adapting rapid…
A Meta-Learning Approach for Custom Model Training
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen +1
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. whe…
Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever
Mixtures of Experts combine the outputs of several "expert" networks, each of which specializes in a different part of the input space. This is achieved by training a "gating" netw…
Understanding Deep Architectures using a Recursive Convolutional Network
David Eigen, Jason Rolfe, Rob Fergus +1
A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kern…