3.5k citations · 7.7k across the 28 of their papers we have counts for
5 papers · 1 filter
Convergent Learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune +2
Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers. Such research is difficult because it req…
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Sina Honari, Jason Yosinski, Pascal Vincent +1
Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision. Max-pooling purposeful…
Understanding Neural Networks Through Deep Visualization
Jason Yosinski, Jeff Clune, Anh Nguyen +2
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recogn…
Can deep learning help you find the perfect match?
Harm de Vries, Jason Yosinski
Is he/she my type or not? The answer to this question depends on the personal preferences of the one asking it. The individual process of obtaining a full answer may generally be d…
GSNs : Generative Stochastic Networks
Guillaume Alain, Yoshua Bengio, Li Yao +4
We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based…