3.5k citations · 7.7k across the 28 of their papers we have counts for
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RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian +9
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the…
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Rosanne Liu, Joel Lehman, Piero Molino +4
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional ne…
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…
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen, Jason Yosinski, Jeff Clune
Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given…