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Eric Frank

Eric Frank
1 BLOG ARTICLES 2 RESEARCH PAPERS
Before joining Uber AI Labs as a researcher, Eric invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and makes art in his free time.

Engineering Blog Articles

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Eric Frank
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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Uber uses convolutional neural networks in many domains that could potentially involve coordinate transforms, from designing self-driving vehicles to automating street sign detection to build maps and maximizing the efficiency of spatial movements in the Uber Marketplace.

In deep learning, …

Research Papers

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, R. Liu
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. [PDF]
International Conference on Learning Representations (ICLR), 2020

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

R. Liu, J. Lehman, P. Molino, F.i Such, E. Frank, A. Sergeev, J. Yosinski
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 neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space. [...] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2018

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