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FeatureX Machine Learning Seminar

  • CIC Boston 50 Milk Street, 5th floor, Compass Boston, MA, 02110 United States (map)

Machine Learning Meetup

This weekly seminar series, hosted by the computer vision research team at FeatureX, is open to students and professionals who share an interest in gathering with like-minded machine learning researchers. This series focuses on current and influential papers in machine learning, and brings active participants together around one relevant paper each week. The presenter will introduce the background of the paper and review the findings. Attendees are expected to have read the paper and be ready to participate in group discussions about the research content and its implications.

Space is limited and RSVP’s are mandatory for this event. Please email Emily Rogers at emily.rogers@featurex.ai if you plan to attend. If your plans change, please update us so we can offer space to someone else. 

Upcoming Seminar:

Paper Title and LinkImage Transformer by Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku

Abstract: Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. We propose another extension of self-attention allowing it to efficiently take advantage of the two-dimensional nature of images. While conceptually simple, our generative models significantly outperform the current state of the art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet from 3.83 to 3.77. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve significantly over previously published super-resolution models. Images generated by the model fool human observers three times more often than the previous state of the art.

Earlier Event: May 17
Venture Café