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Machine Learning Engineer & Grace Hopper ABIE Award Recipient: Mehul Smriti Raje
Mehul Smriti Raje was formerly a Ground Truth Engineer at Nuro and a Brain-Computer Interaction researcher, having previously held engineering/research positions at Google, MIT Media Lab and Spotify. She was also awarded the Grace Hopper ABIE Student of V
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Posted Nov 24 | Views 118
Posted Oct 24 | Views 1.2K
# TransformX 2022
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How to Build a Faster Vision Transformer for Supervised Image Classification
A vision transformer learns from image data by treating image patches as tokens. Adding a token learning layer to a vision transformer speeds up its operations.
Sep 21st, 2022
How to Improve Content Moderation on Inputs to Large ML Models with Synthetic Data
The DALL·E 2 team at OpenAI recently described an intriguing way to limit certain sensitive training images from their image-generation model. We hypothesize that you could improve on this technique by expanding the training dataset for DALL·E 2 with synthetic data.
Sep 6th, 2022
CVPR Research Roundup: Four Papers on Synthetic Data that Will Have an Immediate Impact on Your Data-Centric AI Workflows
New research on synthetic models investigate ways to curate, generate, or label data in resource-constrained environments, using techniques such as generative modeling or weak supervision—and the need for labeled data to train deep neural networks is a priority in AI.
Aug 22nd, 2022
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