Algorithms
Learning in Data Scarce Visual and Multimodal Applications Using Vectorized and Composable Representations
The vision and learning group at CVT (Center for Vision Technologies) at SRI has developed a framework and a suite of algorithms for machine learning in data scarce conditions. We present our results on zero…
Adversarial Benchmarks for Commonsense Reasoning
Human intelligence involves comprehending new situations through a rich model of the world. Given a single image from a movie, or a paragraph from a novel, we can easily infer people’s intentions, mental states, and…
Microsoft @ PPoPP/HPCA/CGO 2019
Microsoft is excited to be part of the PPoPP/HPCA/CGO 2019 co-located conferences and is a Gold sponsor of PPoPP 2019 and HPCA 2019. Stop by our booth to learn about our latest research and find…
Polarization Through the Lens of Learning Theory
What are the biases in my data?
One challenge with AI algorithmic fairness is that one usually has to know the potential group(s) that an algorithm might discriminate against in the first place. However, in joint work with Maria De-Arteaga, Nathaniel Swinger,…
Competing in the X Games of machine learning with Dr. Manik Varma
Episode 63, February 13, 2019 – Dr. Varma tells us all about extreme classification (including where in the world you might actually run into 10 or 100 million options), reveals how his Parabel and Slice…
Everything you always wanted to know about extreme classification (but were afraid to ask)
Varma’s team published a paper that exploded the number of choices that could be considered from a search engine from five thousand to ten million. This changed the nature of the game and led to…
