In this work, we present a system that can automatically generate high-quality audiobooks from online e-books.
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected.
1 code implementation • 14 Jul 2022 • Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
no code implementations • 15 Apr 2022 • Miriam Cha, Kuan Wei Huang, Morgan Schmidt, Gregory Angelides, Mark Hamilton, Sam Goldberg, Armando Cabrera, Phillip Isola, Taylor Perron, Bill Freeman, Yen-Chen Lin, Brandon Swenson, Jean Piou
The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions.
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.
Ranked #2 on Unsupervised Semantic Segmentation on Potsdam-3
Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide.
Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax.
1 code implementation • 14 Jul 2020 • Mark Hamilton, Stephanie Fu, Mindren Lu, Johnny Bui, Darius Bopp, Zhenbang Chen, Felix Tran, Margaret Wang, Marina Rogers, Lei Zhang, Chris Hoder, William T. Freeman
We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia.
Joint optimization of these "likelihood parameters" with model parameters can adaptively tune the scales and shapes of losses in addition to the strength of regularization.
This work aims to improve semi-supervised learning in a neural network architecture by introducing a hybrid supervised and unsupervised cost function.
1 code implementation • 20 Oct 2018 • Mark Hamilton, Sudarshan Raghunathan, Ilya Matiach, Andrew Schonhoffer, Anand Raman, Eli Barzilay, Karthik Rajendran, Dalitso Banda, Casey Jisoo Hong, Manon Knoertzer, Ben Brodsky, Minsoo Thigpen, Janhavi Suresh Mahajan, Courtney Cochrane, Abhiram Eswaran, Ari Green
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.
1 code implementation • 11 Apr 2018 • Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala, Danil Kirsanov, Eduardo de Leon, Eli Barzilay, Ilya Matiach, Joe Davison, Maureen Busch, Miruna Oprescu, Ratan Sur, Roope Astala, Tong Wen, ChangYoung Park
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark.