Search Results for author: Mark Hamilton

Found 13 papers, 7 papers with code

Large-Scale Automatic Audiobook Creation

no code implementations7 Sep 2023 Brendan Walsh, Mark Hamilton, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger, William T. Freeman, Markus Weimer

In this work, we present a system that can automatically generate high-quality audiobooks from online e-books.

MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge

1 code implementation7 Jun 2023 Miriam Cha, Gregory Angelides, Mark Hamilton, Andy Soszynski, Brandon Swenson, Nathaniel Maidel, Phillip Isola, Taylor Perron, Bill Freeman

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.

Representation Learning

Exploring Gender and Race Biases in the NFT Market

no code implementations29 Mar 2023 Howard Zhong, Mark Hamilton

We test the statistical significance of race and gender biases in the prices of CryptoPunks and present the first study of gender bias in the broader NFT market.

MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge

no code implementations15 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.

Image-to-Image Translation Matrix Completion +2

Large-Scale Intelligent Microservices

1 code implementation17 Sep 2020 Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, Lucy Zhang, Mei Gao, Lei Zhang, William T. Freeman

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.

Anomaly Detection

It Is Likely That Your Loss Should be a Likelihood

no code implementations12 Jul 2020 Mark Hamilton, Evan Shelhamer, William T. Freeman

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.

Outlier Detection

Semi-Supervised Translation with MMD Networks

no code implementations28 Oct 2018 Mark Hamilton

This work aims to improve semi-supervised learning in a neural network architecture by introducing a hybrid supervised and unsupervised cost function.

Single Image Deraining Translation

MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales

1 code implementation20 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.

BIG-bench Machine Learning Distributed Computing +2

Flexible and Scalable Deep Learning with MMLSpark

1 code implementation11 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.

Distributed Computing

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