Machine Learning Blog | ML@CMU | Carnegie Mellon University

Educational machine learning

F19 – Topics in data analysis

by Data Analysis Course / August 31, 2020

What is data analysis? A simple definition is: the application of machine learning and statistical methods to real world data to solve a problem. While this statement is simple, data analysis eventually requires expertise from a vast number of disciplines such as the real world domain in question (e.g. healthcare, specific scientific field, finance, etc.) and machine learning and statistics, but could also require knowledge from other fields as diverse as computing or policy or law. The complexity of data…

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machine learning Research

High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

August 14, 2020

There are many works aiming to explain the generalization behavior of neural networks using heavy mathematical machinery, but we will do something different here: with a simple and intuitive twist of data, we will show that many generalization mysteries (like…

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machine learning Research

Maintaining the Illusion of Reality:
Transfer in RL by Keeping Agents in the DARC

July 31, 2020

Reinforcement learning (RL) is often touted as a promising approach for costly and risk-sensitive applications, yet practicing and learning in those domains directly is expensive. It costs time (e.g., OpenAI’s Dota2 project used 10,000 years of experience), it costs money…

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machine learning Research

In defense of weight-sharing for neural architecture search: an optimization perspective

July 17, 2020

Neural architecture search (NAS) -- selecting which neural model to use for your learning problem -- is a promising but computationally expensive direction for automating and democratizing machine learning. The weight-sharing method, whose initial success at dramatically accelerating NAS surprised…

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machine learning Research

Carnegie Mellon University at ICML 2020

July 13, 2020

Carnegie Mellon University is proud to present 44 papers at the 37th International Conference on Machine Learning (ICML 2020), which will be held virtually this week. CMU is also involved in organizing 5 workshops at the conference, and our faculty…

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computer vision machine learning Research

CATER: A diagnostic dataset for Compositional Actions and Temporal Reasoning

July 3, 2020

Introducing CATER: A diagnostic dataset for video understanding that, by design, requires temporal reasoning to be solved. While deep features have revolutionized static image analysis, deep video descriptors have struggled to outperform classic hand-crafted descriptors. Though recent works have shown…

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deep learning machine learning reinforcement learning Research

Learning to Explore using Active Neural SLAM

June 19, 2020

Active Neural SLAM is a modular navigation system that learns to explore unseen indoor environments. Advances in machine learning, computer vision and robotics have opened up avenues of building intelligent robots which can navigate in the physical world and perform…

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Educational machine learning

Understanding the Prevalence of SARS-CoV-2 with Limited Diagnostic Testing Capacity

June 12, 2020

The work presented in this post is not peer reviewed. It should not be taken as clinical advice nor be reported as established information without consulting medical experts. During a disease outbreak, incidence (the rate of new cases) and prevalence (the fraction of the…

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machine learning natural language processing Research

Differentiable Reasoning over Text

May 15, 2020

We all rely on search engines to navigate the massive amount of online information published every day. Modern search engines not only retrieve a list of pages relevant to a query but often try to directly answer our questions by…

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machine learning reinforcement learning Research

Unsupervised Meta-Learning: Learning to Learn without Supervision

May 1, 2020

The history of machine learning has largely been a story of increasing abstraction. In the dawn of ML, researchers spent considerable effort engineering features. As deep learning gained popularity, researchers then shifted towards tuning the update rules and learning rates…

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