Frontiers in Machine Learning 2020

Session Lead: Ahmed Hassan Awadallah, Microsoft

Session Abstract: Modern machine learning applications have enjoyed a great boost utilizing neural networks models, allowing them to achieve state-of-the-art results on a wide range of tasks. Such models, however, require large amounts of annotated data for training. In many real-world scenarios, such data is of limited availability making it difficult to translate these gains into real-world impact. Collecting large amounts of annotated data is often difficult or even infeasible due to the time and expense of labelling data and the private and personal nature of some of these datasets. This session will discuss several approaches to address the labelled data scarcity. In particular, the session will discuss work on: (1) transfer learning techniques that can transfer knowledge between different domains or languages to reduce the need for annotated data; (2) weakly-supervised learning where distant or heuristic supervision is derived from the data itself or other available metadata; (3) and techniques which learn from user interactions or other reward signals directly with techniques such as reinforcement learning. The discussion will be grounded on real-world applications where we aspire to bring AI experiences quickly and efficiently to everyone in more tasks, markets, languages, and domains.

Marti Hearst, University of California, Berkeley
Summarization without the Summaries

Graham Neubig, Carnegie Mellon University
Lessons from the Long Tail: Methods for NLP in the Next 1,000 Languages

Alex Ratner, University of Washington
ML Development with Weak Supervision: Notes from the Field

Q&A panel with all 4 speakers

Session Lead: Philip Rosenfield, Microsoft

Session Abstract: Microsoft has made an ambitious commitment to remove its carbon footprint in response to the overwhelming urgency of addressing climate change. Meanwhile, recent advances in machine learning (ML) models, such as transformer-based NLP, have produced substantial gains in accuracy at the cost of exceptionally large compute resources and, correspondingly, carbon emissions from energy consumption. Understanding and mitigating the climate impact of ML has emerged at the frontier of ML research, spanning multiple areas including hardware design, computational efficiency, and incentives for carbon efficiency.

The goal of this session is to identify priority areas to drive research agendas that are best-suited to efforts in academia, in industry, or in collaboration. We aim to inspire research advances and action, within both academia and industry, to improve the sustainability of machine learning hardware, software and frameworks.

Emma Strubell, Carnegie Mellon University
Learning to Live with BERT

Vivienne Sze, Massachusetts Institute of Technology
Reducing the Carbon Emissions of ML Computing – Challenges and Opportunities

Diana Marculescu, University of Texas at Austin
When Climate Meets Machine Learning: The Case for Hardware-ML Model Co-design

Q&A panel with all 4 speakers

Vani Mandava, Director
Microsoft Research Outreach