Fundamental Science in the era of AI
Richard Armstrong · Bruce Bassett · Michelle Lochner · Nadeem Oozeer
How can AI help deal rigorously with the data deluge of 'big science'? How can AI help amplify the social impact of fundamental science?
AI for Overcoming Global Disparities in Cancer Care
Thomas Fuchs · Gabriele Campanella · Dig Vijay Kumar Yarlagadda · Christina Virgo · Hassan Muhammad · Johan Lundin · Peter Kingham · Olusegun Isaac Alatise
Identify the potential of AI to overcome global disparities in access, diagnosis, and treatment in cancer care.
AI for Earth Sciences
Surya Karthik Mukkavilli · Kelly Kochanski · Johanna Hansen · Trooper Sanders · Pierre Gentine · Mary C Hill · Gregory Dudek · Aaron Courville · Vipin Kumar
Bring cutting edge geoscientific and planetary challenges to the fore for the machine learning and deep learning communities.
Bridging AI and Cognitive Science (BAICS)
Aida Nematzadeh · Jessica Hamrick · Kaylee Burns · Joshua B Tenenbaum · Alison Gopnik · Emmanuel Dupoux
Inspire connections between AI and cognitive science across a broad set of topics.
Integration of Deep Neural Models and Differential Equations
Tan M Nguyen · Richard Baraniuk · Animesh Garg · Stanley J Osher · Anima Anandkumar · Bao Wang
Where theoretical and experimental researchers can come together to join forces towards the goal of integration of deep neural networks and differential equations.
Towards Trustworthy ML: Rethinking Security and Privacy for ML
Nicolas Papernot · Carmela Troncoso · Nicholas Carlini · Florian Tramer · Shibani Santurkar
Bring together experts from a variety of communities (ML, computer security, data privacy, fairness, & ethics) to work on promising ideas and research directions.
Tackling Climate Change with ML
Priya Donti · David Rolnick · Lynn Kaack · Sasha Luccioni · Kris Sankaran · Sharon Zhou · Moustapha Cisse · Carla Gomes · Andrew Ng · Yoshua Bengio
Show that ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.
ML-IRL: Machine Learning in Real Life
Samantha Kleinberg · Rumi Chunara
Examine how we develop, evaluate, and deploy ML in real life applications, and discover problematic implications.
Neural Architecture Search
Frank Hutter · Arber Zela · Aaron Klein · Jan Metzen · Liam Li
Build a strong, open, inclusive, and welcoming community of colleagues working on neural architecture search.
AfricaNLP - Unlocking Local Languages
Kathleen Siminyu · Laura Martinus · Vukosi Marivate · Davor Orlic
Showcase work on NLP for African languages, which are typically low resource languages, and bring it to a global audience.
AI for Affordable Healthcare
Alison O'Neil · Aneta Lisowska · Tewodros Bekele · Ashenafi Gutema · Emanuele Trucco
Highlight recent advances in AI for enabling, democratising, and upholding high standards of healthcare worldwide.
Beyond 'tabula rasa' in reinforcement learning: agents that remember, adapt, and generalize
Louis Kirsch · Ignasi Clavera · Kate Rakelly · Chelsea Finn · Jane Wang · Jeff Clune
Bring together researchers from different backgrounds working on how to extend current RL algorithms to operate in changing environments and tasks.
Computer Vision for Agriculture (CV4A)
Yannis Kalantidis · Laura Sevilla · Ernest Mwebaze · David Guerena · Hamed Alemohammad · Dina Machuve
Expose the progress and unsolved problems of computational agriculture to the AI research community.
Workshop on Causal Learning For Decision Making
Nan Rosemary Ke · Anirudh Goyal Alias Parth Goyal · Jane Wang · Silvia Chiappa · Jovana Mitrovic · Theophane Weber · Danilo Jimenez Rezende · Stefan Bauer
Investigate how much progress is possible by framing the learning problem beyond learning correlations, that is, by uncovering and leveraging causal relations.
Practical ML for Developing Countries: learning under limited/low resource scenarios
Esube Bekele · Ioana Baldini · Nyalleng Moorosi · Vukosi Marivate · VICTOR Dibia · Amanuel Mersha · Tewodros Gebreselassie · Meareg Hailemariam · Michael Melese · Timnit Gebru · Red Abebe · Waheeda Saib
Bring together researchers, experts, policy makers, and related stakeholders under the umbrella of practical ML for developing countries.