The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.



ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

ICLR is one of the fastest growing artificial intelligence conferences in the world. Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

The rapidly developing field of deep learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. ICLR takes a broad view of the field and includes topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization.

A non-exhaustive list of relevant topics explored at the conference include:



  • Unsupervised, Semi-supervised, and Supervised Representation Learning
  • Representation Learning for Planning and Reinforcement Learning
  • Metric Learning and Kernel Learning
  • Sparse Coding and Dimensionality Expansion
  • Hierarchical Models

  • Optimization for Representation Learning
  • Learning Representations of Outputs or States
  • Implementation Issues, Parallelization, Software Platforms, Hardware
  • Applications in Vision, Audio, Speech, Natural Language Processing, Robotics, Neuroscience, or Any Other Field


For more information or registration, please visit the official website.

Get hands-on with data cleaning techniques using Python and AI tools. Join SBU Libraries' Data Literacies Lead, Ahmad Pratama, to learn how to identify and rectify errors, handle missing data, and prepare your dataset for analysis. This workshop introduces you to powerful yet easy-to-use tools and techniques that make data cleaning efficient and effective, turning chaotic data into valuable insights.

Please register for the Data Cleaning with Python and AI here.
https://meetings.cshl.edu/meetings.aspx?meet=naisys&year=20  


November 9 - 12, 2020 Virtual
Abstract Deadline: August 14, 2020


Organizers:

Raia Hadsell, DeepMind, United Kingdom
Blake Richards, Mila, Québec AI Institute, Canada
Anthony Zador, Cold Spring Harbor Laboratory

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The current COVID-19 situation is challenging and difficult for all of us - we hope this virtual conference will allow colleagues to share and discuss their latest research, while under travel and stay-at-home restrictions.

Because of the ongoing COVID-19/SARS-CoV-2 outbreak, CSHL and the organizers have now reached the difficult decision to restructure the meeting on From Neuroscience to Artificially Intelligent Systems into a virtual meeting scheduled November 9-12, 2020.  NAISys will begin at 10 am (EDT)  on Monday, November 9 and ending with an afternoon session on Thursday, November 12, 2020. Oral sessions will be confined to later morning and afternoon sessions EST to maximize access by participants from around the world. 

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Artificial intelligence (AI) and neural networks have long drawn on neuroscience for inspiration. However, in spite of tremendous recent advances in AI, natural intelligence is still far more adept at interacting with the real world in real-time, adapting to changes, and doing so under significant physical and energetic constraints. The goal of this meeting is to bring together researchers at the intersection of AI and neuroscience, and to identify insights from neuroscience that can help catalyze the development of next-generation artificial systems.

Abstracts are welcomed on all scientific topics related to how principles and insights from neuroscience can lead to better artificial intelligence. Topics of interest include but are not limited to network architectures, computing with spiking networks, learning algorithms, active perception, inductive bias, meta-learning, and online learning. Please note that abstracts should be ONE page (~2900 characters).   




Keynote speakers (pending reconfirmation):Yoshua Bengio, Université de Montréal
Yann Lecun, NYU/Facebook


Invited Speakers (pending reconfirmation):Kwabena Boahen, Stanford University
Dmitri Chklovskii, Simons Foundation
Anne Churchland, Cold Spring Harbor Laboratory
Claudia Clopath, Imperial College London, UK
Jim DiCarlo, MIT
Chelsea Finn, Stanford University
Surya Ganguli, Stanford University
Jeff Hawkins, Numenta
Konrad Kording, University of Pennsylvania
Timothy Lillicrap, DeepMind
Yael Niv, Princeton University
Bruno Olshausen, UC Berkeley
Cristina Savin, New York University
Terry Sejnowski, Salk Institute for Biological Studies
Sebastian Seung, Princeton University
Eero Simoncelli, New York University
Sara A. Solla, Northwestern University
David Sussillo, Google Brain
Andreas Tolias, Baylor College of Medicine


New and revised abstracts should be submitted by the resubmission deadline, Friday, August 14. Individuals originally selected for talks should assume they will still be speaking, but we may select additional talks based on the number of invited and selected speakers who cannot reconfirm.

Abstracts should contain only new and unpublished material and must be submitted electronically by the abstract deadline. Selection of material for oral and poster presentation will be made by the organizers and individual session chairs. Status (talk/poster) of abstracts will be posted on our web site as soon as decisions have been made by the organizers.

We are eager to have as many students and postdocs as possible to attend since they are likely to benefit most from this meeting. We have applied for funds from industry and foundations to partially support graduate students and postdocs. Apply in writing stating need for financial support to Catie Carr at carr@cshl.edu. Preference is given to those submitting abstracts. 

All questions pertaining to registration, fees, abstract submission or any other matters may be directed to Catie Carr at carr@cshl.edu.

We anticipate the following support :

National Science Foundation

Social Media:

The designated hashtag for this meeting is #cshlNeuroAI. Note that you must obtain permission from an individual presenter before live-tweeting or discussing his/her talk, poster, or research results on social media. Click the Policies tab above to see our full Confidentiality & Reporting Policy.


Pricing:

Virtual Academic Package: $285
Virtual Graduate Student Package: $175
Virtual Corporate Package: $425

Lab Group Discounts (not departmental or institutional discounts):

Labs registering 4 people: 20% discount off applicable fees
Labs registering 5 people: 25% discount off applicable fees
Labs registering 6 people: 30% discount off applicable fees

To be eligible for lab group discounts, please submit a list of lab members planning to attend in advance of registration to Catie Carr  to establish appropriate discounted fees. Please include a link to your lab web page for verification purposes. Prior payments will be included in the group discount calculation.

IBRO/International Brain Research Organization are generously supporting the participation of a limited number of individuals from US/Canadian Minority Serving Institutions (check eligibility): $25
Limit: 65 attendees / limit per institution: 5 (contact Catie Carr  to confirm eligibility/availability prior to registering) 

Reduced Pricing for Individuals from US/Canadian Minority Serving Institutions (check eligibility): $50

Are you interested in understanding the challenges that lie ahead as Artificial Intelligence (AI) systems become increasingly autonomous, dynamically acquire information, and adapt behaviors?
 
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
 
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
 
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
 
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
 
Keynote speakers include Dr. David Goldstein of  Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
 
This event is designed for academic and government researchers, university students, and small businesses.
 
Would you like to understand some of the most formidable technical challenges in future autonomous systems?  Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
 
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
 
Please see additional information on the three-part series here. Registration details to follow! 
 
Stay tuned: https://www.innovare.org/news-events  
AI3, SBU Libraries and IACS present
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)

Special Talk and Panel Discussion

How I Learned to Stop Worrying and Love AI (For Now)

with Paul Fain from The Job and Work Shift

A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.

Panel discussion to follow with:

  • Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute
  • Nicholas Johnson, Director of AI, SBU Libraries
  • Marianna Savoca, Associate Vice President for Career Readiness and Experiential Education
Paul Fain is co-founder of Work Shift, editor of the must-read newsletter, The Job, and host of The Cusp podcast. A veteran higher education reporter, Paul is perhaps the nation's top journalist focused on connections between education and work. He started Work Shift after a decade as a senior reporter and then news editor at Inside Higher Ed, where he led the outlet's coverage of low-income and first-generation students, college completion, community colleges, federal policy, and emerging models of higher education. He also was the founding host of the successful podcast, The Key with Inside Higher Ed, and has contributed chapters for books on innovation in higher education, published by the Harvard University Press and the Stanford University Press. Earlier in his career, Paul was a senior reporter at The Chronicle of Higher Education.

Limited Seats!

Registration is required.

Designed for faculty, staff, presidents, provosts, academic leaders, student affairs professionals, IT specialists, librarians, researchers, administrators, institutional decision-makers, and other higher education stakeholders, the conference highlights practical strategies institutions can implement now while exploring longer-term governance, policy, and ethical considerations. Participants will leave with concrete tools, cross-institutional insights, and collaborative connections that support mission-aligned AI innovation.

Hosted by: AAC&U

Location: Atlanta, GA and Virtual

Register here.