HAI Weekly Seminar with Leonidas Guibas | Stanford HAI
Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
eventSeminar

HAI Weekly Seminar with Leonidas Guibas

Status
Past
Date
Wednesday, March 10, 2021 10:00 AM - 11:00 AM PST/PDT
Topics
Machine Learning
Overview
Watch Event Recording

Joint Learning Over Visual and Geometric Data

Overview
Watch Event Recording
Share
Link copied to clipboard!
Event Contact
Celia Clark
celia.clark@stanford.edu

Related Events

Kristina McElheran | The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
May 11, 202612:00 PM - 1:00 PM
May
11
2026

We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.

Event

Kristina McElheran | The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)

May 11, 202612:00 PM - 1:00 PM

We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.

Wolfgang Lehrach | Code World Models for General Game Playing
SeminarMay 13, 202612:00 PM - 1:15 PM
May
13
2026

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

Seminar

Wolfgang Lehrach | Code World Models for General Game Playing

May 13, 202612:00 PM - 1:15 PM

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

Arvind Narayanan | Adapting to the Transformation of Knowledge Work
May 18, 202612:00 PM - 1:00 PM
May
18
2026

The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.

Event

Arvind Narayanan | Adapting to the Transformation of Knowledge Work

May 18, 202612:00 PM - 1:00 PM

The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.

Many challenges remain in applying machine learning to domains where obtaining massive annotated data is difficult. We discuss a number of approaches that aim to reduce supervision load for learning algorithms in the visual and geometric domains by leveraging correlations among data, among representations, and among learning tasks -- what we call joint learning. The basic notion is that inference problems do not occur in isolation but rather in a social context that can be exploited to provide self-supervision by enforcing consistency among them, thus improving performance and increasing sample efficiency. An example is shape co-segmentation, where we can use structural correlations between related shapes to regularize the segmentation of any particular shape. Another is the use of cross-task consistency constraints, as in the case of inferring depth and normals from an image, which are obviously related. Even at the level of representations, joint learning can avoid blind-spots of any one individual representation and better adapt to data particularities – just as we get with multiple 2D views of a 3D object. The talk will present a number of examples of joint learning, including the above as well as 3D object detection and pose estimation.

Leonidas Guibas
Paul Pigott Professor of Computer Science; Professor, by courtesy, of Electrical Engineering, Stanford University