ARNI

Continual Learning Working Group
2024-02-29 13:30 - 14:40 UTC | CEPSR 620, Schapiro 530 W. 120th St
Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.00487
Zoom: https://columbiauniversity.zoom.us/j/97515072030?pwd=[REDACTED]

Continual Learning Working Group
2024-02-22 13:30 - 14:40 | CEPSR 620, Schapiro 530 W. 120th St
Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.00487
Zoom: https://columbiauniversity.zoom.us/j/3658091817?pwd=[REDACTED]

Generative AI Freespeech & Public Discourse
2024-02-20 13:00 - 14:15 | Forum Auditorium, 601 W 125th St, New York, NY, 10027, United States
ARNI coPI Kathy McKeown and ARNI faculty Carl Vondrick participate in the
Panel 1: Empirical and Technological Questions: Current Landscape, Challenges, and Opportunities
Link: https://www.engineering.columbia.edu/symposium-generative-ai-free-speech-public-discourse
Article: https://www.engineering.columbia.edu/news/navigating-generative-ai-and-its-impact-future-public-discourse?utm_source=newsletter&utm_medium=email&utm_campaign=highlights030124

Animal Behavior Video Analysis Working Group
2024-02-16 15:00 - 17:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Mapping the landscape of social of social behavior using high-resolution 3D tracking of freely interacting animals
Presenter: Ugne Klibaite, PhD
Harvard University, Department of Organismic & Evolutionary Biology (PI, Bence P. Ölveczky)
Abstract: Social interaction is a fundamental component of animal behavior. However, we lack tools to describe it with quantitative rigor, limiting our understanding of its principles and the neuropsychiatric disorders, like autism, that perturb it. To address these limitations, I and collaborators have developed a technique for high-resolution 3D tracking of freely interacting animals and their body-wide social touch patterns, solving the challenging subject occlusion and part assignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. Using this technology, I have collected and annotated over 34 million 3D postures in interacting rats, featuring five new monogenic autism models lacking reports of social behavioral phenotypes. I will introduce a novel multi-scale approach which I have used to identify a rich landscape of stereotyped interactions, synchrony, and body contact across strains. This deep phenotyping approach revealed a spectrum of changes in rat autism models and in response to amphetamine, and this framework has the potential to facilitate quantitative studies of social behaviors and their neurobiological underpinnings.
Join Zoom Meeting:
https://columbiauniversity.zoom.us/j/94848687512pwd=[REDACTED]
Meeting ID: 948 4868 7512
Passcode: [REDACTED]

Animal Behavior Video Analysis Working Group
2024-01-19 15:00 - 17:00 | CSB 480, Mudd Building, 500 W 120th Street
Title: Multimodal Learning from Pixels to People
Presenter: Carl Vondrick
Abstract: People experience the world through modalities of sight, sound, words, touch, and more. By leveraging their natural relationships and developing multimodal learning methods, my research creates artificial perception systems with diverse skills, including spatial, physical, logical, and cognitive abilities, for flexibly analyzing visual data. This multimodal approach provides versatile representations for tasks like 3D reconstruction, visual question answering, and object recognition, while offering inherent explainability and excellent zero-shot generalization across tasks. By closely integrating diverse modalities, we can overcome key challenges in machine learning and enable new capabilities for computer vision, especially for the many upcoming applications where trust is required.
Join Zoom Meeting:
https://columbiauniversity.zoom.us/j/96127949475pwd=[REDACTED]
Meeting ID: 948 4868 7512
Passcode: [REDACTED]

Animal Behavior Video Analysis Working Group
2023-12-01 15:00 - 17:00 | Zuckerman Institute – L5-084, 3227 Broadway, New York, NY, United States
Title: Precise quantification of natural behavior with computer vision
Abstract: To understand the neural control of movement, cognition, and social interaction, we need to precisely quantify motor behaviors. Deep learning tools now enable to extract meaningful behavioral signals from raw videos, in high spatiotemporal resolution. These technologies are gaining increasing adoption in system neuroscience and are transforming the field in many ways. We will provide an overview of the field, present the limitations of some of the standard approaches, and present some of our own work on pose tracking (keypoint detection) and perhaps behavioral segmentation (discovering discrete behavioral motifs). We look forward to exploring fresh perspectives on this important problem.
Join Zoom Meeting:
https://columbiauniversity.zoom.us/j/95557736296pwd=[REDACTED]
Meeting ID: 948 4868 7512
Passcode: [REDACTED]
