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DTSTART;TZID=America/New_York:20260116T113000
DTEND;TZID=America/New_York:20260116T130000
DTSTAMP:20260525T211149
CREATED:20260113T180942Z
LAST-MODIFIED:20260113T180942Z
UID:2191-1768563000-1768568400@arni-institute.org
SUMMARY:CTN: Nao Uchida
DESCRIPTION:Title: A normative perspective on diversity of dopamine neurons \nAbstract: TBD
URL:https://arni-institute.org/event/ctn-nao-uchida/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260120T150000
DTEND;TZID=America/New_York:20260120T160000
DTSTAMP:20260525T211149
CREATED:20251211T202316Z
LAST-MODIFIED:20260113T180504Z
UID:2162-1768921200-1768924800@arni-institute.org
SUMMARY:Speaker: Xaq Pitkow ARNI WG Multi-resource-cost optimization of neural network models
DESCRIPTION:Title: Frugal Inference for Control\n\nAbstract: A key challenge in advancing artificial intelligence is achieving the right balance between utility maximization and resource use by both external movement and internal computation. While this trade-off has been studied in fully observable settings\, our understanding of resource efficiency in partially observable environments remains limited. Motivated by this challenge\, we develop a version of the POMDP framework where the information gained through inference is treated as a resource that must be optimized alongside task performance and motion effort. By solving this problem in environments described by linear-Gaussian dynamics\, we uncover fundamental principles of resource efficiency. Our study reveals a phase transition in the inference\, switching from a Bayes-optimal approach to one that strategically leaves some uncertainty unresolved. This frugal behavior gives rise to a structured family of equally effective strategies\, facilitating adaptation to later objectives and constraints overlooked during the original optimization. We illustrate the applicability of our framework and the generality of the principles we derived using two nonlinear tasks. Overall\, this work provides a foundation for a new type of rational computation that both brains and machines could use for effective but resource-efficient control under uncertainty.\n\nZoom Link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-xaq-pitkow-arni-wg-multi-resource-cost-optimization-of-neural-network-models/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
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DTSTART;TZID=America/New_York:20260122T150000
DTEND;TZID=America/New_York:20260122T153000
DTSTAMP:20260525T211149
CREATED:20260121T163934Z
LAST-MODIFIED:20260121T163934Z
UID:2248-1769094000-1769095800@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:To kick off the Continual Learning Working Group activities for this year\, the Continual Learning working group will meet on Thursday\, Jan 22 at 3pm on Zoom and in CEPSR 620.\n\nThe meeting will be brief\, and we’ll discuss our agenda and scheduling for the coming semester\, including our goals for the benchmark project.\nJoin via Zoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/continual-learning-working-group-11/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260123T113000
DTEND;TZID=America/New_York:20260123T130000
DTSTAMP:20260525T211149
CREATED:20260113T192529Z
LAST-MODIFIED:20260121T163725Z
UID:2193-1769167800-1769173200@arni-institute.org
SUMMARY:CTN: Scott Linderman
DESCRIPTION:Title: When and How to Parallelize Seemingly Sequential Models\n \nAbstract: Transformers have become the de facto model for sequential data in large part because they are well adapted to modern hardware: At training time\, the loss can be evaluated in parallel over the sequence length on GPUs and TPUs. By contrast\, evaluating nonlinear recurrent neural networks (RNNs) appears to be an inherently sequential problem. However\, recent advances like DEER (arXiv:2309.12252) and DeepPCR (arXiv:2309.16318) have shown that evaluating a nonlinear recursion can be recast as solving a parallelizable optimization problem\, and sometimes this approach can yield dramatic speed-ups in wall-clock time. However\, the factors that govern the difficulty of these optimization problems remain unclear\, limiting the larger adoption of the technique. I will present a recent line of work from my lab that further develops these methods in both theory and practice. We establish a precise relationship between the dynamics of a nonlinear system and the conditioning of its corresponding optimization formulation. We show that the predictability of a system\, defined as the degree to which small perturbations in state influence future behavior\, impacts the number of optimization steps required for evaluation. In predictable systems\, the state trajectory can be computed in O(log2T) time\, where T is the sequence length\, a major improvement over the conventional sequential approach. In contrast\, chaotic or unpredictable systems exhibit poor conditioning\, with the consequence that parallel evaluation converges too slowly to be useful. We validate our claims through extensive experiments\, with a particular emphasis on parallelizing nonlinear RNNs and Markov chain Monte Carlo (MCMC) algorithms for Bayesian statistics. I will provide practical guidance on when nonlinear dynamical systems can be efficiently parallelized\, and highlighting predictability as a key design principle for parallelizable models.
URL:https://arni-institute.org/event/ctn-scott-linderman/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260123T120000
DTEND;TZID=America/New_York:20260123T130000
DTSTAMP:20260525T211149
CREATED:20260113T193600Z
LAST-MODIFIED:20260113T193600Z
UID:2194-1769169600-1769173200@arni-institute.org
SUMMARY:Language and Vision Working Group
DESCRIPTION:Initial Meeting! \nAbout: \nThe ARNI Language & Vision Working Group aims to bring together researchers across neuroscience\, cognitive science\, computer science\, and AI to collaboratively advance our understanding of how humans and machines construct multimodal experiences. Its goal is to create a space for discussing ongoing language- and vision-focused projects\, identifying natural points of overlap\, and transforming them into larger\, interdisciplinary initiatives. Grounded in the idea that language and vision form a dynamic\, symbiotic system rather than isolated modules\, the group seeks to explore how this integration is represented in the brain and in the machine. Strengthening collaboration between these domains is essential for building the next generation of AI systems that learn from continual\, multimodal input\, reflect human cognitive principles\, and ultimately support real-world human needs. \nMore questions: Contact Anna Krason (akrason@gc.cuny.edu) \nZoom: upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/language-and-vision-working-group/
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