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DTSTART;TZID=America/New_York:20250402T140000
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DTSTAMP:20260629T182358
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SUMMARY:Learning Dynamical Transport without Data
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Michael Albergo (Harvard) \nTitle: Learning Dynamical Transport without Data \nAbstract: Algorithms based on dynamical transport of measure\, such as score-based diffusion models\, have resulted in great progress in the field of generative modeling. However\, these algorithms rely on access to an abundance of data from the target distribution. A complementary problem to this is learning to generate samples from a target distribution when only given query access to the unnormalized log-likelihood or energy function associated to it\, with myriad application in statistical physics\, chemistry\, and Bayesian inference. I will present an algorithm based on dynamical transport to sample from a target distribution in this context\, which can be seen as an augmentation of annealed importance sampling and sequential Monte Carlo. Time permitting\, I will also discuss how to generalize these ideas to dynamics of discrete distributions. This is joint work with Eric Vanden-Eijnden\, Peter Holderrieth\, and Tommi Jaakkola. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_4225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-4.2.2025.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250409T140000
DTEND;TZID=America/New_York:20250409T150000
DTSTAMP:20260629T182358
CREATED:20250128T214458Z
LAST-MODIFIED:20250410T150618Z
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SUMMARY:Can Transformers Do Enumerative Geometry?
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Baran Hashemi\, Technical University of Munich \nTitle: Can Transformers Do Enumerative Geometry? \nAbstract: How can Transformers model and learn enumerative geometry? What is a systematic procedure for using Transformers in abductive knowledge discovery within a mathematician-machine collaboration? In this work\, we introduce a Neural Enumerative Reasoning model for computation of ψ-class intersection numbers on the moduli space of curves. By reformulating the problem as a continuous optimization task\, we compute intersection numbers across a wide value range from 10e-45 to 10e45. To capture the recursive nature inherent in these intersection numbers\, we propose the Dynamic Range Activator (DRA)\, a new activation function that enhances the Transformer’s ability to model recursive patterns and handle severe heteroscedasticity. Given precision requirements for computing the intersections\, we quantify the uncertainty of the predictions using Conformal Prediction with a dynamic sliding window adaptive to the partitions of equivalent number of marked points. Beyond simply computing intersection numbers\, we explore the enumerative “world-model” of Transformers. Our interpretability analysis reveals that the network is implicitly modeling the Virasoro constraints in a purely data-driven manner. Moreover\, through abductive hypothesis testing\, probing\, and causal inference\, we uncover evidence of an emergent internal representation of the large-genus asymptotic of ψ-class intersection numbers. This opens up new possibilities in inferring asymptotic closed-form expressions directly from limited amount of data. \nThis talk is based on https://openreview.net/pdf?id=4X9RpKH4Ls. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_4925/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-4.9.2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250423T140000
DTEND;TZID=America/New_York:20250423T150000
DTSTAMP:20260629T182358
CREATED:20250128T214818Z
LAST-MODIFIED:20250311T184354Z
UID:10003709-1745416800-1745420400@cmsa.fas.harvard.edu
SUMMARY:Machine learning for analytic calculations in theoretical physics
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Matthias Wilhelm (University of Southern Denmark) \nTitle: Machine learning for analytic calculations in theoretical physics \nAbstract: In this talk\, we will present recent progress on applying machine-learning techniques to improve calculations in theoretical physics\, in which we desire exact and analytic results. One example are so-called integration-by-parts reductions of Feynman integrals\, which pose a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics. These reductions rely on heuristic approaches for selecting a finite set of linear equations to solve\, and the quality of the heuristics heavily influences the performance. In this talk\, we investigate the use of machine-learning techniques to find improved heuristics. We use funsearch\, a genetic programming variant based on code generation by a Large Language Model\, in order to explore possible approaches\, then use strongly typed genetic programming to zero in on useful solutions. Both approaches manage to re-discover the state-of-the-art heuristics recently incorporated into integration-by-parts solvers\, and in one example find a small advance on this state of the art.
URL:https://cmsa.fas.harvard.edu/event/newtech_42325/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-4.23.2025.docx-1.png
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