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X-WR-CALDESC:Events for CMSA
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DTSTART;TZID=America/New_York:20221019T140000
DTEND;TZID=America/New_York:20221019T150000
DTSTAMP:20240215T095357Z
CREATED:20230808T184955Z
LAST-MODIFIED:20240215T095357Z
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SUMMARY:Towards Faithful Reasoning Using Language Models
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Antonia Creswell\, DeepMind \nTitle: Towards Faithful Reasoning Using Language Models \nAbstract: Language models are showing impressive performance on many natural language tasks\, including question-answering. However\, language models – like most deep learning models – are black boxes. We cannot be sure how they obtain their answers. Do they reason over relevant knowledge to construct an answer or do they rely on prior knowledge – baked into their weights – which may be biased? An alternative approach is to develop models whose output is a human interpretable\, faithful reasoning trace leading to an answer. In this talk we will characterise faithful reasoning in terms of logically valid reasoning and demonstrate where current reasoning models fall short. Following this\, we will introduce Selection-Inference\, a faithful reasoning model\, whose causal structure mirrors the requirements for valid reasoning. We will show that our model not only produces more accurate reasoning traces but also improves final answer accuracy. \n  \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/nt-101922/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/10.19.2022.png
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DTSTART;TZID=America/New_York:20221005T140000
DTEND;TZID=America/New_York:20221005T160000
DTSTAMP:20240214T110102Z
CREATED:20230808T184616Z
LAST-MODIFIED:20240214T110102Z
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SUMMARY:Minerva: Solving Quantitative Reasoning Problems with Language Models
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Guy Gur-Ari\, Google Research \nTitle: Minerva: Solving Quantitative Reasoning Problems with Language Models \nAbstract: Quantitative reasoning tasks which can involve mathematics\, science\, and programming are often challenging for machine learning models in general and for language models in particular. We show that transformer-based language models obtain significantly better performance on math and science questions when trained in an unsupervised way on a large\, math-focused dataset. Performance can be further improved using prompting and sampling techniques including chain-of-thought and majority voting. Minerva\, a model that combines these techniques\, achieves SOTA on several math and science benchmarks. I will describe the model\, its capabilities and limitations.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/minerva-solving-quantitative-reasoning-problems-with-language-models/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/10.05.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220914T140000
DTEND;TZID=America/New_York:20220914T150000
DTSTAMP:20240301T091205Z
CREATED:20230808T183823Z
LAST-MODIFIED:20240301T091205Z
UID:10001210-1663164000-1663167600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Breaking the one-mind-barrier in mathematics using formal verification
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Johan Commelin\, Mathematisches Institut\, Albert-Ludwigs-Universität Freiburg \nTitle: Breaking the one-mind-barrier in mathematics using formal verification \nAbstract: In this talk I will argue that formal verification helps break the one-mind-barrier in mathematics. Indeed\, formal verification allows a team of mathematicians to collaborate on a project\, without one person understanding all parts of the project. At the same time\, it also allows a mathematician to rapidly free mental RAM in order to work on a different component of a project. It thus also expands the one-mind-barrier. \nI will use the Liquid Tensor Experiment as an example\, to illustrate the above two points. This project recently finished the formalization of the main theorem of liquid vector spaces\, following up on a challenge by Peter Scholze. \nVideo
URL:https://live-hu-cmsa-222.pantheonsite.io/event/breaking-the-one-mind-barrier-in-mathematics-using-formal-verification/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220415T090000
DTEND;TZID=America/New_York:20220415T130000
DTSTAMP:20240229T102446Z
CREATED:20230705T083343Z
LAST-MODIFIED:20240229T102446Z
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SUMMARY:Workshop on Machine Learning and Mathematical Conjecture
DESCRIPTION:On April 15\, 2022\, the CMSA will hold a one-day workshop\, Machine Learning and Mathematical Conjecture\, related to the New Technologies in Mathematics Seminar Series. \nLocation: Room G10\, 20 Garden Street\, Cambridge\, MA 02138. \nOrganizers: Michael R. Douglas (CMSA/Stony Brook/IAIFI) and Peter Chin (CMSA/BU). \nMachine learning has driven many exciting recent scientific advances. It has enabled progress on long-standing challenges such as protein folding\, and it has helped mathematicians and mathematical physicists create new conjectures and theorems in knot theory\, algebraic geometry\, and representation theory. \nAt this workshop\, we will bring together mathematicians\, theoretical physicists\, and machine learning researchers to review the state of the art in machine learning\, discuss how ML results can be used to inspire\, test and refine precise conjectures\, and identify mathematical questions which may be suitable for this approach. \nSpeakers: \n\nJames Halverson\, Northeastern University Dept. of Physics and IAIFI\nFabian Ruehle\, Northeastern University Dept. of Physics and Mathematics and IAIFI\nAndrew Sutherland\, MIT Department of Mathematics\n\n  \n \n  \n  \n \n 
URL:https://live-hu-cmsa-222.pantheonsite.io/event/workshop-on-machine-learning-and-mathematical-conjecture/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/Machine-Learning.png
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