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DTSTART;TZID=America/New_York:20210113T150000
DTEND;TZID=America/New_York:20210113T160000
DTSTAMP:20240517T201031Z
CREATED:20240126T093843Z
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UID:10001441-1610550000-1610553600@cmsa.fas.harvard.edu
SUMMARY:AI and Theorem Proving
DESCRIPTION:Speaker: Josef Urban\, Czech Technical University \nTitle: AI and Theorem Proving \nAbstract: The talk will discuss the main approaches that combine machine learning with automated theorem proving and automated formalization. This includes learning to choose relevant facts for “hammer” systems\, guiding the proof search of tableaux and superposition automated provers by interleaving learning and proving (reinforcement learning) over large ITP libraries\, guiding the application of tactics in interactive tactical systems\, and various forms of lemmatization and conjecturing. I will also show some demos of the systems\, and discuss autoformalization approaches such as learning probabilistic grammars from aligned informal/formal corpora\, combining them with semantic pruning\, and using neural methods to learn direct translation from Latex to formal mathematics.
URL:https://cmsa.fas.harvard.edu/event/1-13-2021-new-technologies-in-mathematics/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-01.13.21.png
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DTSTART;TZID=America/New_York:20210120T150000
DTEND;TZID=America/New_York:20210120T160000
DTSTAMP:20240515T191339Z
CREATED:20240126T093733Z
LAST-MODIFIED:20240515T191339Z
UID:10001440-1611154800-1611158400@cmsa.fas.harvard.edu
SUMMARY:Language Modeling for Mathematical Reasoning
DESCRIPTION:Speaker: Christian Szegedy \nTitle: Language Modeling for Mathematical Reasoning \nAbstract: In this talk\, I will summarize the current state of the art of transformer based language models and give examples on non-trivial reasoning task language models can solve in higher order logic reasoning. I will also discuss how to inject injective bias into transformer networks via pretraining on very simple synthetic tasks and representing graph structures for transformer networks. \n 
URL:https://cmsa.fas.harvard.edu/event/1-20-2021-new-tech-in-math/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-01.20.21.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210127T150000
DTEND;TZID=America/New_York:20210127T160000
DTSTAMP:20240515T191649Z
CREATED:20240126T092449Z
LAST-MODIFIED:20240515T191649Z
UID:10001435-1611759600-1611763200@cmsa.fas.harvard.edu
SUMMARY:Knowledge graph representation: From recent models towards a theoretical understanding
DESCRIPTION:Speaker: Carl Allen and Ivana Balažević – University of Edinburgh School of Informatics \nTitle: Knowledge graph representation: From recent models towards a theoretical understanding \nAbstract: Knowledge graphs (KGs)\, or knowledge bases\, are large repositories of facts in the form of triples (subject\, relation\, object)\, e.g. (Edinburgh\, capital_of\, Scotland). Many models have been developed to succinctly represent KGs such that known facts can be recalled (question answering) and\, more impressively\, previously unknown facts can be inferred (link prediction). Subject and object entities are typically represented as vectors in R^d and relations as mappings (e.g. linear transformations) between them. Such representation can be interpreted as positioning entities in a space such that relations are implied by their relative locations. In this talk we give an overview of knowledge graph representation including select recent models; and\, by drawing a connection to word embeddings\, explain a theoretical model for how semantic relationships can correspond to geometric structure.
URL:https://cmsa.fas.harvard.edu/event/1-27-2021-new-tech-in-math-seminar/
LOCATION:MA
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-in-Mathematics-01.27.21.png
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