Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
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Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
From Word Prediction to Complex Skills: Data Flywheels for Mathematical Reasoning
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United Stateshttps://youtu.be/OYOuSAAE7QQ New Technologies in Mathematics Seminar Speaker: Anirudh Goyal (University of Montreal) Title: From Word Prediction to Complex Skills: Data Flywheels for Mathematical Reasoning Abstract: This talk examines how large language models (LLMs) evolve from simple word prediction to complex skills, with a focus on mathematical problem solving. A major driver of AI products today is the […]
Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United Stateshttps://youtu.be/C6NDdnSaluU New Technologies in Mathematics Seminar Speaker: Aryo Lotfi (EPFL) Title: How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad Abstract: Can Transformers predict new syllogisms by composing established ones? More generally, what type of targets can be learned by such models from scratch? Recent works show that Transformers can be Turing-complete in terms of […]
Math and Machine Learning Program Discussion
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesMath and Machine Learning Program Discussion
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United Stateshttps://youtu.be/KOgh-FFDlvg New Technologies in Mathematics Seminar Speaker: Dylan Foster, Microsoft Research Title: Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning Abstract: Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied to robotics, autonomous driving, and autoregressive language […]
Frontier of Formal Theorem Proving with Large Language Models: Insights from the DeepSeek-Prover Series
Virtualhttps://youtu.be/qC60ZgsIFvk New Technologies in Mathematics Seminar Speaker: Huajian Xin, DeepSeek Title: Frontier of Formal Theorem Proving with Large Language Models: Insights from the DeepSeek-Prover Series Abstract: Recent advances in large language models have markedly influenced mathematical reasoning and automated theorem proving within artificial intelligence. Yet, despite their success in natural language tasks, these models face notable obstacles […]
Thinking Like Transformers – A Practical Session
VirtualNew Technologies in Mathematics Seminar Speaker: Gail Weiss, EPFL Title: Thinking Like Transformers - A Practical Session Abstract: With the help of the RASP programming language, we can better imagine how transformers---the powerful attention based sequence processing architecture---solve certain tasks. Some tasks, such as simply repeating or reversing an input sequence, have reasonably straightforward solutions, […]
Can Transformers Reason Logically? A Study in SAT-Solving
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United Stateshttps://youtu.be/o7uac6DuzcQ New Technologies in Mathematics Seminar Speaker: Leyan Pan, Georgia Tech Title: Can Transformers Reason Logically? A Study in SAT-Solving Abstract: Transformer-based LLMs have apparently demonstrated capabilities that resembles human reasoning. In our recent work, we investigated the Boolean reasoning abilities of decoder-only Transformers equipped with Chain-of-Thought, establishing that a Transformer model can decide all […]