Early Dark Energy
Georg Robbers - Universität Heidelberg
15661 - 15672 of 17905 Results
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M2-branes and AdS/CFT Correspondence
Igor Klebanov - Princeton University
Culs-de-sac and open ends
David Gross - Universität zu Köln
Unitary design: bounds on their size
Andrew Scott - Griffith University
Quantum state tomography from yes/no measurements
Martin Roetteler - NEC Laboratories America (Princeton)
Seeking Symmetries of SIC-POVMs
Markus Grassl - Max Planck Institute for the Science of Light
SIC Triple Products
Marcus Appleby - Queen Mary University of London
Why I care
Chris Fuchs - University of Massachusetts Boston
IR modification of gravity and the forbidden mass range of graviton in de Sitter Space
Yanwen Shang - Citigroup Incorporated
New CMB results
Richard Bond - University of Toronto , Jon Sievers - McGill University - Department of Physics , Mike Nolta - University of Toronto
Observational Evidence for Massive Gravity
Niayesh Afshordi - University of Waterloo
Towards a “Theoretical Minimum” for Physicists in AI
Yonatan Kahn - Princeton University
As progress in AI hurtles forward at a speed seldom seen in the history of science, theorists who wish to gain a first-principles understanding of AI can be overwhelmed by the enormous number of papers, notational choices, and assumptions in the literature. I will make a pitch for developing a…
Creativity by Compositionality in Generative Diffusion Models
Alessandro Favero - École Polytechnique Fédérale de Lausanne
Diffusion models have shown remarkable success in generating high-dimensional data such as images and language – a feat only possible if data has strong underlying structure. Understanding deep generative models thus requires understanding the structure of the data they learn from. In particular…
Causal Inference Meets Quantum Physics
Robert Spekkens - Perimeter Institute for Theoretical Physics
Can the effectiveness of a medical treatment be determined without the expense of a randomized controlled trial? Can the impact of a new policy be disentangled from other factors that happen to vary at the same time? Questions such as these are the purview of the field of causal inference, a general…
Lecture - Mathematical Physics, PHYS 777
Kevin Costello - Perimeter Institute for Theoretical Physics
Scaling Limits for Learning: Dynamics and Statics
Blake Bordelon - Harvard University
In this talk, I will discuss how physics can help improve our understanding of deep learning systems and guide improvements to their scaling strategies. I will first discuss mathematical results based on mean-field techniques from statistical physics to analyze the feature learning dynamics of…
LitLLMs, LLMs for Literature Review: Are We There Yet?
Gaurav Sahu - Mila - Quebec Artificial Intelligence Institute
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. In this talk, we will explore the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the…
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
Yuri Chervonyi - Deep Mind
We present AlphaGeometry2, a significantly improved version of AlphaGeometry introduced in Trinh et al. (2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle harder problems…
Bound on the dynamical exponent of frustration-free Hamiltonians and Markov processes
Tomohiro Soejima - Harvard University
Exactly solvable models have tremendously helped our understanding of condensed matter systems. A notable number of them are "frustration-free" in the sense that all local terms of the Hamiltonian can be minimized simultaneously. It has been particularly successful at describing the physics of…
