Quick Summary: What happens when AI models have to run on tiny wireless devices, squeeze into just 2 bits of memory, or shrink from massive ... Jordan's Keynote Speech on the 14th Computing in the 21st Century Conference co-hosted by Microsoft Research Asia ...

Computationally Statistically Efficient Distributed Inference With Theoretical Guarantees -

What happens when AI models have to run on tiny wireless devices, squeeze into just 2 bits of memory, or shrink from massive ... Jordan's Keynote Speech on the 14th Computing in the 21st Century Conference co-hosted by Microsoft Research Asia ... Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech.

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  • What happens when AI models have to run on tiny wireless devices, squeeze into just 2 bits of memory, or shrink from massive ...
  • Jordan's Keynote Speech on the 14th Computing in the 21st Century Conference co-hosted by Microsoft Research Asia ...
  • Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech.
  • Guy Bresler, Massachusetts Institute of Technology Program Presentations 6th Annual Industry Day.
  • In this talk, we explore the advancements in making generative models more

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Computationally & Statistically Efficient Distributed Inference with Theoretical Guarantees

Computationally & Statistically Efficient Distributed Inference with Theoretical Guarantees

Dr. Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. In this recording, he ...

Theoretical guarantees for sampling and inference in generative models with latent diffusions

Theoretical guarantees for sampling and inference in generative models with latent diffusions

Read more details and related context about Theoretical guarantees for sampling and inference in generative models with latent diffusions.

Computational Barriers in Statistical Estimation and Learning

Computational Barriers in Statistical Estimation and Learning

Andrea Montanari (Stanford) Richard M. Karp Distinguished Lecture.

Compressing AI at the Edge: Distributed Inference, 2-Bit Caches, and Tiny Students

Compressing AI at the Edge: Distributed Inference, 2-Bit Caches, and Tiny Students

What happens when AI models have to run on tiny wireless devices, squeeze into just 2 bits of memory, or shrink from massive ...

Divide-and-Conquer and Statistical Inference for Big Data

Divide-and-Conquer and Statistical Inference for Big Data

Michael I. Jordan's Keynote Speech on the 14th Computing in the 21st Century Conference co-hosted by Microsoft Research Asia ...

Computational Complexity of Statistical Inference | Program Presentations | 6th Annual Industry Day

Computational Complexity of Statistical Inference | Program Presentations | 6th Annual Industry Day

Guy Bresler, Massachusetts Institute of Technology Program Presentations 6th Annual Industry Day.

Scaling Up Bayesian Inference for Big and Complex Data

Scaling Up Bayesian Inference for Big and Complex Data

Read more details and related context about Scaling Up Bayesian Inference for Big and Complex Data.

PyTorch Expert Exchange: Efficient Generative Models: From Sparse to Distributed Inference

PyTorch Expert Exchange: Efficient Generative Models: From Sparse to Distributed Inference

In this talk, we explore the advancements in making generative models more

Rui Duan, PhD - Distributed Statistical Learning & Inference in EHR and Other Healthcare Datasets

Rui Duan, PhD - Distributed Statistical Learning & Inference in EHR and Other Healthcare Datasets

Rui Duan, PhD, Assistant Professor of Biostatistics at Harvard T.H. Chan School of Public Health, discusses integrative learning ...

PyHEP 2021: Distributed statistical inference with pyhf

PyHEP 2021: Distributed statistical inference with pyhf

Read more details and related context about PyHEP 2021: Distributed statistical inference with pyhf.