News

Postdoctoral fellow in the Foundations of Machine Learning

October 25, 2022

The Harvard Machine Learning Foundations group invites applications for a postdoctoral fellowships in a multidisciplinary study of the underpinnings of deep learning. These include representation learning, transfer learning, generalization, applications to scientific computing, equivariance,  and connections between artificial neural networks and natural learning systems such as human and animal brains. We are looking for exceptional junior scientists to work collaboratively. Potential advisors include Demba Ba, Boaz Barak, Lucas Janson, Sham Kakade, and Cengiz Pehlevan. Candidates...

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APMTH 226

APMTH 226: Theory of Neural Computation

August 19, 2022

Cengiz and Jacob are teaching APMTH 226: Theory of Neural Computation again this fall.

Description: This course is an introduction to the theory of computation with biological and artificial neural networks. We will cover selected topics from theoretical neuroscience and deep learning theory with an emphasis on topics at the research frontier. These topics include expressivity and generalization in deep learning models; infinite-width limit of neural networks and kernel machines; deep learning dynamics; biologically-plausible training of neural networks and models of...

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Dr. Canatar

Congratulations Dr. Canatar!

August 12, 2022

Abdul passed his thesis defense with flying colors and became the first PhD graduate of our group! Congratulations Dr. Canatar! Best of luck in your future endeavors!

 

APS March Meeting Abstracts

March 16, 2022

Join us at the APS March Meeting!

1. S. Qin, S. Farashahi, D. Lipshutz, A. M. Sengupta, D. B. Chklovskii, C. Pehlevan, Unveiling the dynamics and structure of drifting neural representations,  B03: Neural Systems I

2. A. Atanasov, B. Bordelon, C. Pehlevan, When are Neural Networks Kernel Learners?, F03: Physics of Learning II: Artificial systems

3. A. Canatar, B. Bordelon, C. Pehlevan, Statistical Mechanics of Kernel Regression and Wide Neural Networks, F09: Physics of Machine Learning I

4. J. Zavatone-Veth, A. Canatar, B. Ruben, C....

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