1 hr 12 min
Overparameterization, Deep Ensembles and Gaussian Processes with Geoff Pleiss The Ensemble Podcast, by CrunchDAO
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- Mathematics
Geoff Pleiss is a Postdoctoral researcher at Columbia University, with affiliations in the Department of Statistics and the Zuckerman Institute. He holds a PhD in Computer Science from Cornell University, and is Co-founder and maintainer of the GPyTorch software library.
His research places him at the nexus of deep learning, probabilistic modeling, and numerical linear algebra, enabling him to address both of these challenges. One line of his work focuses directly on neural networks, improving their uncertainty estimates while understanding their predictive capabilities through the lens of probabilistic models.
Another line focuses on the inductive biases of Gaussian processes (GP), improving their computational efficiency and ultimately replicating their desirable properties in neural networks.
This research profile ideally situates him to unite these paradigms, transforming today’s powerful models into general reasoning models. In addition, he has a proven record of coupling his findings with performant and easy-to-use software used widely throughout research and industry, facilitating adoption and innovation in this area.
Hosted by Ausha. See ausha.co/privacy-policy for more information.
Geoff Pleiss is a Postdoctoral researcher at Columbia University, with affiliations in the Department of Statistics and the Zuckerman Institute. He holds a PhD in Computer Science from Cornell University, and is Co-founder and maintainer of the GPyTorch software library.
His research places him at the nexus of deep learning, probabilistic modeling, and numerical linear algebra, enabling him to address both of these challenges. One line of his work focuses directly on neural networks, improving their uncertainty estimates while understanding their predictive capabilities through the lens of probabilistic models.
Another line focuses on the inductive biases of Gaussian processes (GP), improving their computational efficiency and ultimately replicating their desirable properties in neural networks.
This research profile ideally situates him to unite these paradigms, transforming today’s powerful models into general reasoning models. In addition, he has a proven record of coupling his findings with performant and easy-to-use software used widely throughout research and industry, facilitating adoption and innovation in this area.
Hosted by Ausha. See ausha.co/privacy-policy for more information.
1 hr 12 min