BDL

Connecting the dots: is mode-connectedness the key to feasible sample-based inference in Bayesian neural networks?
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks’ parameter …
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
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Position: Bayesian deep learning is needed in the age of large-scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised …
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael a Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
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Bayesian neural networks and dimensionality reduction
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a …
Deborshee Sen, Theodore Papamarkou, David Dunson
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Towards efficient MCMC sampling in Bayesian neural networks by exploiting symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density …
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
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Approximate blocked Gibbs sampling for Bayesian neural networks
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample …
Theodore Papamarkou
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The premise of approximate MCMC in Bayesian deep learning (UCSB)
Talk on an approximate MCMC sampling approach to Bayesian deep learning.
Sep 26, 2022 3:30 PM — 4:30 PM University of California, Santa Barbara, Department of Computer Science
The premise of approximate MCMC in Bayesian deep learning (University of Warwick)
Talk on an approximate MCMC sampling approach to Bayesian deep learning.
Sep 8, 2022 9:10 AM — 9:35 AM University of Warwick, Department of Statistics
Slides Video
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews …
Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble
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Challenges in Markov chain Monte Carlo for Bayesian neural networks
Talk on the predictive capacity of Bayesian marginalization for neural networks in the absence of MCMC convergence.
Apr 23, 2021 3:00 PM — 4:00 PM University of Glasgow, School of Mathematics and Statistics
Code Video
Wide neural networks with bottlenecks are deep Gaussian processes
There has recently been much work on the ‘wide limit’ of neural networks, where Bayesian neural networks (BNNs) are shown …
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
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