BNNs

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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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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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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