Search Results for author: Jorg Bornschein

Found 5 papers, 1 papers with code

Prequential MDL for Causal Structure Learning with Neural Networks

no code implementations2 Jul 2021 Jorg Bornschein, Silvia Chiappa, Alan Malek, Rosemary Nan Ke

Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology.

A study on the plasticity of neural networks

no code implementations31 May 2021 Tudor Berariu, Wojciech Czarnecki, Soham De, Jorg Bornschein, Samuel Smith, Razvan Pascanu, Claudia Clopath

One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task.

Continual Learning Transfer Learning

Small Data, Big Decisions: Model Selection in the Small-Data Regime

no code implementations ICML 2020 Jorg Bornschein, Francesco Visin, Simon Osindero

Highly overparametrized neural networks can display curiously strong generalization performance - a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it.

Model Selection

Bidirectional Helmholtz Machines

1 code implementation12 Jun 2015 Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio

We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized.

Towards Biologically Plausible Deep Learning

no code implementations14 Feb 2015 Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, Zhouhan Lin

Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology.

Denoising Representation Learning

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