Poor generalization can be dangerous in RL!

Zac Kenton, Angelos Filos, Yarin Gal, 02 Jul 2019

We want to develop reinforcement learning (RL) agents that can be trusted to act in high-stakes situations in the real world. That means we need to generalize about common dangers that we might have experienced before, but in an unseen setting. For example, we know it is dangerous to touch a hot oven, even if it’s in a room we haven’t been in before. …

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Human in the Loop: Deep Learning without Wasteful Labelling

Andreas Kirsch, Joost van Amersfoort, Yarin Gal, 24 Jun 2019

In Active Learning we use a “human in the loop” approach to data labelling, reducing the amount of data that needs to be labelled drastically, and making machine learning applicable when labelling costs would be too high otherwise. In our paper [1] we present BatchBALD: a new practical method for choosing batches of informative points in Deep Active Learning which avoids labelling redundancies that plague existing methods. Our approach is based on information theory and expands on useful intuitions. We have also made our implementation available on GitHub at …

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Bayesian Deep Learning Benchmarks

Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal, 14 Jun 2019

In order to make real-world difference with Bayesian Deep Learning (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy MNIST-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints. …

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Targeted Dropout

Aidan Gomez, 05 Jun 2019

Neural networks can represent functions to solve complex tasks that are difficult — if not impossible — to write instructions for by hand, such as understanding language and recognizing objects. Conveniently, we’ve seen that task performance increases as we use larger networks. However, the increase in computational costs also increases dollars and time required to train and use models. Practitioners are plagued with networks that are too large to store in on-device memory, or too slow for real-world utility. …

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An imPACtful, BAYESic result

Clare Lyle, 09 Apr 2019

The applications of probably approximately correct (PAC) learning results to deep networks have historically been about as interesting as they sound. For neural networks of the scale used in practical applications, bounds involving concepts like VC dimension conclude that the algorithm will have no more than a certain error rate on the test set with probability at least zero. Recently, some work by Dziugaite and Roy, along with some folks from Columbia has managed to obtain non-vacuous generalization bounds for more realistic problems using a concept introduced by McAllester (1999) called PAC Bayes bounds. …

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