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Interpreting Shared Deep Learning Models via Explicable Boundary Trees.
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Citations
Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.
Varnavas D. Mouchlis,Antreas Afantitis,Angela Serra,Michele Fratello,Anastasios G. Papadiamantis,Vassilis Aidinis,Iseult Lynch,Dario Greco,Georgia Melagraki +8 more
TL;DR: Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures as mentioned in this paper, which has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural network, generative adversarial networks, and autoencoders.
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Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
TL;DR: This work proposes a novel algorithm which only increases tree size when the estimated discounted future reward of the overall policy would increase by a sufficient amount, and shows that its performance is comparable or superior to traditional tree-based approaches and that it yields a more succinct policy.
34
Toward Sampling for Deep Learning Model Diagnosis
Parmita Mehta,Stephen K. N. Portillo,Magdalena Balazinska,Andrew J. Connolly +3 more
- 01 Apr 2020
TL;DR: A novel data sampling technique is developed that produces approximate but accurate results for these model debugging queries and utilizes the lower dimension representation learned by the DL model and focuses on model decision boundaries for the data in this lower dimensional space.
11
•Posted Content
Sampling for Deep Learning Model Diagnosis (Technical Report).
TL;DR: This paper articulate DL diagnosis as a data management problem, and proposes a general, yet representative, set of queries to evaluate systems that strive to support this new workload and develops a novel data sampling technique that produces approximate but accurate results for these model debugging queries.
4
References
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
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TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro,Sameer Singh,Carlos Guestrin +2 more
- 13 Aug 2016
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
- 06 Sep 2014
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
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