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We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference.
Microeconomic theory and applications
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Elevated systolic blood pressure as a cardiovascular risk factor
William B Kannel · 2000
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Posterior regularization for structured latent variable models
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Harnessing deep neural networks with logic rules
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Deep learning scaling is predictable, empirically, 2017
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Attention is all you need
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Learning beyond simulated physics
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Boolean decision rules via column generation
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Mitigating unwanted biases with adversarial learning
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Deep lagrangian networks: Using physics as model prior for deep learning, 2019
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Neural oblivious decision ensembles for deep learning on tabular data
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Fixing the train-test resolution discrepancy
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Adversarial examples: Attacks and defenses for deep learning
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Protoattend: Attention-based prototypical learning
Sercan O Arık and Tomas Pfister · 2020
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Underspecification presents challenges for credibility in modern machine learning, 2020
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
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Newton vs the machine: solving the chaotic three-body problem using deep neural networks
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Lagrangian duality for constrained deep learning, 2020
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Temporal fusion transformers for interpretable multi-horizon time series forecasting, 2020
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Very deep transformers for neural machine translation
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Controlvae: Controllable variational autoencoder
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