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Nenad Tomasev
@weballergy
Senior staff research scientist at DeepMind. Opinions are my own. Re-tweets and favorites not to be considered as endorsements.
London, England
Joined May 2009
Posts
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    Excited to share our work on envisioning Intelligent AI Delegation (arxiv.org/abs/2602.11865). Delegation in most existing AI systems is brittle, and relies on simplified hand-crafted control flows. As such, it fails to meet the requirements of what is needed to truly scale
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    Deep learning models are often perceived as black boxes. In our most recent work, Acquisition of Chess Knowledge in AlphaZero arxiv.org/abs/2111.09259 , we try to unpack how AlphaZero represents knowledge, where it resides within the network, and when it is acquired in training
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    I'm happy to share that I got promoted to the role of Senior Staff Research Scientist here at Google DeepMind. It's been an incredibly exciting year, though the truly exciting work, as always, lies ahead.
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    I'm excited to share a new paper: "Mastering Board Games by External and Internal Planning with Language Models" storage.googleapis.com/deepmind-media… (also soon to be up on Arxiv, once it's been processed there)
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    'Adversarial Generation of Natural Language': producing realistic sentences arxiv.org/abs/1705.10929 #deeplearning #machinelearning #NLP #AI
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    DeepMoji: Predicting emojis for classifying text sentiment/emotion/sarcasm arxiv.org/abs/1708.00524 #NLP #deeplearning #AI
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    'Relational recurrent neural networks': performing complex relational reasoning in memory networks. arxiv.org/abs/1806.01822 #DeepLearning #AI #MachineLearning
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    "To Trust Or Not To Trust A Classifier" by Google Research arxiv.org/abs/1805.11783 : beyond simple confidence scores. The ability to auto-detect bad predictions in critical for safe deployments in sensitive applications. #MachineLearning #DataScience #AI
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    Proud to share the results of our work on applying deep learning for early prediction of future acute kidney injury from electronic health records in our collaboration with the US Department of Veterans Affairs - just published in Nature: nature.com/articles/s4158…
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    'Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning' arxiv.org/abs/1709.00103 #MachineLearning
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    'A Probabilistic U-net for Segmentation of Ambiguous Images': a cool new paper by my colleagues at DeepMind on how to deal with uncertainty in segmentation models. arxiv.org/abs/1806.05034 #DeepLearning #MachineLearning
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    'Hyperbolic Entailment Cones for Learning Hierarchical Embeddings': viewing hierarchical relations as partial orders based on a family of nested geodesically convex cones arxiv.org/abs/1804.01882 #AI #MachineLearning
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    'Dilated Convolutions for Modeling Long-Distance Genomic Dependencies' arxiv.org/abs/1710.01278 #DeepLearning #Genomics #AI
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    'A Distributional Perspective on Reinforcement Learning': modeling the full distribution of return. arxiv.org/abs/1707.06887 #machinelearning