Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Neural Information Processing Systems
SMTO s usually require access to per-task gradients either with respect to the shared parameters, or to the shared representation.
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Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
Neural Information Processing Systems
One such domain is cryptic crossword puzzles.
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Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
Neural Information Processing Systems
A common lens to theoretically study neural net architectures is to analyze the functions they can approximate.
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