Yong Kiam Tan

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When doing an interactive proof about a piece of software, it is important that the underlying programming language's semantics does not make the proof unnecessarily difficult or unwieldy. Both small-step and big-step semantics are commonly used, and the latter is typically given by an inductively defined relation. In this paper, we consider an alternative:(More)
Deep Neural Networks (DNN) have been successful in enhancing noisy speech signals. Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal. The quality of predicted features can be improved by providing additional side channel information that is robust to(More)
Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data(More)
We have developed and mechanically verified a new compiler backend for CakeML. Our new compiler features a sequence of intermediate languages that allows it to incrementally compile away high-level features and enables verification at the right levels of semantic detail. In this way, it resembles mainstream (unverified) compilers for strict functional(More)
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