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01/31/2023
Training with Mixed-Precision Floating-Point Assignments
When training deep neural networks, keeping all tensors in high precisio...
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01/31/2023
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Recent work has shown that automatic differentiation over the reals is a...
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08/22/2022
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
We present a static analysis for discovering differentiable or more gene...
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06/12/2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Differentiation lies at the core of many machine-learning algorithms, an...
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11/22/2019
Differentiable Algorithm for Marginalising Changepoints
We present an algorithm for marginalising changepoints in time-series mo...
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07/20/2019
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Probabilistic programming is the idea of writing models from statistics ...
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06/01/2018