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07/25/2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large...
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07/12/2023
Decalf: A Directed, Effectful Cost-Aware Logical Framework
We present decalf, a directed, effectful cost-aware logical framework fo...
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09/26/2022
A metalanguage for cost-aware denotational semantics
We present two metalanguages for developing synthetic cost-aware denotat...
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10/04/2021
AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference
Leveraging parallel hardware (e.g. GPUs) to conduct deep neural network ...
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07/09/2021
A cost-aware logical framework
We present calf, a cost-aware logical framework for studying quantitativ...
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11/07/2020
Cost-Aware Type Theory
Although computational complexity is a fundamental aspect of program beh...
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10/16/2019
