no code implementations • 5 Apr 2022 • Amir Khoshaman, Giuseppe Castiglione, Christopher Srinivasa
We explore training Binary Neural Networks (BNNs) as a discrete variable inference problem over a factor graph.
no code implementations • 4 Dec 2019 • Walter Vinci, Lorenzo Buffoni, Hossein Sadeghi, Amir Khoshaman, Evgeny Andriyash, Mohammad H. Amin
The hybrid structure of QVAE allows us to deploy current-generation quantum annealers in QCH generative models to achieve competitive performance on datasets such as MNIST.
no code implementations • 15 Feb 2018 • Amir Khoshaman, Walter Vinci, Brandon Denis, Evgeny Andriyash, Hossein Sadeghi, Mohammad H. Amin
We show that our model can be trained end-to-end by maximizing a well-defined loss-function: a 'quantum' lower-bound to a variational approximation of the log-likelihood.
no code implementations • ICML 2018 • Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult.
Ranked #53 on Image Generation on CIFAR-10 (bits/dimension metric)