360 citations · 512 across the 6 of their papers we have counts for
6 papers
All SMILES Variational Autoencoder
Zaccary Alperstein, Artem Cherkasov, Jason Tyler Rolfe
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolut…
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian +4
Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA r…
Discrete Variational Autoencoders
Jason Tyler Rolfe
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation…
Quantum Boltzmann Machine
Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe +2
Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a tra…
Understanding Deep Architectures using a Recursive Convolutional Network
David Eigen, Jason Rolfe, Rob Fergus +1
A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kern…
Discriminative Recurrent Sparse Auto-Encoders
Jason Tyler Rolfe, Yann LeCun
We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connecte…