29 citations · 41 across the 7 of their papers we have counts for
18 papers
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn +2
Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design.…
Learning stochastic dynamics and predicting emergent behavior using transformers
Corneel Casert, Isaac Tamblyn, Stephen Whitelam
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of th…
Electronic Response Quantities of Solids and Deep Learning
Kevin Ryczko, Olivier Malenfant-Thuot, Michel Côté +1
We introduce a deep neural network (DNN) framework called the \textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work (\textsc{radnet}), which is capable of mak…
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
Pedram Abdolghader, Andrew Ridsdale, Tassos Grammatikopoulos +5
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios. Here…
Neural evolution structure generation: High Entropy Alloys
Conrard Giresse Tetsassi Feugmo, Kevin Ryczko, Abu Anand +2
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) struc…
Weakly-supervised multi-class object localization using only object counts as labels
Kyle Mills, Isaac Tamblyn
We demonstrate the use of an extensive deep neural network to localize instances of objects in images. The EDNN is naturally able to accurately perform multi-class counting using o…