7 citations · 8 across the 5 of their papers we have counts for
5 papers
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli +1
It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations…
Tourbillon: a Physically Plausible Neural Architecture
Mohammadamin Tavakoli, Peter Sadowski, Pierre Baldi
In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need fo…
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
Mohammadamin Tavakoli, Aaron Mood, David Van Vranken +1
There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and doe…
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi
We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to a…
Continuous Representation of Molecules Using Graph Variational Autoencoder
Mohammadamin Tavakoli, Pierre Baldi
In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is empl…