Search Results for author:
Found 8 papers, 0 papers with code
Unraveling the Molecular Magic: AI Insights on the Formation of Extraordinarily Stretchable Hydrogels
The deliberate manipulation of ammonium persulfate, methylenebisacrylamide, dimethyleacrylamide, and polyethylene oxide concentrations resulted in the development of a hydrogel with an exceptional stretchability, capable of extending up to 260 times its original length.
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky.
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data.
Tourbillon: a Physically Plausible Neural Architecture
The circular autoencoders are trained in self-supervised mode by recirculation algorithms and the top layer in supervised mode by stochastic gradient descent, with the option of propagating error information through the entire stack using non-symmetric connections.
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry.
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Furthermore, we show that SPLASH units significantly increase the robustness of deep neural networks to adversarial attacks.
Continuous Representation of Molecules Using Graph Variational Autoencoder
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.
Symmetric-APL Activations: Training Insights and Robustness to Adversarial Attacks
Finally, we show that the use of Symmetric-APL activations can significantly increase the robustness of deep neural networks to adversarial attacks.
