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- University of TorontoCA238 papers
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70 papers · 1 filter
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Neuroevolutionary learning of particles and protocols for self-assembly
Stephen Whitelam, Isaac Tamblyn
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without i…
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
Jixuan Wang, Kai Wei, Martin Radfar +2
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into…
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
Luca A. Thiede, Mario Krenn, AkshatKumar Nigam +1
Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where…
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
Cynthia Shen, Mario Krenn, Sagi Eppel +1
Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs fro…
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao +3
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on to…