Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on 3D Geometry Prediction on Molecule3D val
Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs.
In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism.
We propose a novel non-randomized anytime orienteering algorithm for finding k-optimal goals that maximize reward on a specialized graph with budget constraints.
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).
The asymmetric structure enables the two data streams to interlace each other, which allows for the informative comparison between new data pairs over iterations.