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Found 5 papers, 2 papers with code
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
The power of motifs as inductive bias for learning molecular distributions
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
Neural Flows: Efficient Alternative to Neural ODEs
Neural ordinary differential equations describe how values change in time.
Ranked #3 on
Multivariate Time Series Forecasting
on MIMIC-III
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features.
Learning to Tune XGBoost with XGBoost
In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH).
