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Found 8 papers, 3 papers with code
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
For comparison, Sch\"utt et al. (2019) spent 626 hours creating a dataset on which they trained their NN for 160h, for a total of 786h; our method achieves comparable performance within 31h.
Generating QM1B with PySCF$_{\text{IPU}}$
Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples.
Backpropagating through Fréchet Inception Distance
Using FID as an additional loss for Generative Adversarial Networks improves their FID.
What if Neural Networks had SVDs?
We present an algorithm that is fast enough to speed up several matrix operations.
Margin-Based Generalization Lower Bounds for Boosted Classifiers
To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013).
Optimal Minimal Margin Maximization with Boosting
A common goal in a long line of research, is to maximize the smallest margin using as few base hypotheses as possible, culminating with the AdaBoostV algorithm by (R{\"a}tsch and Warmuth [JMLR'04]).
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
We present all the existing work that had been overlooked and compare the various solutions theoretically.
