Hugo Penedones

TL;DR: It is shown that a neural network can be trained to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions, and the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures.

TL;DR: AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13, shows that simple gradient descent on a properly constructed potential is able to perform on par with more expensive traditional search techniques and without requiring domain segmentation.

TL;DR: This work proposes a method that exploits pose information in order to improve object classification, and investigates both Multi-layer Perceptrons and Convolutional Neural Network architectures, and achieves state-of-the-art results in the challenging NORB dataset.

TL;DR: The $\lambda$ selection problem is formalized as a bias-variance trade-off where the solution is the value of $\ lambda$ that leads to the smallest Mean Squared Value Error (MSVE).

TL;DR: The issue of approximation errors in areas of sharp discontinuities of the value function being further propagated by bootstrap updates is investigated, and empirical evidence of leakage propagation is shown, and analytically it is shown that it must occur, in a simple Markov chain, when function approximation errors are present.