Search Results for author: Faustino Gomez

Found 10 papers, 4 papers with code

Automatic design of novel potential 3CL$^{\text{pro}}$ and PL$^{\text{pro}}$ inhibitors

no code implementations28 Jan 2021 Timothy Atkinson, Saeed Saremi, Faustino Gomez, Jonathan Masci

With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we propose the general molecule optimization framework, Molecular Neural Assay Search (MONAS), consisting of three components: a property predictor which identifies molecules with specific desirable properties, an energy model which approximates the statistical similarity of a given molecule to known training molecules, and a molecule search method.

Safe Interactive Model-Based Learning

no code implementations15 Nov 2019 Marco Gallieri, Seyed Sina Mirrazavi Salehian, Nihat Engin Toklu, Alessio Quaglino, Jonathan Masci, Jan Koutník, Faustino Gomez

A min-max control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set.

Safe Exploration

Model-Based Active Exploration

2 code implementations29 Oct 2018 Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez

Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations.

Efficient Exploration reinforcement-learning

NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

1 code implementation NeurIPS 2018 Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez

This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system.

Understanding Locally Competitive Networks

no code implementations5 Oct 2014 Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber

Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets.

Kernel-based Information Criterion

no code implementations25 Aug 2014 Somayeh Danafar, Kenji Fukumizu, Faustino Gomez

This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis.

GPR Model Selection

Deep Networks with Internal Selective Attention through Feedback Connections

no code implementations NeurIPS 2014 Marijn Stollenga, Jonathan Masci, Faustino Gomez, Juergen Schmidhuber

It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters.

Deep Attention General Classification

A Clockwork RNN

5 code implementations14 Feb 2014 Jan Koutník, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.

General Classification

Compete to Compute

no code implementations NeurIPS 2013 Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber

Local competition among neighboring neurons is common in biological neural networks (NNs).

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