Search Results for author: Jonathan Masci

Found 25 papers, 8 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.

Learning to Detect Objects with a 1 Megapixel Event Camera

no code implementations NeurIPS 2020 Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, Amos Sironi

However, due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions.

Event-based vision object-detection +1

Tractogram filtering of anatomically non-plausible fibers with geometric deep learning

no code implementations24 Mar 2020 Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti, Jonathan Masci, Davide Boscaini, Paolo Avesani

The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties.


Deep Graph Matching Consensus

2 code implementations ICLR 2020 Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.

Ranked #12 on Entity Alignment on DBP15k zh-en (using extra training data)

Entity Alignment Graph Matching +2

Infinite-Horizon Differentiable Model Predictive Control

no code implementations ICLR 2020 Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutnik, Mark Cannon

This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning.

Imitation Learning

No Representation without Transformation

no code implementations9 Dec 2019 Giorgio Giannone, Saeed Saremi, Jonathan Masci, Christian Osendorfer

To explicitly demonstrate the effect of these higher order objects, we show that the inferred latent transformations reflect interpretable properties in the observation space.

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

SNODE: Spectral Discretization of Neural ODEs for System Identification

no code implementations ICLR 2020 Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník

This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification.

Recurrent Neural Processes

2 code implementations13 Jun 2019 Timon Willi, Jonathan Masci, Jürgen Schmidhuber, Christian Osendorfer

We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.

Gaussian Processes Time Series +1

Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

1 code implementation CVPR 2020 Jan Svoboda, Asha Anoosheh, Christian Osendorfer, Jonathan Masci

This paper introduces a neural style transfer model to generate a stylized image conditioning on a set of examples describing the desired style.

Image Generation Style Transfer +1

Deep Iterative Surface Normal Estimation

2 code implementations CVPR 2020 Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci

This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.

Surface Normal Estimation Surface Normals Estimation

ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

no code implementations14 Jun 2018 Francesco Lattari, Marco Ciccone, Matteo Matteucci, Jonathan Masci, Francesco Visin

We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

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.

Geometric deep learning on graphs and manifolds using mixture model CNNs

4 code implementations CVPR 2017 Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

Document Classification Graph Classification +8

Learning shape correspondence with anisotropic convolutional neural networks

no code implementations NeurIPS 2016 Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein

Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications.

Geodesic convolutional neural networks on Riemannian manifolds

no code implementations26 Jan 2015 Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation.


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.


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

Sparse similarity-preserving hashing

no code implementations19 Dec 2013 Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro

In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.

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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