Search Results for author:
Found 25 papers, 8 papers with code
Supervised Tractogram Filtering using Geometric Deep Learning
A tractogram is a virtual representation of the brain white matter.
Automatic design of novel potential 3CL$^{\text{pro}}$ and PL$^{\text{pro}}$ inhibitors
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
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.
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
INODE is trained like a standard RNN, it learns to discriminate short event sequences and to perform event-by-event online inference.
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
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
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)
Infinite-Horizon Differentiable Model Predictive Control
This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning.
No Representation without Transformation
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
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.
SNODE: Spectral Discretization of Neural ODEs for System Identification
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
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer
This paper introduces a neural style transfer model to generate a stylized image conditioning on a set of examples describing the desired style.
Deep Iterative Surface Normal Estimation
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.
Ranked #7 on
Surface Normals Estimation
on PCPNet
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation
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.
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Deep learning systems have become ubiquitous in many aspects of our lives.
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
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
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.
Ranked #4 on
Document Classification
on Cora
Learning shape correspondence with anisotropic convolutional neural networks
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
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
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
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.
Ranked #193 on
Image Classification
on CIFAR-10
Sparse similarity-preserving hashing
In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.
Compete to Compute
Local competition among neighboring neurons is common in biological neural networks (NNs).
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
Deep Neural Networks now excel at image classification, detection and segmentation.
High-Performance Neural Networks for Visual Object Classification
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants.
