12 citations · 16 across the 7 of their papers we have counts for
8 papers
A 3D mesh convolution-based autoencoder for geometry compression
Germain Bregeon, Marius Preda, Radu Ispas +1
In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing no…
Refining Gaussian Splatting: A Volumetric Densification Approach
Mohamed Abdul Gafoor, Marius Preda, Titus Zaharia
Achieving high-quality novel view synthesis in 3D Gaussian Splatting (3DGS) often depends on effective point primitive management. The underlying Adaptive Density Control (ADC) pro…
MeshConv3D: Efficient convolution and pooling operators for triangular 3D meshes
Germain Bregeon, Marius Preda, Radu Ispas +1
Convolutional neural networks (CNNs) have been pivotal in various 2D image analysis tasks, including computer vision, image indexing and retrieval or semantic classification. Exten…
Induced Feature Selection by Structured Pruning
Nathan Hubens, Victor Delvigne, Matei Mancas +3
The advent of sparsity inducing techniques in neural networks has been of a great help in the last few years. Indeed, those methods allowed to find lighter and faster networks, abl…
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
Neural network pruning is a widely used strategy for reducing model storage and computing requirements. It allows to lower the complexity of the network by introducing sparsity in…
One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
Introducing sparsity in a neural network has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced us…
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Max Cohen, Sylvain Le Corff, Maurice Charbit +2
In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We intr…
End-to-end deep metamodeling to calibrate and optimize energy loads
Max Cohen, Maurice Charbit, Sylvain Le Corff +2
In this paper, we propose a new end-to-end methodology to optimize the energy performance and the comfort, air quality and hygiene of large buildings. A metamodel based on a Transf…