7 citations · 20 across the 9 of their papers we have counts for
5 papers · 1 filter
Towards Meta-Pruning via Optimal Transport
Alexander Theus, Olin Geimer, Friedrich Wicke +3
Structural pruning of neural networks conventionally relies on identifying and discarding less important neurons, a practice often resulting in significant accuracy loss that neces…
Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization
Elior Benarous, Sotiris Anagnostidis, Luca Biggio +1
Recent advancements in deep learning have been primarily driven by the use of large models trained on increasingly vast datasets. While neural scaling laws have emerged to predict…
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes
Alexandros Delitzas, Maria Parelli, Nikolas Hars +4
Training models to apply common-sense linguistic knowledge and visual concepts from 2D images to 3D scene understanding is a promising direction that researchers have only recently…
CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes
Maria Parelli, Alexandros Delitzas, Nikolas Hars +4
Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to e…
Mastering Spatial Graph Prediction of Road Networks
Sotiris Anagnostidis, Aurelien Lucchi, Thomas Hofmann
Accurately predicting road networks from satellite images requires a global understanding of the network topology. We propose to capture such high-level information by introducing…