477 citations · 1.4k across the 57 of their papers we have counts for
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Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference
Bartosz Wójcik, Alessio Devoto, Karol Pustelnik +2
While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a sma…
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez +3
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to over…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
Probabilistic load forecasting with Reservoir Computing
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric pow…
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov +3
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…