18 citations
- Carole-Jean Wu2 profiles2 · h 7
- Djork-Arné Clevert2 · h 29
- F. Montanari2 · h 19
- Ryan Henderson2 · h 3
- Steffen Vogler2 profiles2 · h 3
- Abel Gawel1 · h 2
- A. Bochicchio1 · h 6
- Alexander Liniger1 · h 27
- Alexandre Siqueira Guedes Coelho1 · h 1
- Andrea Boscolo Camiletto1 · h 3
- Bruna De Mello Florêncio1 · h 1
- Bruno Achcar Trevisan1 · h 1
- Google (United States)US2 papers
- Bayer (Germany)DE1 paper
- Data Archiving and Networked Services (DANS)NL1 paper
- FACE FoundationUS1 paper
- Federal University of São João del-ReiBR1 paper
- Hugging FaceUS1 paper
- King's College LondonGB1 paper
- Massachusetts Institute of TechnologyUS1 paper
- Open Data InstituteGB1 paper
- Universidade Federal de GoiásBR1 paper
- Universitat Oberta de CatalunyaES1 paper
- University of KonstanzDE1 paper
7 papers
A Framework for Building Enviromics Matrices in Mixed Models
B. A. Trevisan, V. S. Junqueira, B. M. Florencio +3
This study introduces a framework for constructing enviromics matrices in mixed models to integrate genetic and environmental data to enhance phenotypic predictions in plant breedi…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…
U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization
Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger +2
Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird's-Eye-View (BEV) segme…
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?
Tuan Truong, Sadegh Mohammadi, Matthias Lenga
Transfer learning has become a standard practice to mitigate the lack of labeled data in medical classification tasks. Whereas finetuning a downstream task using supervised ImageNe…
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularizati…
Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network. As a mitigation str…