Automated Author ProfileTohouri, Pascal
Tohouri, Pascal
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 7.8 (sum of 13 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal
This study examines a feed-forward neural network embedded in Euclidean R3 space. Nuclei coordinates condition network parameters. Backpropagating from loss-parameter to loss-coordinate derivatives enables spatially-embedded gradient descent. A dense multi-layer perceptron (MLP) learns price-prediction on the California Housing dataset. The model demonstrates performance comparable to conventional, non-spatial MLP predictions. Robustness examinations via re-initialisation sensitivity tests, and spatial analysis via node ablation and activation imaging, reveal complexity and interpretability characteristics unique to the spatially embedded model.
Authors
- Tohouri, Pascal