| Beyond Bouma's window: How to explain global aspects of crowding? A Doerig, A Bornet, R Rosenholtz, G Francis, AM Clarke, MH Herzog PLoS computational biology 15 (5), e1006580, 2019 | 45 | 2019 |
| Crowding reveals fundamental differences in local vs. global processing in humans and machines A Doerig, A Bornet, OH Choung, MH Herzog Vision research 167, 39-45, 2020 | 39 | 2020 |
| Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing A Bornet, OH Choung, A Doerig, D Whitney, MH Herzog, M Manassi Journal of vision 21 (12), 10-10, 2021 | 18 | 2021 |
| A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities B Lonnqvist, A Bornet, A Doerig, MH Herzog Journal of vision 21 (10), 17-17, 2021 | 15 | 2021 |
| Shrinking Bouma’s window: How to model crowding in dense displays A Bornet, A Doerig, MH Herzog, G Francis, E Van der Burg PLoS computational biology 17 (7), e1009187, 2021 | 12 | 2021 |
| Dissecting (un) crowding OH Choung, A Bornet, A Doerig, MH Herzog Journal of vision 21 (10), 10-10, 2021 | 11 | 2021 |
| Running large-scale simulations on the Neurorobotics Platform to understand vision–the case of visual crowding A Bornet, J Kaiser, A Kroner, E Falotico, A Ambrosano, K Cantero, ... Frontiers in neurorobotics 13, 33, 2019 | 9 | 2019 |
| Transformer performance for chemical reactions: Analysis of different predictive and evaluation scenarios F Jaume-Santero, A Bornet, A Valery, N Naderi, D Vicente Alvarez, ... Journal of chemical information and modeling 63 (7), 1914-1924, 2023 | 6 | 2023 |
| Comparing neural language models for medical concept representation and patient trajectory prediction A Bornet, D Proios, A Yazdani, F Jaume-Santero, G Haller, E Choi, ... medRxiv, 2023.06. 01.23290824, 2023 | 3 | 2023 |
| Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study R Gouareb, A Bornet, D Proios, SG Pereira, D Teodoro Health Data Science 3, 0099, 2023 | 2* | 2023 |
| Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data D Proios, A Yazdani, A Bornet, J Ehrsam, I Rekik, D Teodoro 2023 IEEE 10th International Conference on Data Science and Advanced …, 2023 | 1 | 2023 |
| A model with top-down control of the range of perceptual grouping G Francis, A Bornet Journal of Vision 19 (10), 151a-151a, 2019 | 1 | 2019 |
| Shrinking Bouma's window: Visual crowding in dense displays A Bornet, A Doerig, G Francis, MH Herzog, E Van der Burg Perception 48, 27-27, 2019 | 1 | 2019 |
| Crowding asymmetries in a neural model of image segmentation A Bornet, A Doerig, M Herzog, G Francis Journal of Vision 17 (10), 365-365, 2017 | 1 | 2017 |
| ProcNet: Deep Predictive Coding Model for Robust-to-occlusion Visual Segmentation and Pose Estimation M Zechmair, A Bornet, Y Morel arXiv preprint arXiv:2310.18009, 2023 | | 2023 |
| BioWiC: An Evaluation Benchmark for Biomedical Concept Representation H Rouhizadeh, I Nikishina, A Yazdani, A Bornet, B Zhang, J Ehrsam, ... bioRxiv, 2023.11. 08.566170, 2023 | | 2023 |
| CONORM: Context-Aware Entity Normalization for Adverse Drug Event Detection A Yazdani, H Rouhizadeh, A Bornet, D Teodoro medRxiv, 2023.09. 26.23296150, 2023 | | 2023 |
| Global information processing in feedforward deep networks B Lonnqvist, A Bornet, A Doerig, MH Herzog Journal of Vision 22 (14), 3212-3212, 2022 | | 2022 |
| (Un) crowding is pre-attentive Y Markov, A Bornet, N Tiurina, M Herzog Perception 51, 190, 2022 | | 2022 |
| How crowding challenges (feedforward) convolutional neural networks B Lonnqvist, A Doerig, A Bornet, G Francis, L Schmittwilken, MH Herzog Journal of Vision 21 (9), 2039-2039, 2021 | | 2021 |
Michael HerzogEPFL, SwitzerlandVerified email at epfl.ch
