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Mario Lučić
Mario Lučić
Research Scientist, Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
MLP-Mixer: An All-MLP Architecture for Vision
I Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ...
Neural Information Processing Systems, 2021
22772021
ViViT: A Video Vision Transformer
A Arnab, M Dehghani, G Heigold, C Sun, M Lučić*, C Schmid*
International Conference on Computer Vision, 2021
19442021
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, S Gelly, B Schölkopf, O Bachem
International Conference on Machine Learning (Best Paper Award), 2019
14952019
Are GANs Created Equal? A Large-scale Study
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
Advances in neural information processing systems 31, 2018
11812018
Gemini: A family of highly capable multimodal models
Gemini Team
arXiv preprint arXiv:2312.11805, 2023
7762023
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
Journal of Machine Learning Research, 2020
6992020
Assessing Generative Models via Precision and Recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in Neural Information Processing Systems, 2018
5612018
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
5252018
On Mutual Information Maximization for Representation Learning
M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations, 2020
5142020
Self-Supervised GANs via Auxiliary Rotation Loss
T Chen, X Zhai, M Ritter, M Lucic, N Houlsby
Conference on Computer Vision and Pattern Recognition, 2019
3782019
A Large-Scale Study on Regularization and Normalization in GANs
K Kurach, M Lucic, X Zhai, M Michalski, S Gelly
International Conference on Machine Learning, 2018
358*2018
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
353*2019
Scaling vision transformers to 22 billion parameters
M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ...
International Conference on Machine Learning, 7480-7512, 2023
3102023
Revisiting the Calibration of Modern Neural Networks
M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ...
Neural Information Processing Systems, 2021
2722021
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in Neural Information Processing Systems, 2016
1922016
High-Fidelity Image Generation With Fewer Labels
M Lučić*, M Tschannen*, M Ritter*, X Zhai, O Bachem, S Gelly
International Conference on Machine Learning, 2019
1762019
Practical coreset constructions for machine learning
O Bachem*, M Lucic*, A Krause
arXiv preprint arXiv:1703.06476, 2017
1722017
Approximate K-Means++ in Sublinear Time
O Bachem, M Lucic, SH Hassani, A Krause
AAAI Conference on Artificial Intelligence, 2016
1722016
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
International Conference on Knowledge Discovery & Data Mining, 2018
1582018
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
MSM Sajjadi, H Meyer, E Pot, U Bergmann, K Greff, N Radwan, S Vora, ...
Computer Vision and Pattern Recognition, 2022
1382022
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