Mario Lučić
Mario Lučić
Research Scientist, Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Are GANs Created Equal? A Large-Scale Study
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
Advances in Neural Information Processing Systems, 2017
6782017
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
5582019
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
220*2018
Assessing Generative Models via Precision and Recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in Neural Information Processing Systems, 2018
1882018
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
1842018
On Mutual Information Maximization for Representation Learning
M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations, 2020
1762020
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
167*2019
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in Neural Information Processing Systems, 2016
1232016
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
1212020
Approximate K-Means++ in Sublinear Time
O Bachem, M Lucic, SH Hassani, A Krause
AAAI Conference on Artificial Intelligence, 2016
1012016
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
982019
On Self Modulation for Generative Adversarial Networks
T Chen, M Lucic, N Houlsby, S Gelly
International Conference on Learning Representations, 2019
892019
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
802021
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
792017
Deep Generative Models for Distribution-Preserving Lossy Compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems, 2018
692018
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
66*2019
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
M Lucic, O Bachem, A Krause
International Conference on Artificial Intelligence and Statistics, 2016
642016
Coresets for Nonparametric Estimation - the Case of DP-Means
O Bachem, M Lucic, A Krause
International Conference on Machine Learning, 2015
642015
Training Gaussian mixture models at scale via coresets
M Lucic, M Faulkner, A Krause, D Feldman
The Journal of Machine Learning Research, 2017
612017
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
International Conference on Knowledge Discovery & Data Mining, 2018
592018
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