Παρακολούθηση
Mario Lučić
Mario Lučić
Research Scientist, Google DeepMind
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα google.com - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
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
14462021
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
12272019
ViViT: A Video Vision Transformer
A Arnab, M Dehghani, G Heigold, C Sun, M Lučić*, C Schmid*
International Conference on Computer Vision, 2021
11902021
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
10512018
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
5372020
On Mutual Information Maximization for Representation Learning
M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations, 2020
4482020
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
4302018
Assessing Generative Models via Precision and Recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in Neural Information Processing Systems, 2018
4262018
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
3332019
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
332*2018
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
233*2019
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in Neural Information Processing Systems, 2016
1772016
Revisiting the Calibration of Modern Neural Networks
M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ...
Neural Information Processing Systems, 2021
1632021
Approximate K-Means++ in Sublinear Time
O Bachem, M Lucic, SH Hassani, A Krause
AAAI Conference on Artificial Intelligence, 2016
1562016
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
1532019
Practical coreset constructions for machine learning
O Bachem*, M Lucic*, A Krause
arXiv preprint arXiv:1703.06476, 2017
1442017
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
International Conference on Knowledge Discovery & Data Mining, 2018
1402018
Training Gaussian mixture models at scale via coresets
M Lucic, M Faulkner, A Krause, D Feldman
The Journal of Machine Learning Research, 2017
1182017
Deep Generative Models for Distribution-Preserving Lossy Compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems, 2018
1152018
On robustness and transferability of convolutional neural networks
J Djolonga, J Yung, M Tschannen, R Romijnders, L Beyer, A Kolesnikov, ...
Computer Vision and Pattern Recognition, 2021
1102021
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