Mario Lucic

End-to-End Spatio-Temporal Action Localisation with Video Transformers CVPR 2024 On Scaling Up a Multilingual Vision and Language Model CVPR 2024 Audiovisual Masked Autoencoders ICCV 2023 Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution NeurIPS 2023 RUST: Latent Neural Scene Representations From Unposed Imagery CVPR 2023 Scaling Vision Transformers to 22 Billion Parameters ICML 2023 Video OWL-ViT: Temporally-consistent Open-world Localization in Video ICCV 2023 Object Scene Representation Transformer NeurIPS 2022 Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations CVPR 2022 Underspecification Presents Challenges for Credibility in Modern Machine Learning JMLR 2022 VCT: A Video Compression Transformer NeurIPS 2022 Which Model To Transfer? Finding the Needle in the Growing Haystack CVPR 2022 A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models NeurIPS 2021 MLP-Mixer: An all-MLP Architecture for Vision NeurIPS 2021 On Robustness and Transferability of Convolutional Neural Networks CVPR 2021 Representation Learning From Videos In-the-Wild: An Object-Centric Approach WACV 2021 Revisiting the Calibration of Modern Neural Networks NeurIPS 2021 ViViT: A Video Vision Transformer ICCV 2021 A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020 On Mutual Information Maximization for Representation Learning ICLR 2020 Precision-Recall Curves Using Information Divergence Frontiers AISTATS 2020 Self-Supervised Learning of Video-Induced Visual Invariances CVPR 2020 A Large-Scale Study on Regularization and Normalization in GANs ICML 2019 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations ICML 2019 High-Fidelity Image Generation With Fewer Labels ICML 2019 On Self Modulation for Generative Adversarial Networks ICLR 2019 Self-Supervised GANs via Auxiliary Rotation Loss CVPR 2019 Are GANs Created Equal? A Large-Scale Study NeurIPS 2018 Assessing Generative Models via Precision and Recall NeurIPS 2018 Deep Generative Models for Distribution-Preserving Lossy Compression NeurIPS 2018 One-shot Coresets: The Case of k-Clustering AISTATS 2018 Training Gaussian Mixture Models at Scale via Coresets JMLR 2018 Distributed and Provably Good Seedings for k-Means in Constant Rounds ICML 2017 Stochastic Submodular Maximization: The Case of Coverage Functions NeurIPS 2017 Uniform Deviation Bounds for k-Means Clustering ICML 2017 Fast and Provably Good Seedings for k-Means NeurIPS 2016 Horizontally Scalable Submodular Maximization ICML 2016 Linear-Time Outlier Detection via Sensitivity IJCAI 2016 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016 Coresets for Nonparametric Estimation - the Case of DP-Means ICML 2015 Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning AISTATS 2015 Fast and Robust Least Squares Estimation in Corrupted Linear Models NeurIPS 2014