René's URL Explorer Experiment


Title: Michael Tschannen

direct link

Domain: mitscha.github.io

NoneIE=edge

Links:

Abouthttps://mitscha.github.io/
Newshttps://mitscha.github.io/#news
Publicationshttps://mitscha.github.io/#pubs
https://scholar.google.com/citations?user=TSj_8nYAAAAJ
https://github.com/mitscha
https://www.linkedin.com/in/michael-tschannen-31119094
https://twitter.com/mtschannen
Google DeepMindhttps://deepmind.google/
Applehttps://machinelearning.apple.com/
Google Researchhttps://research.google/
ETH Zurichhttps://ethz.ch
Helmut Bölcskeihttps://www.mins.ee.ethz.ch/people/show/boelcskei
ETH Zurichhttps://ethz.ch
EPFLhttps://www.epfl.ch
Amazon AIhttps://aws.amazon.com/ai/
Google Researchhttps://research.google/
linkhttps://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/givt/README.md
linkhttps://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/cappa/README.md
linkhttps://www.youtube.com/watch?v=rUwY4IvfLmQ
linkhttps://neurips.cc/virtual/2023/oral/73871
HiFiChttps://arxiv.org/abs/2006.09965
demo pagehttps://hific.github.io/
Hacker News Threadhttps://news.ycombinator.com/item?id=23652753
Twohttps://arxiv.org/abs/1912.02783
papershttps://arxiv.org/abs/2003.10184
ETH Medalhttps://ethz.ch/en/the-eth-zurich/education/awards/eth-medal.html
PhD thesishttps://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/322751/eth-25498.pdf
LocCa: Visual Pretraining with Location-aware Captionershttps://arxiv.org/abs/2403.19596
PaLI-X: On scaling up a multilingual vision and language modelhttps://arxiv.org/abs/2305.18565
Finite scalar quantization: VQ-VAE made simplehttps://arxiv.org/abs/2309.15505
[colab]https://github.com/google-research/google-research/tree/master/fsq
Towards truly zero-shot compositional visual reasoning with LLMs as programmershttps://arxiv.org/abs/2401.01974
GIVT: Generative Infinite-Vocabulary Transformershttps://arxiv.org/abs/2312.02116
[code]https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/givt/README.md
[colab]https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/givt/givt_demo_colab.ipynb
Image captioners are scalable vision learners toohttps://arxiv.org/abs/2306.07915
[talk]https://neurips.cc/virtual/2023/oral/73871
[code]https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/cappa/README.md
M2T: Masking transformers twice for faster decodinghttps://arxiv.org/abs/2304.07313
Scaling vision transformers to 22 billion parametershttps://arxiv.org/abs/2302.05442
CLIPPO: Image-and-Language Understanding from Pixels Onlyhttps://arxiv.org/abs/2212.08045
[code]https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/clippo/README.md
[colab]https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/clippo/clippo_colab.ipynb
[talk]https://www.youtube.com/watch?v=rUwY4IvfLmQ
FlexiViT: One Model for All Patch Sizeshttps://arxiv.org/abs/2212.08013
[code]https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/flexivit/README.md
Neural Face Video Compression using Multiple Viewshttps://arxiv.org/abs/2203.15401
On Robustness and Transferability of Convolutional Neural Networkshttps://arxiv.org/abs/2007.08558
Representation learning from videos in-the-wild: An object-centric approachhttps://arxiv.org/abs/2010.02808
High-Fidelity Generative Image Compressionhttps://arxiv.org/abs/2006.09965
Automatic shortcut removal for self-supervised representation learninghttps://arxiv.org/abs/2002.08822
Weakly-supervised disentanglement without compromiseshttps://arxiv.org/abs/2002.02886
Self-supervised learning of video-induced visual invarianceshttps://arxiv.org/abs/1912.02783
Learning better lossless image compression using lossy compressionhttps://arxiv.org/abs/2003.10184
On mutual information maximization for representation learninghttps://arxiv.org/abs/1907.13625
Disentangling factors of variation using few labelshttps://arxiv.org/abs/1905.01258
Semantic bottleneck scene generationhttps://arxiv.org/abs/1911.11357
The visual task adaptation benchmarkhttps://arxiv.org/abs/1910.04867
Generative adversarial networks for extreme learned image compressionhttps://arxiv.org/abs/1804.02958
Practical full resolution learned lossless image compressionhttps://arxiv.org/abs/1811.12817
High-fidelity image generation with fewer labelshttps://arxiv.org/abs/1903.02271
Noisy subspace clustering via matching pursuitshttps://arxiv.org/abs/1612.03450
Deep generative models for distribution-preserving lossy compressionhttps://arxiv.org/abs/1805.11057
Recent advances in autoencoder-based representation learninghttps://arxiv.org/abs/1812.05069
StrassenNets: Deep learning with a multiplication budgethttp://proceedings.mlr.press/v80/tschannen18a.html
Born-again neural networkshttp://proceedings.mlr.press/v80/furlanello18a.html
Conditional probability models for deep image compressionhttps://arxiv.org/abs/1801.04260
Towards image understanding from deep compression without decodinghttps://arxiv.org/abs/1803.06131
Unsupervised learning: Model-based clustering and learned compressionhttps://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/322751/eth-25498.pdf
Robust nonparametric nearest neighbor random process clusteringhttps://arxiv.org/abs/1612.01103
Dimensionality-reduced subspace clusteringhttps://academic.oup.com/imaiai/article/6/3/246/3070510
A unified optimization view on generalized matching pursuit and Frank-Wolfehttps://arxiv.org/abs/1702.06457
Greedy algorithms for cone constrained optimization with convergence guaranteeshttps://arxiv.org/abs/1705.11041
Soft-to-hard vector quantization for end-to-end learning compressible representationshttps://arxiv.org/abs/1704.00648
Convolutional recurrent neural networks for electrocardiogram classificationhttps://arxiv.org/abs/1710.06122
Deep structured features for semantic segmentationhttps://arxiv.org/abs/1609.07916
Discrete deep feature extraction: A theory and new architectureshttp://jmlr.org/proceedings/papers/v48/wiatowski16.html
Heart sound classification using deep structured featureshttp://www.cinc.org/archives/2016/pdf/162-186.pdf
Regression forest-based automatic estimation of the articular margin plane for shoulder prosthesis planninghttp://www.sciencedirect.com/science/article/pii/S1361841516000232
Nonparametric nearest neighbor random process clusteringhttps://arxiv.org/abs/1504.05059
Subspace clustering of dimensionality-reduced datahttps://arxiv.org/abs/1404.6818
Dimensionality reduction for sparse subspace clusteringhttps://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/155169/eth-47901-01.pdf
A learning-based approach for fast and robust vessel tracking in long ultrasound sequenceshttp://link.springer.com/chapter/10.1007/978-3-642-40811-3_65

Viewport: width=device-width initial-scale=1

URLs of crawlers that visited me.