Scientific seminars

Termin / Date Name Thema / Topic Organization / Organisation Anmerkung / Remark
08.10.20 - 14:30 Uhr - GPT Improving Language Understanding by Generative Pre-Training Journal-Club / Book Club Zur Vorbereitung muss folgendes Paper durchgearbeitet werden: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
15.10.20 - 14:30 Uhr - Chapter 5 Bayesian Statistics - K. Murphy Machine Learning Journal-Club / Book Club bis einschlie├člich Unter-Kapitel 5.4
22.10.20 - 14:45 Uhr - BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding Journal-Club / Book Club https://arxiv.org/abs/1810.04805
29.10.20 - 14:45 Uhr - Bayesian modeling and inference via probabilistic programming with pyro Journal-Club / Book Club https://pyro.ai/examples/, https://towardsdatascience.com/probabilistic-programming-with-pyro-and-kitchen-scale-f8d6a5d9ae0f, https://gitlab.com/deep.TEACHING/educational-materials/-/blob/master/notebooks/probabilistic-programming/pyro/exercise-pyro-simple-gaussian.ipynb
05.11.20 - 14:45 Uhr Rasmus Kiehl, Sebastian Lohmann, Tom Bisson Daten MAI-Projekt wissenschaftliches Seminar
12.11.20 - 14:45 Uhr - Bayesian Methods for Machine Learning Journal Club / Book Club Week 1 of https://www.coursera.org/learn/bayesian-methods-in-machine-learning
19.11.20 - 14:45 Uhr - Deep neural network models for computational histopathology: A survey Journal Club / Book Club Besprechung des Paper https://www.sciencedirect.com/science/article/pii/S1361841520301778
26.11.20 - 14:45 Uhr - Bayesian Methods for Machine Learning Journal Club / Book Club Week 2 of https://www.coursera.org/learn/bayesian-methods-in-machine-learning, https://gitlab.com/deep.TEACHING/educational-materials/-/blob/master/notebooks/graphical-models/directed/exercise-EM-simple-example.ipynb , https://gitlab.com/deep.TEACHING/educational-materials/-/blob/master/notebooks/graphical-models/directed/exercise-1d-gmm-em.ipynb
03.12.20 - 14:45 Uhr - Image GPT https://openai.com/blog/image-gpt/ Journal Club / Book Club Besprechung des Paper https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf
10.12.20 - 14:45 Uhr - Bayesian Methods for Machine Learning Journal Club / Book Club Week 3 of https://www.coursera.org/learn/bayesian-methods-in-machine-learning only part "Variational Inference" and not LDA
17.12.20 - 14:45 Uhr - SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) Journal Club / Book Club https://arxiv.org/pdf/2002.05709.pdf , https://amitness.com/2020/03/illustrated-simclr/
07.01.21 - 14:45 Uhr - SVI with pyro and example Bayesian Regression with Pyro Journal Club / Book Club http://pyro.ai/examples/svi_part_i.html http://pyro.ai/examples/svi_part_ii.html http://pyro.ai/examples/svi_part_iii.html http://pyro.ai/examples/bayesian_regression.html http://pyro.ai/examples/bayesian_regression_ii.html
14.01.21 - 14:45 Uhr - Hyperparameter tuning for deep learning / Part I Journal Club / Book Club https://arxiv.org/pdf/1810.05934.pdf
21.01.21 - 14:45 Uhr - Monte Carlo Methods / MCMC Journal Club / Book Club https://www.coursera.org/learn/bayesian-methods-in-machine-learning/home/week/4 http://www.inference.org.uk/mackay/erice.pdf
28.01.21 - 14:45 Uhr - Hyperparameter tuning for deep learning / Part II Journal Club / Book Club Jamieson, K. and Talwalkar, A. Non-stochastic best arm identification and hyperparameter optimization. In AISTATS, 2015: https://arxiv.org/abs/1502.07943
04.02.21 - 14:45 Uhr - Bayesian Neural Networks: Bayes by Backprop Journal Club / Book Club Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra: Weight Uncertainty in Neural Networks https://arxiv.org/abs/1505.05424 https://gitlab.com/deep.TEACHING/educational-materials/-/blob/master/notebooks/variational/exercise-bayesian-by-backprop.ipynb
11.02.21 - 14:45 Uhr - Hyperparameter tuning for deep learning / Part III Journal Club / Book Club Hyperband: https://arxiv.org/abs/1603.06560
18.02.21 - 14:45 Uhr - Variational Autoencoder Journal Club / Book Club Diederik P Kingma, Max Welling: Auto-Encoding Variational Bayes https://arxiv.org/abs/1312.6114 https://gitlab.com/deep.TEACHING/educational-materials/-/blob/master/notebooks/variational/exercise-variational-autoencoder.ipynb
25.02.21 - 14:45 Uhr - Hyperparameter tuning for deep learning / Part IV Journal Club / Book Club A System for Massively Parallel Hyperparameter Tuning - https://arxiv.org/pdf/1810.05934.pdf
xx.0x.21 - 14:45 Uhr - Hyperparameter tuning for deep learning / Part V Journal Club / Book Club Population based Training https://deepmind.com/blog/article/population-based-training-neural-networks https://arxiv.org/abs/1711.09846