sebastianjaszczur / machinelearningseminar

Seminarium Magisterskie Machine Learning

Machine Learning

Seminarium magisterskie MIMUW 2018/2019

USOS

Plan spotkań 2018/2019

• 2018-10-11; Zuzanna Pilat - Learning algorithms with neural gpu
• 2018-10-18; Bartosz Biskupski (TCL) - prezentacja tematów prac magisterskich
• 2018-10-25; (seminarium odwołane) Paweł Gora - prezentacja tematów prac magisterskich
• 2018-11-08; Paweł Gora - prezentacja tematów prac magisterskich
• 2018-11-15; Sebastian Jaszczur - Unifying distillation and privileged information
• 2018-11-22; Przemysław Sadownik
• 2018-11-29; Marcin Papierzyński - Opening the black box of Deep Neural Networks via Information

Plan spotkań 2017/2018

• 2017-10-05; Spotkanie organizacyjne
• 2017-10-12; Propozycje prac magisterskich - NVidia
• 2017-10-19; Propozycje prac magisterskich - Henryk Michalewski
• 2017-10-26; Understanding deep learning requires rethinking generalization, https://arxiv.org/abs/1611.03530 , prezentacja
• 2017-11-02; Distilling the Knowledge in a Neural Network, https://arxiv.org/abs/1503.02531
• 2017-11-09; "Why Should I Trust You?": Explaining the Predictions of Any Classifier, https://arxiv.org/abs/1602.04938 prezentacja
• 2017-11-16; Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, https://arxiv.org/abs/1412.1897
• 2017-11-23; Visualizing statistical models: Removing the blindfold, http://had.co.nz/stat645/model-vis.pdf
• 2017-11-30; mlr: Machine Learning in R, http://jmlr.org/papers/v17/15-066.html
• 2017-12-07
• 2017-12-14; Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics, https://arxiv.org/pdf/1705.07115.pdf
• 2017-12-21; Deep Reinforcement Learning: Pong from Pixels, http://karpathy.github.io/2016/05/31/rl/ https://docs.google.com/presentation/d/1lHvatcCaJXit7Uub4pBDOD_s32VZKq5NRZPpxcaDf8M/edit#slide=id.p3
• 2018-01-11; Scikit-learn & Pandas
• 2018-01-18; Visualizing and Understanding Convolutional Networks, https://arxiv.org/abs/1311.2901
• 2018-01-25; A Critical Review of Recurrent Neural Networks for Sequence Learning https://arxiv.org/abs/1506.00019
• 2018-03-01; A Unified Approach to Interpreting Model Predictions https://arxiv.org/abs/1705.07874
• 2018-03-08
• 2018-03-15; Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm https://arxiv.org/abs/1712.01815, Mastering the game of Go without human knowledge https://www.nature.com/articles/nature24270?sf123103138=1
• 2018-03-22; Playing Atari with Deep Reinforcement Learning https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf oraz Continuous control with deep reinforcement learning https://arxiv.org/abs/1509.02971
• 2018-03-28; Paweł Góra, zaczynamy 10:45
• 2018-04-05; brak seminarium
• 2018-04-12; Dynamic Routing Between Capsules https://arxiv.org/abs/1710.09829
• 2018-04-19
• 2018-04-26; Matrix Capsules with EM Routing https://openreview.net/forum?id=HJWLfGWRb
• 2018-05-10; GA2M - Interpretable Generalized Additive Models (+ applications) - Przemysław Horban
• 2018-05-17; NeuroSAT - Learning a SAT Solver from Single-Bit Supervision https://arxiv.org/abs/1802.03685
• 2018-05-24; Anchors: High-Precision Model-Agnostic Explanations https://homes.cs.washington.edu/~marcotcr/aaai18.pdf
• 2018-06-07; Continuous control with deep reinforcement learning https://arxiv.org/abs/1509.02971