Machine Learning

Seminarium magisterskie MIMUW 2019/2020

USOS

Link do spotkań: http://meet.google.com/seb-fjag-hyj

Plan spotkań 2020/2021

2020-10-15;
2020-10-22;
2020-11-05; Jacek Cyranka (tematy prac mgr.), Wojciech Jablonski, Zuzanna Kwiatkowska, Deep Learning Based Open Set Acoustic Scene Classification
2020-11-19; Zuzanna Opala, Maciej Sypetkowski
2020-12-03; Jakub Modrzewski, Marta Mysiursk, ...
2020-12-17; Michal Raszkowski, Piotr Krzywicki, ...
2021-01-14; Karol Kowalski, Marcin Wierzbiński (Gravitational-wave detectors with machine learning), ...
2021-01-28; Maciej Mikula, ..., ...

Plan spotkań 2019/2020

2019-10-03
2019-10-10; Umair Rasheed - Volkswagen
2019-10-17; Michał Futrega (Neural Architecture Search) + Jacek Cyranka (prezentacja tematów prac magisterskich)
2019-10-24; Maciej Biernaczyk - AutoGAN: Neural Architecture Search for Generative Adversarial Networks
2019-10-31; Michał Zawalski - Knowledge-free solving the Rubik’s cube
2019-11-07; Krystian Koziatek - Learning the Depths of Moving People by Watching Frozen People + Marek Cygan: prezentacja tematów prac magisterskich
2019-11-14; Mateusz Doliński - NGBoost: Natural Gradient Boosting for Probabilistic Prediction
2019-11-21; Jakub Skorupski - TossingBot: Learning to Throw Arbitrary Objects with Residual Physics | Prezentacja
2019-11-28; Michał Kukuła - Graph-based sparse neural networks for traffic signal optimization + (Paweł Gora: prezentacja tematów prac magisterskich
2019-12-05 Przemek Biecek: prezentacja tematów prac magisterskich & Piotr Miłoś: prezentacja tematów prac magisterskich
2019-12-12; Mateusz Doliński - Wild patterns: Ten years after the rise of adversarial machine learning
2019-12-19 Henryk Michalewski, Mateusz Malinowski - Prezentacja tematów prac magisterskich
2020-01-09 brak seminarium
2020-01-16 Jakub Jasiulewicz - Playing hard exploration games by watching YouTube (paper)
2020-01-23 Kamil Faber - Optymalizacja Hiperparametrów, Tomasz Kurzelewski
2020-02-27 Zuzanna Kwiatkowska - Introduction to Topological Data Analysis
2020-03-05
2020-03-12 Jakub Sieroń - Emergent Tool Use from Multi-Agent Interaction
2020-03-19
2020-03-26 Maciej Sypetkowski -- Presentation, Self-training with Noisy Student improves ImageNet classification
2020-04-02 Jakub Sieroń
2020-04-16 brak seminarium
2020-04-23 brak seminarium
2020-04-30 (10:15-13:15) Michał Kukuła, Jakub Skorupski, Kamil Tokarski - Autoencoders
2020-05-07
2020-05-14
2020-05-21 (10:15-13:15) Zuzanna Kwiatkowska (AudioAI), Mieczysław Krawiarz (ERASER), Krystian Koziatek (Skin cancer detection & ResNeSt), Michał Futrega Pruning - DL for audio, NLP and video
2020-05-28 Piotr Piękos - Mathematical tasks with nlp methods
2020-06-04

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 - Distillation and privileged information
2018-11-22; Przemysław Sadownik - Tree-based Pipeline Optimization Tool for Automating Data Science
2018-11-29; Marcin Papierzyński - Opening the black box of Deep Neural Networks via Information
2018-12-13; Piotr Biliński - prezentacja tematów prac magisterskich.
2018-12-20; Michał Zawalski
2018-01-10; Rafał Sadziak - Adversarial attacks and defences
2018-01-17; Michał Łuszczyk - Realistic Evaluation of Deep Semi-Supervised Learning Algorithms.
2018-01-24; Wojciech Mańke - Neural Architecture Search With Reinforcement Learning
2019-01-31;
2019-02-07;
2019-02-28; Paweł Zięcik - Large-Scale Study of Curiosity-Driven Learning
2019-03-07; Michał Kukuła Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
2019-03-14; Jakub Sieroń Thinking Fast and Slow with Deep Learning and Tree Search
2019-03-21; Maciej Biernaczyk
2019-03-28; Adam Dobrakowski
2019-04-04; Sebastian Jaszczur - Concrete Problems in AI Safety
2019-04-11; Jakub Skorupski - Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
2019-04-18;
2019-04-25; Mikołaj Błaż - Single-Agent Policy Tree Search With Guarantees
2019-05-02;
2019-05-09; Przemysław Sadownik "Layer-based AutoML with evolutionary algorithms"
2019-05-16; Jacek Maksymiuk - “What is Relevant in a Text Document?”: An Interpretable Machine Learning Approach
2019-05-23; Mateusz Doliński - doc2vec
2019-05-30; Piotr Piękos - Hindsight Experience Replay
2019-06-06; Paweł Zięcik - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
2019-06-13; Krystian Koziatek - Depth map prediction problem overview, Tomasz Kępa - AutoAugment: Learning Augmentation Strategies from Data

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

quinor / MachineLearningSeminar

Machine Learning

Plan spotkań 2020/2021

Plan spotkań 2019/2020

Plan spotkań 2018/2019

Plan spotkań 2017/2018

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