Seminarium magisterskie MIMUW

• 2023-10-05 spotkanie organizacyjne
• 2023-10-12 przykłady projektów magisterskich
• 2023-10-19 Maksymilian Grochowski VOYAGER: An Open-Ended Embodied Agent with Large Language Models
• 2023-10-26 -
• 2023-11-09 Władek Pałucki Reward Design with Language Models, Maciej Sygnowski ProAgent: Building Proactive Cooperative AI with Large Language Models
• 2023-11-16 Witold Drzewakowski (Eureka), Mateusz Boruń (Universal Approximation Theorem for MLP)
• 2023-11-23 Franciszek Szarwacki (Can Large Language Models Reason about Program Invariants? Paper, Slides)
• 2023-11-30 Ewa Dobrowolska Code as Policies: Language Model Programs for Embodied Control, Kamil Jonak (LLM as General Pattern Machines)
• 2023-12-07 Kamil Ciebiera LLMs and the Abstraction and Reasoning Corpus, Krystian Król Low-Resource Languages Jailbreak GPT-4
• 2023-12-14 Bartłomiej Sadlej Autogen
• 2023-12-21
• 2024-01-11 Przemysław Pietrzak ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate slides
• 2024-01-18 Maciej Białobrzeski The Reversal Curse:LLMs trained on “A is B” fail to learn “B is A”
• 2024-01-25 Mikael Jaworowski (zastosowania Random Matrix Theory w przewidywaniu siły wiązania się potencjalnych leków do wybranych receptorów), Maciej Raczuk Discovering faster matrix multiplication algorithms with reinforcement learning
• 2024-02-29 spotkanie organizacyjne - semestr letni
• 2024-03-07 Kamil Ciebiera + Krystian Król: MoE-Mamba + background (Mamba/Switch) Slajdy
• 2024-03-14 Bartłomiej Sadlej i Stanisław Świątkowski - Consistency Models https://arxiv.org/abs/2303.01469
• 2024-03-21
• 2024-03-26 Krzysztof Tomala Solving olympiad geometry without human demonstrations
• 2024-04-04 Ewa Dobrowolska Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
• 2024-04-11 Przemysław Pietrzak i Paweł Wojciechowski AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers Slides
• 2024-04-18
• 2024-04-25 Franciszek Szarwacki (TBA)
• 2024-05-09 Michał Sala (TBA)
• 2024-05-16
• 2024-05-23
• 2024-06-06 Władek Pałucki (TBD), Maks Grochowski (TBD)
• 2024-06-13 Kamil Jonak (TBD), Maciej Sygnowski (TBD)

• 2022-10-20 Łukasz Kuciński, Research topics in RL and Deep Learning.
• 2022-11-03 Maciej Wojtala, Best First Search-based planning algorithms in the Rubik's Cube environment
• 2022-11-17 Szymon Tworkowski & Szymon Antoniak, Formal Premise Selection With Language Models
• 2022-12-01 Bartłomiej Krzepkowski, A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models; Aleksandra Mysiak, Transformer probing
• 2022-12-15 Franciszek Szarwacki Make-a-video: Text-to-Video Generation without Text-Video Data; Philip Smolenski-Jensen Bayesian Neural Networks
• 2023-01-12 Jakub Modrzewski ???; Krzysztof Tomala Structural re-parametrization of neural networks
• 2023-01-26 Krystian Król, State of knowledge on Automatic Music Transcription; Szymon Sadkowski How Close is ChatGPT to Human Experts
• 2023-03-09 Spotkanie organizacyjne
• 2023-03-23 Jakub Modrzewski ConvNeXt - A ConvNet for the 2020s; Propozycje tematów Transformers for ML Seminar
• 2023-04-13 Igor Witkowski, Learning to Play Atari with the Help of Instruction Manuals
• 2023-05-04 Mateusz Kaluzny, A Mathematical Framework for Transformer Circuits
• 2023-05-11 Michał Sala ???
• 2023-05-18 Michał Tyrolski (Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search)
• 2023-05-25
• 2023-06-01 Witold Drzewakowski (TBA)
• 2023-06-15 Radosław Jurczak (Transformers on protein sequences: how & why); Alek Leśniak (TBA)

• 2021-10-07 Spotkanie organizacyjne. Łukasz Kuciński : Prezentacja tematów z RL-a
• 2021-10-14 NCBJ NOMATEN Stefanos Papanikolaou : Prezentacja tematów
• 2021-10-21 TCL - Prezentacja tematów
• 2021-11-04 Zuzanna Opała "Extreme Classification", Maciej Wojtala "AlphaStar Algorithm and Its Adaptation in Portfolio Optimisation"
• 2021-11-18 Zuzanna Kwiatkowska, "State of Audio"
• 2021-12-02 Michał Raszkowski "Hyperparameter optimisation", Szymon Tworkowski & Michał Tyrolski "Hierarchical Transformers Are More Efficient Language Models"
• 2021-12-16 Maciej Mikuła Enhancing Goal-Conditioned Reinforcement Learning agents via Planning, Szymon Haponiuk "Neural ordinary differential equations"
• 2022-01-13 Paweł Gora "RL do optymalizacji strategii w modelach nowotworów"
• 2022-01-27 Cancelled
• 2022-03-03 Kałużny Mateusz Jakub "???"
• 2022-03-17 Mysiak Aleksandra "Dependency trees in NLP", Błajda Mateusz "Style GAN"
• 2022-03-31 Tomala Krzysztof Jakub, Antoniak Szymon "A glimpse into the world of Meta Learning"
• 2022-04-21 Smolenski-Jensen Philip Alexander
• 2022-05-05 Król Krystian Kamil "All you need to know about DALL·E 2", Wierzbiński Marcin Józef
• 2022-05-19 Bochenek Maria Teresa, Mysiurska Marta
• 2022-06-02 Modrzewski Jakub Maksymilian "NAS - Neural Predictor Approach"

• 2020-10-15;
• 2020-10-22;
• 2020-11-05; Jacek Cyranka (tematy prac mgr.), Wojciech Jablonski, Neural Architecture Search for Segmentation (na podstawie DARTS, Auto-DeepLab, DCNAS), Zuzanna Kwiatkowska, Deep Learning Based Open Set Acoustic Scene Classification
• 2020-11-19; Zuzanna Opala(Training a Cooperative Team in Google Football Environment Using Multi-Agent Reinforcement Learning), Maciej Sypetkowski (Latent space editing in GANs & PGGAN, StyleGAN paper scraps)
• 2020-12-03; Jakub Modrzewski(COVID-19 AI Diagnosis using only Cough Recordings. Presentation, Article), Marta Mysiurska Multi-objective counterfactual explanation
• 2020-12-17; Michal Raszkowski (Computer Vision for Autonomous Vehicles), Piotr Krzywicki: Deep Learning in robotic manipulation
• 2021-01-14; Karol Kowalski, Marcin Wierzbiński Gravitational-wave detectors with machine learning)
• 2021-01-28; Philip Smolenski-Jensen (AlphaFold - state-of-the-art protein folding model ), Maciej Mikula Agent57- Outperforming the Atari Human Benchmark
• 2021-03-04
• 2021-03-18; Marcin Wierzbiński - LSTM vs GRU; Zuzanna Opała (Robust High-dimensional Memory-augmented Neural Networks)
• 2021-04-08; Maciej Mikuła - Relational inductive biases, deep learning, and graph networks; Jakub Modrzewski - Stock Market’s Price Movement Prediction With LSTM Neural Networks
• 2021-04-29; Philip Smolenski-Jensen (CURL - Contrastive Unsupervised Representations for Reinforcement Learning)
• 2021-05-20; Zuzanna Kwiatkowska (On gradient-based saliency maps in deep learning: presentation, paper);
• 2021-06-10; Wojciech Jabłoński (TurboDARTS - better, faster, more efficient Neural Architecture Search); Jakub Jasiulewicz - Pretrained Transformers As Universal Computation Engines (paper, paper, presentation), Marta Mysiurska - ?; Piotr Krzywicki – Lessons learned implementing real-world robot learning system

• 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

• 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

• 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