Repository files navigation
2025-10-02 spotkanie organizacyjne
2025-10-09
2025-10-16 Ewa Szczurek - t.b.d.
2025-10-23
2025-10-30 Jan Urbanek Subliminal Learning: Language models transmit behavioral traits via hidden signals in data https://arxiv.org/pdf/2507.14805 , Łukasz Smoliński Training Large Language Models to Reason in a Continuous Latent Space
2025-11-06 Marek Lisowski: Scaling Scaling Laws with Board Games , Cezary Botta: Learn To be Efficient: Build Structured Sparsity in Large Language Models
2025-11-20 Jakub Kołaczyński: Spurious Rewards: Rethinking Training Signals in RLVR , Ignacy Stępniewski: T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models
2025-11-27 Mikołaj Garstecki: DeepSeek-OCR: Contexts Optical Compression
2025-12-4 Mateusz Winiarek: The Impact of Depth on Compositional Generalization in Transformer Language Models
2025-12-11 Stanisław Kiedrzyński: GraphCast: Learning skillful medium-range global weather forecasting , Ignacy Kozakiewicz: SnapFusion: Text-to-Image Diffusion Model on Mobile
2025-12-18 Mateusz Boryczka: What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT , Michał Nowicki Compute Better Spent: Replacing Dense Layers with Structured Matrices
2026-01-08 Marek Lisowski: AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
2026-01-15 Jan Urbanek: Activation-Informed Merging of Large Language Models , Mikołaj Garstecki: Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Training
2026-01-22 Cezary Botta: Muon: An optimizer for hidden layers in neural networks , Michał Nowicki: Evaluating multiple models using labeled and unlabeled data
2026-02-26 Ignacy Stępniewski: Memory Mosaics at scale , Jakub Kołaczyński Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
2026-03-05 Łukasz Smoliński: Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inference
2026-03-12 Ignacy Kozakiewicz Back to Basics: Let Denoising Generative Models Denoise
2026-03-19 Mateusz Boryczka: 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities , Mateusz Winiarek (Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free)[https://arxiv.org/pdf/2505.06708 ]
2026-03-26 Stanisław Kiedrzyński: Superposition Yields Robust Neural Scaling
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 (slides )
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 Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (Slides )
2024-05-09 Michał Sala Musika! Fast Infinite Waveform Music Generation
2024-05-16 Radosław Jurczak Evaluating the Moral Beliefs Encoded in LLMs slides , Piotr Kamiński GPT-4 IS TOO SMART TO BE SAFE: STEALTHY CHAT WITH LLMS VIA CIPHER
2024-05-23 Witold Drzewakowski NLHF , Mateusz Szymański VampNet: Music Generation via Masked Acoustic Token Modeling
2024-06-06 Władek Pałucki Contrastive Reinforcement Learning , Maks Grochowski Learning Curricula in Open-Ended Worlds
2024-06-13 Szymon Sadkowski Efficient Streaming Language Models with Attention Sinks , Maciej Raczuk Language models can explain neurons in language models
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
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
You can’t perform that action at this time.
