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Title: GitHub - MI2DataLab/MI2DataLab_Seminarium: Materiały z seminariów prowadzonych w MI^2 DataLabie.

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Description: Materiały z seminariów prowadzonych w MI^2 DataLabie. - MI2DataLab/MI2DataLab_Seminarium

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https://meet.drwhy.aihttps://meet.drwhy.ai
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#year-20252026
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#spring-semester
Maximilian Dreyerhttps://www.maxdreyer.de/
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#fall-semester
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#schedule
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025-10-13_Towards_scientific_discovery_with_dictionary_learning
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_10_20_inductive_bias_probe_for_world_models
ConceptAttention: Diffusion Transformers Learn Highly Interpretable Featureshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_11_03_concept_attention/Concept_Attention.pdf
Counterfactual Explanations on Robust Perceptual Geodesicshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_11_17_pcg/pcg.pdf
ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppressionhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_12_01_ImageNet_trained_CNNs_are_not_biased_towards_texture/2025_12_01_ImageNet_trained_CNNs_are_not_biased_towards_texture.pdf
Attention (as Discrete-Time Markov) Chainshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_12_08_attention_markov_chains/attention_markov_chains.pdf
Emergent Misalignment: Controlling Persona Drift with Vectors and Inoculationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2026/2026_01_12-mitigation-of-emergent-misalignment.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#invited-lectures
Jan Betleyhttps://www.janbetley.net/
Anna Sztyber-Betleyhttps://repo.pw.edu.pl/info/author/WUT60693/Profil+osoby+%E2%80%93+Anna+Sztyber-Betley+%E2%80%93+Politechnika+Warszawska
Jeremi Ochabhttp://cs.if.uj.edu.pl/jeremi/
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#year-20242025
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#spring-semester-1
Tracking information flow in biosystems from high-throughput datahttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_02_24_tracking_information_flow
ReLUhttps://transformer-circuits.pub/2024/scaling-monosemanticity/
TopKhttps://openreview.net/forum?id=tcsZt9ZNKD
JumpReLUhttps://openreview.net/forum?id=XkMrWOJhNd
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelshttps://openreview.net/forum?id=I4e82CIDxv
Sparse Autoencoders Do Not Find Canonical Units of Analysishttps://openreview.net/forum?id=9ca9eHNrdH
Interpreting CLIP with Hierarchical Sparse Autoencodershttps://arxiv.org/abs/2502.20578
ProtoViT: Interpretable image classification with adaptive prototype-based vision transformershttps://openreview.net/forum?id=hjhpCJfbFG
PIP-Net: Patch-based intuitive prototypes for interpretable image classificationhttps://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html
Birds look like cars: Adversarial analysis of intrinsically interpretable deep learninghttps://arxiv.org/abs/2503.08636
Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Modelshttps://arxiv.org/abs/2412.18604
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_05_12_denoising_diffusion_null_space_models
Diffusion Posterior Sampling for General Noisy Inverse Problemshttps://arxiv.org/abs/2209.14687
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanationshttps://arxiv.org/abs/2311.17833
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#fall-semester-1
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#schedule-for-the-first-half-of-the-semester-track-context-matters-in-deep-learning-models---computer-vision
Positional Label for Self-Supervised Vision Transformerhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_10_21_Positional_Label_for_Self-Supervised_Vision_Transformer
Adversarial examples vs. context consistency defense for object detectionhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_10_28_Adversarial_attacks_against_object_detection.md
Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-traininghttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_11_04_Unlocking_the_Power_of_Spatial_and_Temporal_Information_in_Medical_Multimodal_Pre-training
Vision Transformers provably learn spatial structurehttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_11_25_Vision_Transformers_provably_learn_spatial_structure
Null-text Inversion for Editing Real Images using Guided Diffusion Modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_12_02_Null_text_optimization_for_editing_real_images
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Traininghttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_12_09_Diffusion_Based_Scene_Graph_to_Image_Generation_with_Masked_Contrastive_Pre_Training
Rethinking Visual Counterfactual Explanations Through Region Constrainthttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_12_16_rethinking_visual_counterfactual_explanations_through_region_constraint
Controlling The World by Sleight of Handhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2025/2025_01_13_controlling_the_world_by_sleight_of_hand
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#year-20232024
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#summer-semester
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#schedule-1
Introduction to counterfactual explanations trackhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_04_08_Intro_to_CEs
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attackshttps://github.com/HubertR21/MI2DataLab_Seminarium/tree/master/2024/2024_04_15_explanation_linkage_attacks
Text-to-Image Models for Counterfactual Explanations: A Black-Box Approachhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_04_22_TIME_Text-To-Image_For_Counterfactual_Explanations
GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanationshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_05_06_GLOBE-CE
Introduction to ViT and transformer attributionshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_05_13_Introduction_to_Visual_Transformers_and_Transformer_Attributions/
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_06_03_AtMan
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnethttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2024/2024_06_03_Scaling_Monosemanticity
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#winter-semester
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#schedule-2
Organizational mattershttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_10_09_Organizational_matters_winter_edition
On Minimizing the Impact of Dataset Shifts on Actionable Explanationshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2023/2023_10_16_impact_of_dataset_shifts_on_actionable_eplanations.txt
On the Robustness of Removal-Based Feature Attributionshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_10_23_removal_based_attributions_robustness
Representation Engineeringhttps://github.com/maciejchrabaszcz/MI2DataLab_Seminarium/tree/master/2023/2023_11_06_Representation_Engineering
Adaptive Testing of Computer Vision Modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_11_13_Adaptive_Testing_of_Computer_Vision_Models
Red Teaming Language Models with Language Modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_11_20_Red_Teaming_Language_Models_with_Language_Models
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_11_27_Red_Teaming_Language_Models_to_Reduce_Harms
Introduction to Diffusion Modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_12_11_intro_to_diffusion_models
Glaze: Protecting artists from style mimicry by text-to-image modelhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_12_18_glaze_protecting_artists_from_style_mimicry
FLIRT: Feedback Loop In-context Red Teaminghttps://github.com/HubertR21/MI2DataLab_Seminarium/tree/patch-2/2024/2024_01_08_FLIRT_Feedback_Loop_In-context_Red_Teaming
Red-Teaming the Stable Diffusion Safety Filterhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2024_01_15_red_teaming_stable_diffusion_safety_filter
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#year-20222023
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#spring-semester-2
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#schedule-3
Acquisition of chess knowledge in AlphaZerohttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_02_27_Acquisition_of_chess_knowledge_in_AlphaZero
Aggregation of explanationshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_03_06_Ensemble_XAI
Ensembles of models - how to get the most benefitshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_03_13_ensemble_selection
Unfooling Perturbation-Based Post Hoc Explainershttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_03_20_unfooling
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_03_27_globally_consistent
From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_04_03_from_where_to_what
Shapley values and functional decompositionshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_04_24_shap_functional_decomposition
Scalable Gaussian process-based transfer surrogates for hyperparameter optimizationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_05_08_Scalable_Gaussian_process-based_transfer_surrogates_for_hyperparameter_optimization
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizershttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_05_15_Automated_Benchmark_Driven_Design_and_Explanation_of_Hyperparameter_Optimizers
Adaptive Testing and Debugging of NLP modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_05_29_Adaptive_Testing_And_Debugging_Of_NLP_Models
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#fall-semester-2
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#overview-phase
Organisation of the seminarhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_10_03_Winter_2022_Seminar_Org/2022_10_03_Winter_2022_Seminar_Org.pdf
xLungs - Efficient and Explainable Diagnostics’ Support for Radiologistshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_10_10_Overview_xLungs/xLungs%20-%20overview%20presentation.pdf
HOMER - Achieving Responsible ML by Human Oriented autoMated machinE leaRninghttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_10_17_Overview_HOMER/HOMER-overview-presentation.pdf
ATLAS - Automated documenT anaLysis for sociAl awareneSshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_10_24_ATLAS_Overview/ATLAS_Overview.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#research-phase
Explainable machine learning for survival analysishttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_11_07_survsSHAP_survex
Lungs vector representationshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_11_14_xLungs_vector_representations
ATLAS: Explaining abstractive summarizationhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_11_21_TLDR
How to organize our work - discussionhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_11_28_MI2_Discussion
New era of AutoML?https://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_12_12_HOMER_AutoML
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#update-phase
HOMER updatehttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_01_02_HOMER_update
ATLAS updatehttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2023/2023_01_09_ATLAS_update
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#year-20212022
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#spring-semester-3
Industrial PhD (lesson learned)https://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_05_30_Industrial_PhD_lesson_learnd/Industrial_PhD_lesson_learned.pdf
Hyperparameter optimization with Reinforcement Learninghttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_05_23_Hyperparameter_optimization_with_reinforcement_learning/Hyp_RL.pdf
Monitoring of AI regulations using natural language processinghttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_05_16_MAIR/MAIR.pdf
Extensions of the SHAP method: using Shapley value to interpret models with dependencies and survival analysis modelshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_05_09_SHAP_Extensions_survSHAP/SHAP_extensions_survSHAP.pdf
Comparative analysis of veins and arteries used in coronary artery bypass grafting. PhD Thesishttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_04_25_Comparative_analysis_of_veins_and_arteries_used_in_coronary_artery_bypass_grafting/PhD%20defense.pdf
Running services and calculations in our Kubernetes clusterhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/tree/master/2022/2022_03_28_kubernetes_and_akira
Hybrid model of Fuzzy Cognitive Map - Decision Tree in making medical decisionshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_03_21_Fuzzy_cognitive_maps_%26_tree_based_models_in_making_medical_decisions/Fuzzy_Map%20%26%20%20DT%20-%20Thien.pdf
Duel-based Deep Learning system for solving IQ testshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_03_14_Duel_based_DL_system_for_IQ_tests/Duel-IQ.pdf
Expected goal (xG) models and XAIhttps://github.com/mcavs/MI2DataLab_Seminarium/blob/master/2022/2022_03_07_Expected_goal_models_and_XAI/Expected_goal_models_and_XAI.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#fall-semester-3
Models selecting prototypes with ProtoPNethttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_01_24_Models_selecting_prototypes_with_ProtoPNet/Models_selecting_prototypes_with_ProtoPNet.pdf
Bayesian Optimization with a Prior for the Optimumhttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_01_17_Bayesian_Optimization_with_a_Prior_for_the_Optimum/Bayesian_Optimization.pdf
Evaluating explanationshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_01_10_evaluating_explanations/evaluating-explanations.pdf
Explanations of multimodal neural networkshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2022/2022_01_03_Explanations_of_multimodal_neural_networks/multimodal.pdf
NeurIPS2021 – takeawayshttps://github.com/MI2DataLab/MI2DataLab_Seminarium/blob/master/2021/2021_12_20_NeurIPS2021_takeaways/NeurIPS2021_takeaways.pdf
Auto-sklearn:Automating Design Decisions in AutoMLhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_12_14_AutoML_Autosklearn_2/Autosklearn_2.pdf
Malaria pipelineshttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_12_06_Malaria_Pipeline/Malaria_Pipeline.pdf
Auto-sklearn: Portfolio Successive Halving in PoSHhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_11_29_AutoML_PoSH/AutoML3.pdf
MONAI framework for deep learning in healthcare imaginghttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_11_22_monai/MONAI.pdf
Auto-sklearn: problem statement + challenge descriptionhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_10_25_AutoML_intro/prezentacja_autoML_intro.pdf
How to increase the visibility of a statistician in the modern world of collaborative research?https://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_10_18_increase_the_visibility/increase_the_visibility.pdf
Multivariate data vis, Linear projections, and their application to local explanations of modelshttps://nspyrison.github.io/mi2_talk/#1
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#rok-20202021
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#semestr-letni
Conference Bookhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/MI2SummerCONF.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#mi2-summer-conf-august-2-2021---conference-book
SUCCESSful data visualization using International Business Communication Standardhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/successful/SUCCESSful.pdf
triplot4python: the remedy for dealing with correlated features in explanationshttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/triplot4python/triplot.pdf
The medLIME algorithmhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/medLIME
Multitasking and transfer learning capabilities in Deep Learninghttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/multitasking
Guide through jungle of models! forester: An R package to automatically select between tree-based modelshttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/forester/forester.pdf
FairPANs - bringing fairness to neural networkshttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/fairPAN/FairPAN.pdf
metaMIMIC: an analysis of hyperparameter transfer possibilities for tabular data using MIMIC-IV databasehttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/metaMIMIC/metaMIMIC.pdf
Monitoring of AI regulationhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/MI2SummerCONF/AIregulation/ai.pdf
Sickest-first policy & Predictive Models for Liver Transplant Candidates in the US - Hoang Thien Lyhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_08_26_Sickest-first_policy_%26_Predictive_Models_for_Liver_Transplant_Candidates_in_the_US/Sickest-first_policy_%26_Predictive_Models_for_Transplant_Candidates_in_the_US.pdf
NeurIPS 2021 Datasets and Benchmarks Track - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_08_12-NeurIPS_2021_Datasets_and_Benchmarks_Track/Datasets_and_Benchmarks_Track.pdf
The Lean Startup - Maria Kałuskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_08_12_the_lean_startup/TheLeanStartup.pdf
A brief introduction to object detection - Jakub Wiśniewskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_08_05_intro_to_object_detection/A%20brief%20introduction%20to%20object%20detection.pdf
The use of denoising autoencoders for categorical and continuous variables - Anna Kozakhttps://insurancedatascience.org/downloads/London2021/Session_3b/Anna_Kozak.pdf
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions - Mateusz Krzyzińskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_29_Shapley_Flow/2021_07_29_Shapley_Flow.pdf
Hackathons - Zuzanna Trafashttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_29_hackathons/hackathons.pdf
DVC (Data Version Control) - Stanisław Gizińskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_22_DVC/2021_07_22_DVC.pdf
Metaheuristic optimization - Mateusz Grzybhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_22_metaheuristics/metaheurystyki_optymalizacyjne.pdf
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks - Hubert Banieckihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_15_iAlgebra/2021_07_15_iAlgebra.pdf
CVPR - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_07_15_CVPR/2021_07_15_CVPR.pdf
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department - Farah Shamouthttps://www.nature.com/articles/s41746-021-00453-0
What Will You Tell Me About the Chart? - Recognition and Description Process - Karolina Seweryn, Katarzyna Lorenchttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_06_07_methods_for_detecting/2021_06_07_methods_for_detecting.pdf
Robustness in Computer Vision - Sebastian Cygerthttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_05_31_robustness_in_computer_vision/2021_05_31_robustness_in_computer_vision.pdf
Protagonists’ Catcher in Novels - A Dataset and A Tool - Weronika Łajewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_05_24_protagonists_catcher_in_novels/2021_05_24_protagonists_catcher_in_novels.pdf
Cross-validation: what does it estimate and how well does it do it? - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_05_19_cv/2021_05_19_cv.pdf
The deriving of a computational approach test for testing equality of several populations’ means in the presence of nuisance parameter - Mustafa Cauvushttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_05_17_mcavus/2021_05_17_mcavus.pdf
Monitor wariantów SARS-CoV-2 - Hubert Baniecki, Piotr Piątyszek, Hanna Zdulskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_05_10_monitor/2021_05_10_monitor.pdf
Towards Explainable AutoML: xAutoML - Marius Lindauerhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_04_26_xautoml/2021_04_26_xautoml.pdf
Generative Adversarial Network in Medical Imaging - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_04_12_GAN/2021_04_12_GAN.pdf
Marginal Attribution by Conditioning on Quantiles - Anna Kozakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_03_29_MACQ/2021_03_29_MACQ.pdf
Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision - Piotr Czarneckihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_03_22_beyond_fairness/2021_03_22_beyond_fairness.pdf
MIMIC III - Medical Information Mart for Intensive Care - Katarzyna Kobylińskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_03_08_mimic/2021_03_08_mimic.pdf
Cleora - Barbara Rychalskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_03_01_cleora/2021_03_01_cleora.pdf
Gonitohttps://gonito.net/presentation/psnc-2019#/title
& Information extraction challenges and their evaluation - Filip Gralińskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_02_22_Information_extraction_challenges_and_their_evaluation/2021_02_22_Information_extraction_challenges_and_their_evaluation.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#semestr-zimowy
Transfer Learning of pre-trained CNN representations - Michał Sokólskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_01_18_transfer_learning_of_pre-trained_cnn_representations/2021_01_18_transfer_learning_of_pre-trained_cnn_representations.pdf
Adversarial attacks on Explainable AI methods - Hubert Baniecki, Wojciech Kretowiczhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2021/2021_01_11_adversarial_attacks_on_explainable_ai_methods/2021_01_11_adversarial_attacks_on_explainable_ai_methods.pdf
Towards explainable meta-learning - Kasia Woźnicahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_12_14_Towards_explainable_meta-learning/2020_12_14_Towards_explainable_meta-learning.pdf
XAI for image based COVID-19 detection - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_11_30_XAI_for_image_based_COVID_19_detection__works/2020_11_30_XAI_for_image_based_COVID_19_detection__works.pdf
Looking to listen: Audio separation and localization with visual clues - Piotr Czarneckihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_11_23_Looking_to_listen_Audio_separation_and_localization_with_visual_clues/2020_11_23_Looking_to_listen_Audio_separation_and_localization_with_visual_clues.pdf
Interpretable Machine Learning with application to Credit Scoring - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_11_16_Interpretable_Machine_Learning_with_application_to_Credit_Scoring/2020_11_16_Interpretable_Machine_Learning_with_application_to_Credit_Scoring.pdf
XAI in medicine - Katarzyna Kobylińskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_11_09_xai_in_medicine/2020_11_09_xai_in_medicine.pdf
Graph-based Recommendation Systems - Barbara Rychalskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_11_02_Graph-based_Recommendation_Systems/2020_11_02_Graph-based_Recommendation_Systems.pdf
2020 NLP trendshttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_10_26_NLP/2020_10_26_nlp_trend_2020.pdf
& UniCase - Rethinking Casing in Language Models - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_10_26_NLP/2020_10_26_UniCase.pdf
Arena - the universal dashboard for models exploration & Telemetry - API for collecting anonymized data from DrWhy.AI tools - Piotr Piątyszekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_10_19_telemetry_arena/2020_10_19_telemetry_arena.pdf
fairmodels - flexible tool for bias detection, visualization, and mitigation - Jakub Wiśniewskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_10_19_fairmodels_blog/2020_10_19_fairmodels_blog.pdf
Autumn update - Hubert Banieckihttps://sway.office.com/zfC1GiJUk9mDc0f1?ref=Link
Landscape of R packages for eXplainable Artificial Intelligence - Szymon Maksymiukhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_10_12_Landscape_of_R_packages_for_eXplainable_Artificial_Intelligence/2020_10_12_Landscape_of_R_packages_for_eXplainable_Artificial_Intelligence.pdf
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#rok-20192020
https://patch-diff.githubusercontent.com/MI2DataLab/MI2DataLab_Seminarium#semestr-letni-1
Semantyczne modelowanie danych doświadczalnych i jego zastosowanie w analizie wyników fenotypowania roślin - Hanna Ćwiek-Kupczyńskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_09_11_semantyczne_modelowanie_danych_doswiadczalnych/2020_09_11_semantyczne_modelowanie_danych_doswiadczalnych.pdf
Review of chest imaging findings in COVID-19 - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_08_03_review_chest_covid19/2020_08_03_review_chest_covid19.pdf
Introduction to fairness - basic concepts and implementation of fairmodels package - Jakub Wiśniewskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_07_28_fairmodels/2020_07_28_fairmodels.pdf
XAI for modelling of covid-19 mortality for the Polish population - Przemysław Biecekhttps://github.com/MOCOS-COVID19/mortality
Detecting and overcoming bias in data with explainable artificial intelligence - Agnieszka Mikołajczykhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_06_01_detecting_and_overcoming_bias_in_data/2020_06_01_detecting_and_overcoming_bias_in_data.pdf
Graph Convolutional Networks - Piotr Czarneckihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_25_graph_convolutional_networks/2020_05_25_graph_convolutional_networks.pdf
Hyperparameter importance - Kasia Woźnicahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_18_hyperparameter_importance/2020_05_18_hyperparameter_importance.pdf
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior - Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_18_Evaluating_Explainable_AI/2020_05_18_Evaluating_Explainable_AI.pdf
Benchmart dla NLP ERASER - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_11_ERASER/2020_05_11_ERASER.pdf
Weight Poisoning Attacks on Pre-trained Models - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_11_attacks/2020_05_11_attacks.pdf
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_05_04_prostate_biopsies/2020_05_04_prostate_biopsies.pdf
Learning Disentangled Representations for Recommendation - Barbara Rychalskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_04_27_macridvae/2020_04_27_macridvae.pdf
AI dla raka płuc - Katarzyna Kobylińskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_04_27_AI_dla_raka_pluc/2020_04_27_AI_dla_raka_pluc.pdf
Visualizing and Measuring the Geometry of BERT - Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_04_06_BERT_geometry/2020_04_06_BERT_geometry.pdf
The Mythos of Model Interpretability - Kasia Woźnica, Anna Kozakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_03_30_XAI/2020_03_30_XAI.pdf
AI Explainability 360 by IBM - Michał Kuźbahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_03_23_AIX360/2020_03_23_AIX360.pdf
NLP: Current trends in language modelling and transfer learning - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_03_09_NLP/2020_03_09_NLP.pdf
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering (SAFE) - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_03_02_SAFE/2020_03_02_SAFE.pdf
Wyjaśnienia sieci ARA - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_02_24_ARA_XAI/2020_02_24_ARA_XAI.pdf
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flashlight czyli prześwietlanie czarnych skrzynek - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_01_27_flashlight/2020_01_27_flashlight.pdf
Wyjaśnialny ML w naukach medycznych - Kasia Kobylińskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_01_27_ML_nauki_medyczne/2020_01_27_ML_nauki_medyczne.pdf
The World of Decisions - Stanisław Łaniewskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2020/2020_01_20_The_World_of_Decision/2020_01_20_The_World_of_Decision.pdf
ACL, ICONIP - Barbara Rychalska, Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2020/2020_01_20_ACL_ICONIP/2020_01_20_ACL_ICONIP.pdf
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning - Łukasz Rączkowskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2020/2020_01_13_ARA/2020_01_13_ARA.pdf
Multiple Defaults - Kasia Woźnicahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_12_02_multiple_defaults/2019_12_02_multiple_defaults.pdf
Internships - Michał Kuźbahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_12_02_internships/2019_12_02_internships.pdf
Neural Machine Translation: achievements, challenges and the way forward - Barbara Rychalskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_11_25_Neural_Machine_Translation/2019_11_25_Neural_Machine_Translation.pdf
CONLL & EMNLP - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_11_18_conll_and_emnlp/2019_11_18_conll_and_emnlp.pdf
Inpainting - Weronika Hryniewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_10_28_inpainting/2019_10_28_Inpainting.pdf
Analiza dźwięku - Piotr Czarneckihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_10_21_analiza%20dzwieku/2019_10_21_analiza_dzwieku.pdf
Elo - Kasia Woźnicahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_10_07_elo/2019_10_07_elo.pdf
Funnel plot - Szymon Maksymiukhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2019/2019_10_07_funnel_plot/2019_10_07_funnel_plot.pdf
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Mikrosegmentacja - Adam Dobrakowskihttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_05_14_mining/AdamDobrakowski_StructureMining.pdf
Tunability: Importance of Hyperparameters of Machine Learning Algorithms - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_04_15_tuning_hyperparameters/tunability.pdf
Feast - Przemysław Biecekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_04_08_feast/feast.pdf
Named Entity Recognition - is there a glass ceiling? - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_04_01_ner/NER.pdf
ExplainPrediction & sales example - Mateusz Staniakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_03_18_b2b/prezentacja_marzec.pdf
TFML - Michał Kuźbahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_03_11_tfml/TFML_19-2.pdf
Explaining Classifications for Individual Instances - Katarzyna Kobylińskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_03_04_explainprediction/Prezentacja.pdf
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Przegląd prac z konferencji FATML - Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_01_28_fatml/fat_ml_basaj.pdf
Wizualna eksploracja modeli - Agnieszka Ciepielewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_01_21_modelvis/Wizualna_eksploracja_modeli.pdf
NA - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2019/2019_01_14_na/prezentacja_braki_danych_seminariumMI2.pdf
Wrażenia z Krakowa - Alicja Gosiewska, Mateusz Staniakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2018/2018_12_03_krakow
EMNLP Conference Highlights - Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_11_26_emnlp/EMNLP_reading_club.pdf
Atencja - Tomasz Stanisławekhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_11_19_atencja/TS_Atencja_19.11.2018.pdf
Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_11_06_survxai/survxai.pdf
Klasyfikacja tekstów z użyciem Transfer Learning i Active Learning - Michał Kuźbahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_11_05_active/active.pdf
Mythos of Interpretability - Mateusz Staniakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_10_29_interpretability/Interpretowalnosc.pdf
The Family of Relief algorithms - Alicja Gosiewskahttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_10_22_RReliefF/RReliefF.pdf
Concept drift & IML - Mateusz Staniakhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_10_15_Drift/Concept_drift___IML.pdf
Interpretability & Security in NLP models - Dominika Basajhttps://github.com/mi2-warsaw/MI2DataLab_Seminarium/blob/master/2018/2018_10_08_QA/seminarum_08_10.pdf
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