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(Spotlight)",{"topic":211,"people":212,"link":214,"kind":58,"year":73,"info":215},"Honorable mention in QAIF competition for best master's thesis in AI",[213],"\u003Cb>Piotr Borycki\u003C\u002Fb>","https:\u002F\u002Fwww.qaif.org\u002Fcontests\u002Fkonkurs-na-najlepsze-prace-dyplomowe-w-obszarze-ai","QAIF",{"topic":217,"people":218,"link":223,"kind":72,"year":73,"info":224},"SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense",[219,220,221,222,70],"\u003Cb>Patryk Krukowski\u003C\u002Fb>","\u003Cb>Łukasz Gorczyca\u003C\u002Fb>","\u003Cb>Piotr Helm\u003C\u002Fb>","\u003Cb>Kamil Książek\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.08255","CVPR (Findings)",{"topic":226,"people":227,"link":235,"kind":72,"year":73,"info":236},"LLM-as-a-judge is bad, based on AI attempting the exam qualifying for the member of the Polish National Board of Appeal",[228,229,230,231,232,233,234],"Michał Karp","Anna Kubaszewska","Magdalena Król","Robert Król","\u003Cb>Witold Wydmański\u003C\u002Fb>","Aleksander Smywiński-Pohl","Mateusz Szymański","https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.04205","Artificial Intelligence and Law",{"topic":238,"people":239,"link":243,"kind":72,"year":73,"info":244},"FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification",[222,240,241,242,228,135],"\u003Cb>Hubert Jastrzębski\u003C\u002Fb>","Krzysztof Pniaczek","\u003Cb>Bartosz Trojan\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.14301","Knowledge-Based Systems",{"topic":246,"people":247,"link":253,"kind":72,"year":73,"info":254},"Universal Properties of Activation Sparsity in Modern Large Language Models",[177,248,249,179,250,251,252,178],"Patryk Będkowski","Alessio Devoto","Pasquale Minervini","\u003Cb>Mikołaj Piórczyński\u003C\u002Fb>","Simone Scardapane","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.00454","ICLR",{"topic":256,"people":257,"link":263,"kind":72,"year":73,"info":264},"HyConEx: Hypernetwork classifier with counterfactual explanations",[258,259,260,222,261,262],"\u003Cb>Patryk Marszalek\u003C\u002Fb>","\u003Cb>Ulvi Movsum-zada\u003C\u002Fb>","\u003Cb>Oleksii Furman\u003C\u002Fb>","\u003Cb>Przemyslaw Spurek\u003C\u002Fb>","\u003Cb>Marek Śmieja\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.02382","Neurocomputing",{"topic":266,"people":267,"link":263,"kind":72,"year":59,"info":268},"Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-Ray: Summary of the PENGWIN 2024 Challenge",[93],"IEEE Transactions on Medical Imaging",{"topic":270,"people":271,"link":275,"kind":72,"year":59,"info":276},"VeGaS: Video Gaussian splatting",[272,132,273,274,134,70],"\u003Cb>Weronika Smolak-Dyżewska\u003C\u002Fb>","\u003Cb>Kornel Howil\u003C\u002Fb>","\u003Cb>Jan Kaczmarczyk\u003C\u002Fb>","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0020025525011703","Information Sciences",{"topic":278,"people":279,"link":281,"kind":151,"year":59,"info":152},"Analyzing Deep Neural Networks from a Graph Perspective",[280],"\u003Cb>Aleksandra Nowak\u003C\u002Fb>","https:\u002F\u002Frozprawy-doktorskie.bip.uj.edu.pl\u002Fnauki-inzynieryjno-techniczne\u002F-\u002Fjournal_content\u002F56_INSTANCE_ddQveR9Wo24Y\u002F143381600\u002F159654608",{"topic":283,"people":284,"link":286,"kind":151,"year":59,"info":152},"Deep learning under distribution shifts: continual learning and beyond",[285],"\u003Cb>Michał Zając\u003C\u002Fb>","https:\u002F\u002Frozprawy-doktorskie.bip.uj.edu.pl\u002Fnauki-inzynieryjno-techniczne\u002F-\u002Fjournal_content\u002F56_INSTANCE_ddQveR9Wo24Y\u002F143381600\u002F159633739",{"topic":288,"people":289,"link":291,"kind":163,"year":59,"info":292},"Boosting Neural Network Efficiency by Examining Early Training Patterns",[290],"\u003Cb>Mateusz Pyla\u003C\u002Fb>","https:\u002F\u002Fncn.gov.pl\u002Faktualnosci\u002F2025-11-28-wyniki-opus29-preludium24","PRELUDIUM 24",{"topic":294,"people":295,"link":291,"kind":163,"year":59,"info":292},"Democratizing Diffusion Models: Guidance-Driven Optimization of Existing Models for Enhanced Quality and Diversity with Compliance-Preserving Unlearning",[133],{"topic":297,"people":298,"link":291,"kind":163,"year":59,"info":292},"Explanation of Pre-Trained deep learning models via Prototypes",[213],{"topic":300,"people":301,"link":291,"kind":163,"year":59,"info":292},"Editing of a 3D object represented by Gaussian Splatting",[79],{"topic":303,"people":304,"link":291,"kind":163,"year":59,"info":305},"Multimodal learning for medical image analysis",[93],"OPUS 29",{"topic":307,"people":308,"link":309,"kind":72,"year":59,"info":310},"Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge",[93],"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.media.2025.103883","Medical Image Analysis",{"topic":312,"people":313,"link":317,"kind":72,"year":59,"info":244},"Hypernetwork Approach to Rapid NeRF Adaptation",[314,132,315,134,316,261],"\u003Cb>Paweł Batorski\u003C\u002Fb>","\u003Cb>Marcin Przewięźlikowski\u003C\u002Fb>","\u003Cb>Sławomir Tadeja\u003C\u002Fb>","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.knosys.2025.114861",{"topic":319,"people":320,"link":324,"kind":72,"year":59,"info":325},"Enhancing Chemical Explainability Through Counterfactual Masking",[321,322,117,323],"\u003Cb>Łukasz Janisiów\u003C\u002Fb>","Marek Kochańczyk","\u003Cb>Tomasz Danel\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2508.18561","AAAI",{"topic":327,"people":328,"link":332,"kind":72,"year":59,"info":325},"EPIC: Explanation of Pretrained Image Classification Networks via Prototypes",[213,329,330,135,261,331,169],"\u003Cb>Magda Trędowicz\u003C\u002Fb>","\u003Cb>Szymon Janusz\u003C\u002Fb>","Arkadiusz Lewicki","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2505.12897",{"topic":334,"people":335,"link":337,"kind":151,"year":59,"info":338},"New properties of optimization of artificial neural networks",[336],"\u003Cb>Stanisław Jastrzębski\u003C\u002Fb>","https:\u002F\u002Fhabilitacje.bip.uj.edu.pl\u002Fstart?p_p_id=56_INSTANCE_xI82GrjZ1BqU&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-3&p_p_col_count=1&groupId=143381317&articleId=158324404&&version=1.3","Habilitation",{"topic":340,"people":341,"link":342,"kind":151,"year":59,"info":152},"Adapting Deep Learning Architectures for Drug Discovery",[56],"https:\u002F\u002Frozprawy-doktorskie.bip.uj.edu.pl\u002Fstart\u002F-\u002Fjournal_content\u002F56_INSTANCE_tAB5GL1X6sZA\u002F143381600\u002F159417565",{"topic":344,"people":345,"link":347,"kind":348,"year":59,"info":349},"A method and a system for identifying polyculture bacteria on microscopic images using deep learning",[117,118,119,120,123,121,346],"Barbara Brzychczy","https:\u002F\u002Fregister.epo.org\u002Fapplication?number=EP22461550","patent","European Patent Office",{"topic":351,"people":352,"link":353,"kind":58,"year":59,"info":354},"Witold Lipski Award for Young Computer Scientists (for the achievements in applied computer science)",[149],"https:\u002F\u002Fnagrodalipskiego.ideas.org.pl\u002F","Witold Lipski Award",{"topic":356,"people":357,"link":363,"kind":72,"year":59,"info":103},"GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation",[358,93,359,360,361,362,180,97],"Tomasz Szczepański","Michal K Grzeszczyk","Arleta Adamowicz","Piotr Fudalej","\u003Cb>Przemysław Korzeniowski\u003C\u002Fb>","https:\u002F\u002Ftomek1911.github.io\u002FGEPAR3D\u002F",{"topic":365,"people":366,"link":369,"kind":72,"year":59,"info":370},"Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation",[93,367,176,368,97],"Gizem Mert","Ewa Szczurek","https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.06363","NeurIPS",{"topic":372,"people":373,"link":380,"kind":72,"year":59,"info":370},"URB - Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles",[374,375,376,220,377,156,378,379],"\u003Cb>Ahmet Onur Akman\u003C\u002Fb>","\u003Cb>Anastasia Psarou\u003C\u002Fb>","\u003Cb>Michał Hoffmann\u003C\u002Fb>","Lukasz Kowalski","\u003Cb>Grzegorz Jamróz\u003C\u002Fb>","\u003Cb>Rafal Kucharski\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.17734",{"topic":382,"people":383,"link":388,"kind":72,"year":59,"info":370},"FlySearch: Exploring how vision-language models explore",[116,384,385,386,117,387],"\u003Cb>Dominik Matuszek\u003C\u002Fb>","\u003Cb>Mateusz Przebieracz\u003C\u002Fb>","\u003Cb>Marek Cygan\u003C\u002Fb>","\u003Cb>Maciej Wołczyk\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.02896",{"topic":390,"people":391,"link":393,"kind":72,"year":59,"info":370},"ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data",[392,143,232,135,262],"\u003Cb>Patryk Marszałek\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.10704",{"topic":395,"people":396,"link":397,"kind":72,"year":59,"info":370},"DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control",[260,259,392,69,262],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.23700",{"topic":399,"people":400,"link":409,"kind":72,"year":59,"info":370},"Scalable and Cost-Efficient de Novo Template-Based Molecular Generation",[401,402,403,404,405,406,407,408],"\u003Cb>Piotr Gaiński\u003C\u002Fb>","Oussama Boussif","Andrei Rekesh","Dmytro Shevchuk","Ali Parviz","Mike Tyers","Robert A. Batey","\u003Cb>Michał Koziarski\u003C\u002Fb>","https:\u002F\u002Fwww.arxiv.org\u002Fabs\u002F2506.19865",{"topic":411,"people":412,"link":414,"kind":72,"year":59,"info":370},"CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting",[273,79,213,413,134,261],"\u003Cb>Tadeusz Dziarmaga\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2505.22854",{"topic":416,"people":417,"link":427,"kind":72,"year":59,"info":428},"Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals",[222,418,419,420,421,422,423,424,425,426],"Wilhelm Masarczyk","Przemysław Głomb","Michał Romaszewski","Krisztián Buza","Przemysław Sekuła","Michał Cholewa","Katarzyna Kołodziej","Piotr Gorczyca","Magdalena Piegza","https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1012983","PLOS Computational Biology",{"topic":430,"people":431,"link":433,"kind":72,"year":59,"info":434},"Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA",[392,432,135,143],"\u003Cb>Klaudia Bałazy\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.12122","EMNLP",{"topic":436,"people":437,"link":444,"kind":72,"year":59,"info":445},"Large protein databases reveal structural complementarity and functional locality",[438,439,440,441,442,443],"Paweł Szczerbiak*","Lukasz M. Szydlowski*","\u003Cb>Witold Wydmański*\u003C\u002Fb>","P. Douglas Renfrew","Julia Koehler Leman","Tomasz Kosciolek","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-63250-3","Nature Communications",{"topic":447,"people":448,"link":452,"kind":72,"year":59,"info":453},"RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles",[375,220,449,450,451],"\u003Cb>Zoltán György Varga\u003C\u002Fb>","\u003Cb>Grzegorz Jamroz\u003C\u002Fb>","\u003Cb>Rafał Kucharski\u003C\u002Fb>","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.softx.2025.102279","SoftwareX",{"topic":455,"people":456,"link":460,"kind":72,"year":59,"info":461},"HyperNeRFGAN: Camera-Free 3D Scene Generation via Hypernetwork-Driven Neural Radiance Fields",[457,133,458,459,134,69,261],"\u003Cb>Adam Kania\u003C\u002Fb>","\u003Cb>Jakub Kościukiewicz\u003C\u002Fb>","Artur Górak","https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.11631","CIKM",{"topic":463,"people":464,"link":468,"kind":72,"year":59,"info":469},"As Good as It KAN Get: High-Fidelity Audio Representation",[392,465,466,261,467],"\u003Cb>Maciej Rut\u003C\u002Fb>","\u003Cb>Piotr Kawa\u003C\u002Fb>","\u003Cb>Piotr Syga\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2503.02585v2","DSAA",{"topic":471,"people":472,"link":473,"kind":72,"year":59,"info":474},"PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification",[329,169,134,330,331,135],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11443","ECAI",{"topic":476,"people":477,"link":483,"kind":72,"year":59,"info":474},"One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression",[478,479,480,481,482],"\u003Cb>Mikołaj Janusz\u003C\u002Fb>","\u003Cb>Tomasz Wojnar\u003C\u002Fb>","Yawei Li","Luca Benini","\u003Cb>Kamil Adamczewski\u003C\u002Fb>",null,{"topic":485,"people":486,"link":488,"kind":72,"year":59,"info":474},"Classifier-free Guidance with Adaptive Scaling",[132,133,487,135,261],"Maciej Zięba","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2502.10574",{"topic":490,"people":491,"link":483,"kind":151,"year":59,"info":152},"Deep Learning Techniques for Accelerating Microscopy Image Analysis",[118],{"topic":493,"people":494,"link":495,"kind":58,"year":59,"info":496},"Scholarship of the Minister of Science and Higher Education for Outstanding Young Scientists",[315],"https:\u002F\u002Fwww.gov.pl\u002Fweb\u002Fnauka\u002Fogloszenie-wynikow-postepowania-w-sprawie-przyznania-stypendiow-ministra-nauki-i-szkolnictwa-wyzszego-dla-wybitnych-mlodych-naukowcow-w-2025-r-edycja-20","MNISW",{"topic":498,"people":499,"link":503,"kind":72,"year":59,"info":504},"Beyond [cls]: Exploring the true potential of Masked Image Modeling representations",[500,501,502,262,117],"\u003Cb> Marcin Przewięźlikowski\u003C\u002Fb>","Randall Balestriero","Wojtek Jasiński","https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.03215","ICCV",{"topic":506,"people":507,"link":511,"kind":72,"year":59,"info":103},"PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy",[508,272,132,509,510,261],"Joanna Kaleta","Diego Dall'Alba","Przemyslaw Korzeniowski","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2411.12510",{"topic":513,"people":514,"link":515,"kind":72,"year":59,"info":516},"HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning",[222,261],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.00113","Neural Networks",{"topic":518,"people":519,"link":521,"kind":72,"year":59,"info":137},"Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations",[149,520,143],"Amin Sorkhei","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.15777",{"topic":523,"people":524,"link":530,"kind":72,"year":59,"info":172},"Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers",[177,525,526,527,528,529],"Yaoyue Zheng","Fei Yang","Tomasz Trzcinski","Bartłomiej Twardowski","Joost van de Weijer","https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07404",{"topic":532,"people":533,"link":535,"kind":72,"year":59,"info":172},"MiraGe: Editable 2D Images using Gaussian Splatting",[79,200,213,316,534,261],"Thomas Bohné","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.01521",{"topic":537,"people":538,"link":483,"kind":72,"year":59,"info":172},"How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies",[186,178,539,540,527,541,542],"Bartłomiej Krzepkowski","Monika Michaluk","Jary Pomponi","Aamil Adamczewski",{"topic":544,"people":545,"link":553,"kind":72,"year":59,"info":172},"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models",[546,547,548,549,149,550,551,552],"Siddarth Venkatraman","Mohsin Hasan","Minsu Kim","Luca Scimeca","Yoshua Bengio","Glen Berseth","Nikolay Malkin","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.06999",{"topic":555,"people":556,"link":558,"kind":72,"year":59,"info":172},"SEMU: Singular Value Decomposition for Efficient Machine Unlearning",[149,169,222,557,135,119],"Kryspin Musiol","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.07587",{"topic":560,"people":561,"link":565,"kind":72,"year":59,"info":276},"HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning",[219,562,222,563,564,261],"Anna Bielawska","Paweł Wawrzyński","Paweł Batorski","https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.15444",{"topic":567,"people":568,"link":569,"kind":163,"year":59,"info":570},"A virtual assistant for medical screening tests powered by artificial intelligence",[323],"https:\u002F\u002Fwww.gov.pl\u002Fweb\u002Fncbr\u002Flider-xv---wyniki-oceny-merytorycznej-wnioskow-zlozonych-w-konkursie","LIDER XV",{"topic":572,"people":573,"link":575,"kind":72,"year":59,"info":276},"NegGS: Negative Gaussian Splatting",[133,574,134,135,261],"\u003Cb>Bartosz Czekaj\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.18163",{"topic":577,"people":578,"link":582,"kind":72,"year":59,"info":254},"FreSh: Frequency Shifting for Accelerated Neural Representation Learning",[579,580,581,135,261],"Adam Kania","Marko Mihajlovic","Sergey Prokudin","https:\u002F\u002Fopenreview.net\u002Fforum?id=zMjjzXxS64",{"topic":584,"people":585,"link":590,"kind":72,"year":59,"info":254},"LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision",[586,587,135,588,589],"\u003Cb>Mateusz Pach\u003C\u002Fb>","\u003Cb>Koryna Lewandowska\u003C\u002Fb>","\u003Cb>Bartosz Michał Zieliński\u003C\u002Fb>","\u003Cb>Dawid Damian Rymarczyk\u003C\u002Fb>","https:\u002F\u002Fopenreview.net\u002Fforum?id=BM9qfolt6p&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2025%2FConference%2FAuthors%23your-submissions)",{"topic":592,"people":593,"link":483,"kind":72,"year":59,"info":595},"VisTabNet: Adapting Vision Transformers for Tabular Data",[232,594,135,262],"Ulvi Movsum-zada","SDM",{"topic":597,"people":598,"link":483,"kind":72,"year":59,"info":595},"Parameter-Efficient Interventions for Enhanced Model Merging",[599,600,117],"\u003Cb>Marcin Osial\u003C\u002Fb>","Daniel Marczak",{"topic":602,"people":603,"link":483,"kind":72,"year":59,"info":609},"Workshop on Computer Vision for Drug Discovery: Where Are We and What is Beyond?",[119,604,605,606,607,608],"\u003Cb>Ada Borowa\u003C\u002Fb>","Ilknur Icke","Chao-Hui Huang","Ana Sanchez-Fernandez","Anne Carpenter","CVPR",{"topic":611,"people":612,"link":614,"kind":72,"year":59,"info":325},"Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference",[178,249,613,250,252],"Karol Pustelnik","https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10193",{"topic":616,"people":617,"link":625,"kind":72,"year":59,"info":254},"Workshop on Sparsity in LLMs (SLLM): Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference",[618,619,620,621,622,623,280,624],"Tianlong Chen","Utku Evci","Yani Ioannou","Berivan Isik","Shiwei Liu","Mohammed Adnan","Ashwinee Panda","https:\u002F\u002Fopenreview.net\u002Fforum?id=AqpDRGnu82",{"topic":627,"people":628,"link":483,"kind":58,"year":629,"info":630},"Outstanding doctoral dissertation: Deep Learning Methods in Pharmaceutical Sciences",[323],2024,"Prime Minister's Prize in the category of outstanding doctoral dissertation.",{"topic":632,"people":633,"link":637,"kind":72,"year":59,"info":638},"Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers",[116,634,635,636,117],"Grzegorz Kurzejamski","Jan Olszewski","Tomasz Trzciński","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13353","WACV",{"topic":640,"people":641,"link":642,"kind":72,"year":629,"info":244},"Augmentation-aware Self-supervised Learning with Conditioned Projector",[315,290,117,528,135,262],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.06082",{"topic":644,"people":645,"link":648,"kind":72,"year":629,"info":370},"D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup",[79,646,508,647,70],"Piotr Borycki","Sławomir Tadeja","https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.14276",{"topic":650,"people":651,"link":654,"kind":72,"year":629,"info":370},"Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion",[652,178,653,252],"Filip Szatkowski","Mikołaj Piórczyński","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04361",{"topic":656,"people":657,"link":661,"kind":72,"year":629,"info":370},"RGFN: Synthesizable Molecular Generation Using GFlowNets",[658,403,404,659,401,550,660,406,407],"Michał Koziarski","Almer van der Sloot","Cheng-Hao Liu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.08506",{"topic":663,"people":664,"link":676,"kind":72,"year":629,"info":370},"Amortizing intractable inference in diffusion models for vision, language, and control",[665,666,667,668,669,547,670,671,672,673,674,675,550,551,552],"Siddarth Venkatraman*","Moksh Jain*","Luca Scimeca*","Minsu Kim*","\u003Cb>Marcin Sendera*\u003C\u002Fb>","Luke Rowe","Sarthak Mittal","Pablo Lemos","Emmanuel Bengio","Alexandre Adam","Jarrid Rector-Brooks","https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.20971",{"topic":678,"people":679,"link":680,"kind":72,"year":629,"info":370},"Improved off-policy training of diffusion samplers",[149,548,671,672,549,675,674,550,552],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.05098",{"topic":682,"people":683,"link":686,"kind":72,"year":59,"info":638},"Token Recycling for Efficient Sequential Inference with Vision Transformers",[635,119,684,685,117],"Piotr Wójcik","Mateusz Pach","https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.15335",{"topic":688,"people":689,"link":691,"kind":72,"year":59,"info":638},"GeoGuide: Geometric guidance of diffusion models",[690,135,70],"Mateusz Poleski","https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.12889",{"topic":693,"people":694,"link":696,"kind":72,"year":629,"info":264},"HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks",[315,695,135,69,70],"Przemysław Przybysz","https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.15745",{"topic":698,"people":699,"link":701,"kind":72,"year":629,"info":74},"AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale",[116,700,387,482,636,117],"Michał Wronka","https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.03482",{"topic":703,"people":704,"info":705,"kind":163,"link":706,"year":629},"Interpretowalne i interaktywne wielomodalne wyszukiwanie w procesie odkrywania leków.",[117],"FIRST TEAM FENG","https:\u002F\u002Fwww.fnp.org.pl",{"topic":708,"people":709,"info":705,"kind":163,"link":710,"year":629},"Efektywne renderowanie obiektów 3D reprezentowanych za pomocą NeRF w środowisku rozszerzonej rzeczywistości.",[261],"https:\u002F\u002Fwww.fnp.org.pl\u002F",{"topic":712,"people":713,"link":714,"kind":72,"year":629,"info":145},"A deep cut into Split Federated Self-Supervised Learning",[315,599,262,117],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.08267",{"topic":716,"people":717,"link":719,"kind":72,"year":629,"info":172},"Sparser, Better, Deeper, Stronger: Improving Sparse Training with Exact Orthogonal Initialization",[280,718,652,135],"Łukasz Gniecki","https:\u002F\u002Fopenreview.net\u002Fforum?id=3mY9aGiMn0",{"topic":721,"people":722,"link":725,"kind":72,"year":629,"info":254},"Prediction Error-based Classification for Class-Incremental Learning",[285,723,724],"Tinne Tuytelaars","Gido M. van de Ven","https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18806",{"topic":727,"people":728,"link":732,"kind":72,"year":629,"info":254},"Divide and not forget Ensemble of selectively trained experts in Continual Learning",[729,181,730,731,117,528],"Grzegorz Rypeść","Valeriya Khan","\u003Cb>Tomasz Trzcinski\u003C\u002Fb>","https:\u002F\u002Fopenreview.net\u002Fpdf?id=sSyytcewxe",{"topic":734,"people":735,"link":737,"kind":72,"year":629,"info":738},"Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation",[736,451],"\u003Cb>Farnoud Ghasemi\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.02496","AAMAS",{"topic":740,"people":741,"link":744,"kind":72,"year":629,"info":325},"Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations",[742,743,119,169,135,117],"\u003Cb>Mikołaj Sacha\u003C\u002Fb>","Bartosz Jura","https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.08162",{"topic":746,"people":747,"info":748,"kind":163,"link":749,"year":750},"Meta-learning in Deep Neural Networks ",[135],"OPUS 25","https:\u002F\u002Fncn.gov.pl\u002Faktualnosci\u002F2023-11-23-wyniki-opus22-preludium25",2023,{"topic":752,"people":753,"info":754,"kind":163,"link":749,"year":750},"Where to look next - guiding active visual exploration with internal model uncertainty",[116],"PRELUDIUM 22",{"topic":756,"people":757,"info":754,"kind":163,"link":749,"year":750},"Improving the transferability of self-supervised learning models",[315],{"topic":759,"people":760,"info":754,"kind":163,"link":749,"year":750},"Computationally efficient dynamic neural networks",[178],{"topic":762,"people":763,"link":765,"kind":151,"year":750,"info":152},"Deep generative models in image processing",[764],"\u003Cb>Szymon Knop\u003C\u002Fb>","https:\u002F\u002Fmatinf.uj.edu.pl\u002Fobrony-prac-doktorskich\u002F-\u002Fjournal_content\u002F56_INSTANCE_SaA7HRzna0dW\u002F41633\u002F154585657",{"topic":767,"people":768,"link":769,"kind":151,"year":750,"info":152},"Deep learning methods in pharmaceutical sciences",[323],"https:\u002F\u002Frozprawy-doktorskie.bip.uj.edu.pl\u002Fnauki-inzynieryjno-techniczne\u002F-\u002Fjournal_content\u002F56_INSTANCE_ddQveR9Wo24Y\u002F143381600\u002F154219491",{"topic":771,"people":772,"link":774,"kind":72,"year":750,"info":775},"Zero time waste in pre-trained early exit neural networks",[178,315,652,387,432,539,773,135,262,180],"\u003Cb>Igor Podolak\u003C\u002Fb>","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0893608023005555","Neural Networks, vol. 168",{"topic":777,"people":778,"info":370,"kind":72,"year":750,"link":781},"Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training",[280,779,780,135],"Bram Grooten","Decebal Constantin Mocanu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.12230",{"topic":783,"people":784,"info":370,"kind":72,"year":750,"link":791},"Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery",[785,285,280,786,787,788,789,790],"Mateusz Olko","Nino Scherrer","Yashas Annadani","Stefan Bauer","Łukasz Kuciński","Piotr Miłoś","https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.13715",{"topic":793,"kind":72,"people":794,"info":370,"year":750,"link":798},"Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders",[179,795,796,180,797],"Stanisław Pawlak","Franzisca Boenisch","Adam Dziedzic","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08571",{"topic":800,"people":801,"info":370,"kind":72,"link":806,"year":750},"The Tunnel Effect: Building Data Representations in Deep Neural Networks",[802,803,804,805,790,180],"Wojciech Masarczyk","Mateusz Ostaszewski","Ehsan Imani","Razvan Pascanu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19753",{"topic":808,"people":809,"info":504,"kind":72,"year":750,"link":823},"Document Understanding Dataset and Evaluation (DUDE)",[810,811,812,813,814,815,816,817,818,819,820,821,822],"Jordy Van Landeghem","Rubén Tito","Łukasz Borchmann","\u003Cb>Michał Pietruszka\u003C\u002Fb>","Paweł Józiak","Rafał Powalski","Dawid Jurkiewicz","Mickaël Coustaty","Bertrand Ackaert","Ernest Valveny","Matthew Blaschko","Sien Moens","Tomasz Stanisławek","https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.08455",{"topic":825,"people":826,"info":504,"kind":72,"year":750,"link":827},"ICICLE: Interpretable Class Incremental Continual Learning",[119,529,117,528],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.07811",{"topic":829,"people":830,"info":474,"kind":72,"year":750},"CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer",[116,119,831,832,833,834,835,836,837,838,839,117],"Joanna Jaworek-Korjakowska","Dariusz Kucharski","Andrzej Brodzicki","Julia Lasek","Zofia Schneider","Iwona Kucybała","Andrzej Urbanik","Rafał Obuchowicz","Zbisław Tabor",{"topic":841,"people":842,"info":474,"kind":72,"year":750,"link":844},"ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging",[169,119,331,843,135,117],"Robert Sabiniewicz","https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.10535",{"topic":846,"people":847,"info":849,"kind":72,"year":750,"link":850},"Hypernetworks build Implicit Neural Representations of Sounds",[652,848,261,135,731],"Karol Piczak","ECML","https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04959",{"topic":852,"people":853,"info":849,"kind":72,"year":750},"ChiENN: Embracing Molecular Chirality with Graph Neural Networks",[401,658,135,262],{"topic":855,"people":856,"info":849,"kind":72,"year":750,"link":860},"Contrastive Hierarchical Clustering",[857,858,859,135,262],"Michał Znaleźniak","Przemysław Rola","Patryk Kaszuba","https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.03389",{"topic":862,"people":863,"info":864,"kind":163,"year":750,"link":865},"Interpretowalne metody zrównoważonej sztucznej inteligencji tłumaczące decyzje w sposób intuicyjny",[117],"OPUS 24","https:\u002F\u002Fwww.ncn.gov.pl\u002Fsites\u002Fdefault\u002Ffiles\u002Flisty-rankingowe\u002F2022-09-15-opu8jisl\u002Fstreszczenia\u002F573201-pl.pdf",{"topic":867,"people":868,"info":869,"kind":72,"year":750,"link":870},"Active Visual Exploration Based on Attention-Map Entropy",[116,729,634,117,180],"IJCAI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06457",{"topic":872,"people":873,"info":874,"kind":163,"year":750,"link":875},"Sieci prototypowe jako krok do interpretowalnej analizy funkcji białek",[232],"Perły Nauki 2023","https:\u002F\u002Fwww.gov.pl\u002Fattachment\u002F1b3c2e9f-9ca9-4ee5-ae8c-e620aa7422bd",{"topic":877,"people":878,"info":883,"kind":72,"year":750,"link":884},"Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models",[879,432,880,881,135,882],"Mohammadreza Banaei","Artur Kasymov","Remi Lebret","Karl Aberer","EACL","https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04045",{"topic":886,"people":887,"info":883,"kind":72,"year":750,"link":888},"Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks",[401,432],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.05120",{"topic":890,"people":891,"info":595,"kind":72,"link":893,"year":750},"ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction",[119,892,323],"Daniel Dobrowolski","https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.03745",{"topic":895,"people":896,"info":897,"kind":163,"link":898,"year":899},"Conditional deep generative models",[262],"OPUS 23","https:\u002F\u002Fncn.gov.pl\u002Faktualnosci\u002F2022-12-06-wyniki-opus23-preludium21-polonezbis2",2022,{"topic":901,"people":902,"info":904,"kind":163,"link":898,"year":899},"Balancing priors in Bayesian Neural Networks",[903],"Tomasz Kuśmierczyk","POLONEZ BIS 2",{"topic":906,"people":907,"info":908,"kind":163,"link":898,"year":899},"Improving interpretability in deep neural networks",[119],"PRELUDIUM 21",{"topic":910,"people":911,"info":908,"kind":163,"link":898,"year":899},"Better adaptation in Meta-Learning",[149],{"topic":913,"people":914,"info":915,"kind":163,"link":916,"year":899},"COeXISTENCE between humans and machines in urban mobility",[451],"ERC Starting Grant","https:\u002F\u002Frafalkucharskipk.github.io\u002FCOeXISTENCE\u002F",{"topic":918,"people":919,"info":921,"kind":72,"year":899,"link":922},"Sparsifying Transformer Models with Trainable Representation Pooling",[813,812,920],"Łukasz Garncarek","ACL (Spotlight)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.05169",{"topic":924,"people":925,"info":638,"kind":72,"link":926,"year":750},"SONGs: Self-Organizing Neural Graphs",[169,323,262,135,117],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.13214",{"topic":928,"people":929,"info":638,"kind":72,"year":750},"ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts",[742,119,169,135,117],{"topic":931,"people":932,"info":638,"kind":72,"link":934,"year":750},"HyperShot: Few-Shot Learning by Kernel HyperNetworks",[149,315,933,487,135,70],"Konrad Karanowski","https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.11378",{"topic":936,"people":937,"info":145,"kind":72,"year":899,"link":939},"Discovering wiring patterns influencing neural network performance",[280,938],"Romuald Janik","https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_1358.pdf",{"topic":941,"people":942,"info":145,"kind":72,"year":899,"link":944},"On the relationship between disentanglement and multi-task learning",[56,280,387,943],"\u003Cb>Andrzej Bedychaj\u003C\u002Fb>","https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_1371.pdf",{"topic":946,"people":947,"info":949,"year":899,"kind":163,"link":950},"Multimodal and reinforcement learning platform for personalizing cancer management",[948],"Krzysztof Geras","ARTIQ - AI Centers of Excellence","https:\u002F\u002Fwww.gov.pl\u002Fweb\u002Fncbr\u002Fwyniki-oceny-merytorycznej-wnioskow-zlozonych-w-konkursie-artiq---centra-doskonalosci-ai",{"topic":952,"people":953,"info":370,"year":899,"kind":72},"Disentangling Transfer in Continual Reinforcement Learning",[387,285,805,789,790],{"topic":955,"people":956,"info":370,"year":899,"kind":72},"FlowHMM: Flow-based continuous hidden Markov models",[957,958,180,487],"Paweł Lorek","Rafał Nowak",{"topic":960,"people":961,"info":370,"year":899,"kind":72,"link":965},"On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models",[962,963,180,964],"Kamil Deja","Anna Kuzina","Jakub M. Tomczak","https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00070",{"topic":967,"people":968,"info":74,"year":899,"kind":72,"link":971},"Interpretable Image Classification with Differentiable Prototypes Assignment",[119,169,969,970,135,117],"\u003Cb>Michał Górszczak\u003C\u002Fb>","Koryna Lewandowska","https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.02902",{"topic":973,"people":974,"info":145,"year":899,"kind":72,"link":978},"ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification",[119,116,975,976,977,117],"\u003Cb>Jarosław Kraus\u003C\u002Fb>","\u003Cb>Aneta Kaczyńska\u003C\u002Fb>","\u003Cb>Marek Skomorowski\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.10612",{"topic":980,"people":981,"info":985,"year":899,"kind":72,"link":986},"LIDL: Local Intrinsic Dimension estimation using approximate Likelihood",[982,983,920,70,135,984],"Piotr Tempczyk","Rafał Michaluk","Adam Golinski","ICML (long presentation)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.14882",{"topic":988,"people":989,"info":172,"year":899,"kind":72,"link":992},"Continual Learning with Guarantees via Weight Interval Constraints",[387,848,178,990,991,135,180,70],"Łukasz Pustelnik","\u003Cb>Paweł Morawiecki\u003C\u002Fb>","https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.07996",{"topic":994,"people":995,"info":996,"kind":163,"year":899},"Hypernetworks methods in Meta-Learning",[70],"Opus 22",{"topic":998,"people":999,"info":103,"year":899,"kind":72,"link":1005},"BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video",[1000,1001,1002,1003,1004,180,97],"Szymon Płotka","Michał K. Grzeszczyk","Robert Brawura-Biskupski-Samaha","Paweł Gutaj","Michał Lipa","https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.09382",{"topic":1007,"people":1008,"info":609,"year":899,"kind":72,"link":1013},"CoNeRF: Controllable Neural Radiance Fields. Computer Vision and Pattern Recognition",[1009,1010,1011,180,1012],"Kacper Kania","Kwang Moo Yi","Marek Kowalski","Andrea Tagliasacchi","https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.01983",{"topic":1015,"people":1016,"info":869,"year":899,"kind":72,"link":1017},"Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning",[962,563,600,802,180],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12196",{"topic":1019,"people":1020,"info":325,"year":899,"kind":72,"link":1024},"PluGeN: Multi-Label Conditional Generation From Pre-Trained Models",[387,1021,56,487,1022,1023,262],"\u003Cb>Magdalena Proszewska\u003C\u002Fb>","Patryk Wielopolski","Rafał Kurczab","https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.09011",{"topic":1026,"people":1027,"info":370,"year":1028,"kind":72,"link":1029},"Zero Time Waste: Recycling Predictions in Early Exit Neural Networks",[387,178,432,773,135,262,180],2021,"https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05409",{"topic":1031,"people":1032,"info":370,"year":1028,"kind":72,"link":1033},"Continual World: A Robotic Benchmark For Continual Reinforcement Learning",[387,285,805,789,790],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.10919",{"topic":1035,"people":1036,"info":370,"year":1028,"kind":72,"link":1038},"Non-Gaussian Gaussian Processes for Few-Shot Regression",[149,135,280,943,1037,180,70,487],"Massimiliano Patacchiola","http:\u002F\u002Farxiv.org\u002Fabs\u002F2110.13561",{"topic":1040,"people":1041,"info":370,"kind":72,"year":1028,"link":1045},"DUE: End-to-End Document Understanding Benchmark",[812,813,822,816,1042,1043,1044],"Michał Turski","Karolina Szyndler","Filip Graliński","https:\u002F\u002Fopenreview.net\u002Fforum?id=rNs2FvJGDK",{"topic":1047,"people":1048,"kind":163,"year":1028,"link":1049},"MEiN scholarship for outstanding young scientists 2021",[180],"https:\u002F\u002Fwww.gov.pl\u002Fweb\u002Fedukacja-i-nauka\u002Fogloszenie-wynikow-postepowania-w-sprawie-przyznania-stypendiow-ministra-nauki-i-szkolnictwa-wyzszego-dla-wybitnych-mlodych-naukowcow-w-2021-r",{"topic":1047,"people":1051,"kind":163,"year":1028,"link":1049},[262],{"topic":1053,"people":1054,"kind":163,"link":1055,"year":1028},"START 2021",[336],"https:\u002F\u002Fwww.fnp.org.pl\u002Flaureaci-start-2021\u002F",{"topic":1057,"people":1058,"info":1059,"year":1028,"kind":72,"link":1060},"ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification",[119,169,135,117],"SIGKDD","https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.14340",{"topic":1062,"people":1063,"info":172,"year":1028,"kind":72},"Robust Learning-Augmented Caching: An Experimental Study",[1064,1065,1066,1067],"\u003Cb>Jakub Chłędowski\u003C\u002Fb>","Adam Polak","Bartosz Szabucki","Konrad Żołna",{"topic":1069,"people":1070,"info":172,"year":1028,"kind":72,"link":1079},"Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization",[336,1071,1072,1073,1074,1075,1076,1077,1078],"Devansh Arpit","Oliver Astrand","Giancarlo Kerg","Huan Wang","Caiming Xiong","Richard Socher","Kyunghyun Cho*","Krzysztof Geras*","https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.14193",{"topic":1081,"people":1082,"info":869,"year":1028,"kind":72},"Explaining Self-Supervised Image Representations with Visual Probing",[1083,1084,1085,969,1086,180,117],"Dominika Basaj","Witold Oleszkiewicz","\u003Cb>Igor Sieradzki\u003C\u002Fb>","Barbara Rychalska",{"topic":1088,"people":1089,"info":638,"year":1028,"kind":72,"link":1090},"Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning",[119,118,135,117],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12991",{"topic":1092,"people":1093,"info":1094,"kind":163,"year":1095},"Combination of Molecular Simulation and Deep Learning for De Novo Drug Design",[323],"Preludium 19",2020,{"topic":1097,"people":1098,"info":1100,"year":1095,"kind":72,"link":1101},"Cramer-Wold AutoEncoder",[764,135,70,773,134,1099],"Stanisław Jastrzębski","JMLR","https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09235",{"topic":1103,"people":1104,"info":145,"year":1095,"kind":72,"link":1105},"Finding the Optimal Network Depth in Classification Tasks",[178,387,432,135],"https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.08172",{"topic":1107,"people":1108,"info":172,"year":1095,"kind":72,"link":1111},"Hypernetwork approach to generating point clouds",[70,1109,135,1110,487,636],"\u003Cb>Sebastian Winczowski\u003C\u002Fb>","Maciej Zamorski","https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.00802",{"topic":1113,"people":1114,"info":1115,"year":1095,"kind":163},"Transformer-based methods for novel active chemical compounds",[56],"Preludium ",{"topic":1117,"people":1118,"info":1124,"kind":72,"link":1125,"year":1126},"Evolutionary-Neural Hybrid Agents for Architecture Search",[1119,1120,1121,1122,1123],"\u003Cb>Krzysztof Maziarz\u003C\u002Fb>","Mingxing Tan","Andrey Khorlin","Marin Georgiev","Andrea Gesmundo","ICML Workshop on AutoML 2019; 1st place in Data Science Masters - best Master's thesis competition, Applied track","https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.09828",2019,{"topic":1128,"people":1129,"year":1126,"info":1130,"kind":163,"link":45},"Bio-inspired artificial neural networks",[135],"FNP TEAM-NET",{"topic":1132,"people":1133,"info":1136,"year":1095,"kind":72,"link":1137},"The Break-Even Point on the Optimization Trajectories of Deep Neural Networks",[1099,1134,1135,1071,135,1077,1078],"\u003Cb>Maciej Szymczak\u003C\u002Fb>","Stanislav Fort","ICLR (Spotlight)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.09572",{"topic":1139,"people":1140,"info":869,"year":1126,"kind":72,"link":1145},"Persistence bag of words for topological data analysis",[117,1141,1142,1143,1144],"Michał Lipiński","Mateusz Juda","Matthias Zeppelzauer","Paweł Dłotko","https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.09245",{"topic":1147,"people":1148,"info":172,"year":1126,"kind":72,"link":1155},"Parameter-Efficient Transfer Learning for NLP",[1149,1150,336,1151,1152,1123,1153,1154],"Neil Houlsby","Andrei Giurgiu","Bruna Morrone","Quentin de Laroussilhe","Mona Attariyan","Sylvain Gelly","https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00751",{"topic":1157,"people":1158,"info":254,"year":1126,"kind":72,"link":1163},"On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length",[336,1159,1160,1161,550,1162],"Zachary Kenton","Nicolas Ballas","Asja Fischer","Amos Storkey","https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.05031",{"topic":1165,"people":1166,"info":254,"year":1126,"kind":72,"link":1168},"Distribution-Interpolation Trade off in Generative Models",[1167,1085,773],"\u003Cb>Damian Leśniak\u003C\u002Fb>","https:\u002F\u002Fopenreview.net\u002Fforum?id=SyMhLo0qKQ",{"topic":1170,"people":1171,"info":370,"year":1172,"kind":72},"Processing of missing data by neural networks",[262,169,135,117,70],2018,{"topic":1174,"people":1175,"year":1172,"info":1180,"kind":163},"Efficient unsupervised learning with applications in deep learning",[1176,1177,1178,1179],"prof. Jacek Tabor","dr Marek Śmieja","dr Przemysław Spurek","dr Łukasz Struski","NCN OPUS 13",{"topic":1182,"people":1183,"year":1172,"info":1185,"kind":58},"Deep Learning in representation of long sequential data",[1184],"mgr Konrad Żołna","NCN ETIUDA 6",{"topic":1187,"people":1188,"info":1189,"year":1190,"kind":163},"Additional information in data clustering and related areas",[1177,1178,1179],"2017-2020 NCN SONATA 11",2017,{"topic":1192,"people":1193,"year":1190,"info":1195,"kind":58},"New representation learning methods in Deep Learning",[1194],"mgr Stanisław Jastrzębski","NCN ETIUDA 5",{"topic":1197,"people":1198,"year":1200,"info":1201,"kind":163},"Theory of missing data",[1176,1179,1177,1199],"dr Bartosz Zieliński",2016,"NCN OPUS 10",{"topic":1203,"people":1204,"year":1200,"info":1205,"kind":163},"Clustering algorithm, which uses generalized Gaussian distribution and non-normal distributions",[1178],"NCN SONATA 10",{"topic":1207,"people":1208,"year":1200,"info":1205,"kind":163},"Detectors and descriptors of the key points based on the topological information",[1199,1209],"dr Mateusz Juda",{"topic":1211,"people":1212,"year":1200,"info":1214,"kind":163},"Algorithmical aspects of synchronization",[1213],"dr Adam Roman","NCN OPUS 9",{"topic":1216,"people":1217,"info":1220,"year":1221,"kind":58},"Best paper award at CORES'15",[1218,1219],"mgr Wojciech Czarnecki","dr hab. Jacek Tabor","",2015,{"topic":1223,"people":1224,"year":1221,"info":1226,"kind":163},"Application of neural networks in politology",[1225],"dr hab. Igor Podolak (grant coordinated by dr hab. Łukasz Wordliczek)","NCN OPUS 8",{"topic":1228,"people":1229,"year":1221,"info":1231,"kind":163},"Knowledge enriched sparse word embedding",[1230],"lic. Stanisław Jastrzębski (under supervision of dr hab. Jacek Tabor)","MNiSW Diamentowy Grant 4",{"topic":1233,"people":1234,"year":1221,"info":1240,"kind":163},"Minimal Memory Clustering Paradigm",[1219,1235,1236,1237,1238,1218,1239],"dr hab. Igor Podolak","prof. dr hab. Andrzej Bojarski","mgr Przemysław Spurek","mgr Marek Śmieja","mgr Sabina Smusz","NCN OPUS 7",{"topic":1242,"people":1243,"year":1221,"info":1244,"kind":163},"Development of machine learning methods with applications to chemical compound activity prediction",[1238],"NCN PRELUDIUM 7",{"topic":1246,"people":1247,"year":1221,"info":1249,"kind":58},"Rector prize for scientific achievements",[1176,1248,1178],"dr Wojciech Czarnecki","UJ",{"topic":1251,"people":1252,"year":1221,"info":1254,"kind":58},"Scholarship of Polish Minister Of Science",[1253],"lic. Stanisław Jastrzębski","MNiSW",{"topic":1256,"people":1257,"year":1259,"info":1260,"kind":163},"A novel approach to de novo genome assembly problem based on UCT",[1258],"mgr Ewa Matczyńska",2014,"NCN PRELUDIUM 6",{"topic":1262,"people":1263,"year":1259,"info":1264,"kind":163},"The memory center",[1237],"NCN PRELUDIUM 5",{"topic":1266,"people":1267,"year":1259,"info":1264,"kind":163},"Novel active learning querying strategy for the machine learning models",[1268],"mgr Wojciech M. Czarnecki",{"topic":1270,"people":1271,"year":1259,"kind":58},"4th place in TRADESHIFT competition",[1253,1272,1218],"Rafał Józefowicz (Google)",{"topic":1274,"people":1275,"year":1259,"kind":58},"5th place in CONNECTOMICS competition",[1218,1272],{"topic":1277,"people":1278,"year":1280,"info":1281,"kind":163},"Entropy of the mixture of sources",[1279,1238],"dr hab Jacek Tabor",2013,"NCN OPUS 1",{"topic":1283,"people":1284,"year":1280,"kind":58},"Second prize at the SMP competition",[1285],"Stanisław Jastrzębski (under supervision of dr hab. Igor Podolak)",{"topic":1287,"people":1288,"year":1280,"kind":58},"Best presentation at CISIM'13",[1237],{"id":1290,"extension":5,"meta":1291,"projects":1292,"stem":1388,"__hash__":1389},"projects\u002Fprojects.yml",{},[1293,1302,1311,1320,1324,1334,1341,1350,1355,1360,1364,1370,1374,1379,1384],{"title":154,"authors":1294,"link":1300,"image":1301},[1295,1296,1297,1298,1299],"Grzegorz Wilczyński","Rafał Tobiasz","Paweł Gora","Marcin Mazur","Przemysław Spurek","https:\u002F\u002Fgwilczynski95.github.io\u002FQuantumGS\u002F","quantumgs.png",{"title":1303,"authors":1304,"link":1309,"image":1310},"GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Reality",[1305,646,1306,1307,1308,647,1299],"Anastasiya Pechko","Joanna Waczyńska","Daniel Barczyk","Agata Szymańska","https:\u002F\u002Fanastasiya999.github.io\u002FGS-Verse\u002F","gsverse.png",{"title":1312,"authors":1313,"link":1318,"image":1319},"VeGaS: Video Gaussian Splatting",[1314,1315,1316,1317,1298,1299],"Weronika Smolak-Dyżewska","Dawid Malarz","Kornel Howil","Jan Kaczmarczyk","https:\u002F\u002Fgmum.github.io\u002FVeGaS\u002F","vegas.png",{"title":356,"authors":1321,"link":363,"image":1323},[358,1000,359,360,361,1322,636,97],"Przemysław Korzeniowski","gepar3d.png",{"title":372,"authors":1325,"link":1332,"image":1333},[1326,1327,1328,1329,377,1297,1330,1331],"Ahmet Onur Akman","Anastasia Psarou","Michał Hoffmann","Łukasz Gorczyca","Grzegorz Jamróz","Rafal Kucharski","https:\u002F\u002Furbenchmark.com\u002F","urb.png",{"title":319,"authors":1335,"link":1339,"image":1340},[1336,322,1337,1338],"Łukasz Janisiów","Bartosz Zieliński","Tomasz Danel","https:\u002F\u002Fcounterfactualmasking.gmum.net\u002F","counterfactual_masking.png",{"title":382,"authors":1342,"link":1348,"image":1349},[1343,1344,1345,1346,1337,1347],"Adam Pardyl","Dominik Matuszek","Mateusz Przebieracz","Marek Cygan","Maciej Wołczyk","https:\u002F\u002Fflysearch.gmum.net\u002F","flysearch.jpg",{"title":411,"authors":1351,"link":1353,"image":1354},[1316,1306,646,1352,1298,1299],"Tadeusz Dziarmaga","https:\u002F\u002Fkornelhowil.github.io\u002FCLIPGaussian\u002F","clip_gaussian.jpg",{"title":1356,"authors":1357,"link":1358,"image":1359},"HuSc3D: Human Sculpture dataset for 3D object reconstruction",[1314,1315,1295,1296,1306,646,1299],"https:\u002F\u002Fwmito.github.io\u002FHuSc3D\u002F","husc3d.png",{"title":506,"authors":1361,"link":1362,"image":1363},[508,1314,1315,509,1322,1299],"https:\u002F\u002Fsanoscience.github.io\u002FPR-ENDO\u002F","prendo.png",{"title":532,"authors":1365,"link":1368,"image":1369},[1306,1366,646,647,534,1367],"Tomasz Szczepanik","Przemyslaw Spurek","https:\u002F\u002Fwaczjoan.github.io\u002FMiraGe\u002F","mirage.png",{"title":644,"authors":1371,"link":1372,"image":1373},[1306,646,508,647,1299],"https:\u002F\u002Fwaczjoan.github.io\u002FD-MiSo\u002F","dmiso.png",{"title":1375,"authors":1376,"link":1377,"image":1378},"GASP: Gaussian Splatting for Physic-Based Simulations",[646,1314,1306,1298,647,1299],"https:\u002F\u002Fwaczjoan.github.io\u002FGASP\u002F","gasp.jpg",{"title":698,"authors":1380,"link":1382,"image":1383},[1343,700,1347,1381,636,1337],"Kamil Adamczewski","https:\u002F\u002Fio.pardyl.com\u002FAdaGlimpse\u002F","adaglimpse.png",{"title":867,"authors":1385,"link":1386,"image":1387},[1343,729,634,1337,636],"https:\u002F\u002Fio.pardyl.com\u002FAME\u002F","ame.png","projects","-SrrpU-IuY49DfpeMlg3bF4ww5qGRL93Fi9pMsxdOAg",1786120614985]