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* 03.03 - Introduction to Sparse Autoencoders SAE with [ReLU](https://transformer-circuits.pub/2024/scaling-monosemanticity/) (Anthropic; Blog; 21.05.2024), [TopK](https://openreview.net/forum?id=tcsZt9ZNKD) (OpenAI; ICLR 2025; 06.06.2025) and [JumpReLU](https://openreview.net/forum?id=XkMrWOJhNd) (DeepMind; 09.08.2024; EMNLP 2024 Workshop) - (advising Vladimir)
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* 10.03 - [Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models](https://openreview.net/forum?id=I4e82CIDxv) (Northeastern University; ICLR 2025; 31.03.2025) - (advising Vladimir)
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* 17.03 - [Sparse Autoencoders Do Not Find Canonical Units of Analysis](https://openreview.net/forum?id=9ca9eHNrdH) (Durham University/Independent; ICLR 2025; 07.02.2025) - (advising Vladimir)
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* 24.03 - Interpreting CLIP with Hierarchical Sparse Autoencoders (My paper; Under Review; 30.01.2025) - Vladimir Zaigrajew
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* 31.03 - Short intro (HB) + [PIP-Net: Patch-based intuitive prototypes for interpretable image classification](https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html)(CVPR 2023) - (advising HB)
* 14.04 - Adversarial analysis of intrinsically interpretable deep learning - Hubert Baniecki
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* 28.04 - Vlad
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* 24.03 - [Interpreting CLIP with Hierarchical Sparse Autoencoders](https://arxiv.org/abs/2502.20578) (My paper; Under Review; 30.01.2025) - Vladimir Zaigrajew
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* 31.03 - [PIP-Net: Patch-based intuitive prototypes for interpretable image classification](https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html)- Karol Dobiczek
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* 07.04 - [ProtoViT: Interpretable image classification with adaptive prototype-based vision transformers](https://openreview.net/forum?id=hjhpCJfbFG)- Paweł Gelar
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* 14.04 - [Adversarial analysis of intrinsically interpretable deep learning](https://arxiv.org/abs/2503.08636) - Hubert Baniecki
* 19.05 - [Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models](https://arxiv.org/abs/2412.18604) (advising BS)
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* 26.05 - [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://arxiv.org/abs/2209.14687) (advising BS)
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