Implemented semi-supervised learning for digit recognition using Sparse Autoencoder
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Updated
Apr 16, 2018 - Python
Implemented semi-supervised learning for digit recognition using Sparse Autoencoder
Repository for "From What to How: Attributing CLIP's Latent Components Reveals Unexpected Semantic Reliance"
Implementations and Experiments: Transformers, RoPE, KV cache, SAEs, Tokenisers
Official code for NeurIPS 2025 paper "Revising and Falsifying Sparse Autoencoder Feature Explanations".
Interpret and control dense embedding via sparse autoencoder.
Multi-Layer Sparse Autoencoders (ICLR 2025)
Collection of autoencoder models in Tensorflow
Providing the answer to "How to do patching on all available SAEs on GPT-2?". It is an official repository of the implementation of the paper "Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small"
[NeurIPS 2025] This is the official repository for VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
Official Triton kernels for TopK and HierarchicalTopK Sparse Autoencoder decoders.
Sparse Embedding Compression for Scalable Retrieval in Recommender Systems
[JAMIA] Official repository of Deep Propensity Network - Sparse Autoencoder(DPN-SA)
Sparse Autoencoders (SAE) vs CLIP fine-tuning fun.
SANSA - sparse EASE for millions of items
Official Code for Paper: Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
Pivotal Token Search
Tensorflow Examples
Pytorch implementations of various types of autoencoders
A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.
Implementation of the stacked denoising autoencoder in Tensorflow
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