(Reading for inspiration(十))SPARSE AUTOENCODERS FIND HIGHLY INTER PRETABLE FEATURES IN LANGUAGE MODELS Abstract 1 简介 主题 核心创新点 可解释性与单语义性: 核心假设 解决了什么问题 结论 (Reading for inspiration(十))SPARSE AUTOENCODERS FIND HIGHLY INTER PRETABLE FEATURES IN LANGUAGE MODELS Hoagy ...
Citations and References: Research: Towards Monosemanticy Sparse Autoencoders Find Highly Interpretable Features in Language Model Reference Implementations: Neel Nanda AI-Safety-Foundation. Arthur Conmy. Callum McDougallAbout Training Sparse Autoencoders on Language Models Resources Readme License MIT...
Training a Sparse Autoencoder Join the Slack! Feel free to join the Open Source Mechanistic Interpretability Slack for support! Citations and References Research: Towards Monosemanticy Sparse Autoencoders Find Highly Interpretable Features in Language Model Reference Implementations: Neel Nanda AI-Safety...
resulting into a deep decoder. Encoder plus decoder network together form a (deep) autoencoder. In most cases (as well as in our work) the decoder network has a laterally reversed architecture to the encoder. That means the input to the encoder network has the same number...
Lee, K., Carlberg, K.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. arXiv preprint arXiv:1812.08373 (2018) Lévy, B.: A numerical algorithm for L2 semi-discrete optimal transport in 3D. ESAIM Math. Model. Numer. Anal. 49(6), 1693–17...
As for the sparse MOEAs based on dimensionality reduction techniques, MOEA/PSL [22] adopts the restricted Boltzmann machine (RBM) [23] and denoising autoencoder (DAE) [24] to learn the sparse distribution and compact representation of decision variables, and regards the combination of the learnt...
On this basis, a rotation forest classifier based on sparse auto-encoder is proposed to predict the association between drugs and diseases. In order to evaluate the performance of the proposed model, we used it to implement 10-fold cross validation on two golden standard datasets, Fdataset and...
On the other hand, the convolutional neural network (CNN) and autoencoder (AE) prefer non-sequential data types, such as image input [14]. The algorithms attempt to distinguish between normal and anomalous behavior by establishing a decision boundary, such as with the support vector machine (...
Reasoning: Brute-force snap those geometric bonds, hoping to force CLIP model to find better (less text obsessed) solution 😅 ...Until I learn / find out what I am actually doing here (with regard to Sparse Autoencoders), at least. =) Sparse Autoencoder inspiration: Anthropic.AI researc...
python salad/training/eval_structure_autoencoder.py \ --config small_inner \ --params ae_params/small_inner-200k.jax \ --diagnostics True \ --path path-to-input-pdbs/ \ --num_recycle 10 \ --out_path path-to-ae-outputs/ Remember to match the config and corresponding parameter names. ...