(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
题目:Sparse Autoencoders Find Highly Interpretable Features in Language Models 名称:稀疏自动编码器在语言模型中寻找高度可解释的特征 论文:arxiv.org/abs/2309.0860 代码: 单位:EleutherAI、MATS、BristolAI 出版:Arxiv 2023 SEA 题目:SEA: Sparse Linear Attention with Estimated Attention Mask 名称:SEA:带有...
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...
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...
Conceptual overview about our approach: Multi-omics feature mapping to a specific pathway are summarized into a pathway level score via a sparse denoising multi-modal autoencoder architecture. Hidden layer 1 consists of up to [pj/2] hidden units per omics modality, where p_j is the number of...
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...
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...
这就是 Sparse attention 类的论文的核心出发点，其中的关键就是用什么算法去压缩 token 数量，NSA 也...
One of the most highly acclaimed shallow autoencoders is easer, favored for its competitive recommendation accuracy and simultaneous simplicity. However, the poor scalability of easer (both in time and especially in memory) severely restricts its use in production environments with vast item sets. ...