In this paper, we propose the Deep Autoencoder-like NMF with Contrastive Regularization and Feature Relationship preservation (DANMF-CRFR) to address the above problem. Inspired by contrastive learning, this deep model is able to learn discriminative and instructive deep features while adequately ...
This codebase was designed to replicate Anthropic's sparse autoencoder visualisations, which you can see here. The codebase provides 2 different views: a feature-centric view (which is like the one in the link, i.e. we look at one particular feature and see things like which tokens fire...
Inspired by the unique feature representation learning capability of deep autoencoder, we propose a novel model, named Deep Autoencoder-like NMF (DANMF), for community detection. Similar to deep autoencoder, DANMF consists of an encoder component and a decoder component. This architecture empowers...
Deep Image Inpainting using UNET like Vanilla Autoencoder and Partial Convolution based Autoencoder. - GitHub - ayulockin/deepimageinpainting: Deep Image Inpainting using UNET like Vanilla Autoencoder and Partial Convolution based Autoencoder.
This study proposes a novel autoencoder-like semi-nonnegative matrix factorization (NMF) multiple clustering (ASNMFMC) model that generates multiple non-redundant, high-quality clustering. The nonnegative property of the semi-NMF is utilized by the algorithm to enforce non-redundancy. Extensive ...
To overcome these shortcomings, inspired by deep Autoencoder , we propose two novel deep Autoencoder-like NMF with graph regularized prediction methods for dynamic networks. By fusing encoder component with deep structure into deep NMF model, our algorithms can sufficiently exploit the complex ...
To address these limitations, we propose a novel framework that combines autoencoder-like NMF with dual-graph constraints for multi-view clustering (ADGNMF). This approach unifies data representation learning and data reconstruction into a single framework, enhancing the learning of low-dimensional ...
Deep autoencoder-like NMFL_(2,1) normLink prediction aims to predict missing links or eliminate spurious links and anticipate new links by analyzing observed network topological structure information. Non-negative matrix factorization(NMF) is widely used in solving the issue of link prediction due ...
To address these issues, a novel deep autoencoder-like nonnegative matrix factorization method with L_(2,1) norm for link prediction models(DANMFL) is proposed. Unlike conventional NMF-based approaches, our model contains a decoder component and an encoder component, which capture complex ...
Deep Autoencoder-like NMF Code in Matlab for the paper "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection", CIKM 2018. [paper] The Python version could be foundhere. Thanks for the reproduction. Releases No releases published ...