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Found 6 papers, 2 papers with code
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.
CARL-G: Clustering-Accelerated Representation Learning on Graphs
CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline.
Subspace Clustering Based Analysis of Neural Networks
These experiments reveal that as we go deeper in a network, inputs tend to have an increasing affinity to other inputs of the same class.
Analyzing Representations inside Convolutional Neural Networks
In this work, we propose a framework to categorize the concepts a network learns based on the way it clusters a set of input examples, clusters neurons based on the examples they activate for, and input features all in the same latent space.
A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens
Furthermore, can we characterize a given deep neural network based on it's observed behavior on different inputs?
Relation Schema Induction using Tensor Factorization with Side Information
To the best of our knowledge, this is the first application of tensor factorization for the RSI problem.
