Search Results for author:
Found 4 papers, 3 papers with code
InversionView: A General-Purpose Method for Reading Information from Neural Activations
The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations.
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically
Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias.
In-Context Learning through the Bayesian Prism
One of the main discoveries in this line of research has been that for several function classes, such as linear regression, transformers successfully generalize to new functions in the class.
TAN-NTM: Topic Attention Networks for Neural Topic Modeling
Topic models have been widely used to learn text representations and gain insight into document corpora.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
