1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
Quantifying artificial intelligence through algorithmic generalization
Takuya Ito, Murray Campbell, Lior Horesh +2
The rapid development of artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is imp…
Dense Associative Memory Through the Lens of Random Features
Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt +2
Dense Associative Memories are high storage capacity variants of the Hopfield networks that are capable of storing a large number of memory patterns in the weights of the network o…
Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations
Stephen Carrow, Kyle Harper Erwin, Olga Vilenskaia +5
Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an…
Learning interpretable positional encodings in transformers depends on initialization
Takuya Ito, Luca Cocchi, Tim Klinger +3
In transformers, the positional encoding (PE) provides essential information that distinguishes the position and order amongst tokens in a sequence. Most prior investigations of PE…