2 citations · 2 across the 13 of their papers we have counts for
4 papers · 1 filter
MetaCluster: Enabling Deep Compression of Kolmogorov-Arnold Network
Matthew Raffel, Adwaith Renjith, Lizhong Chen
Kolmogorov-Arnold Networks (KANs) replace scalar weights with per-edge vectors of basis coefficients, thereby increasing expressivity and accuracy while also resulting in a multipl…
FlashKAT: Understanding and Addressing Performance Bottlenecks in the Kolmogorov-Arnold Transformer
Matthew Raffel, Lizhong Chen
The Kolmogorov-Arnold Network (KAN) has been gaining popularity as an alternative to the multilayer perceptron (MLP) due to its greater expressiveness and interpretability. Even so…
ML For Hardware Design Interpretability: Challenges and Opportunities
Raymond Baartmans, Andrew Ensinger, Victor Agostinelli +1
The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. How…
MatrixKAN: Parallelized Kolmogorov-Arnold Network
Cale Coffman, Lizhong Chen
Kolmogorov-Arnold Networks (KAN) are a new class of neural network architecture representing a promising alternative to the Multilayer Perceptron (MLP), demonstrating improved expr…