13 papers
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…
Towards Universal Semantics With Large Language Models
Raymond Baartmans, Matthew Raffel, Rahul Vikram +2
The Natural Semantic Metalanguage (NSM) is a linguistic theory based on a universal set of semantic primes: simple, primitive word-meanings that have been shown to exist in most, i…
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
Matthew Raffel, Victor Agostinelli, Lizhong Chen
This paper discusses the construction, fine-tuning, and deployment of BeaverTalk, a cascaded system for speech-to-text translation as part of the IWSLT 2025 simultaneous translatio…
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…
Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions
Tahmid Hasan Prato, Seijoon Kim, Lizhong Chen +1
Deep neural networks are not resilient to parameter corruptions: even a single-bitwise error in their parameters in memory can cause an accuracy drop of over 10%, and in the worst…