2 citations · 2 across the 8 of their papers we have counts for
9 papers
Hopfield Networks in Non-volatile Memory
Patrick Miller, Ulya R. Karpuzcu
Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumve…
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…
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…
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…
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous Translation
Matthew Raffel, Victor Agostinelli, Lizhong Chen
Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tu…
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Victor Agostinelli, Max Wild, Matthew Raffel +2
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety…
Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation
Matthew Raffel, Lizhong Chen
Simultaneous speech translation is an essential communication task difficult for humans whereby a translation is generated concurrently with oncoming speech inputs. For such a stre…
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation
Matthew Raffel, Drew Penney, Lizhong Chen
Transformer models using segment-based processing have been an effective architecture for simultaneous speech translation. However, such models create a context mismatch between tr…