2 citations · 2 across the 13 of their papers we have counts for
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Swift: High-Performance Sparse Tensor Contraction for Scientific Applications
Andrew Ensinger, Gabriel Kulp, Victor Agostinelli +2
In scientific fields such as quantum computing, physics, chemistry, and machine learning, high dimensional data are typically represented using sparse tensors. Tensor contraction i…
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
Yifan Zeng, Ojas Tendolkar, Raymond Baartmans +3
Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach to sort the ranking lis…
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions
Victor Agostinelli, Sanghyun Hong, Lizhong Chen
A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions…
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…
FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
Gabriel Kulp, Andrew Ensinger, Lizhong Chen
Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking a…