1 citations · 1 across the 3 of their papers we have counts for
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Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Yiheng Tao, Yihe Zhang, Matthew Dearing +4
Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with t…
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam +5
In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the f…
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
Matthew T. Dearing, Xiaoyan Wang
Many interesting datasets ubiquitous in machine learning and deep learning can be described via graphs. As the scale and complexity of graph-structured datasets increase, such as i…