10 citations · 12 across the 4 of their papers we have counts for
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cs.LG2024★ 1 cited
On-device AI: Quantization-aware Training of Transformers in Time-Series
Tianheng Ling, Gregor Schiele
Artificial Intelligence (AI) models for time-series in pervasive computing keep getting larger and more complicated. The Transformer model is by far the most compelling of these AI…
cs.LG2024
An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware
Tianheng Ling, Islam Mansour, Chao Qian +1
Recent advancements in IoT technologies have underscored the importance of using sensor data to understand environmental contexts effectively. This paper introduces a novel embedde…
cs.LG2023★ 1 cited
A Study of Quantisation-aware Training on Time Series Transformer Models for Resource-constrained FPGAs
Tianheng Ling, Chao Qian, Lukas Einhaus +1
This study explores the quantisation-aware training (QAT) on time series Transformer models. We propose a novel adaptive quantisation scheme that dynamically selects between symmet…