15 citations · 29 across the 2 of their papers we have counts for
3 papers
cs.IR2022★ 14 cited
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
Xiaomeng Hu, Shi Yu, Chenyan Xiong +3
Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…
cs.LG2021
A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
Geoffrey X. Yu, Yubo Gao, Pavel Golikov +1
Deep learning researchers and practitioners usually leverage GPUs to help train their deep neural networks (DNNs) faster. However, choosing which GPU to use is challenging both bec…
cs.HC2020★ 15 cited
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
Geoffrey X. Yu, Tovi Grossman, Gennady Pekhimenko
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for…