How to stabilize neural search ranking models during model update is an important but largely unexplored problem. Motivated by trigger analysis, we suggest to balance the trade-off between performance improvement and the amount of affected queries.
Apr 20, 2020 · How to stabilize neural search ranking models during model update is an important but largely unexplored problem. Motivated by trigger analysis, ...
Apr 20, 2020 · We propose two heuristics and one theory-guided stabilization method to solve the optimization problem. Our proposed methods are evaluated on two of the world' ...
Download Citation | On Apr 20, 2020, Ruilin Li and others published Stabilizing Neural Search Ranking Models | Find, read and cite all the research you need ...
Dec 2, 2021 · Ruilin Li, Zhen Qin, Xuanhui Wang, Suming J. Chen, Donald Metzler: Stabilizing Neural Search Ranking Models. WWW 2020: 2725-2732.
Nov 17, 2023 · Ranking is a class of supervised learning algorithms that aim to sort a list of items based on their relevance to a query.
In contrast, the stabilized network achieves ranking scores that are higher than the ranking score of the baseline model on the original dataset. 5.3. Image ...
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May 24, 2021 · Neural rankers fine-tuned from pre-trained language models (PLMs) establish state-of-the-art ranking effectiveness.
Empirical results show that our proposed methods are highly effective in optimizing the proposed objective and are applicable to different model update.
Automatic stabilization of finite-element simulations using ...
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Petrov–Galerkin formulations with optimal test functions allow for the stabilization of finite element simulations.