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Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Proceedings of the VLDB Endowment, 2022 The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck ...
Yang Li +7 more
semanticscholar +3 more sources
The Power of Scale for Parameter-Efficient Prompt Tuning [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 In this work, we explore “prompt tuning,” a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks.
Brian Lester +2 more
semanticscholar +1 more source
Flower pollination algorithm parameters tuning [PDF]
Soft Computing, 2021 The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance.
Mergos, P.E., Yang, X-S.
openaire +5 more sources
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning [PDF]
Neural Information Processing Systems, 2022 Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input.
Haokun Liu +6 more
semanticscholar +1 more source
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and ...
Elad Ben-Zaken +2 more
semanticscholar +1 more source
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning [PDF]
IEEE International Conference on Computer Vision, 2023 Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities.
Ligong Han +5 more
semanticscholar +1 more source
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or ...
Zhiqiang Hu +7 more
semanticscholar +1 more source
Automated video game parameter tuning with XVGDL+ [PDF]
Journal of Universal Computer Science, 2022 Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this is commonly an iterative process in which play-testing is required as well as human interaction for gathering all important ...
Jorge Ruiz Quiñones +1 more
doaj +3 more sources
Rainfall Prediction Using Backpropagation with Parameter Tuning [PDF]
MATEC Web of Conferences, 2022 Rainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed
Setiawan Wahyudi +2 more
doaj +1 more source
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning [PDF]
Neural Information Processing Systems, 2022 Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains recently. However, it is costly to update the entire parameter set of large pre-trained models.
Yi-Lin Sung, Jaemin Cho, Mohit Bansal
semanticscholar +1 more source