SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. By carefully comparing various selection and search strategies, we realize that no single method outperforms the others, and hybrid or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first downstream task-aware, flexible, and efficient model search engine for transfer learning. These properties are enabled by a custom query language SHiFT-QL together with a cost-based decision maker, which we empirically validate. Motivated by the iterative nature of machine learning development, we further support efficient incremental executions of our queries, which requires a careful implementation when jointly used with our optimizations.
Code (1)
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
Transfer learning has recently shown significant performance across various tasks involving deep neural networks. In these transfer learning scenarios, the prior distribution for downstream data becomes crucial in Bayesi…
Transfer LearningFlexible Transfer Learning under Support and Model Shift
Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source/training domain) but only very limited training data for a second task (the target/test domain) tha…
Transfer LearningThe First Differentiable Transfer-Based Algorithm for Discrete MicroLED Repair
Laser-enabled selective transfer, a key process in high-throughput microLED fabrication, requires computational models that can plan shift sequences to minimize motion of XY stages and adapt to varying optimization objec…
Reinforcement LearningPolyimide-Based Flexible Coupled-Coils Design and Load-Shift Keying Analysis
Wireless power transfer using inductive coupling is commonly used for medical implantable devices. The design of the secondary coil on the implantable device is important as it will affect the power transfer efficiency, …
GEO: Generative Engine Optimization
The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formali…