Qing Liu

Incremental Learning for Metric-Based Meta-Learners

2020 · arXiv (Cornell University)

Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and …

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

2021 · arXiv (Cornell University)

Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domain allows knowledge transferring. However, optimizing all the parameters …

A Time Series Classification Dataset Based on the Average Price of Concrete in major Cities in China

2022

Time series classification (TSC) is an important and challenging problem in data mining. Time series data sets are an important basis for this research and are widely used in baseline verification of various algorithm models. …

Temporal Relation Extraction with Joint Semantic and Syntactic Attention

2022 · Computational Intelligence and Neuroscience

Determining the temporal relationship between events has always been a challenging natural language understanding task. Previous research mainly relies on neural networks to learn effective features or artificial language features to extract temporal relationships, which …

Efficient retrieval of power structured data with global data access view

2023

With the deepening of power grid informatization construction, the number of structured data such as equipment, network and operation data used in power system is increasing rapidly. In order to improve the efficiency of power …

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

2022 · arXiv (Cornell University)

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding …