Search Results for author: Caigao Jiang

Found 8 papers, 5 papers with code

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt

1 code implementation19 Oct 2023 Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Y. Zhang, Jun Zhou, Defu Lian, Ying WEI

In this work, we first investigate such anytime fine-tuning effectiveness of existing continual pre-training approaches, concluding with unanimously decreased performance on unseen domains.

Transfer Learning

Continual Learning in Predictive Autoscaling

no code implementations29 Jul 2023 Hongyan Hao, Zhixuan Chu, Shiyi Zhu, Gangwei Jiang, Yan Wang, Caigao Jiang, James Zhang, Wei Jiang, Siqiao Xue, Jun Zhou

In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.

Continual Learning Density Estimation

EasyTPP: Towards Open Benchmarking Temporal Point Processes

1 code implementation16 Jul 2023 Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei

In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.

Benchmarking Point Processes

Learning Large-scale Universal User Representation with Sparse Mixture of Experts

no code implementations11 Jul 2022 Caigao Jiang, Siqiao Xue, James Zhang, Lingyue Liu, Zhibo Zhu, Hongyan Hao

However, unlike natural language processing (NLP) tasks, the parameters of user behaviour model come mostly from user embedding layer, which makes most existing works fail in training a universal user embedding of large scale.

Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Space

1 code implementation NeurIPS 2021 Huiru Xiao, Caigao Jiang, Yangqiu Song, James Zhang, Junwu Xiong

Specifically, we propose to learn the embeddings of hierarchically structured data in the unit ball model of the complex hyperbolic space.

Representation Learning

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