Search Results for author: Yi Liang

Found 14 papers, 3 papers with code

Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors

1 code implementation24 Feb 2024 Shengkun Ma, Jiale Han, Yi Liang, Bo Cheng

Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data.

Contrastive Learning Relation +1

Automated Evaluation of Personalized Text Generation using Large Language Models

no code implementations17 Oct 2023 Yaqing Wang, Jiepu Jiang, Mingyang Zhang, Cheng Li, Yi Liang, Qiaozhu Mei, Michael Bendersky

Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context.

Text Generation text similarity

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

1 code implementation8 Oct 2023 Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu

Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.

Network Pruning

DeeDiff: Dynamic Uncertainty-Aware Early Exiting for Accelerating Diffusion Model Generation

no code implementations29 Sep 2023 Shengkun Tang, Yaqing Wang, Caiwen Ding, Yi Liang, Yao Li, Dongkuan Xu

In this work, we propose DeeDiff, an early exiting framework that adaptively allocates computation resources in each sampling step to improve the generation efficiency of diffusion models.


Teach LLMs to Personalize -- An Approach inspired by Writing Education

no code implementations15 Aug 2023 Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, Michael Bendersky

Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.

Retrieval Text Generation

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model

1 code implementation CVPR 2023 Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang, Yao Li, Caiwen Ding, Yanzhi Wang, Yi Liang, Dongkuan Xu

To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely \textbf{MuE}.

Language Modelling

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

no code implementations3 Nov 2022 Junru Wu, Yi Liang, Feng Han, Hassan Akbari, Zhangyang Wang, Cong Yu

For example, even in the commonly adopted instructional videos, a speaker can sometimes refer to something that is not visually present in the current frame; and the semantic misalignment would only be more unpredictable for the raw videos from the internet.

Contrastive Learning

Self-supervised Domain Adaptation in Crowd Counting

no code implementations7 Jun 2022 Pha Nguyen, Thanh-Dat Truong, Miaoqing Huang, Yi Liang, Ngan Le, Khoa Luu

Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision.

Crowd Counting Domain Adaptation

Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

no code implementations4 May 2022 Yi Liang, Shuai Zhao, Bo Cheng, Yuwei Yin, Hao Yang

Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.

Metric Learning Relation

COVID-19 Forecasts via Stock Market Indicators

no code implementations13 Dec 2021 Yi Liang, James Unwin

Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment.

Optimal Function Approximation with Relu Neural Networks

no code implementations9 Sep 2019 Bo Liu, Yi Liang

We consider in this paper the optimal approximations of convex univariate functions with feed-forward Relu neural networks.

Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field

no code implementations4 Mar 2019 Yi Liang, Xin Zhao, Alan J. X. Guo, Fei Zhu

To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks.

Ranked #8 on Hyperspectral Image Classification on Indian Pines (Overall Accuracy metric)

General Classification Hyperspectral Image Classification +3

When CTC Training Meets Acoustic Landmarks

no code implementations5 Nov 2018 Di He, Xuesong Yang, Boon Pang Lim, Yi Liang, Mark Hasegawa-Johnson, Deming Chen

In this paper, the convergence properties of CTC are improved by incorporating acoustic landmarks.

Automatic Speech Recognition (ASR)

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