Search Results for author: Belinda Zeng

Found 12 papers, 2 papers with code

Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks

no code implementations NAACL (ACL) 2022 Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi

However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.

Knowledge Distillation Multi-Task Learning

ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding

no code implementations7 Oct 2023 Zixuan Liu, Gaurush Hiranandani, Kun Qian, Eddie W. Huang, Yi Xu, Belinda Zeng, Karthik Subbian, Sheng Wang

ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews.

Graph Embedding Link Prediction

Efficient and effective training of language and graph neural network models

no code implementations22 Jun 2022 Vassilis N. Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, George Karypis

The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model.

Edge Classification Language Modelling +1

DynaMaR: Dynamic Prompt with Mask Token Representation

no code implementations7 Jun 2022 Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Belinda Zeng, Trishul Chilimbi

In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues.

Language Modelling

Multi-modal Alignment using Representation Codebook

no code implementations CVPR 2022 Jiali Duan, Liqun Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, Trishul Chilimbi

Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion.

Representation Learning Retrieval

Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning

no code implementations30 Oct 2021 Xuanli He, Iman Keivanloo, Yi Xu, Xiang He, Belinda Zeng, Santosh Rajagopalan, Trishul Chilimbi

To achieve this, we propose a novel idea, Magic Pyramid (MP), to reduce both width-wise and depth-wise computation via token pruning and early exiting for Transformer-based models, particularly BERT.

text-classification Text Classification

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