Search Results for author: Jiachen Zhao

Found 8 papers, 2 papers with code

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

no code implementations • 19 Mar 2024 • Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basant, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Ali, Bhakti Baheti, Yingbin Bai, Ishaan Bhatt, Sabri Can Cetindag, WenTing Chen, Li Cheng, Prasad Dutand, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Oksuz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, Joao Lourenco Silva, Ziga Spiclin, Sanjay Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, Yinglin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard Do, Anton Becker, Amber Simpson, Ender Konukoglu, Andras Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern Menze

The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.

Image Segmentation Medical Image Segmentation +3

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Learning and Forgetting Unsafe Examples in Large Language Models

1 code implementation • 20 Dec 2023 • Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren

As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning from third-party custom finetuning data.

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Student as an Inherent Denoiser of Noisy Teacher

no code implementations • 15 Dec 2023 • Jiachen Zhao

Knowledge distillation (KD) has been widely employed to transfer knowledge from a large language model (LLM) to a specialized model in low-data regimes through pseudo label learning.

Knowledge Distillation Language Modeling +3

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Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation

1 code implementation • 15 Nov 2023 • Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum

In this paper, we present the discovery that a student model distilled from a few-shot prompted LLM can commonly generalize better than its teacher to unseen examples on such tasks.

Constituency Parsing Knowledge Distillation +4

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Large Language Models are In-context Teachers for Knowledge Reasoning

no code implementations • 12 Nov 2023 • Jiachen Zhao, Zonghai Yao, Zhichao Yang, Hong Yu

Furthermore, we reveal that for ICT, rationales from different teacher LLMs or human experts that more resemble the student LLM's self-explanations are better in-context demonstrations.

In-Context Learning Information Retrieval +4