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DETR++: Taming Your Multi-Scale Detection Transformer


Jun 07, 2022
Chi Zhang, Lijuan Liu, Xiaoxue Zang, Frederick Liu, Hao Zhang, Xinying Song, Jindong Chen

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* T4V: Transformers for Vision workshop @ CVPR 2022 

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Chefs' Random Tables: Non-Trigonometric Random Features


May 30, 2022
Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

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Tracing Knowledge in Language Models Back to the Training Data


May 24, 2022
Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu

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* 14 pages, 5 Tables, 5 Figures 

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Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations


Feb 24, 2022
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar

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* AISTATS 2022 

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First is Better Than Last for Training Data Influence


Feb 24, 2022
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar

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EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks


Oct 16, 2021
Frederick Liu, Siamak Shakeri, Hongkun Yu, Jing Li

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Leveraging redundancy in attention with Reuse Transformers


Oct 13, 2021
Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar

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Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles


Jun 29, 2021
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha

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The Penalty Imposed by Ablated Data Augmentation


Jun 08, 2020
Frederick Liu, Amir Najmi, Mukund Sundararajan

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Estimating Training Data Influence by Tracking Gradient Descent


Feb 19, 2020
Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale

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