Search Results for author: Yufei Cui

Found 8 papers, 4 papers with code

Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion

no code implementations4 Jan 2024 Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue

Retrieval-based augmentations that aim to incorporate knowledge from an external database into language models have achieved great success in various knowledge-intensive (KI) tasks, such as question-answering and text generation.

Natural Language Understanding Neural Architecture Search +5

NFL: Robust Learned Index via Distribution Transformation

1 code implementation24 May 2022 Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, Chun Jason Xue

Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).

A Fast Transformer-based General-Purpose Lossless Compressor

1 code implementation30 Mar 2022 Yu Mao, Yufei Cui, Tei-Wei Kuo, Chun Jason Xue

To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.

Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization

no code implementations6 Feb 2021 Ziquan Liu, Yufei Cui, Jia Wan, Yu Mao, Antoni B. Chan

On the one hand, when the non-adaptive learning rate e. g. SGD with momentum is used, the effective learning rate continues to increase even after the initial training stage, which leads to an overfitting effect in many neural architectures.

Crowd Counting Image Classification +3

Variational Nested Dropout

1 code implementation CVPR 2021 Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue

Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.

Representation Learning

Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations

no code implementations ICML Workshop AML 2021 Ziquan Liu, Yufei Cui, Antoni B. Chan

The derived regularizer is an upper bound for the input gradient of the network so minimizing the improved regularizer also benefits the adversarial robustness.

Adversarial Robustness

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex

no code implementations25 Sep 2019 Yufei Cui, Wuguannan Yao, Qiao Li, Antoni Chan, Chun Jason Xue

In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.

Adversarial Attack Bayesian Inference +1

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

1 code implementation29 May 2019 Yufei Cui, Wuguannan Yao, Qiao Li, Antoni B. Chan, Chun Jason Xue

In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.

Adversarial Attack Bayesian Inference +2

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