Search Results for author: Kun Fang

Found 18 papers, 14 papers with code

Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs

1 code implementation19 Feb 2024 Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, Ji-Rong Wen

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies.

Question Answering

Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective

1 code implementation22 Oct 2023 Kun Fang, Qinghua Tao, Xiaolin Huang, Jie Yang

Motivated by such diversities on OoD loss landscape across modes, we revisit the deep ensemble method for OoD detection through mode ensemble, leading to improved performance and benefiting the OoD detector with reduced variances.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Efficient Generalization Improvement Guided by Random Weight Perturbation

1 code implementation21 Nov 2022 Tao Li, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Ming Yang, Xiaolin Huang

To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.

On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness

1 code implementation20 Nov 2022 Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li, Xiaolin Huang, Jie Yang

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances.

Online LiDAR-Camera Extrinsic Parameters Self-checking

1 code implementation19 Oct 2022 Pengjin Wei, Guohang Yan, Yikang Li, Kun Fang, Jie Yang, Wei Liu

This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks.

Autonomous Driving Binary Classification

Unifying Gradients to Improve Real-world Robustness for Deep Networks

1 code implementation12 Aug 2022 Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang

The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i. e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are most threatening since they can effectively hurt a victim network with the only access to model outputs.

Subspace Adversarial Training

1 code implementation CVPR 2022 Tao Li, Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels

1 code implementation13 Apr 2021 Qin Luo, Kun Fang, Jie Yang, Xiaolin Huang

Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.

regression

Test of the superdiffusion model in the interstellar medium around the Geminga pulsar

no code implementations5 Jan 2021 Sheng-Hao Wang, Kun Fang, Xiao-Jun Bi, Peng-Fei Yin

The TeV $\gamma$-ray halo around the Geminga pulsar is an important indicator of cosmic-ray (CR) propagation in the local zone of the Galaxy as it reveals the spatial distribution of the electrons and positrons escaping from the pulsar.

High Energy Astrophysical Phenomena High Energy Physics - Phenomenology

Towards Robust Neural Networks via Orthogonal Diversity

2 code implementations23 Oct 2020 Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li, Jia Cai, Feipeng Cai, Xiaolin Huang, Jie Yang

In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.

Adversarial Robustness Data Augmentation

Learn Robust Features via Orthogonal Multi-Path

no code implementations28 Sep 2020 Kun Fang, Xiaolin Huang, Yingwen Wu, Tao Li, Jie Yang

To defend adversarial attacks, we design a block containing multiple paths to learn robust features and the parameters of these paths are required to be orthogonal with each other.

End-to-end Kernel Learning via Generative Random Fourier Features

1 code implementation10 Sep 2020 Kun Fang, Fanghui Liu, Xiaolin Huang, Jie Yang

In the second-stage process, a linear learner is conducted with respect to the mapped random features.

Adversarial Robustness

Geometric Rényi Divergence and its Applications in Quantum Channel Capacities

1 code implementation12 Sep 2019 Kun Fang, Hamza Fawzi

We present a systematic study of the geometric R\'enyi divergence (GRD), also known as the maximal R\'enyi divergence, from the point of view of quantum information theory.

Quantum Physics Information Theory Mathematical Physics Information Theory Mathematical Physics

One-shot entanglement distillation beyond LOCC

no code implementations4 Jun 2019 Bartosz Regula, Kun Fang, Xin Wang, Mile Gu

We show in particular that the $\varepsilon$-error one-shot distillable entanglement of any pure state is the same under all sets of operations ranging from one-way LOCC to separability-preserving operations or operations preserving the set of states with positive partial transpose, and can be computed exactly as a quadratically constrained linear program.

Quantum Physics Mathematical Physics Mathematical Physics

Non-asymptotic entanglement distillation

1 code implementation19 Jun 2017 Kun Fang, Xin Wang, Marco Tomamichel, Runyao Duan

For isotropic states, it can be further simplified to a linear program.

Quantum Physics

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