activity
20122023
most citedCertified Adversarial Robustness with Additive Noise

185 citations · 1.5k across the 88 of their papers we have counts for

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Showing 2020Show all

17 papers · 1 filter

cs.CL2020★ 7 cited

My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism

Swapnil Parekh, Yaman Kumar Singla, Changyou Chen +2

Significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades. However, little research has been put to understand and int…

cs.CV2020★ 3 cited

ReMP: Rectified Metric Propagation for Few-Shot Learning

Yang Zhao, Chunyuan Li, Ping Yu +1

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space…

cs.CV2020★ 3 cited

Unpaired Image-to-Image Translation via Latent Energy Transport

Yang Zhao, Changyou Chen

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in t…

cs.LG2020★ 7 cited

Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference

Bang An, Jie Lyu, Zhenyi Wang +6

The neural attention mechanism plays an important role in many natural language processing applications. In particular, the use of multi-head attention extends single-head attentio…

cs.CL2020★ 30 cited

MixKD: Towards Efficient Distillation of Large-scale Language Models

Kevin J Liang, Weituo Hao, Dinghan Shen +4

Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power c…

cs.LG2020★ 2 cited

Learning Manifold Implicitly via Explicit Heat-Kernel Learning

Yufan Zhou, Changyou Chen, Jinhui Xu

Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in…