no code implementations • COLING 2022 • BoWen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, Liwen Jing
SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL).
1 code implementation • 31 Jul 2024 • Shanbo Cheng, Zhichao Huang, Tom Ko, Hang Li, Ningxin Peng, Lu Xu, Qini Zhang
Aligned with professional human interpreters, we evaluate CLASI with a better human evaluation metric, valid information proportion (VIP), which measures the amount of information that can be successfully conveyed to the listeners.
no code implementations • 2 Jul 2024 • BoWen Zhang, Zhichao Huang, Genan Dai, Guangning Xu, Xiaomao Fan, Hu Huang
\method{} comprises several key modules, including the core subgraph knowledge submodule, graph domain adaptation module, and few-shot learning module for downstream tasks.
1 code implementation • 23 Mar 2024 • Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu, Xu Huang, Bo Liu, Liwen Jing, BoWen Zhang
To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework.
no code implementations • 21 Dec 2023 • Zhichao Huang, Rong Ye, Tom Ko, Qianqian Dong, Shanbo Cheng, Mingxuan Wang, Hang Li
Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM.
1 code implementation • 31 Aug 2023 • Zhichao Huang, Chutong Meng, Tom Ko
To improve the performance of these discrete speech tokens, we present RepCodec, a novel speech representation codec for semantic speech tokenization.
no code implementations • 6 Jun 2022 • Zhichao Huang, Yanbo Fan, Chen Liu, Weizhong Zhang, Yong Zhang, Mathieu Salzmann, Sabine Süsstrunk, Jue Wang
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet.
1 code implementation • 21 Jan 2022 • Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
1 code implementation • 14 Dec 2021 • Chen Liu, Zhichao Huang, Mathieu Salzmann, Tong Zhang, Sabine Süsstrunk
This lets us demonstrate that the decay in generalization performance of adversarial training is a result of fitting hard adversarial instances.
no code implementations • 29 Sep 2021 • Zhichao Huang, Chen Liu, Mathieu Salzmann, Sabine Süsstrunk, Tong Zhang
Although adversarial training and its variants currently constitute the most effective way to achieve robustness against adversarial attacks, their poor generalization limits their performance on the test samples.
1 code implementation • CVPR 2021 • Zhichao Huang, Xintong Han, Jia Xu, Tong Zhang
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs.
no code implementations • 3 Oct 2020 • Zhichao Huang, Yaowei Huang, Tong Zhang
We show that searching over the structured space can be approximated by a time-varying contextual bandits problem, where the attacker takes feature of the associated arm to make modifications of the input, and receives an immediate reward as the reduction of the loss function.
1 code implementation • CVPR 2021 • Baoquan Zhang, Xutao Li, Yunming Ye, Zhichao Huang, Lisai Zhang
To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples.
no code implementations • NeurIPS 2020 • Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
We consider nonconvex-concave minimax optimization problems of the form $\min_{\bf x}\max_{\bf y\in{\mathcal Y}} f({\bf x},{\bf y})$, where $f$ is strongly-concave in $\bf y$ but possibly nonconvex in $\bf x$ and ${\mathcal Y}$ is a convex and compact set.
1 code implementation • ICLR 2020 • Zhichao Huang, Tong Zhang
We present a new method for black-box adversarial attack.
no code implementations • 29 Dec 2018 • Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
Zeroth-order optimization is an important research topic in machine learning.