1 code implementation • 21 Dec 2023 • Jiaxin Pan, Mojtaba Nayyeri, Yinan Li, Steffen Staab
Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps.
1 code implementation • 27 Oct 2023 • Danni Yang, Jiayi Ji, Xiaoshuai Sun, Haowei Wang, Yinan Li, Yiwei Ma, Rongrong Ji
Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0. 8% and 1. 1% respectively.
no code implementations • 23 Oct 2023 • Yinan Li, Chicheng Zhang
We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise~\citep{tsybakov2004optimal} under structured unlabeled data distributions.
no code implementations • 11 Oct 2023 • Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu, Xin Wang, Jerry Zhijian Yang
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
no code implementations • 19 Feb 2023 • Tian Yan, Yinan Li, Fang Liu
We propose a method named NA$_0$CT$^2$ (Noise Augmentation for $\ell_0$ regularization on Core Tensor in Tucker decomposition) to regularize the parameters in tensor regression (TR), coupled with Tucker decomposition.
1 code implementation • CVPR 2023 • Jingjia Huang, Yinan Li, Jiashi Feng, Xinglong Wu, Xiaoshuai Sun, Rongrong Ji
We then introduce \textbf{Clover}\textemdash a Correlated Video-Language pre-training method\textemdash towards a universal Video-Language model for solving multiple video understanding tasks with neither performance nor efficiency compromise.
Ranked #1 on Video Question Answering on LSMDC-FiB
no code implementations • 18 Apr 2022 • Yinan Li, Fang Liu
We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs).
no code implementations • 16 Oct 2021 • Yinan Li, Fang Liu
When the target regularization is for variable selection, we propose a new regularizer that achieves both privacy and sparsity guarantees simultaneously.
no code implementations • 3 Apr 2021 • Yinan Li, Zhibing Sun, Jun Liu
We show that the proposed algorithm is sound for full LTL specifications, and robustly complete for specifications recognizable by deterministic B\"uchi automata (DBA), the latter in the sense that control strategies can be found whenever the given specification can be satisfied with additional bounded disturbances.
no code implementations • 10 Feb 2021 • Chicheng Zhang, Yinan Li
We give a computationally-efficient PAC active learning algorithm for $d$-dimensional homogeneous halfspaces that can tolerate Massart noise (Massart and N\'ed\'elec, 2006) and Tsybakov noise (Tsybakov, 2004).
no code implementations • 9 Sep 2020 • Yiming Meng, Yinan Li, Maxwell Fitzsimmons, Jun Liu
While the converse Lyapunov-barrier theorems are not constructive, as with classical converse Lyapunov theorems, we believe that the unified necessary and sufficient conditions with a single Lyapunov-barrier function are of theoretical interest and can hopefully shed some light on computational approaches.