1 code implementation • NeurIPS 2023 • Yun Yue, Zhiling Ye, Jiadi Jiang, Yongchao Liu, Ke Zhang
Additionally, we introduce an auto-switching function that enables the preconditioning matrix to switch dynamically between Stochastic Gradient Descent (SGD) and the adaptive optimizer.
no code implementations • 21 Sep 2023 • Uday Kumar Reddy Vengalam, Andrew Hahn, Yongchao Liu, Anshujit Sharma, Hui Wu, Michael Huang
Due to 5G deployment, there is significant interest in LDPC decoding.
1 code implementation • 25 May 2023 • Yun Yue, Jiadi Jiang, Zhiling Ye, Ning Gao, Yongchao Liu, Ke Zhang
Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization.
1 code implementation • 30 Jul 2021 • Yun Yue, Yongchao Liu, Suo Tong, Minghao Li, Zhen Zhang, Chunyang Wen, Huanjun Bao, Lihong Gu, Jinjie Gu, Yixiang Mu
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.
no code implementations • 18 May 2021 • Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
2 code implementations • 13 May 2021 • Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on Node Property Prediction on ogbn-proteins
1 code implementation • 21 Apr 2021 • Yongchao Liu, Houyi Li, Guowei Zhang, Xintan Zeng, Yongyong Li, Bin Huang, Peng Zhang, Zhao Li, Xiaowei Zhu, Changhua He, WenGuang Chen
Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions.
no code implementations • 1 Jan 2021 • Yun Yue, Suo Tong, Zhen Zhang, Yongchao Liu, Chunyang Wen, Huanjun Bao, Jinjie Gu, Yixiang Mu
We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly.
no code implementations • 11 Aug 2020 • Yongchao Liu, Yue Jin, Yong Chen, Teng Teng, Hang Ou, Rui Zhao, Yao Zhang
Accelerating deep model training and inference is crucial in practice.
no code implementations • 16 Oct 2019 • Tianchu Guo, Yongchao Liu, HUI ZHANG, Xiabing Liu, Youngjun Kwak, Byung In Yoo, Jae-Joon Han, Changkyu Choi
For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it.
no code implementations • 12 Apr 2017 • Yongchao Liu, Tony Pan, Oded Green, Srinivas Aluru
Pairwise association measure is an important operation in data analytics.