1 code implementation • 2 Feb 2023 • Junbo Zhao, Xuefei Ning, Enshu Liu, Binxin Ru, Zixuan Zhou, Tianchen Zhao, Chen Chen, Jiajin Zhang, Qingmin Liao, Yu Wang
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
1 code implementation • 10 Oct 2022 • Ru Peng, Yawen Zeng, Junbo Zhao
Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme.
1 code implementation • 21 Sep 2022 • Haobo Wang, Mingxuan Xia, Yixuan Li, YUREN MAO, Lei Feng, Gang Chen, Junbo Zhao
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth.
1 code implementation • 21 Jul 2022 • Haobo Wang, Ruixuan Xiao, Yiwen Dong, Lei Feng, Junbo Zhao
Combining with the small-loss selection, our method is able to achieve a precision of 99. 27 and a recall of 98. 22 in detecting clean samples on the CIFAR-10N dataset.
Ranked #1 on
Learning with noisy labels
on CIFAR-10N-Worst
1 code implementation • Findings (ACL) 2022 • Yiming Zhang, Min Zhang, Sai Wu, Junbo Zhao
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval 2014 Task 4 Sub Task 2
(using extra training data)
Aspect-Based Sentiment Analysis (ABSA)
Multi-Task Learning
+1
1 code implementation • 22 Jan 2022 • Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
no code implementations • 20 Dec 2021 • Yitong Liu, Zhengshuo Li, Junbo Zhao
To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model.
no code implementations • 23 Nov 2021 • Ru Peng, Nankai Lin, Yi Fang, Shengyi Jiang, Tianyong Hao, BoYu Chen, Junbo Zhao
However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness.
1 code implementation • ICLR 2022 • Haobo Wang, Ruixuan Xiao, Sharon Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
no code implementations • 27 May 2021 • Yu Chen, Yuxuan Wang, Bolin Lai, Zijie Chen, Xu Cao, Nanyang Ye, Zhongyuan Ren, Junbo Zhao, Xiao-Yun Zhou, Peng Qi
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment.
no code implementations • 27 May 2021 • Xu Cao, Zijie Chen, Bolin Lai, Yuxuan Wang, Yu Chen, Zhengqing Cao, Zhilin Yang, Nanyang Ye, Junbo Zhao, Xiao-Yun Zhou, Peng Qi
For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images.
no code implementations • 5 Apr 2021 • Marcos Netto, Junbo Zhao, Lamine Mili
Simulations carried out on the IEEE 39-bus test system reveal that our robust extended Kalman filter exhibits good tracking capabilities under Gaussian process and observation noise while suppressing observation outliers, even in position of leverage.
1 code implementation • 22 Dec 2020 • Xuefei Ning, Junbo Zhao, Wenshuo Li, Tianchen Zhao, Yin Zheng, Huazhong Yang, Yu Wang
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
no code implementations • 2 Nov 2020 • Xingyu Lei, Student Member, Zhifang Yang, Member, Junbo Zhao, Juan Yu, Senior Member, IEEE
Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.
Systems and Control Systems and Control
2 code implementations • 17 Aug 2020 • Ziyu Wang, Yiyi Zhang, Yixiao Zhang, Junyan Jiang, Ruihan Yang, Junbo Zhao, Gus Xia
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
no code implementations • 24 Jun 2020 • Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen, Frede Blaabjerg
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.
no code implementations • 31 May 2020 • Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen
This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.
no code implementations • 31 May 2020 • Xingyu Lei, Zhifang Yang, Juan Yu, Junbo Zhao, Qian Gao, Hongxin Yu
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
BIG-bench Machine Learning
Physics-informed machine learning
no code implementations • 11 May 2020 • Junbo Zhao, Marcos Netto, Zhenyu Huang, Samson Shenglong Yu, Antonio Gomez-Exposito, Shaobu Wang, Innocent Kamwa, Shahrokh Akhlaghi, Lamine Mili, Vladimir Terzija, A. P. Sakis Meliopoulos, Bikash Pal, Abhinav Kumar Singh, Ali Abur, Tianshu Bi, Alireza Rouhani
Power system dynamic state estimation (DSE) remains an active research area.
no code implementations • ICLR 2018 • Mikael Henaff, Junbo Zhao, Yann Lecun
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
4 code implementations • 14 Nov 2017 • Mikael Henaff, Junbo Zhao, Yann Lecun
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
no code implementations • NeurIPS 2016 • Michael F. Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann Lecun
The only available source of supervision during the training process comes from our ability to distinguish among different observations belonging to the same category.
3 code implementations • 10 Nov 2016 • Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann Lecun
During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.
3 code implementations • 11 Sep 2016 • Junbo Zhao, Michael Mathieu, Yann Lecun
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
29 code implementations • NeurIPS 2015 • Xiang Zhang, Junbo Zhao, Yann Lecun
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
Ranked #16 on
Sentiment Analysis
on Yelp Fine-grained classification
2 code implementations • 8 Jun 2015 • Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann Lecun
The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet.