Search Results for author: Junbo Zhao

Found 26 papers, 14 papers with code

Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"

1 code implementation2 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.

Neural Architecture Search

Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation

1 code implementation10 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.

Knowledge Distillation Multimodal Machine Translation +2

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

1 code implementation21 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.

Partial Label Learning Weakly-supervised Learning

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

1 code implementation21 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.

Learning with noisy labels

Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis

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.

Aspect-Based Sentiment Analysis (ABSA) Multi-Task Learning +1

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning

1 code implementation22 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.

Contrastive Learning Partial Label Learning +2

Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors

no code implementations20 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.

Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network

no code implementations23 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.

Machine Translation NMT +1

Contrastive Label Disambiguation for Partial Label Learning

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.

Contrastive Learning Partial Label Learning +2

VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images

no code implementations27 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.

Navigate regression

A robust extended Kalman filter for power system dynamic state estimation using PMU measurements

no code implementations5 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.

Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach

1 code implementation22 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.

Neural Architecture Search

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

no code implementations2 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

PIANOTREE VAE: Structured Representation Learning for Polyphonic Music

2 code implementations17 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).

Music Generation Representation Learning

Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning

no code implementations24 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.

Decision Making

Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning

no code implementations31 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.

Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

no code implementations31 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

Prediction Under Uncertainty with Error Encoding Networks

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.

Video Prediction

Prediction Under Uncertainty with Error-Encoding Networks

4 code implementations14 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.

Video Prediction

Disentangling factors of variation in deep representation using adversarial training

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.

Disentangling factors of variation in deep representations using adversarial training

3 code implementations10 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.

Disentanglement

Energy-based Generative Adversarial Network

3 code implementations11 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.

Stacked What-Where Auto-encoders

2 code implementations8 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.

Semi-Supervised Image Classification

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