Search Results for author: Junbo Zhao

Found 51 papers, 23 papers with code

Pre-Trained Model Recommendation for Downstream Fine-tuning

no code implementations11 Mar 2024 Jiameng Bai, Sai Wu, Jie Song, Junbo Zhao, Gang Chen

As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task.

Inductive Bias Model Selection +1

RECOST: External Knowledge Guided Data-efficient Instruction Tuning

no code implementations27 Feb 2024 Qi Zhang, Yiming Zhang, Haobo Wang, Junbo Zhao

When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples.


Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach

no code implementations24 Feb 2024 Alaa Selim, Yanzhu Ye, Junbo Zhao, Bo Yang

To address this challenge, our research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL), specifically utilizing the Importance Weighted Actor-Learner Architecture (IMPALA) algorithm, executed on the RAY platform.

Distributed Computing reinforcement-learning

Energy-based Automated Model Evaluation

1 code implementation23 Jan 2024 Ru Peng, Heming Zou, Haobo Wang, Yawen Zeng, Zenan Huang, Junbo Zhao

The core of the MDE is to establish a meta-distribution statistic, on the information (energy) associated with individual samples, then offer a smoother representation enabled by energy-based learning.

3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?

no code implementations12 Jan 2024 Zeqing Yuan, Haoxuan Lan, Qiang Zou, Junbo Zhao

Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward.

Program Synthesis

Cyber-Physical Testbed Integrating RTAC with RTDS for Game-Theoretic Topology Control Under Load Altering Attacks

no code implementations10 Dec 2023 Alaa Selim, Junbo Zhao

This paper introduces a cyber-physical testbed that integrates the Real-Time Digital Simulator (RTDS) with the Real-Time Automation Controller (RTAC) to enhance cybersecurity in electrical distribution networks.

FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

1 code implementation27 Nov 2023 Ruixuan Xiao, Yiwen Dong, Junbo Zhao, Runze Wu, Minmin Lin, Gang Chen, Haobo Wang

While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention.

Active Learning In-Context Learning

Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems

no code implementations21 Nov 2023 Somayeh Yarahmadi, Daniel Adrian Maldonado, Lamine Mili, Junbo Zhao, Mihai Anitescu

Analyzing these characteristics enables the assessment of the impacts of EMW on the performance of the protection system.

Revisiting the Knowledge Injection Frameworks

no code implementations2 Nov 2023 Peng Fu, Yiming Zhang, Haobo Wang, Weikang Qiu, Junbo Zhao

Briefly, the core of this technique is rooted in an ideological emphasis on the pruning and purification of the external knowledge base to be injected into LLMs.

Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks

no code implementations29 Sep 2023 Yi Zhang, Yichao Wang, Junbo Zhao, Shan Zuo

In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks.

CAME: Contrastive Automated Model Evaluation

1 code implementation ICCV 2023 Ru Peng, Qiuyang Duan, Haobo Wang, Jiachen Ma, Yanbo Jiang, Yongjun Tu, Xiu Jiang, Junbo Zhao

In this work, we propose Contrastive Automatic Model Evaluation (CAME), a novel AutoEval framework that is rid of involving training set in the loop.

Towards Cross-Table Masked Pretraining for Web Data Mining

1 code implementation10 Jul 2023 Chao Ye, Guoshan Lu, Haobo Wang, Liyao Li, Sai Wu, Gang Chen, Junbo Zhao

Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information.

Contrastive Learning

Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

no code implementations16 May 2023 Hao Chen, Yiming Zhang, Qi Zhang, Hantao Yang, Xiaomeng Hu, Xuetao Ma, Yifan Yanggong, Junbo Zhao

Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions.

Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

1 code implementation15 May 2023 Wentao Ye, Mingfeng Ou, Tianyi Li, Yipeng chen, Xuetao Ma, Yifan Yanggong, Sai Wu, Jie Fu, Gang Chen, Haobo Wang, Junbo Zhao

With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses.


Latent Processes Identification From Multi-View Time Series

1 code implementation14 May 2023 Zenan Huang, Haobo Wang, Junbo Zhao, Nenggan Zheng

Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a. k. a.

Contrastive Learning Time Series

Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models

no code implementations2 May 2023 Shuai Zhao, Jinming Wen, Luu Anh Tuan, Junbo Zhao, Jie Fu

Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.

Backdoor Attack Few-Shot Text Classification +1

Better Sign Language Translation with Monolingual Data

1 code implementation21 Apr 2023 Ru Peng, Yawen Zeng, Junbo Zhao

Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relies on the availability of large-scale parallel G2T pairs.

Sign Language Translation Translation

Controllable Textual Inversion for Personalized Text-to-Image Generation

1 code implementation11 Apr 2023 Jianan Yang, Haobo Wang, YanMing Zhang, Ruixuan Xiao, Sai Wu, Gang Chen, Junbo Zhao

The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts.

Active Learning Text-to-Image Generation

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

iDAG: Invariant DAG Searching for Domain Generalization

1 code implementation ICCV 2023 Zenan Huang, Haobo Wang, Junbo Zhao, Nenggan Zheng

In this work, we first characterize that this failure of conventional ML models in DG is attributed to an inadequate identification of causal structures.

Contrastive Learning Domain Generalization

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 Ruixuan Xiao, Yiwen Dong, Haobo Wang, Lei Feng, Runze Wu, Gang Chen, Junbo Zhao

To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples.

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 Aspect-Based Sentiment Analysis (ABSA) +3

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.

Computational Efficiency

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

2 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.


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.

Generative Adversarial Network

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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