Search Results for author: Junbo Zhao

Found 74 papers, 29 papers with code

Pi-GPS: Enhancing Geometry Problem Solving by Unleashing the Power of Diagrammatic Information

no code implementations7 Mar 2025 Junbo Zhao, Ting Zhang, Jiayu Sun, Mi Tian, Hua Huang

Geometry problem solving has garnered increasing attention due to its potential applications in intelligent education field.

Geometry Problem Solving

DataMan: Data Manager for Pre-training Large Language Models

no code implementations26 Feb 2025 Ru Peng, Kexin Yang, Yawen Zeng, Junyang Lin, Dayiheng Liu, Junbo Zhao

In this paper, we train a Data Manager (DataMan) to learn quality ratings and domain recognition from pointwise rating, and use it to annotate a 447B token pre-training corpus with 14 quality ratings and domain type.

In-Context Learning Instruction Following

Category-free Out-of-Distribution Node Detection with Feature Resonance

no code implementations22 Feb 2025 Shenzhi Yang, Junbo Zhao, Shouqing Yang, Yixuan Li, Dingyu Yang, Xiaofang Zhang, Haobo Wang

Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailable.

D.Va: Validate Your Demonstration First Before You Use It

no code implementations19 Feb 2025 Qi Zhang, Zhiqing Xiao, Ruixuan Xiao, Lirong Gao, Junbo Zhao

In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference.

In-Context Learning Natural Language Understanding +1

Towards Robust Incremental Learning under Ambiguous Supervision

no code implementations23 Jan 2025 Rui Wang, Mingxuan Xia, Chang Yao, Lei Feng, Junbo Zhao, Gang Chen, Haobo Wang

Technically, we develop the Prototype-Guided Disambiguation and Replay Algorithm (PGDR) which leverages the class prototypes as a proxy to mitigate two intertwined challenges in IPLL, i. e., label ambiguity and catastrophic forgetting.

Incremental Learning Partial Label Learning +1

Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks

no code implementations31 Dec 2024 Yi Zhang, Mohamadamin Rajabinezhad, Yichao Wang, Junbo Zhao, Shan Zuo

This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks.

Privacy Preserving

AIGT: AI Generative Table Based on Prompt

no code implementations24 Dec 2024 Mingming Zhang, Zhiqing Xiao, Guoshan Lu, Sai Wu, Weiqiang Wang, Xing Fu, Can Yi, Junbo Zhao

Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields.

A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications

no code implementations11 Dec 2024 Tong Su, Jiangkai Peng, Alaa Selim, Junbo Zhao, Jin Tan

The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators.

Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data

no code implementations3 Dec 2024 Mingming Zhang, Jiahao Hu, Pengfei Shi, Ningtao Wang, Ruizhe Gao, Guandong Sun, Feng Zhao, Yulin kang, Xing Fu, Weiqiang Wang, Junbo Zhao

However, financial datasets in the industry often encounter some challenges, such as data heterogeneity, the predominance of numerical features and the large scale of the data, which can range from tens of millions to hundreds of millions of records.

Kolmogorov-Arnold Networks

From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards

no code implementations24 Nov 2024 Ziyu Chen, Zhiqing Xiao, Xinbei Jiang, Junbo Zhao

Large Language Models (LLMs) and Reinforcement Learning (RL) are two powerful approaches for building autonomous agents.

Reinforcement Learning (RL)

Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy

no code implementations13 Nov 2024 Tong Su, Junbo Zhao

Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties.

TableGPT2: A Large Multimodal Model with Tabular Data Integration

1 code implementation4 Nov 2024 Aofeng Su, Aowen Wang, Chao Ye, Chen Zhou, Ga Zhang, Gang Chen, Guangcheng Zhu, Haobo Wang, Haokai Xu, Hao Chen, Haoze Li, Haoxuan Lan, Jiaming Tian, Jing Yuan, Junbo Zhao, Junlin Zhou, Kaizhe Shou, Liangyu Zha, Lin Long, Liyao Li, Pengzuo Wu, Qi Zhang, Qingyi Huang, Saisai Yang, Tao Zhang, Wentao Ye, Wufang Zhu, Xiaomeng Hu, Xijun Gu, Xinjie Sun, Xiang Li, Yuhang Yang, Zhiqing Xiao

In response, we introduce TableGPT2, a model rigorously pre-trained and fine-tuned with over 593. 8K tables and 2. 36M high-quality query-table-output tuples, a scale of table-related data unprecedented in prior research.

Benchmarking Data Integration

Navigate Complex Physical Worlds via Geometrically Constrained LLM

no code implementations23 Oct 2024 Yongqiang Huang, Wentao Ye, Liyao Li, Junbo Zhao

To enhance the comprehension of geometric and spatial relationships in the complex physical world, the study introduces a set of geometric conventions and develops a workflow based on multi-layer graphs and multi-agent system frameworks.

Navigate

A Comparative Study on Reasoning Patterns of OpenAI's o1 Model

1 code implementation17 Oct 2024 Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, J. H. Liu

In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i. e., math, coding, commonsense reasoning).

Math

Comparative Study of Data-driven Area Inertia Estimation Approaches on WECC Power Systems

no code implementations1 Aug 2024 Bendong Tan, Jiangkai Peng, Ningchao Gao, Junbo Zhao, Jin Tan

With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability.

A Review of Safe Reinforcement Learning Methods for Modern Power Systems

no code implementations29 Jun 2024 Tong Su, Tong Wu, Junbo Zhao, Anna Scaglione, Le Xie

Due to the availability of more comprehensive measurement data in modern power systems, there has been significant interest in developing and applying reinforcement learning (RL) methods for operation and control.

energy management Reinforcement Learning (RL) +2

FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents

no code implementations21 Jun 2024 Ruixuan Xiao, Wentao Ma, Ke Wang, Yuchuan Wu, Junbo Zhao, Haobo Wang, Fei Huang, Yongbin Li

Motivated by this, we formalize different formats of workflow knowledge and present FlowBench, the first benchmark for workflow-guided planning.

Benchmarking

On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

no code implementations14 Jun 2024 Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem.

Synthetic Data Generation

DORY: Deliberative Prompt Recovery for LLM

no code implementations31 May 2024 Lirong Gao, Ru Peng, Yiming Zhang, Junbo Zhao

Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc.

Data Contamination Calibration for Black-box LLMs

1 code implementation20 May 2024 Wentao Ye, Jiaqi Hu, Liyao Li, Haobo Wang, Gang Chen, Junbo Zhao

The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size.

Inference Attack Membership Inference Attack

Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models

no code implementations2 Apr 2024 Jiachen Ma, Anda Cao, Zhiqing Xiao, Yijiang Li, Jie Zhang, Chao Ye, Junbo Zhao

In this work, we investigate a more practical and universal attack that does not require the presence of a target model and demonstrate that the high-dimensional text embedding space inherently contains NSFW concepts that can be exploited to generate harmful images.

Adversarial Attack Text-to-Image Generation

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 +2

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.

Diversity Re-Ranking

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

1 code implementation24 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.

Deep Reinforcement Learning Distributed Computing +2

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.

model

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

Targeted Representation Alignment for Open-World Semi-Supervised Learning

1 code implementation CVPR 2024 Ruixuan Xiao, Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang

Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen.

Maximum Separation Open-World Semi-Supervised Learning

Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation

1 code implementation CVPR 2024 Lin Long, Haobo Wang, Zhijie Jiang, Lei Feng, Chang Yao, Gang Chen, Junbo Zhao

To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations.

Binary Classification Contrastive Learning +1

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.

model

Towards Cross-Table Masked Pretraining for Web Data Mining

2 code implementations10 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.

Memorization

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

Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering

no code implementations Conference 2023 Liyao Li, Haobo Wang, Liangyu Zha, Qingyi Huang, Sai Wu, Gang Chen, Junbo Zhao

Further, we posit that the crucial merit of FETCH is its transferability where the yielded policy network trained on a variety of datasets is indeed capable to enact feature engineering on unseen data, without requiring additional exploration.

Automated Feature Engineering Feature Engineering +1

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 Deep Reinforcement Learning +1

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.

Clustering Deep Reinforcement Learning

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.

Prediction Video Prediction

Prediction Under Uncertainty with Error-Encoding Networks

1 code implementation14 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.

Prediction 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.

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.

Decoder Semi-Supervised Image Classification

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