Search Results for author: Hao Hu

Found 24 papers, 8 papers with code

What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?

1 code implementation30 May 2023 Rui Yang, Yong Lin, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang

In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important.

Imitation Learning Offline RL

One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration

no code implementations29 May 2023 Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang

To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration.

The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning

no code implementations27 Feb 2023 Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang

Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations.

Offline RL reinforcement-learning +1

Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting

no code implementations13 Dec 2022 Yiqi Sun, Zhengxin Shi, Jianshen Zhang, Yongzhi Qi, Hao Hu, ZuoJun Max Shen

We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability.

Marketing Time Series Forecasting

On the Role of Discount Factor in Offline Reinforcement Learning

no code implementations7 Jun 2022 Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang

The discount factor, $\gamma$, plays a vital role in improving online RL sample efficiency and estimation accuracy, but the role of the discount factor in offline RL is not well explored.

D4RL Offline RL +2

Optimizing Binary Decision Diagrams with MaxSAT for classification

no code implementations21 Mar 2022 Hao Hu, Marie-José Huguet, Mohamed Siala

Then, we lift the encoding to a MaxSAT model to learn optimal BDDs in limited depths, that maximize the number of examples correctly classified.

Classification Decision Making +2

Offline Reinforcement Learning with Value-based Episodic Memory

1 code implementation ICLR 2022 Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang

Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.

D4RL Offline RL +2

On the Estimation Bias in Double Q-Learning

1 code implementation NeurIPS 2021 Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.

Q-Learning Value prediction

Generalizable Episodic Memory for Deep Reinforcement Learning

1 code implementation11 Mar 2021 Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang

Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning.

Atari Games Continuous Control +2

Surface Dyakonov-Cherenkov Radiation

no code implementations17 Dec 2020 Hao Hu, Xiao Lin, Liang Jie Wong, Qianru Yang, Baile Zhang, Yu Luo

Recent advances in engineered material technologies (e. g., photonic crystals, metamaterials, plasmonics, etc) provide valuable tools to control Cherenkov radiation.


Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2

1 code implementation27 May 2020 Benjamin M. Althouse, Edward A. Wenger, Joel C. Miller, Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne, Hao Hu

SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands.

GraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task

no code implementations27 Apr 2020 Chunhua Jia, Lei Zhang, Hui Huang, Weiwei Cai, Hao Hu, Rohan Adivarekar

Multi-label networks with branches are proved to perform well in both accuracy and speed, but lacks flexibility in providing dynamic extension onto new labels due to the low efficiency of re-work on annotating and training.

General Classification Multi-Label Classification

Companion Surface of Danger Cylinder and its Role in Solution Variation of P3P Problem

no code implementations4 Jun 2019 Bo wang, Hao Hu, Caixia Zhang

And when the optical center moves on the danger cylinder, accordingly the optical centers of the two other solutions of the corresponding P3P problem form a new surface, characterized by a polynomial equation of degree 12 in the optical center coordinates, called the Companion Surface of Danger Cylinder (CSDC).

Learning to Adaptively Scale Recurrent Neural Networks

no code implementations15 Feb 2019 Hao Hu, Liqiang Wang, Guo-Jun Qi

Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series.

Time Series Analysis

New insights on Multi-Solution Distribution of the P3P Problem

no code implementations30 Jan 2019 Bo Wang, Hao Hu, Caixia Zhang

In this work, we show that when the optical center is outside of all the 6 toroids defined by the control point triangle, each positive root of the Grunert's quartic equation must correspond to a true solution of the P3P problem, and the corresponding P3P problem cannot have a unique solution, it must have either 2 positive solutions or 4 positive solutions.

Geometric Interpretation of side-sharing and point-sharing solutions in the P3P Problem

no code implementations29 Jan 2019 Bo wang, Hao Hu, Caixia Zhang

In this work, we provide some new geometric interpretations on the multi-solution phenomenon in the P3P problem, our main results include: (1): The necessary and sufficient condition for the P3P problem to have a pair of side-sharing solutions is the two optical centers of the solutions both lie on one of the 3 vertical planes to the base plane of control points; (2): The necessary and sufficient condition for the P3P problem to have a pair of point-sharing solutions is the two optical centers of the solutions both lie on one of the 3 so-called skewed danger cylinders;(3): If the P3P problem has other solutions in addition to a pair of side-sharing ( point-sharing) solutions, these remaining solutions must be a point-sharing ( side-sharing ) pair.

Image Classification Based on Quantum KNN Algorithm

no code implementations16 May 2018 Yijie Dang, Nan Jiang, Hao Hu, Zhuoxiao Ji, Wenyin Zhang

However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming.

Classification General Classification +1

Global versus Localized Generative Adversarial Nets

2 code implementations CVPR 2018 Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, Xian-Sheng Hua

In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.

General Classification

State-Frequency Memory Recurrent Neural Networks

2 code implementations ICML 2017 Hao Hu, Guo-Jun Qi

Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community.

Temporal Sequences

InAR:Inverse Augmented Reality

no code implementations11 Aug 2015 Hao Hu, Hainan Cui

Augmented reality is the art to seamlessly fuse virtual objects into real ones.

3D Reconstruction

First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos

no code implementations6 Jun 2015 Jun Ye, Hao Hu, Kai Li, Guo-Jun Qi, Kien A. Hua

With the prevalence of the commodity depth cameras, the new paradigm of user interfaces based on 3D motion capturing and recognition have dramatically changed the way of interactions between human and computers.

3D Action Recognition

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