Search Results for author: Mingde Zhao

Found 15 papers, 4 papers with code

Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL

no code implementations21 Mar 2022 Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio

In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping.

Continuous Control Contrastive Learning +1

Exploration-Driven Representation Learning in Reinforcement Learning

no code implementations ICML Workshop URL 2021 Akram Erraqabi, Mingde Zhao, Marlos C. Machado, Yoshua Bengio, Sainbayar Sukhbaatar, Ludovic Denoyer, Alessandro Lazaric

In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space.

reinforcement-learning Representation Learning

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

no code implementations20 Aug 2020 Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information.

Graph Classification Node Classification

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

no code implementations20 Aug 2020 Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning paradigms, are pervasively thought to be caused by the limitations of the GCN layers, including insufficient expressive power, etc.

Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style

no code implementations ICCV 2019 Hongwei Ge, Zehang Yan, Kai Zhang, Mingde Zhao, Liang Sun

In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner.

Image Captioning

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

1 code implementation NeurIPS 2019 Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.

Node Classification

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

2 code implementations25 Apr 2019 Mingde Zhao, Sitao Luan, Ian Porada, Xiao-Wen Chang, Doina Precup

Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well as algorithms which learn how to improve policies.

Meta-Learning

A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation

no code implementations12 Apr 2019 Mingde Zhao, Hongwei Ge, Kai Zhang, Yaqing Hou

The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms.

Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization

no code implementations17 Dec 2018 Mingde Zhao, Hongwei Ge, Yi Lian, Kai Zhang

The generalization abilities of heuristic optimizers may deteriorate with the increment of the search space dimensionality.

Multi-Armed Bandits

A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning

no code implementations3 Mar 2018 Hongwei Ge, Mingde Zhao, Liang Sun, Zhen Wang, Guozhen Tan, Qiang Zhang, C. L. Philip Chen

This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).

Incremental Learning

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