Search Results for author:
Found 18 papers, 6 papers with code
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations.
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes.
Revisiting Heterophily For Graph Neural Networks
ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.
Inductive Bias
Node Classification on Non-Homophilic (Heterophilic) Graphs
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
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.
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Ranked #1 on
Node Classification
on Pubmed
Exploration-Driven Representation Learning in Reinforcement Learning
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.
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning.
Model-based Reinforcement Learning
Out-of-Distribution Generalization
+2
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
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.
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
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.
META-Learning Eligibility Traces for More Sample Efficient Temporal Difference Learning
Our approach can be used both in on-policy and off-policy learning.
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style
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.
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.
Ranked #1 on
Node Classification
on PubMed (0.1%)
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
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.
A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation
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
The generalization abilities of heuristic optimizers may deteriorate with the increment of the search space dimensionality.
A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning
This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).
