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Found 8 papers, 3 papers with code
Coreference by Appearance: Visually Grounded Event Coreference Resolution
Event coreference resolution is critical to understand events in the growing number of online news with multiple modalities including text, video, speech, etc.
Concept Discovery for Fast Adapatation
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task.
ARIEL: Adversarial Graph Contrastive Learning
In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning.
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions.
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
Batch Reinforcement Learning Through Continuation Method
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow.
