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Found 18 papers, 6 papers with code
On Dropping Clusters to Regularize Graph Convolutional Neural Networks
Dropout has been widely adopted to regularize graph convolutional networks (GCNs) by randomly zeroing entries of the node feature vectors and obtains promising performance on various tasks.
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease.
Learning System Dynamics without Forgetting
More importantly, we construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics and significantly enriching the research field of machine learning for dynamic systems.
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
To bridge the gap, we provide a comprehensive review of existing continual graph learning (CGL) algorithms by elucidating the different task settings and categorizing the existing methods based on their characteristics.
Empowering Time Series Analysis with Large Language Models: A Survey
Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs.
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data.
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments.
Deep Bidirectional Language-Knowledge Graph Pretraining
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.
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GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
Hierarchical Prototype Networks for Continual Graph Representation Learning
Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e. g., new research areas in citation networks, or new types of products in co-purchasing networks) and their associated edges are continuously emerging, causing catastrophic forgetting on previous categories.
GreaseLM: Graph REASoning Enhanced Language Models
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Hierarchical Prototype Network for Continual Graph Representation Learning
The key challenge is to incorporate the feature and topological information of new nodes in a continuous and effective manner such that performance over existing nodes is uninterrupted.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
Context Aware Graph Convolution for Skeleton-Based Action Recognition
Extensive experiments on two real-world datasets demonstrate the importance of context information and the effectiveness of the proposed CA-GCN in skeleton based action recognition.
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space.
Learning Dynamic Embeddings from Temporal Interactions
Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions.
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition
Considering the complementarity between graph node convolution and graph edge convolution, we additionally construct two hybrid neural networks to combine graph node convolutional neural network and graph edge convolutional neural network using shared intermediate layers.
