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Found 16 papers, 9 papers with code
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
While such methods could improve GNN predictions, they usually don't perform well on explanations.
Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction
Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships.
EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency
In this paper, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation.
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints.
Retrieval-based Knowledge Augmented Vision Language Pre-training
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params.
Efficient On-device Training via Gradient Filtering
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI.
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition.
Molecular Attributes Transfer from Non-Parallel Data
Optimizing chemical molecules for desired properties lies at the core of drug development.
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning
In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings.
Learning Attributed Graph Representations with Communicative Message Passing Transformer
For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture.
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
BioNavi-NP: Biosynthesis Navigator for Natural Products
Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.
Communicative Message Passing for Inductive Relation Reasoning
Relation prediction for knowledge graphs aims at predicting missing relationships between entities.
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Synthesis planning is the process of recursively decomposing target molecules into available precursors.
