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Found 14 papers, 5 papers with code
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning
This paper describes experiments undertaken and their results as part of the BioNLP MEDIQA 2021 challenge.
Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare Enterprise
Neural Machine Translation (NMT) is a deep learning based approach that has achieved outstanding results lately in the translation community.
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Second, we decouple the parameter space and the partition count making NeuroCUT inductive to any unseen number of partition, which is provided at query time.
Mirage: Model-Agnostic Graph Distillation for Graph Classification
GNNs, like other deep learning models, are data and computation hungry.
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn a good model.
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
In this work, we address these limitations through a novel GNN framework called GRAFENNE.
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics.
Lifelong Learning for Neural powered Mixed Integer Programming
In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs.
On the Generalization of Neural Combinatorial Optimization Heuristics
Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems.
TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
There has been a recent surge in learning generative models for graphs.
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks
First, our study reveals that a significant portion of the routes recommended by existing methods fail to reach the destination.
SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems
The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey.
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
Representation learning of drug and disease terms for drug repositioning
Drug repositioning (DR) refers to identification of novel indications for the approved drugs.
