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Found 5 papers, 4 papers with code
Causal Transformer for Estimating Counterfactual Outcomes
In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.
Estimating average causal effects from patient trajectories
Instead, medical practice is increasingly interested in estimating causal effects among patient subgroups from electronic health records, that is, observational data.
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.
Ranked #3 on
Retinal OCT Disease Classification
on OCT2017
Retinal OCT Disease Classification
Semi-Supervised Image Classification
+1
Unsupervised Anomaly Detection for X-Ray Images
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #25 on
Entity Alignment
on DBP15k zh-en
