SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

arXiv:2606.20451 · stat.ML, cs.LG, stat.AP, stat.CO · Submitted 2026-06-18 · Read on arXiv

Jie Min, Yueyao Wang, Mengkun Chen

stat.ML, cs.LG, stat.AP, stat.CO

Submitted: 2026-06-18

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 25× in this paper · explained in 2 episodes
• Loss Function — 10× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Latent Variables — 4× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Hierarchical Structure — 3× in this paper · explained in 3 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Overfitting — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Contrastive Learning — 2× in this paper · explained in 9 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Copula — 1× in this paper · explained in 2 episodes
• Double Bit Error — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Long Short-Term Memory (LSTM) — 1× in this paper · explained in 2 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Neural Fine Gray — 1× in this paper
• Uncertainty Quantification — 1× in this paper · explained in 9 episodes

Sources

• Modeling Spatially Correlated Failure-time Data Under Two Distance Functions with an Application to Titan GPU Data
• The Use of Variational Inference for Lifetime Data with Spatial Correlations

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