Diagnose overfitting, DenseNet, preprocessing, CV
Overview
This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.
NVIDIA
Oct 13, 2025, 9:49 PM
Data Scientist
HR Screen
Machine Learning
0
0
ML Interview Task: Overfitting, DenseNet vs. ResNet, Medical Imaging Pipeline, Hyperparameter Tuning, and Cross-Validation
1) Overfitting
- Define overfitting.
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Propose three distinct mitigation techniques and, for each one, explain:
- Its effect on bias–variance.
- Its impact on training dynamics.
2) DenseNet vs. ResNet
- Describe DenseNet’s connectivity pattern versus ResNet.
- Explain why DenseNet helps gradient flow and parameter efficiency.
- Discuss scenarios where DenseNet can hurt performance or efficiency.
3) Medical Imaging Pipeline (Leakage-Free)
You are given a supervised classification problem in medical imaging (e.g., 2D X‑ray or 3D MRI). The dataset has:
- Class imbalance (rare positive pathology).
- Scanner/site drift (differences and temporal shifts across scanners/vendors/sites).
Detail a leakage‑free preprocessing pipeline covering normalization, augmentation, and harmonization. Justify your choices.
4) CNN Hyperparameters and Efficient Tuning
- List five common CNN hyperparameters.
- For each, specify a practical search space and how you would tune efficiently (e.g., schedulers, multi‑fidelity search, early stopping).
5) Cross-Validation
- Compare k‑fold, stratified k‑fold, and nested cross‑validation.
- State when each is necessary to avoid optimistic generalization estimates.
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