This is where I spent most of my mental energy.
Start by defining Quantization-Aware Training (QAT) and explaining how it simulates quantization during training to adapt the model. Then compare QAT with Post-Training Quantization (PTQ) in terms of accuracy, computational cost, and deployment flexibility, highlighting trade-offs. Finally, relate the discussion to real-world scenarios, especially for safety-critical systems like autonomous driving at Waymo.
Pro tip: Emphasize that QAT is particularly valuable when deploying to hardware with limited precision support, but it requires a full training pipeline and careful hyperparameter tuning. Mention that PTQ is often preferred for rapid prototyping or when training resources are constrained.
Explain that QAT inserts fake quantization nodes during training to simulate the effects of quantization, allowing the model to learn parameters robust to low-precision inference.
Describe PTQ as a post-training process that quantizes weights and activations without retraining, often using calibration data to determine scaling factors.
Discuss how QAT typically achieves higher accuracy than PTQ, especially for low-bit widths (e.g., 4-bit), because the model can adapt to quantization noise during training.
Highlight that QAT requires additional training time and resources, while PTQ is faster and cheaper but may suffer accuracy loss, particularly for aggressive quantization.
Mention that QAT can be more complex to implement due to the need for quantization-aware training frameworks, but it often yields better performance on target hardware; PTQ is simpler and more flexible for quick deployment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining knowledge distillation and its core mechanism (teacher-student training with soft targets). Then, compare it to other compression techniques like pruning and quantization, highlighting trade-offs in accuracy, latency, and deployment constraints. Finally, explain when distillation is the preferred choice, especially in scenarios requiring model generalization or ensemble compression, and tie it to Waymo's autonomous driving context.
Pro tip: Emphasize that distillation is not just for compression but also for transferring knowledge from large ensembles or multi-modal teachers to a single deployable model, which is crucial for real-time safety-critical systems like self-driving cars.
Explain the teacher-student paradigm: a large, high-capacity teacher model (or ensemble) transfers knowledge to a smaller student model via soft targets (logits with temperature). Mention the loss function combining distillation loss and student loss.
Contrast distillation with pruning (removing weights/neurons), quantization (reducing precision), and low-rank factorization. Highlight that distillation is model-agnostic and can be combined with others.
Discuss scenarios: when you have a high-performing but slow teacher, need a smaller model with minimal accuracy drop, want to compress an ensemble, or require better generalization on noisy data. Also, when other methods cause too much accuracy loss.
Mention trade-offs: distillation requires training a teacher and a student (more compute upfront), may not reduce inference latency as much as quantization, and can be sensitive to temperature and loss weighting. Also, note that it can be combined with pruning/quantization for greater compression.
Tie the answer to autonomous driving: distillation can compress large perception or prediction models for real-time inference on vehicle hardware, transfer knowledge from simulation to real-world, or combine multiple sensor modalities into a single model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Given it's Waymo, I figured this would come up.
Start by acknowledging that standard accuracy metrics are insufficient for safety-critical systems, then outline a multi-faceted evaluation framework that includes robustness, uncertainty, and fairness. Emphasize the need for rigorous testing, interpretability, and alignment with safety standards, and tie your answer to Waymo's context of autonomous driving.
Pro tip: Demonstrate maturity by discussing the limitations of metrics and the importance of a safety culture, including continuous monitoring and post-deployment analysis. Mention specific techniques like adversarial testing and uncertainty quantification, and relate them to real-world consequences.
Identify the specific safety requirements and failure modes relevant to the application, such as pedestrian detection or rare edge cases. Establish clear evaluation criteria that go beyond accuracy, like false negative rates and time-to-failure.
Choose metrics that capture different aspects of performance: precision-recall curves, F1 score, AUC-ROC for imbalanced data, and calibration metrics for uncertainty. Include domain-specific metrics like intersection over union (IoU) for object detection.
Test the model under distribution shifts, adversarial attacks, and noisy inputs to assess robustness. Quantify uncertainty using techniques like Bayesian neural networks or ensemble methods to ensure the model knows when it's unsure.
Check for performance disparities across different subgroups (e.g., pedestrians of different ages, lighting conditions). Use fairness metrics and bias detection tools to ensure equitable performance.
Set up a system for ongoing monitoring in deployment, with fallback mechanisms and human oversight. Conduct root cause analysis for any failures and iterate on the model and evaluation process.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through the idea of pulling similar pairs together and pushing dissimilar ones apart in embedding space.
Start by defining contrastive learning as a self-supervised technique that learns representations by pulling positive pairs together and pushing negative pairs apart. Then contrast it with supervised representation learning, which relies on explicit labels and typically optimizes a task-specific loss. Finally, discuss trade-offs and why contrastive learning is valuable for autonomous driving domains like Waymo.
Pro tip: Emphasize that contrastive learning can leverage massive unlabeled data (e.g., driving logs) to learn general-purpose representations, reducing labeling costs and improving robustness—key for real-world deployment at scale.
Explain that it's a self-supervised approach where the model learns by comparing similar and dissimilar examples, often using a contrastive loss like InfoNCE. Mention that positive pairs are different views of the same instance, and negatives are other instances.
Describe it as learning representations using labeled data, where the objective is typically to minimize a supervised loss (e.g., cross-entropy) for a specific task like classification or detection. Representations are shaped by the labeled task.
Contrast the reliance on labels: contrastive learning uses unlabeled data and learns from data structure, while supervised learning requires labels. Also contrast the objective: contrastive aims for general-purpose representations, supervised for task-specific ones.
Mention that contrastive learning can scale with unlabeled data but may require careful negative sampling and augmentation design. Supervised learning is more direct but label-hungry. Relate to Waymo: contrastive learning can pretrain on unlabeled driving data, then fine-tune with labels for perception tasks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.