Neural Network Architectures: Recurrent & Sequence Models Questions
Comprehensive understanding of RNNs, LSTMs, GRUs, and Transformer architectures for sequential data. Understand the motivation for each (vanishing gradient problem, LSTM gates), attention mechanisms, self-attention, and multi-head attention. Know applications in NLP, time series, and other domains. Discuss Transformers in detail—they've revolutionized NLP and are crucial for generative AI.
HardTechnical
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Propose techniques to reduce the O(T^2) memory and compute of full self-attention for very long sequences (T up to 1M tokens). Discuss sparse attention, locality-sensitive hashing (LSH) attention, linearized attention, memory-compressed attention, and trade-offs each approach introduces.
MediumTechnical
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Explain knowledge distillation for sequence models. Describe loss terms you might use beyond standard student-teacher KL on logits (e.g., attention transfer, hidden state imitation), and when intermediate layer matching is beneficial for sequence tasks.
HardTechnical
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Design an experiment and metrics to compare attention visualization techniques for a Transformer summarization model. Include how you'd quantify whether attention maps correlate with human notions of important phrases and how to test reliability across examples.
MediumTechnical
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Implement backpropagation-through-time (BPTT) for a simple RNN across a fixed sequence length in pseudocode. Describe how you would apply gradient clipping and why it is necessary. Assume you already have per-step loss values.
MediumTechnical
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Compare sequence models for time-series forecasting: RNN/LSTM/GRU versus Transformer-based architectures. Discuss advantages and disadvantages in terms of handling long-range dependencies, training parallelism, data efficiency, and inference latency in production.
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