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Model family that processes inputs and/or outputs as ordered sequences, capturing temporal or positional dependencies.
DESCRIPTION
Sequence models handle data where order matters, such as language, speech, and time series. Architectures include RNNs (LSTM, GRU), sequence-to-sequence frameworks, and transformers with attention. Tasks range from labeling, prediction, and translation to speech recognition and forecasting. Training can be teacher-forced or autoregressive. Challenges include long-range dependencies, vanishing gradients, and exposure bias. Recent advances favor attention-based models for scalability and expressiveness.
ALTERNATIVE DEFINITIONS
Stanford CS224n
Sequence models are models where the inputs and/or outputs are sequences, such as sentences, audio, or DNA.
General pedagogical definition spanning input and output sequences.
Goodfellow, Bengio, Courville
Recurrent neural networks are general-purpose sequence models designed to process sequences by maintaining a state that can capture information about previous elements.
Frames RNNs as a core class of sequence models with memory.
RELATED TERMS
Recurrent Neural Network LSTM GRU Transformer Model Hidden Markov Model Sequence-to-Sequence Autoregressive Model Time Series Modeling Attention Mechanism Connectionist Temporal Classification
COMPETING TERMS
Feedforward Neural Network
RELATIONS
- Broader: Model
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Recurrent neural network
- Christopher Olah - Explains LSTMs and sequence modeling.
- TensorFlow - Keras guide to RNNs.
- Sutskever et al. - Sequence-to-sequence learning paper.
TOP QUESTIONS
What problems are best suited for Sequence Models?
Language modeling, machine translation, speech recognition, and time-series forecasting benefit from modeling order and dependencies.
Category: Applications
How do Sequence Models handle long-range dependencies?
LSTMs/GRUs use gating; transformers use attention to connect distant positions. Techniques like segment recurrence and memory compressions help further.
Category: Concept
What is exposure bias in Sequence Models?
It arises when models see ground-truth histories during training but must rely on their own predictions at inference, causing errors to compound.
Category: Risks
How do transformers compare to RNNs for sequences?
Transformers parallelize and handle long contexts better, while RNNs may be lighter for small problems and low-latency edge cases.
Category: Comparison
What losses are used in Sequence Models?
Cross-entropy for classification/labeling, CTC for alignment-free speech, and regression losses for continuous outputs.
Category: Practice
What is teacher forcing in Sequence Models?
A training strategy feeding ground-truth tokens to stabilize learning. Scheduled sampling gradually reduces reliance on ground truth.
Category: Concept
How to evaluate Sequence Models?
Use perplexity for language models, BLEU/ROUGE in translation/summarization, and task-specific metrics for sequence labeling.
Category: Testing
What are common mistakes when training Sequence Models?
Insufficient regularization, poor tokenization, and not clipping gradients can cause instability or overfitting.
Category: Mistakes
Can Sequence Models operate in real time?
Yes; streaming inference and chunked processing enable low-latency scenarios, but require careful architecture choices.
Category: Deployment