In traditional machine learning, feature engineering—selecting and transforming raw data into features—is often manual and task-specific. Representation learning aims to automate this process, discovering better data representations that improve model performance. Meanwhile, structured prediction deals with outputs that have interdependent components, such as sequences, trees, or graphs, common in tasks like NLP, computer vision, and bioinformatics. This chapter explores both paradigms, their interconnections, and techniques used in advanced ML systems.

11.1 Fundamentals of Representation Learning 11.1.1 What is Representation Learning? Representation learning is the set of techniques that allow a system to automatically learn features from raw data that can be useful for downstream tasks such as classification, regression, or clustering.

• Generalization: Good representations help models generalize better. • Compactness: Learn compressed but informative representations. • Disentanglement: Separate out independent factors of variation in data.

11.2 Types of Representation Learning 11.2.1 Unsupervised Representation Learning • Autoencoders: o Learn to reconstruct input. o Structure: encoder → bottleneck → decoder. • Principal Component Analysis (PCA): o Projects data onto lower-dimensional space. • t-SNE and UMAP: o Non-linear embeddings used for visualization.

11.2.2 Supervised Representation Learning • Deep Neural Networks: o Hidden layers act as feature extractors. o Representations learned through backpropagation. • Transfer Learning: o Pre-trained models (e.g., ImageNet) offer strong feature extractors for new tasks.

11.2.3 Self-Supervised Learning • Contrastive Learning (e.g., SimCLR, MoCo): o Learn representations by distinguishing between similar and dissimilar pairs. • Masked Prediction Models: o BERT-style language models mask tokens and predict them to learn word representations.

• Invariance: Should be stable under input transformations. • Sparsity: Only a few dimensions active for a given input. • Hierarchical Composition: Capture abstract features at higher layers. • Smoothness: Nearby inputs should have nearby representations.

11.4 Structured Prediction: An Overview 11.4.1 What is Structured Prediction? Structured prediction refers to tasks where outputs are interdependent and structured, such as: • Sequences (e.g., part-of-speech tagging), • Trees (e.g., syntactic parsing), • Graphs (e.g., molecular structure prediction).

11.4.2 Challenges • Exponential Output Space: Hard to enumerate or search all outputs. • Interdependencies: Must consider how parts of the output relate to each other. • Inference Complexity: Finding the best structure often requires complex algorithms.

11.5 Structured Prediction Models 11.5.1 Conditional Random Fields (CRFs) • Used for sequence labeling. • Models conditional probabilities of labels given inputs. • Supports global feature dependencies and Markov assumptions.

11.5.2 Structured SVMs • Extends SVMs to structured outputs. • Solves max-margin learning over structured spaces. • Uses a loss-augmented inference step.

11.5.3 Sequence-to-Sequence (Seq2Seq) Models • Used in NLP (e.g., machine translation). • Encoder-decoder architecture with RNNs, LSTMs, or Transformers. • Handles variable-length inputs and outputs.

11.6 Learning and Inference in Structured Models 11.6.1 Exact vs Approximate Inference • Exact: Dynamic programming (e.g., Viterbi). • Approximate: Beam search, sampling, loopy belief propagation.

11.6.2 Loss Functions • Structured Hinge Loss • Negative Log-Likelihood • Task-specific Evaluation Metrics (e.g., BLEU, IoU)

11.6.3 Joint Learning and Inference • Some models (e.g., neural CRFs) learn parameters and perform inference jointly. • Often uses backpropagation through inference.

11.7 Deep Structured Prediction 11.7.1 Neural CRFs • Combines deep feature learning (via CNN/RNN) with CRF output layers. • Used in semantic segmentation, NER, etc.

11.7.2 Graph Neural Networks (GNNs) • Predict structured outputs on graphs. • Nodes, edges, and their relationships are jointly modeled. • Powerful for molecule modeling, social networks, etc.

11.7.3 Energy-Based Models (EBMs) • Learn an energy landscape over structured outputs. • Inference = minimizing energy. • Used in image generation and structured decision making.

11.8 Applications of Representation & Structured Learning Domain Use Case NLP Named entity recognition, POS tagging, machine translation Vision Semantic segmentation, object detection Bioinformatics Protein folding, gene interaction networks Robotics Motion planning and control Recommender Systems Structured user-item interactions

11.9 Integration: Representation + Structured Learning Modern ML integrates both paradigms: • Representations learned by deep models feed into structured output layers. • Example: In semantic segmentation, CNNs extract pixel-level features, CRFs enforce label consistency. This hybrid approach enables scalable, accurate, and interpretable models.

11.10 Summary In this chapter, we explored two powerful ideas in advanced machine learning: • Representation Learning focuses on automatically extracting meaningful features from raw data, replacing manual feature engineering and improving model generalization. Techniques like autoencoders, contrastive learning, and transformers exemplify this trend. • Structured Prediction tackles tasks where output variables are interrelated, requiring models like CRFs, structured SVMs, and sequence-to-sequence architectures. These models are essential for domains like NLP, bioinformatics, and computer vision. By combining these paradigms, modern machine learning systems can handle complex real-world tasks that require both rich feature representations and sophisticated output modeling.