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Fundamentals of Cost-Efficient AI : In Healthcare and Biomedicine
Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Chantilly : Elsevier Science & Technology, 2025Beschreibung: 1 Online-Ressource (670 p.)ISBN:- 9780443333637
- 610.28563
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PPN: PPN: 1970428163Package identifier: Produktsigel: BSZ-4-NLEBK-KAUB | ZDB-4-NLEBK
Front Cover -- Fundamentals of Cost-Efficient AI -- Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine -- Copyright -- Contents -- 1 -- Introduction -- Why does efficient AI matter? -- Model training pains -- Model serving pains -- Making AI accessible -- Modern machine learning -- Bringing simplicity to complexity -- Book organization -- Transformer Architectures -- Model Fine-Tuning -- Model Compression Techniques -- Efficient Reinforcement Learning -- Efficient Graph Techniques -- Training Data -- Training Data Augmentation -- Training Data Generation -- Efficient Mixture of Expert Models -- Hardware awareness -- GPU fundamentals and model inference -- Fast Matrix Multiplications -- Running Models Locally -- Expert Interviews and Use Cases -- Relevance to healthcare and biomedicine -- Book audience -- History of machine learning -- Future of AI in healthcare and biomedicine -- Types of machine learning -- All the best! -- References -- 2 -- Efficient transformer architectures -- Introduction -- Attention and optimization -- Introducing attention -- Optimizing attention -- Key-value cache -- Without key-value cache -- With key-value cache -- Efficient sequence modeling -- Order matters -- Learning position encodings -- Sinusoidal positional encoding -- Sparse and localized attention -- Sparse attention -- Localized Receptive Fields -- Sliding Window Attention -- Top-K Attention -- Random feature attention -- Specialized architectures -- Linformer -- Longformer -- Routing Transformer -- Kronecker factorization -- Nystromformer -- Compressive transformer -- Perceiver -- Set Transformer -- TokenLearner -- Sinkhorn Transformer -- Funnel Transformer -- BigBird -- Swin transformer -- Long-short transformer -- CharFormer -- Axial Transformer -- Progressive sparsification -- Efficient activation functions -- ReLU linear attention.
Polynomial attention -- Cosine-based attention -- Conclusion -- AI disclosure -- References -- 3 -- Efficient model fine-tuning -- Introduction -- The need for fine-tuning -- Challenges and how to overcome them -- Data challenges -- Computational constraints -- Catastrophic forgetting in fine-tuning -- Parameter selection strategies -- Specification-based selection -- Heuristic-based approaches -- Learning-based approaches -- Masking techniques for parameter selection -- Reparameterization techniques -- Intrinsic dimensionality -- Low-rank adaptation and variants -- LoRA: Low-rank adaptation -- LoRA-FA: LoRA with fine-tuning activation -- MoELoRA: Mixture of Experts with LoRA -- Layer-wise LoRA -- LoRAPrune -- Incremental LoRA -- Dynamic LoRA -- Adaptive LoRA -- QLoRA: Quantized LoRA -- QA-LoRA: Quantization-aware LoRA -- Low-rank fine-tuning via quantization -- Laplace-LoRA -- KronA: Kronecker-factored adaptation -- Hybrid fine-tuning techniques -- MAM Adapter -- Universal adaptation subspace -- Gradual unfreezing -- Adapter tuning methods -- Pfeiffer adapter -- Houlsby adapter -- Parallel Adapter -- Adapter fusion -- Sparse Adapter -- Compacter -- Prefix tuning -- Optimization techniques -- Layer-wise learning rate scheduling -- Training dynamics -- Domain-adaptive pretraining -- Memory-efficient fine-tuning (MeZO) -- Key advantages of MeZO -- How MeZO works -- Tips for maximizing memory efficiency -- Tokenizer optimization -- Caching frequent tokens -- Batch tokenization strategies -- Sharpness-aware minimization -- Research context and future directions -- Conclusion -- AI disclosure -- References -- 4 -- Model compression techniques -- Introduction -- Quantization -- Representing real numbers -- Floating point operations per second (FLOPS) -- Why floating point? -- Quantization types -- Downcasting -- Uniform quantization.
Symmetric versus asymmetric quantization -- Nonuniform quantization -- Power-of-two quantizer -- Quantization decisions -- Granularity -- Other layers -- Activation functions -- Calibration methods -- KL Divergence Calibration -- Entropy-based calibration -- Activation-aware Weight Quantization (AWQ) -- Selection and preservation of weights -- Scaling as an alternative -- Generative pretrained transformers quantization (GPTQ) -- Arbitrary order -- Hessian matrix -- Lazy update -- Numerical stability -- Weight grouping -- Outline placeholder -- Quantization aware training (QAT) -- Quantization-aware training for Graph Neural Networks -- XNOR nets -- Mixed precision training -- Low-rank and pruning -- Low rank adaptation (LoRA) -- Why LLMs have low-rank structure -- Low rank and quantization -- Pruning types -- Pruning by structure -- Unstructured pruning -- Structured pruning -- Pruning granularity -- Temporal pruning -- Static pruning -- L2 norm-based pruning -- Geometric median based pruning -- Dynamic pruning -- Nuclear norm-based pruning -- Connection-based pruning -- Penalty-based pruning -- Sensitivity estimation algorithm -- Sparse training from scratch -- Efficiency under compute constraints -- Cramming -- Gradient checkpointing -- DeepSpeed -- Scaling principles -- Depth -- Sequence length -- Curriculum learning -- Reducing memory waste with batch shuffling -- Tucker decomposition -- Asynchronous updates -- Hyperparameters -- Model checkpointing -- Gradient checkpointing -- Layer wise checkpointing -- Uniform checkpointing -- Nonuniform checkpointing -- Weight sharing -- K-means weight sharing -- HashNet style parameter hashing -- Conclusion -- AI disclosure -- References -- 5 -- Efficient reinforcement learning -- Introduction -- What is RL? -- Core concepts -- Environment -- State -- State space approaches -- State space representation.
Adaptive decision-making in dynamic environments -- Belief updates -- Planning horizon -- Replay and experience methods -- Hindsight Experience Replay (HER) -- Prioritized Experience Replay (PER) -- Improving value-based stability -- Overestimation bias/double DQN -- Categorical DQN/C51 -- Sim2Real -- Population and search-based methods -- Population-based training (PBT) -- Monte Carlo Tree Search (MCTS) -- Factorization techniques -- Accelerated and efficient training strategies -- Accelerated learning/curriculum learning -- Efficient hyper-parameter search -- Debugging RL training -- Reward design pitfalls -- Conclusion -- AI disclosure -- References -- 6 -- Efficient graph algorithms -- Introduction -- Graph sampling -- Edge sampling -- Node sampling -- Snowball sampling -- Random walk sampling -- Metropolis-Hastings (MH) -- Graph representation -- Edge list -- Dynamic graphs -- Knowledge graphs -- Co-reference resolution -- Entity extraction -- Relationship classification -- End-to-end autoregressive models -- Training -- Embeddings -- Dependency-based embeddings -- n-Gram language models -- Graph representation learning -- Node similarity metrics -- Betweenness centrality -- Closeness centrality -- PageRank -- Degree centrality -- Structural Equivalence -- Eigenvector Centrality -- Graphlets -- Clustering co-efficient -- SimRank -- Jaccard Similarity -- Efficiency -- Node embedding algorithms -- DeepWalk -- Node2Vec -- LINE (large-scale information network embedding) -- High-Order Proximity preserved Embedding (HOPE) -- GraRep -- Graph neural network-based approaches -- Graph convolutional network -- GraphSAGE -- FastGCN -- Graph Attention Networks (GAT) -- Graph autoencoders (GAE) -- Variational GAE (VGAE) -- Deep Graph Infomax (DGI) -- Scalable Inception Graph Neural Network (SIGN) -- Graph Transformers -- Metapath2Vec -- Community detection.
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