Machine Learning Guide

MLG 036 AutoencodersAuto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links • Notes and resources at ocdevel.com/mlg/36 Try a walking desk • - stay healthy & sharp while you learn & code • Build the future of multi-agent software with AGNTCY • . • Thanks to T.J. Wilder • from intrep.io • for recording this episode! Fundamentals of Autoencoders • Autoencoders are neural networks designed to reconstruct their input data by passing data through a compressed intermediate representation called a "code." • The architecture typically follows an hourglass shape: a wide input and output separated by a narrower bottleneck layer that enforces information compression. • The encoder compresses input data into the code, while the decoder reconstructs the original input from this code. Comparison with Supervised Learning • Unlike traditional supervised learning, where the output differs from the input (e.g., image classification), autoencoders use the same vector for both input and output. Use Cases: Dimensionality Reduction and Representation • Autoencoders perform dimensionality reduction by learning compressed forms of high-dimensional data, making it easier to visualize and process data with many features. • The compressed code can be used for clustering, visualization in 2D or 3D graphs, and input into subsequent machine learning models, saving computational resources and improving scalability. Feature Learning and Embeddings • Autoencoders enable feature learning by extracting abstract representations from the input data, similar in concept to learned embeddings in large language models (LLMs). • While effective for many data types, autoencoder-based encodings are less suited for variable-length text compared to LLM embeddings. Data Search, Clustering, and Compression • By reducing dimensionality, autoencoders facilitate vector searches, efficient clustering, and similarity retrieval. • The compressed codes enable lossy compression analogous to audio codecs like MP3, with the difference that autoencoders lack domain-specific optimizations for preserving perceptually important data. Reconstruction Fidelity and Loss Types • Loss functions in autoencoders are defined to compare reconstructed outputs to original inputs, often using different loss types depending on input variable types (e.g., Boolean vs. continuous). • Compression via autoencoders is typically lossy, meaning some information from the input is lost during reconstruction, and the areas of information lost may not be easily controlled. Outlier Detection and Noise Reduction • Since reconstruction errors tend to move data toward the mean, autoencoders can be used to reduce noise and identify data outliers. • Large reconstruction errors can signal atypical or outlier samples in the dataset. Denoising Autoencoders • Denoising autoencoders are trained to reconstruct clean data from noisy inputs, making them valuable for applications in image and audio de-noising as well as signal smoothing. • Iterative denoising as a principle forms the basis for diffusion models, where repeated application of a denoising autoencoder can gradually turn random noise into structured output. Data Imputation • Autoencoders can aid in data imputation by filling in missing values: training on complete records and reconstructing missing entries for incomplete records using learned code representations. • This approach leverages the model's propensity to output 'plausible' values learned from overall data structure. Cryptographic Analogy • The separation of encoding and decoding can draw parallels to encryption and decryption, though autoencoders are not intended or suitable for secure communication due to their inherent lossiness. Advanced Architectures: Sparse and Overcomplete Autoencoders • Sparse autoencoders use constraints to encourage code representations with only a few active values, increasing interpretability and explainability. • Overcomplete autoencoders have a code size larger than the input, often in applications that require extraction of distinct, interpretable features from complex model states. Interpretability and Research Example • Research such as Anthropic's "Towards Monosemanticity" applies sparse autoencoders to the internal activations of language models to identify interpretable features correlated with concrete linguistic or semantic concepts. • These models can be used to monitor and potentially control model behaviors (e.g., detecting specific language usage or enforcing safety constraints) by manipulating feature activations. Variational Autoencoders (VAEs) • VAEs extend autoencoder architecture by encoding inputs as distributions (means and standard deviations) instead of point values, enforcing a continuous, normalized code space. • Decoding from sampled points within this space enables synthetic data generation, as any point near the center of the code space corresponds to plausible data according to the model. VAEs for Synthetic Data and Rare Event Amplification • VAEs are powerful in domains with sparse data or rare events (e.g., healthcare), allowing generation of synthetic samples representing underrepresented cases. • They can increase model performance by augmenting datasets without requiring changes to existing model pipelines. Conditional Generative Techniques • Conditional autoencoders extend VAEs by allowing controlled generation based on specified conditions (e.g., generating a house with a pool), through additional decoder inputs and conditional loss terms. Practical Considerations and Limitations • Training autoencoders and their variants requires computational resources, and their stochastic training can produce differing code representations across runs. • Lossy reconstruction, lack of domain-specific optimizations, and limited code interpretability restrict some use cases, particularly where exact data preservation or meaningful decompositions are required.

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MLG 035 Large Language Models 2At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction. Links • Notes and resources at ocdevel.com/mlg/mlg35 • Build the future of multi-agent software with AGNTCY Try a walking desk • stay healthy & sharp while you learn & code In-Context Learning (ICL) Definition: • LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters. Types: • Zero-shot • : Direct query, no examples provided. One-shot • : Single example provided. Few-shot • : Multiple examples, balancing quantity with context window limitations. • Mechanism: • ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations. Emergent Properties: • ICL is an "inference-time training" approach, leveraging the model's pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples. • Retrieval Augmented Generation (RAG) and Grounding Grounding: • Connecting LLMs with external knowledge bases to supplement or update static training data. Motivation • : LLMs' training data becomes outdated or lacks proprietary/specialized knowledge. Benefit • : Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information. • RAG Workflow: • Embedding: 1. Documents are converted into vector embeddings (using sentence transformers or representation models). Storage: 2. Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant). Retrieval: 3. When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing. Augmentation: 4. Retrieved chunks are added to the prompt to provide up-to-date context for generation. Generation: 5. The LLM generates responses informed by the augmented context. • Advanced RAG: • Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge). • LLM Agents Overview: • Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage. Key Components: • Reasoning Engine (LLM Core): • Interprets goals, states, and makes decisions. Planning Module: • Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment. Memory: • Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems. Tools and APIs: • Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models. • Capabilities: • Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability. Current Trends: • Research and development are shifting toward these agentic paradigms as LLM core scaling saturates. Multimodal Large Language Models (MLLMs) Definition: • Models capable of ingesting and generating across different modalities (text, image, audio, video). Architecture: • Modality-Specific Encoders: • Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images). Fusion/Alignment Layer: • Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content. Unified Transformer Backbone: • Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format. • Recent Advances: • Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models. Functionality: • Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation. Advanced LLM Architectures and Training Directions Predictive Abstract Representation: • Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders). Patch-Level Training: • Predicting larger "patches" of tokens to reduce sequence lengths and computation. Concept-Centric Modeling: • Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta's Large Concept Model). Multi-Token Prediction: • Training models to predict multiple future tokens for broader context capture. Evaluation Benchmarks (as of 2025) Key Benchmarks Used for LLM Evaluation: • GPQA (Diamond): • Graduate-level STEM reasoning. SWE Bench Verified: • Real-world software engineering, verifying agentic code abilities. MMMU: • Multimodal, college-level cross-disciplinary reasoning. HumanEval: • Python coding correctness. HLE (Human's Last Exam): • Extremely challenging, multimodal knowledge assessment. LiveCodeBench: • Coding with contamination-free, up-to-date problems. MLPerf Inference v5.0 Long Context: • Throughput/latency for processing long contexts. MultiChallenge Conversational AI: • Multiturn dialogue, in-context reasoning. TAUBench/PFCL: • Tool utilization in agentic tasks. TruthfulnessQA: • Measures tendency toward factual accuracy/robustness against misinformation. • Prompt Engineering: High-Impact Techniques Foundational Approaches: • Few-Shot Prompting: • Provide pairs of inputs and desired outputs to steer the LLM. Chain of Thought: • Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality. Clarity and Structure: • Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring. Affirmative Directives: • Phrase instructions positively ("write a concise summary" instead of "don't write a long summary"). Iterative Self-Refinement: • Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality. System Prompt/Role Assignment: • Assign a persona or role to the LLM for tailored behavior (e.g., "You are an expert Python programmer"). • Guideline: • Regularly consult official prompting guides from model developers as model capabilities evolve. Trends and Research Outlook Inference-time compute • is increasingly important for pushing the boundaries of LLM task performance. Agentic LLMs • and multimodal reasoning • represent the primary frontiers for innovation. Prompt engineering • and benchmarking • remain essential for extracting optimal performance and assessing progress. • Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.

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