optimization - Concepts

Explore concepts tagged with "optimization"

Total concepts: 18

Concepts

Exploration vs Exploitation - A fundamental tradeoff in decision-making between trying new things to discover opportunities and using what you already know works.
Local Optimum - A solution that is best within a limited neighborhood but not the globally best solution.
Conversion Rate - The percentage of visitors or leads who complete a desired action.
Knowledge Distillation - A model compression technique where a smaller student model is trained to reproduce the behavior and outputs of a larger, more capable teacher model.
A/B Testing - A method of comparing two versions of something to determine which performs better.
Speculative Decoding - An inference acceleration technique where a smaller draft model proposes multiple tokens that a larger target model verifies in parallel, speeding up generation without changing output quality.
Mixture of Experts - A neural network architecture that uses a gating network to route inputs to specialized sub-networks called experts, enabling efficient scaling by activating only a subset of parameters for each input.
Sparse Models - Neural network architectures where only a fraction of parameters are activated for any given input, enabling larger model capacity with lower computational cost.
Direct Preference Optimization - A simplified alternative to RLHF that fine-tunes language models directly on human preference data without training a separate reward model.
Reinforcement Learning - A machine learning paradigm where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties as feedback.
Model Quantization - A technique for reducing the numerical precision of a neural network's weights and activations to decrease model size, memory usage, and inference latency.
Meta-Prompting - Using AI to generate, refine, or improve prompts themselves, creating a recursive improvement loop.
Ensemble Learning - A machine learning paradigm that combines predictions from multiple models to produce more accurate and robust results than any single model alone.
Critical Path Method - A project scheduling technique identifying the longest sequence of dependent tasks.
Model Pruning - A neural network compression technique that removes redundant or low-impact weights, neurons, or entire layers to create smaller, faster models.
Multi-Task Learning - A machine learning approach where a single model is trained on multiple related tasks simultaneously, leveraging shared representations to improve generalization.
Model Scaling - The study and practice of increasing neural network size, data, or compute to improve model performance, guided by empirical scaling laws.
Pareto Efficiency - A state of resource allocation where no individual can be made better off without making at least one other individual worse off.

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