explainable recommendation,recommender systems
Provide reasons for recommendations.
3,145 technical terms and definitions
Provide reasons for recommendations.
Exponential backoff increases delay between retry attempts preventing thundering herd.
Exponential smoothing forecasts by weighted averaging past observations with exponentially decreasing weights for older data.
Molecular fingerprints.
Extended Kalman Filter linearizes nonlinear dynamics locally enabling state estimation in nonlinear systems.
Extended producer responsibility obligates manufacturers to manage product end-of-life.
Costs from defects at customer.
Eyring model generalizes acceleration relationships for multiple stress factors.
Design without manufacturing.
Check factual accuracy.
Encourage disentanglement through discrimination.
Track fact retrieval process.
Store and retrieve facts.
Fail fast: quick experiments, learn from failures, pivot. AI is uncertain; embrace experimentation.
Fail-safe design defaults to safe state when failures occur protecting people and equipment.
Investigate failed chips to find root cause.
Study how failures occur.
Systematically find failure cases.
Systematic equipment reliability analysis.
Frequency of different failure types.
Systematic reliability analysis.
Failure modes describe how equipment can fail characterizing symptoms and mechanisms.
Frequency of failures over time.
Fair DARTS addresses performance collapse in differentiable NAS through early stopping regularization and fair operation selection.
Ensure fairness across clients.
Equitable resource allocation.
Fairness constraints enforce equitable treatment during model training.
Fairness constraints in recommendations ensure equitable treatment across demographic groups or item categories.
Ensure fair exposure for all items.
Fairness metrics quantify equitable performance across populations.
Quantify and measure bias across different demographic groups.
Quantify bias and fairness (demographic parity equalized odds).
Fairness-aware recommendation incorporates constraints or regularization to ensure equitable exposure and outcomes across user groups.
AI fairness ensures equal treatment across groups. Demographic parity, equalized odds. Audit for bias.
PyTorch extension for large-scale training.
Library for efficient similarity search in high dimensions.
FAISS provides library for efficient similarity search and clustering of dense vectors.
Library for efficient vector search.
FAISS is Facebook similarity search. Billion-scale.
Ensure reasoning steps are logically sound.
Generate only from provided documents.
Faithfulness ensures generated answers are supported by provided context.
Open-source LLM trained on high-quality web data.
Fallback models provide backup responses when primary systems fail.
Missed harmful content.
Incorrectly flagged safe content.
Efficient adversarial training.
Collect models along training trajectory.
fastai is practical deep learning library. Built on PyTorch.
Pinpoint error-causing statements.