Learn Before
Approximate Search in Scored Inference
A scored inference system may compute a score for each possible output but still need an approximate search algorithm to find a high-scoring output, because exhaustive enumeration can be far too large. These approximate algorithms are not guaranteed to find the output that maximizes the score.
0
1
References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Data Science
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Approximate Search in Scored Inference
Distance-Based Scoring Function
Semantic Matching Scoring Function
In Andrew Ng's speech recognition example, what does Score_A(S) represent?
True or False: A scoring function alone is sufficient; no separate search or maximization step is needed.
An inference system searches for the output that _____ the scoring function.
Match each scoring function component to its role in the speech recognition example.
Order the steps of the scoring function design pattern in AI system design.
Explain why learning a scoring function and searching separately is a useful AI design pattern.
Diagnose a speech recognition error using the scoring function framework.
What two components make up the common AI design pattern described for scoring functions?
When can you apply the Optimization Verification test according to the source?
True or False: In the common design pattern, both the scoring function and the maximization algorithm are typically exact rather than approximate.
Learn After
Beam Search as Approximate Inference Search
Why can't a scored inference system exhaustively enumerate all possible output sentences to find the highest-scoring one?
True or False: Approximate search algorithms used in scored inference are guaranteed to find the output that maximizes the score function.
Because exhaustively enumerating all possible outputs is infeasible, a scored inference system must apply a(n) _____ algorithm to find a high-scoring output.
Why must a scored inference system use approximate search rather than exhaustively enumerating all possible output sentences?
Approximate search algorithms used in scored inference are guaranteed to find the output S that maximizes Score_A(S).
In a scored inference system, the formal search objective is to compute the _____ of Score_A(S) over all possible output sentences S.
Match each term to its role in a scored inference system for speech recognition.
Order the steps a scored inference system follows to produce a transcription from an audio clip.
What does the quantity P(S|A) represent when used as Score_A(S) in a scored speech recognition system?
For a vocabulary of 50,000 words, there are (50,000)^N possible output sentences of exactly length N.
Approximate search algorithms are _____ to find the output that maximizes the score function, yet they make search computationally tractable.
Match each challenge in scored inference to the concept that best characterizes it.
Order the reasoning steps that build the argument for why approximate search is necessary in scored inference.
Explain the computational challenge and theoretical limitation of search in a scored inference system.
Diagnose search suboptimality in a scored speech recognition system using approximate search.
State the trade-off of using approximate search algorithms in scored inference.