Bloom filters check if an element is possibly in a set with high probability, avoiding false negatives
Image: Nandanupadhyay, CC BY-SA 3.0, via Wikimedia Commons
Bloom filter
Bloom filters check if an element is possibly in a set with high probability, avoiding false negatives
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Related concepts
log-probabilities are used instead of probabilities: avoids numerical underflow
Why can't we just add up tiny chances over time?
importance sampling does: reweights samples from proposal to estimate target expectation
Importance sampling estimates target expectations using samples from a different distribution
Top-k vs top-p sampling: top-k fixes candidate count, top-p fixes cumulative probability mass
Top-k sampling fixes candidate count; top-p sampling fixes cumulative probability mass
Randomized algorithm
Randomized algorithms use random bits for expected polynomial time
rejection sampling does: samples from target by accepting/rejecting proposals
Rejection sampling generates observations from a target distribution
Mixture of experts
Mixture of experts (MoE) divides problem space into homogeneous regions
Swipe through 100 ML concepts daily
Open Pocket Polymath