# Tag Info

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The only general situation that comes to my mind where BFS could be preferred over A* is when your graph is unweighted and the heuristic function is $h(n) = 0, \forall n \in V$. However, in that case, A* (which is equivalent to UCS) behaves like BFS (except for the goal test: see section 3.4.2 of this book), i.e. it will first expand nodes at level $l$, then ...

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Both Belief-MDPs and Bayes-Adaptive MDPs (BAMDPs) are special cases of POMDPs and their state space is augmented with a belief over their unobserved/hidden variables. In a belief-MDP, the hidden variables can change over the course of an episode. (Eg. Both the position and the uncertainty in the position of the robot can vary during an episode). In a BAMDP, ...

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There is an inherent assumption in heuristic search that the heuristic function points you in the right direction. A* largely depends on how good the heuristic function is. Two nice properties for the heuristic function are for it to be admissible and consistent. If the latter stands, I can't think of any case where BFS would outperform A*. However, this ...

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Can't see that this has been mentioned yet - there are ways to generate text non-sequentially using a non-autoregressive transformer, where you produce the entire response to the context at once. This typically produces worse accuracy scores because there are interdependencies within the text being produced - a model translating "thank you" could ...

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A perceptron is a linear threshold function. That means it has a weight vector $w$, and it outputs $w \cdot x > t$, where $x$ is the input vector and $t$ the threshold. Naïve Bayes makes the assumption that all features are independent (hence the term naïve). It predicts the most likely class by using Bayesian probability, for each class multiplying the ...

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Naive Bayes is a generative algorithm while Perceptron is a discriminative algorithm. That is the main difference.

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