New answers tagged comparison
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Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see here and here), I would say tree-based models e.g. Random Forests, Decision Trees, Gradient Boosting are favorable over neural networks when working with low-dimensional data and easy interpretable features (usually simple tabular data with ...
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What is eager learning or lazy learning?
Eager learning is when a model does all its computation before needing to make a prediction for unseen data. For example, Neural Networks are eager models.
Lazy learning is when a model doesn't require any training, but all of its computation during inference. An example of such a model is k-NN. Eager learning is ...
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The difference between Vanilla Policy Gradient (VPG) with a baseline as Value function and Advantage Actor Critic (A2C) is very similar to the difference between Monte Carlo Control and SARSA:
The value estimates used in updates for VPG are based on full sampled returns, calculated at the end of episodes.
The value estimates used in updates for A2C are ...
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I'm not sure any intelligent mechanism can be entirely free of symbolic logic.
Even where a decision is statistically based, a machine that takes actions must include some form of:
IF {some condition}
THEN {some action}
As to the popularity of newly proven statistical AI methods (ANN and genetic algorithms), this derives from the greater utility they ...
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You might also ask if there's any particular reason why we would use a neural net. If we're to train a neural net to play chess, we need to be able to:
1. Feed it positions as input vectors (easy enough),
2. Decide on an output format. Perhaps a distribution over possible moves (but then, how to represent that such that the meaning of a specific output cell ...
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ANNs as used today need 1. a lot of data 2. a lot of computational power. Before we had any of the above two, we didn't really know how to properly build ANNs since we didn't quite have the means to train the network, and thus couldn't evaluate it.
"Symbolic AI" on the other hand, is very much just a bunch of if-else/logical conditions, much like ...
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After diving deeper into the material I am able to answer my own question:
Simulated Annealing tries to optimize a energy (cost) function by stochastically searching for minima at different temparatures via a Markov Chain Monte Carlo method. The stochasticity comes from the fact that we always accept a new state $c'$ with lower energy ($\Delta E < 0$), ...
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