Artificial Intelligence Stack Exchange
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1500 questions
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What are the advantages of the Kullback-Leibler over the MSE/RMSE?

I've recently encountered different articles that are recommending to use the KL divergence instead of the MSE/RMSE (as the loss function), when trying to learn a probability distribution, but none of the articles are giving a clear reasoning why…
razvanc92
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How do I know when to use which Monte Carlo method?

I'm a bit confused with extensive number of different Monte Carlo methods such as: Hamiltonian/Hybrid Monte Carlo (HMC), Dynamic Monte Carlo (DMC), Markov chain Monte Carlo (MCMC), Kinetic Monte Carlo (KMC), Dynamic Monte Carlo (DMC) Quasi-Monte…
kenorb
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Why did a Tesla car mistake a truck with a bright sky?

Do we know why Tesla's autopilot mistaken empty sky with a high-sided lorry which resulted in fatal crash involving a car in self-drive mode? Was it AI fault or something else? Is there any technical explanation behind this why this happened? The…
kenorb
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How is the gradient of the loss function in DQN derived?

In the original DQN paper, page 1, the loss function of the DQN is $$ L_{i}(\theta_{i}) = \mathbb{E}_{(s,a,r,s') \sim U(D)} [(r+\gamma \max_{a'} Q(s',a',\theta_{i}^{-}) - Q(s,a;\theta_{i}))^2] $$ whose gradient is presented (on page…
Dimitris Monroe
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Benchmarks for reinforcement learning in discrete MDPs

To compare the performance of various algorithms for perfect information games, reasonable benchmarks include reversi and m,n,k-games (generalized tic-tac-toe). For imperfect information games, something like simplified poker is a reasonable…
user76284
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What is the current state-of-the-art in Reinforcement Learning regarding data efficiency?

In other words, which existing reinforcement method learns with fewest episodes? R-Max comes to mind, but it's very old and I'd like to know if there is something better now.
rcpinto
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What are the state-of-the-art approaches for continual learning with neural networks?

There seems to be a lot of literature and research on the problems of stochastic gradient descent and catastrophic forgetting, but I can't find much on solutions to perform continual learning with neural network architectures. By continual learning,…
gcorso
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Is there any use of using 3D convolutions for traditional images (like cifar10, imagenet)?

I am curious if there is any advantage of using 3D convolutions on images like CIFAR-10/100 or ImageNet. I know that they are not usually used on this data set, though they could because the channel could be used as the "depth" channel. I know that…
Charlie Parker
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How to show temporal difference methods converge to MLE?

In chapter 6 of Sutton and Barto (p. 128), they claim temporal difference converges to the maximum likelihood estimate (MLE). How can this be shown formally?
fool
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Video summarization similar to Summe's TextRank

We have the popular TextRank API which given a text, ranks keywords and can apply summarization given a predefined text length. I am wondering if there is a similar tool for video summarization. Maybe a library, a deep model or ML-based tool that…
Mary
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Are simple animal snares and traps a form of automation? Of computation?

I'm trying to understand the relationship of humans and automation, historically and culturally. I ask because the waterclock is generally considered the earliest form of automation, but snares and deadfall traps constitute simple switch…
DukeZhou
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Are fully connected layers necessary in a CNN?

I have implemented a CNN for image classification. I have not used fully connected layers, but only a softmax. Still, I am getting results. Must I use fully-connected layers in a CNN?
SARIKA
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What is the difference between asymmetric and depthwise separable convolution?

I have recently discovered asymmetric convolution layers in deep learning architectures, a concept which seems very similar to depthwise separable convolutions. Are they really the same concept with different names? If not, where is the difference?…
Pierre Gramme
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Is it ok to struggle with mathematics while learning AI as a beginner?

I have a decent background in Mathematics and Computer Science .I started learning AI from Andrew Ng's course from one month back. I understand logic and intuition behind everything taught but if someone asks me to write or derive mathematical…
user27556
6
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2 answers

What is the difference between learning without forgetting and transfer learning?

I would like to incrementally train my model with my current dataset and I asked this question on Github, which is what I'm using SSD MobileNet v1. Someone there told me about learning without forgetting. I'm now confused between learning without…
abhimanyuaryan
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