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1500 questions
7
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4 answers
Can the mean squared error be negative?
I'm new to machine learning. I was watching a Prof. Andrew Ng's video about gradient descent from the machine learning online course. It said that we want our cost function (in this case, the mean squared error) to have the minimum value, but that…
Borna Ghahnoosh
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7
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Deep Q-Learning poor convergence on Stochastic Environment
I'm trying to implement a Deep Q-network in Keras/TF that learns to play Minesweeper (our stochastic environment). I have noticed that the agent learns to play the game pretty well with both small and large board sizes. However, it only…
Sanavesa
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7
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What is an objective function?
Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function.
My question is what is the objective function?
Abbas Ali
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7
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Why doesn't VAE suffer mode collapse?
Mode collapse is a common problem faced by GANs. I am curious why doesn't VAE suffer mode collapse?
Trect
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7
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Which Rosenblatt's paper describes Rosenblatt's perceptron training algorithm?
I struggle to find Rosenblatt's perceptron training algorithm in any of his publications from 1957 - 1961, namely:
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
The perceptron: A probabilistic model for information…
Cryptiex
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7
votes
1 answer
What does the agent in reinforcement learning exactly do?
What is an agent in reinforcement learning (RL)? I think it is not the neural network behind. What does the agent in RL exactly do?
TVSuchty
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7
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3 answers
Would AlphaGo Zero become perfect with enough training time?
Would AlphaGo Zero become theoretically perfect with enough training time? If not, what would be the limiting factor?
(By perfect, I mean it always wins the game if possible, even against another perfect opponent.)
Christopher King
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7
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3 answers
Is there research that employs realistic models of neurons?
Is there research that employs realistic models of neurons? Usually, the model of a neuron for a neural network is quite simple as opposed to the realistic neuron, which involves hundreds of proteins and millions of molecules (or even greater…
TomR
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7
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How does the Dempster-Shafer theory differ from Bayesian reasoning?
How does the Dempster-Shafer theory differ from Bayesian reasoning? How do these two methods handle uncertainty and compute posterior distributions?
rudresh dwivedi
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7
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1 answer
In imitation learning, do you simply inject optimal tuples of experience $(s, a, r, s')$ into your experience replay buffer?
Due to my RL algorithm having difficulties learning some control actions, I've decided to use imitation learning/apprenticeship learning to guide my RL to perform the optimal actions. I've read a few articles on the subject and just want to confirm…
Rui Nian
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7
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2 answers
What are the best machine learning models for music composition?
What are the best machine learning models that have been used to compose music? Are there some good research papers (or books) on this topic out there?
I would say, if I use a neural network, I would opt for a recurrent one, because it needs to have…
Ben
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2 answers
Why is the log probability replaced with the importance sampling in the loss function?
In the Trust-Region Policy Optimisation (TRPO) algorithm (and subsequently in PPO also), I do not understand the motivation behind replacing the log probability term from standard policy gradients
$$L^{PG}(\theta) = \hat{\mathbb{E}}_t[\log…
Mark
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7
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What makes multi-layer neural networks able to perform nonlinear operations?
As I know, a single layer neural network can only do linear operations, but multilayered ones can.
Also, I recently learned that finite matrices/tensors, which are used in many neural networks, can only represent linear operations.
However,…
KYHSGeekCode
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3 answers
More effective way to improve the heuristics of an AI... evolution or testing between thousands of pre-determined sets of heuristics?
I'm making a Connect Four game where my engine uses Minimax with Alpha-Beta pruning to search. Since Alpha-Beta pruning is much more effective when it looks at the best moves first (since then it can prune branches of poor moves), I'm trying to come…
Inertial Ignorance
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How does rotating an image and adding new 'rotated classes' prevent overfitting?
From Meta-Learning with Memory-Augmented Neural Networks in section 4.1:
To reduce the risk of overfitting, we performed data augmentation by randomly translating and rotating character images. We also created new classes through 90◦, 180◦ and 270◦…
AAC
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