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2

Yes, there are different ways. What I think you are looking for is under the research field of Localization and Mapping. Which divides in the following subfields: For getting current (the robot) position and trajectory go to models for Odometry Estimation For getting a representation of the world around the robot go to models for Mapping If you want both of ...


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You do not always need a fitness function to perform genetic algorithm searches. The more general need is for a selection process that favours individuals that perform better at the core tasks in an environment. One way of assessing performance is to award a score, but other approaches are possible, including: Tournament selection where two or more ...


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As you say, the outputs are modeled as a vector, each output in one vector component. In regression problems: The most common loss function, like in the scalar case, is the square error. Skipping constants, it is defined as: $$E=\sum_i ||\mathbf{y_i}-\mathbf{\hat{y_i}}||^2 = \sum_i (\mathbf{y_i}-\mathbf{\hat{y_i}})(\mathbf{y_i}-\mathbf{\hat{y_i}})$$ where: $...


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Assuming you're using softmax on the last layer for classification, it sounds like a simple application of cross entropy loss from here on out: https://datascience.stackexchange.com/questions/20296/cross-entropy-loss-explanation Edit:


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Turns out the reason is because, for places where a dot is shown in the image above, they're actually element-wise multiplications, not dot products. A lot of sources use an X or . to denote multiplication, but don't clearly indicate they mean element-wise multiplication.


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It is not clear form your question, how you use your replay buffer. Basically, you have to store all states/reward tuples and train your agent on the entire buffer. Moreover, you should give the agent time to explore (all) states of the world. But if you want to speed up training, you can try to implement importance sampling


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Before anything, the function you have wrote for the network lacks the bias variables (I'm sure you used bias to get those beautiful images, otherwise your tanh network had to start from zero). Generally I would say it's impossible to have a good approximation of sinus with just 3 neurons, but if you want to consider one period of sinus, then you can do ...


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A layer with bigger number of nodes than previous one is something very common. Some examples are: strategies encoder-decoder (autoencoders) where the encoder typically has layers with a decreasing number of nodes (until the compressed/encoded data) and the decoder has layers increasing in number of nodes. bidirectional recurrent networks where in the ...


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Several ways to add new class to trained model which require just training for new classes. Incremental training Transfer Learning Twice Continual learning approaches (Regularization, Expansion, Rehearsal)


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The state space is certainly continuous, assuming that you can somehow feed that AI exact coordinates. You may have to resort to CNNs if you do not have access to this information. For the action space, you should consider how the game actually plays. Since you use a mouse to simply show the direction, you could use (x,y) positions of the mouse as an action, ...


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The first thing I spotted was that a(t)=∂L/∂z(t) should be a(t)=-∂L/∂z(t). Later you have the correct value so this is probably a typo. I do not fully understand every step of N-ODE and I do not fully understand your question. Nevertheless... First, a forward pass is done to obtain predictions of z, at every t. Then the adjoint state is run backwards in time ...


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In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all ...


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Monocular depth estimation basically does this and it implicitly brings in knowledge about object size. But you need to have prior information.


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is it common to deal with weights and biases in everyday tasks or in most of the cases existing algorithms do it well? No; and it is no coincidence that you will not be able to find any reference to such a practice in any course or tutorial about neural networks. Such a practice would require a whole additional level of (business/SME) know-how in order to ...


1

Can my loss function be evaluating the model until it dies? 1/survival time could be the loss value to be minimized by gradient descent. In order to use backpropagation and gradient descent, you have to relate the loss function directly to the output of the neural network. Your proposed loss function is too indirect, it is not possible to turn it directly ...


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Short answer: larger gradients That is not the derivative of the softmax function. $t - o$ is the combined derivative of the softmax function and cross entropy loss. Cross entropy loss is used to simplify the derivative of the softmax function. In the end, you do end up with a different gradients. It would be like if you ignored the sigmoid derivative when ...


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"Will a neural network adapt to that ?" No. The big functional difference between human mind and neural networks : human mind learns by itself, a NN not. If we call NN the net with its layers, weights, ... this is a static system, unable to learn anything new. The back-propagation algorithm that made intelligent the NN runs outside the NN itself, ...


1

The behaviour when playing against "cheats" depends on how the agent has been trained, and how different the game becomes from the training scenarios. It will also depend on how much of the agent's behaviour is driven by training, and how much by just-in-time planning. In general, unless game playing bots are written specifically to detect or cope ...


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If you look at the definition of the cross-entropy (e.g. here), you will see that it is defined for probability distributions (in fact, it comes from information theory). You can also show that the maximization of the (binomial/Bernoulli) log-likelihood is equivalent to the minimization of the cross-entropy, i.e. when you minimize the cross-entropy you ...


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I only have one good news... There is nothing wrong with your code. Neural networks tend to do that. Especially with a really complex function. Increasing the amount of neurons will not decrease how the error is distributed. There are better loss functions for each case but is not a really effective solution. Neural networks are really good managing noise. ...


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A very wide but shallow neural network is going to be harder to train. You can check that with the playground of tensorflow or with the MPG example in Google Colab. The relationship between architecture and learning capabilities is not fully understood, but, empirically, thats what you see. But making the network too deep creates more problems: Vanishing ...


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You might be able to glean what you want from Chapter 13 or Sutton & Barto's Reinforcement Learning: An Introduction, which deals with policy gradient algorithms, and includes pseudocode for a variety of agents based on linear approximation using softmax regression. From your description, you appear to be using - or should consider - softmax regression ...


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Another good (although a bit old) and freely available online book (apart from the one suggested in this answer) is Neural Networks - A Systematic Introduction (1996) by Raul Rojas. This book contains several exercises at the end of each chapter and covers topics that you will not find in many online courses.


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They're pretty much the same thing - in that the underlying logic of neural networks is fuzzy. A neural network will take a variety of valued inputs, give them different weight in relation to eachother, and arrive at a decision which normally also has a value. Nowhere in that process is there anything like the sequences of either-or decisions which ...


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ML is full with things that supposed to work better (in theory). Sigmoid function seems better than ReLu. L1 seems way better than L2. Spikes neural network seem to be better than standard neural network. A shallow neural network with a lot of neurons has more parameters than a deep one with the same amount of neurons. So, in theory, has to be more ...


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After reading your question I can relate it to the Representation Learning papers such as SimCLR and SwAV. These models use a "Big Task agnostic CNN" to obtain smaller representations of the images and then they train another CNN for classification. I suggest you read Big Self-Supervised Models are Strong Semi-Supervised Learners by Ting Chen, ...


1

Use of Transposed Convolution can lead to checkerboard artifacts. So we prefer to up-sample and then apply convolution. You can check this article for more information https://distill.pub/2016/deconv-checkerboard/.


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Disclaimer: I am not an attorney and this does not constitute formal legal advice. If the output is novel the copyright resides with the creator In this case, almost certainly the human who utilizes the algorithm†. There was a recent US patent case "Dabus" [U.S. Patent Application No.: 16/524,350] where the human programmers tried to claim an AI ...


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Firstly, note that the Gaussian policies you describe are not equivalent to $\epsilon$-greedy, mainly for this reason: for a fixed policy, the policy's variance in the Gaussian case depends on the state, while in the $\epsilon$-greedy case it does not. Right off the bat, the Gaussian policy should achieve less regret than $\epsilon$-greedy. Other approaches ...


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This update rule can still be applied in the continuous domain. As pointed out in the comments, suppose we are parameterising our policy using a Gaussian distribution, where our neural networks take as input the state we are in and output the parameters of a Gaussian distribution, the mean and the standard deviation which we will denote as $\mu(s, \theta)$ ...


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? This means that there are not promising versions of this algorithm fro regression until 2012. After your question, I have found one of the survey research paper which is done or ensemple methods for regression. This table also extracted from this paper. Read this paper, it will help you a lot more This one is latest paper published on object detection with ...


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In practice you never want to classify just a single digit rather than series. In such case you have to pass a patch of image to multiple network, which would make it inconvenient. If you built different accurate models, training parameters will not significantly reduced. For example sloppy written 6, in a single model the probability of being 6 and 0 would ...


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One of the essential pre-processing we do on the corpus involves treating the variable-length sentences to a fixed length. There are various ways in which we can do this: Truncate This involves reducing the length of all the sentences to the length of the shortest sentence in the corpus. This is generally not done as it reduces the amount of information ...


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In rehearsal, you do not necessarily train with all old training data, but you can just use some of it [1], which you add to your current (or new) training data. In batch learning, at every epoch, you typically train with all training data, every step with a different batch (or subset) of the training data; so, if you have $N$ training examples and your ...


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I think it makes sense to use a Conv2D net for evaluating each position where you have different input channels for each figure type on the board. For example one channel for pawns: an 8x8 matrix with 1's where there are white pawns, and -1's where there are black pawns. the rest should be 0. Also input channels for bishops, knights etc... and then ...


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Check out Figure 6 in this paper: PyTorch Distributed: Experiences on Accelerating Data Parallel Training It breaks down the latency of the forward pass, the backward pass, the communication step, and the optimization step for running both ResNet50 and BERT on a NVIDIA Tesla V100 GPUs. From measuring the pixels in the figure, I estimated the times for the ...


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Normally you only have two classes along with a threshold probability. It's how systems like YOLO work.


1

You are right. If you don't continuously train the neural network after you have deployed it, there is no way it can continuously learn or be updated with more information. You need to program the neural network to learn even after it has been deployed. There is no such thing as a neural network that decides what it does without a human deciding first what ...


1

So if I understand correctly, you're proposing to use a neutral net with $N$ input units (let's say data is in $\mathbf{R}^N$), 1 hidden unit, and whatever the necessary output needs to be. Let's say we try to do this. Then each unit of the output layer is responsible for computing its output based on a single scalar input. So it's like as if you're saying ...


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Yes, if you have 3 images (and by images I assume you mean samples) the flatten layer will be of the shape $12288*3$ ($64*64*3=12288$). The size of $W$ however does not change, and nor does the size of $b$ as these are parameters and are independent of the amount of samples passed through the network. ETA: I only answered the "Am I right?" part of ...


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