Questions tagged [weights]

For questions about the concept of weight (or parameter) of a machine learning model, such as a neural network or a linear regression model.

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Are there any recommendations on initialising a single parameter in deep learning?

I want to initialize a parameter, which is a single real number in my model. If you want the role of the parameter in the model, you can assume it as the parameter to multiply with the output of the ...
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Custom layers in Keras -- defining weights [closed]

I am trying to understand how to build custom layers in Keras and I went through a couple examples: here and here. The syntax is, of course, similar, but in non of the cases it is addressed why ...
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Are there neural networks with (hard) constraints on the weights?

I don't know too much about Deep Learning, so my question might be silly. However, I was wondering whether there are NN architectures with some hard constraints on the weights of some layers. For ...
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17 views

Rank of gradient-of-loss with respect to layer weights in an MLP

The paper: https://arxiv.org/abs/2110.11309, makes the following claim at the end of page 3: The gradient of loss $L$ with respect to weights $W_l$ of an MLP is a rank-1 matrix for each of B batch ...
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Derivation of weights for linear function approximation of Temporal difference method

https://web.stanford.edu/class/cs234/slides/lecture5.pdf I am reading the above material. On page 38, the update for the parameters for the linear function approximation of TD(0) is given. I have a ...
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1answer
30 views

model and trained model parameters on CIFAR-10 [closed]

I'm looking for different models (specifically ResNet18/20, ResNet32/34, VGG16, MobileNet and SqueezeNet) and their parameters after training (i.e., .pth file) that ...
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1answer
38 views

Is there a way to update the neural network to fit the new data without the time required for retraining?

I built a basic neural network in MATLAB. The neural network classifies points on the X-Y axis system into two classes (0 and 1). (I try to get the function that represents a shape from this photo) ...
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48 views

What would be the total number of learnable parameters of the RNN encoder of this encoder-decoder architecture for machine translation?

Here's a quiz. My answer is different from the teacher's, so I'm wondering what answer would you pick up. We use a sequence-to-sequence (encoder-decoder) system to perform machine translation. We ...
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23 views

In mini-batch gradient descent, are the weights updated after each batch or after all the batches have gone through an epoch?

Say I have a mini-batch of size 32, and I have 10 such batches. Assuming I only run it for one epoch (just for the sake of understanding it), Will the weights be updated using the gradients of one ...
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51 views

Correctly input additional values into CNN

I understand that in order to add additional inputs to a CNN, e.g. in self driving, I can append the data to a flattened layer after the convolutions and before the fully connected layers. However, a ...
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Using parameter estimation for training a neural network

Assume that we have 4 layers in a neural network. $$z_1 = L_1(x, W_1)$$ $$z_2 = L_2(z_1, W_2)$$ $$z_3 = L_3(z_2, W_3)$$ $$y = L_1(z_3, W_4)$$ Where $x$ is the vector input, $y$ is the vector output ...
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How did authors ensure that critical points do exist in GAN?

Using an MLP as a generator introduces multiple critical points in parameter space. You can read this excerpt from the research paper titled Generative Adversarial Nets by Ian J. Goodfellow et al. In ...
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Is there a proper initialization technique for the weight matrices in multi-head attention?

Self-attention layers have 4 learnable tensors (in the vanilla formulation): Query matrix $W_Q$ Key matrix $W_K$ Value matrix $W_V$ Output matrix $W_O$ Nice illustration from https://jalammar....
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Are there any connectionist parametric models with non-neuron building blocks?

Parametric models allows learning by converging to the desired parameters, which are randomly initialized initially. Among the parametric models, especially in connectionist AI, neural networks are ...
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How to handle critical points during generator training?

Using an MLP as a generator introduces multiple critical points in parameter space. You can read this excerpt from the research paper titled Generative Adversarial Nets by Ian J. Goodfellow et al. In ...
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Recursive Least squares (RLS) for mini batch

For my application I am considering a learning problem where I simulate a bunch of episodes say '$n$' first, and than carry out the recursive least squares update. Similar to $TD(1)$. I know that RLS ...
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43 views

Is precision of weights unimportant in neural networks?

While reading about Module in PyTorch, I came across a new data type called half datatype. half() method when calls on a Module ...
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49 views

What are the numbers that are useful (may need to be stored) other than parameters of a model?

Consider the following method related to buffers in PyTorch ...
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58 views

Can some of the weights be fixed during the training of a neural network?

Is it possible to exclude specific layers from the optimization? For example, let's say I have an input layer, 2 hidden layers, and the output layer. I know there is a perfect solution for my problem ...
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Prioritized Experience Replay, clarifications for Important Sampling

I can't seem to understand how the weight equation is dissected and how it really works when combined with the TD-error value. The weight equation is: I can understand what N, P(i) and beta represent,...
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1answer
51 views

Which solutions are there to the problem of having too large activations before the softmax (or sigmoid) layer?

I'm trying to build a neural network (NN) for classification using only N-bit integers for both the activations and weights, then I will train it with some heuristic algorithm, based only on the NN ...
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If I have a feature matrix of shape $1200 \times 2000$, should I have $2000$ input neurons?

I have an input feature matrix (row-normalized) of size $1200 \times 2000$. When I am writing my neural network, is the number of input neurons equal to the number of columns, that is $2000$? Is it ...
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1answer
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Why are weights not initialized with mean=1?

I wonder why weights are initialized with zero-mean. It is one of the reasons, why deep architectures cannot be trained without skip connections. Without the skip connections, the zero initialization ...
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Which methods for weight initialization in Neural Networks are currently common practice?

I am currently researching the topic of weight initialization methods for (deep) neural networks and I'm a little stuck. The result of my work should be an overview of methods that are currently in ...
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Is the bias also a "weight" in a neural network?

I'm learning about how neural networks are trained. I understand how a neuron works, backpropagation, and all that. In neurons, there is a clear distinction between a "weight" and a "...
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What is the effect and behavior of using mixed weight instead of normal weight matrix?

Suppose I try to find appropriate matrix A in differential equation $\dot{X}=A X$ using RNN. Current state is $X=\begin{bmatrix} x_{1}\\ x_{2}\\ \end{bmatrix}$, and desired trajectory state is $X_d=\...
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How to update all the weights in case only one data out of n signals is observable

If we have cost function as $$E_i = (D_i -Y_i)^T Q (D_i -Y_i)$$, where $$Q=\begin{bmatrix} 1 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0 \end{bmatrix}$$( in case only one data signal can ...
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1answer
56 views

Is this LSTM layer learning anything?

I've trained a CNN-LSTM model but the results weren't satisfactory, so I took a look at my weight distributions and this is what I got: I don't understand. Is this layer learning anything? Or no? ...
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1answer
40 views

How to train a neural network with few weights and biases held constant?

I am a beginner in neural networks. I am building a neural network with 3 layers. The input $X$ has 7 features and the output $Y$ is a real number. In the hidden layer, there are two nodes. The bottom ...
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149 views

Why did the developement of neural networks stop between 50s and 80s?

In a video lecture on the development of neural networks and the history of deep learning (you can start from minute 13), the lecturer (Yann LeCunn) said that the development of neural networks ...
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1answer
47 views

Does the weight vector form imply feature space curvature?

I came across this sentence when exploring a simple nearest neighbor classifier method using Euclidean distance (link): The slightly odd thing about using the Euclidean distance to compare features ...
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1answer
109 views

Is there a convention on the order of multiplication of the weights with the inputs in neural nets?

Is there a convention on how the input data and the weights are multiplied? The input data can be anything, including the result from the previous layers. There are two options: Option 1: $$\begin{...
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Why does sigmoid saturation prevent signal flow through the neuron?

As per these slides on page 35: Sigmoids saturate and kill gradients. when the neuron's activation saturates at either tail of 0 or 1, the gradient at these regions is almost zero. the gradient and ...
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Are there any new weight initialization techniques for DNN published after 2015?

Considering weights initialization in my personal projects, I always used some standard techniques such as: Glorot (also known as Xavier) initialization (2010). Mertens initialization (2010). He ...
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1answer
54 views

What are acting as weights in a convolution neural network?

Looking at some old notes I took on CNN's and I wrote down that the weights in a CNN are acting like filters in a CNN but to be honest I don't really know what the weights are acting as in a CNN and ...
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25 views

How do we interpret the images of weights in logistic regression

The following images are a) The weights of a logistic regression model trained on MNIST. b) The sign of the weights of a logistic regression How do these images represent the weights? Would be ...
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1answer
37 views

What does "adding class weights for an imbalanced dataset" mean in the case of multi-label classification?

Suppose I have the following toy data set: Each instance has multiple labels at a time. You can see I have 2 instances for Label2. However, only one instance for the other labels. It means that we ...
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When is using weight regularization bad?

Regularization of weights (e.g. L1 or L2) keeps them small and standardized, which can help reduce data overfitting. From this article, regularization sounds favorable in many cases, but is it always ...
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Is the performance of a neural network, which was trained with encrypted data and weights, affected if the weights are decrypted?

Suppose that a neural network is trained with encrypted (for example, with homomorphic encryption and, more precisely, with the Paillier partial scheme) data. Moreover, suppose that it is also trained ...
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Why is there a Uniform and Normal version of He / Xavier initialization in DL libraries?

Two of the most popular initialization schemes for neural network weights today are Xavier and He. Both methods propose random weight initialization with a variance dependent on the number of input ...
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1answer
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In attention models with multiple layers, are weight matrices shared across layers?

In articles that describe neural architectures with multiple attention layers of the same form, are the weight matrices usually the same across the layers? Consider as an example, "Attention is ...
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2answers
141 views

In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$?

In the Attention is all you need paper, on the 4th page, we have equation 1, which describes the self-attention mechanism of the transformer architecture $$ \text { Attention }(Q, K, V)=\operatorname{...
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Why should variance(output) equal variance(input) in Xavier Initialisation?

In a lot of explanations online for Xavier Initialization, I see the following: With each passing layer, we want the variance to remain the same. This helps us keep the signal from exploding to a ...
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How many parameters does the AI of a typical self driving car have?

I can find the number of parameters AIs such as GPT3 or OpenAI Five have, but I'm having a difficult time googling the number of parameters a self driving car has because Google searches keeps using ...
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How efficient is SCAWI weight initialization method?

I'm currently in the middle of a project (for my thesis) constructing a deep neural network. Since I'm still in the research part, I'm trying to find various ways and techniques to initialize weights. ...
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1answer
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Should the range and initial values of weights and biases be adjusted to fit input and output data?

As a routine (in typical everyday tasks) of a data scientist, should they usually decide about weights and biases range and initial values as a function of which data they are planning to insert as ...
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1answer
98 views

Which hyperparameters in neural network are accesible to users adjustment

I am new to Neural Networks and my questions are still very basic. I know that most of neural networks allow and even ask user to chose hyper-parameters like: amount of hidden layers amount of ...
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160 views

What is the goal of weight initialization in neural networks?

This is a simple question. I know the weights in a neural network can be initialized in many different ways like: random uniform distribution, normal distribution, and Xavier initialization. But what ...
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1answer
75 views

What are some examples of functions that machine learning models compute?

My simple understanding of AI is that it is based on a mathematical model of a problem. If I understood correctly, the model is a polynomial equation and its weights are calculated by training the ...
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Are there examples of agents that use a more modest number of parameters on Pendulum (or similar environments)?

I'm looking at some baseline implementations of RL agents on the Pendulum environment. My guess was to use a relatively small neural net (~100 parameters). I'm comparing my solution with some ...