Questions tagged [objective-functions]
For questions related to the concept of loss (or cost) function in the context of machine learning.
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Application thats compatible with android that allows you to access the wifi signals around you and constructs its own
Can we develop a application thats compatible with android that allows you to access the wifi signals around you and constructs its own?
By continuously encryption data by the wifi signal by ...
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Why do diffusions learns accumulated noise and not intermediate noise?
The core step is to learn scoring function: $L=\int E_{X_t}\|S_\theta(X_t,t)-\nabla\ln p_{t}(X_t)\|dt$, where $X_t$ is a forward-noise process and $p_{t}(x)$ is a density of X_t.
The trick to actually ...
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Custom Loss Function Traps Network in Local Optima
I am working with a feedforward neural network to fit the following simple function:
N(1) = -1
N(2) = -1
N(3) = 1
N(4) = -1
But I don't want to use the Mean-...
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Transformer Loss Function for Music Generation
I am working on a Midi Generation project that takes tracks as inputs, and outputs a complimentary track of notes.
The tracks are basically a list of notes created of:
Time
Duration
Pitch
Velocity
I ...
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Using conditional probability as an estimate in a loss function
I have a rather large ML framework that takes multiple conditional probability terms that are computed via classifiers/neural networks. This arbitrary loss function is computed via a function:
...
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Can local learning rules minimize a global loss?
It is widely believed that synaptic plasticity is the way biological brains learn. Artificial implementations of this mechanism are for instance local weight-update rules in Spiking Neural Networks. ...
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Sparse Cross Entropy
I've been attempting to mess around with Sparse Categorical Cross Entropy Loss for the MNIST dataset. I can't seem to figure out what might be wrong with my implementation, the loss seems to ...
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Optimizing a nonlinear objective function in Deep Reinforcement Learning
I'm working on a reinforcement learning problem where the environment returns a reward pair $(r_{t+1}^{(a)}, r_{t+1}^{(b)})$. The goal is to maximize the following nonlinear objective function.
$$
E[\...
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Why do we need the min in PPO objective function?
I don't understand why we need to take the minimum of the unclipped part and the clipped part in PPO's objective function. Why not just use the clipped term? How can the clipped part ever be bigger ...
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Multi-task objective sometimes improve single-task performance, but is this true when fine tuning?
It is known that multitask objectives in neural networks sometimes have the effect of improving the performance of the neural network for each of the tasks individually (versus training the same ...
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Why does an action cost function dependes on result state in search problems?
In the famous AI book Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (4th edition), in chapter 3, the action cost function of a problem solver agent denoted as $c(s, a, ...
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Can you explain the Hinton's comment "Rprop is equivalent to using the gradient, but also dividing by the size of the gradient"?
Been reviewing some old foundational material and ran into this comment by Hinton on Rprop in his old Coursera class:
Rprop is equivalent to using the gradient, but also dividing by the
size of the ...
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Non differentiable loss function train with actor critic style
I'm working on a project where a non differentiable loss is there. I'm thinking about how should I deal with them.
My model is a very big lstm model (about 1M parameter), and after 500 steps (not sure ...
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What is the loss function used when pre-training BERT on MLM & NSP tasks?
I'm new to NLP and was reading through the 2019 BERT paper and am confused about the loss function used during pre-training.
As I understand it, the model is trained on the MLM and NSP tasks. The MLM ...
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What Kind of Models and Loss Functions for User Churn Prevention by Promo Codes?
The Company Business Model
Bike rental with an app, where riders pay for the time they rented the bikes for.
The Business Case
User (rider) attrition prediction, and ideally, prevention. Basically, ...
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Has There Been Research on Using a Neural Network as a Loss Function for Another Neural Network?
I'm intrigued by the idea of employing a separate neural network (which I'll refer to as the "loss network") to compute the loss for a primary network based on its inputs and outputs. The ...
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How do LGBM rankers train?
I'm looking into Learning to Rank models - specifically, the LGBMRanker model - and I want to understand how it's able to train. It takes in features, group sizes and labels, and optimizes for a ...
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Search recall optimization - what appropriate loss function to use?
I am studying machine learning and wanted to work on a project of my own so that I have better chances after graduating college. I'm studying the application of ML to improve searches using a toy ...
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why learn an observation model when training latent space model in model based rl
I'm currently studying reinforcement learning through CS 285 provided by UC Berkeley.
At 1:52 of the part 5 of the lecture 11, I got confused on why one would want to learn an observation model $p(o_t ...
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How to check clustering performance?
Background
I'm implementing the DBScan algorithm. I have trained it to cluster a small dataset of random clusters, and want to be able to get a decimal for its accuracy of clustering the groups.
...
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In logistic regression, do I try to fit the graph perfectly or mimimize the error in the predicted probabilities?
In linear regression, I train the model so the graph runs best through the data points, so the geometric distance between f(x) and $y^i$ is minimized.
Now, is it correct that in logistic regression I ...
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Can gradient descent cause loss to increase in some situations?
Is a gradient descent step always supposed to decrease loss? I can think of a situation where it would seem that gradient descent would increase loss but maybe it I am misunderstanding a part of ...
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How do I assign a weight to an additional loss?
I am trying to do multi-spectral image fusion. I am using the following paper as a reference.
https://arxiv.org/pdf/1804.08361.pdf
The code available on GitHub works well. But, I am trying to add some ...
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What is MLM & NSP loss function
Two objective functions are used during the BERT language
model pretraining step.
The first one is masked language
model (MLM) that randomly masks
15% of the
input tokens and the objective is to ...
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What is the best way to combine or weight multiple losses with gradient descent?
I am optimizing a neural network with Adam using 3 different losses. Their scale is very different, and the current method is to either sum the losses and clip the gradient or to manually weight them ...
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Which loss / activation function with 2 classes that do not occur often and do not sum to one?
I have a neural network that predicts 2 classes of a time series (bottom and top). Currenlty my Y labels are size 2: [1 0] for bottom and [0 1] for top. The NN has 2 output nodes.
Of course not every ...
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What is the correct loss function for binary classification: Cross entropy or Binary cross entropy?
Let's say I have a binary classification problem and I want to solve it by means of FC neural net. So which approach will be correct: 1) define the last layer of NN like this ...
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What's the difference between classification and segmentation in deep learning?
What's the difference between classification and segmentation in deep learning?
In particular, can the classification loss function be used for segmentation problems?
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Image classification problem with multiple right classes
I have a use case where the model needs to detect fabricdefects. There are 15+ different kinds of defects. In one image there can be multiple defects present. The straight forward solution for this ...
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Why MSE and MAE yield poor results when used with gradient-based optimization for classification?
Deep learning book chapter 6: In 6.2.1.2 last paragraph:
Unfortunately, mean squared error and mean absolute error often lead to poor results when used with gradient-based optimization. Some output ...
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Why is `SigmoidBinaryCrossEntropyLoss` in `DJL` implemented this way?
SigmoidBinaryCrossEntropyLoss implementation in DJL accepts two kinds of outputs from NNs:
where sigmoid activation has already been applied.
where raw NN output ...
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Loss Function for Binary Classification with Multiple Correct Choices
I have a binary classification problem, where there are multiple correct predictions, however, I would consider the prediction to be correct if the highest confidence prediction of a 1 is correct.
I ...
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Learning curve converges with huge errors
I am training an auto-encoder over $10^4$ epochs. I get a converging learning curve. However the error at the last stages stays huge $\sim10^{15}$. What does this mean? does it mean that my auto-...
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Training a neural network simultaneously with two different loss functions rather than considering the weighted sum
This is a follow up on the already asked question: Is the neural network 100% accurate on training data if epoch loss is minimized to 0?
I want to train a neural network that works as an approximator ...
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Left-to-Right vs Encoder-decoder Models
Xu et al. (2022) distinguishes between popular pre-training methods for language modeling: (see Section 2.1 PRETRAINING METHODS)
Left-to-Right:
Auto-regressive, Left-to-right models, predict the ...
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Do we need to know or verify properties of loss functions / metrics' implementations?
I will start with an example, in order to get to the general question.
I was reading the following paper (https://www.cns.nyu.edu/pub/lcv/wang03-preprint.pdf) about Structural Similarity Index (SSIM), ...
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Is the discriminator of a GAN network embedded in VAE?
From what I understand, a Generative Adversarial Network (GAN) is composed of an encoder (generator), some synthetic data (fake data) and a discriminator that will penalize any distinguishable real ...
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What loss function should I use if I only care about the accuracy of one class?
CrossEntropyLoss optimizes the overall classification accuracy as
$$ {n_{\text{correct}} \over N} $$
What loss function should I use if I only care about increasing the true positive rate of one class?...
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How to define a loss function for multi-label problem?
I have voice recordings which are labelled by not only a single label but multiple labels. Each voice recording corresponds to one of class labels within a set. In other words, the training instance ...
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What is the difference between the triplet loss and the contrastive loss?
What is the difference between the triplet loss and the contrastive loss?
They look same to me. I don't understand the nuances between the two. I have the following queries:
When to use what?
What ...
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What should I think about when designing a custom loss function?
I'm trying to get my toy network to learn a sine wave.
I output (via tanh) a number between -1 and 1, and I want the network to minimise the following loss, where ...
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What is the domain of the discriminator of a GAN?
I've read that the discriminator $D$ validates an image $D(x)$, where $x$ is either a real image or a fake one created by the generator, i.e. $ D(G(x))$.
What does the function of the discriminator ...
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How to create a loss function that penalizes duplicate indices in the output tensor?
We're working on a sequence-to-sequence problem using pytorch, and are using cross-entropy to calculate the loss when comparing the output sequence to the target sequence. This works fine and ...
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Why do we use "true labels" that are based on the output of our network in Deep Q-Learning?
In the original DQN paper, the $\ell_2$ loss is taken over the distance between our network output, $\hat{q}(s_j,a_j,w)$ and the labels $y_j=r_j+\gamma \cdot \max\limits_{a'} \hat{q}(s_{j+1},a',w^-)$, ...
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Learning values in open ball: which final layers to employ?
I'm fairly new to deep learning and looking for some reference literature... Specifically, I want to train a neural network to predict vectors $v \in \mathbb{R}^3$ under the constraint $||v||\leq 1$.
...
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How is catastrophic cancellation dealt with in loss functions?
It just occurred to me that this seems like it should be a very common problem that must have some kind of solution... Yet I'm not sure what it is...
If there is no solution, does this mean once a ...
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how to define or calculate the similarity betweeen two curves as the loss funtion to optimize in the generative model?
I want to train a neural network as the curve productor that can generate the specific type of curves (e.g. exponential decay curves). I take the encoder-decoder structure, the curves in a dataset is ...
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What is the reason we loop over epochs when training a neural network?
After reading through this thread and some other resources online, I still do not understand the role of epochs in training a neural network. I understand that one epoch is one iteration through the ...
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What is the difference between a loss function and reward/penalty in Deep Reinforcement Learning?
In Deep Reinforcement Learning (DRL) I am having difficulties in understanding the difference between a Loss function, a reward/penalty and the integration of both in DRL.
Loss function: Given an ...
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In CVAE's objective function, why do both terms condition on $\textbf{c}$?
I don't quite understand why, in Conditional Variational Autoencoder (CVAE), we concatenate a conditioning vector two times, at encoder and decoder respectively.
After we concatenate it once at the ...
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