Answers.Body: <p>Achieving the global minimum of a loss function doesn't guarantee it's the best metric if there are problems with the architecture or training dataset.</p>
  <p>For example, if the training dataset contains mislabeled examples or very noisy data, the model might overfit to these flawed data at the global minimum of the right loss function even with quantum computing, leading to poor metrics like low accuracy or high RMSE due to large variance.</p>
  <p>Or if your model architecture is misconfigured to be underparameterized, such a simple model might minimize the loss but lack the capacity to capture complex patterns in the data, leading to suboptimal metrics due to large bias.</p>
  
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Answers.CreationDate: 2024-11-28T05:45:25.800Z
Answers.LastActivityDate: 2024-11-28T05:45:25.800Z
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Body: <p>Suppose the hardware constraint is not a problem anymore, so that the quantum computer is everywhere.</p>
  <p>If we define a neural network model that has many params, traditionally (using gradient descent) its params (weights) is adjusted based on direction (gradient) of next layer reference (from output layer).</p>
  <p>In quantum annealing (tunneling), it doesn't relly on weights initialization as it replaced by superposition state which all possible states of weights has explored simultenously.</p>
  <p>So, it's possible that in future, what deep learning engineer tune is only about the layers used for the hyperparameters and the dataset preprocessing, but still need to monitor the metric even though the loss is always in global minimum.</p>
  <p>Learning rate (optimizer hyperparams) would be not used anymore as it replaced by quantum superposition.</p>
  <p>Speaking generally, regardless of metric being used (classification like accuracy and regression like rmse), is global minimum loss always correlation and causation to the metric?</p>
  <p>Like, <code>global minimum = best metric</code>?</p>
  <p>Or, is it possible that the global minimum which not achieving best metric is due to misconfigured in defining the architecture or miss annotated the dataset?</p>
  
Tags: <gradient-descent><loss><weights-initialization><metric><quantum-annealing>
Title: global minimum loss always best metric?
UserDisplayName: Muhammad Ikhwan Perwira
ThreadText: # global minimum loss always best metric?
  
  (Asked by: username_0 on 2024-11-27)
  
  Suppose the hardware constraint is not a problem anymore, so that the quantum computer is everywhere.
  
  If we define a neural network model that has many params, traditionally (using gradient descent) its params (weights) is adjusted based on direction (gradient) of next layer reference (from output layer).
  
  In quantum annealing (tunneling), it doesn't relly on weights initialization as it replaced by superposition state which all possible states of weights has explored simultenously.
  
  So, it's possible that in future, what deep learning engineer tune is only about the layers used for the hyperparameters and the dataset preprocessing, but still need to monitor the metric even though the loss is always in global minimum.
  
  Learning rate (optimizer hyperparams) would be not used anymore as it replaced by quantum superposition.
  
  Speaking generally, regardless of metric being used (classification like accuracy and regression like rmse), is global minimum loss always correlation and causation to the metric?
  
  Like, `global minimum = best metric`?
  
  Or, is it possible that the global minimum which not achieving best metric is due to misconfigured in defining the architecture or miss annotated the dataset?
  
  **Tags:** gradient-descent, loss, weights-initialization, metric, quantum-annealing
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-11-28. Score: 1 | Accepted answer
  
  Achieving the global minimum of a loss function doesn't guarantee it's the best metric if there are problems with the architecture or training dataset.
  
  For example, if the training dataset contains mislabeled examples or very noisy data, the model might overfit to these flawed data at the global minimum of the right loss function even with quantum computing, leading to poor metrics like low accuracy or high RMSE due to large variance.
  
  Or if your model architecture is misconfigured to be underparameterized, such a simple model might minimize the loss but lack the capacity to capture complex patterns in the data, leading to suboptimal metrics due to large bias.
  
  
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