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The '''Earth Mover's Distance''' (EMD), also known as the '''Wasserstein distance''' or '''Mallows distance''', is a measure of dissi ...duct of the mass being moved and the distance it is moved. Mathematically, the EMD between two probability distributions P and Q, defined over a common me

...g a large dataset or model into a simplified representation, which retains the most essential information. This can be done through various methods, such ...while discarding less informative features. Reducing the dimensionality of the input data can help prevent overfitting, speed up training, and simplify vi

...(EMD), is a metric used in the field of machine learning, particularly in the training of Generative Adversarial Networks (GANs). Introduced by [[Martin ...training procedure involves minimizing a specific loss function, known as the Jensen-Shannon (JS) divergence. However, this loss function can lead to sev

...PR AUC is particularly useful when dealing with imbalanced datasets, where the proportion of positive and negative samples is unequal. ...ecision measures the accuracy of positive predictions, and recall measures the ability to identify all relevant instances.

...the [[loss]] on the mini-batch of examples is also far more efficient than the entire dataset. ...ent]] to train the model on all data in one iteration. Unfortunately, when the dataset grows large, this approach becomes computationally expensive and ma

...new text generated (output) by the [[LLM]] after you [[inference]] it with the [[prompt]] '''[[The Pile]]'''

...]]''' - steer AGI towards the goals of humans. Prevent AGI from destroying the world - https://www.reddit.com/r/ControlProblem/wiki/faq ...lip/ - encode images and text into representations that can be compared in the same space. basis for many [[Text-to-Image Models]] like [[Stable Diffusion

...he relationship between the input features ([[independent variables]]) and the output ([[dependent variable]]) can be represented by a straight line, or m ...hyperplane) that describes the relationship between the input features and the output variable.

...this agglomerative cluster starts as a singleton cluster and at each step the two closest clusters are merged until an ending criterion is met. ...a point is assigned its own cluster; then the algorithm iteratively merges the two closest clusters until an ending criterion is met.

...aw data itself, with a central server. In this article, we will delve into the concepts, techniques, and applications of federated learning. ...with a central server. This approach ensures that the raw data remains on the clients' devices, thus preserving data privacy and security.

...raph data. Utilizing sparse features effectively can significantly improve the efficiency and performance of machine learning algorithms by reducing memor ...in the COO list contains a tuple with the index of the non-zero value and the value itself. This format is memory-efficient and allows for easy manipulat

...ic gradient descent]] (SGD), which itself is a stochastic approximation of the [[gradient descent]] optimization algorithm. The mini-batch stochastic gradient descent algorithm can be summarized in the following steps:

...ten requires domain expertise. Semi-supervised learning takes advantage of the available unlabeled data to improve model performance without requiring a s ...a points. The model is then retrained on the combined labeled dataset, and the process is repeated iteratively until a desired level of performance is ach