Adaptive Softmax: An Efficient Computation Technique for Probability Distributions Over Words
If you have ever used a smartphone's text prediction feature or a virtual assistant, then you have interacted with language models that compute probability distributions over words. However, these models can be computationally intensive, especially when dealing with large vocabularies. Adaptive Softmax is a technique that speeds up this computation and makes it more efficient.
The Inspiration Behind
Have you ever wondered how computers can understand language? One way computers do this is through natural language processing, which involves using algorithms to analyze and interpret human language. One important aspect of natural language processing is language modeling, or predicting the likelihood of a word occurring in a given context. Hierarchical Softmax is one technique that can be used for efficient language modeling.
What is Hierarchical Softmax?
Hierarchical Softmax is an alternat
The Mixture of Logistic Distributions (MoL) is an output function used in deep learning models to predict discrete values. It is an alternative to the traditional softmax layer that has been a staple in deep learning models. The MoL is used in models such as PixelCNN++ and WaveNet to enhance these models' ability to predict discrete values. The discretized logistic mixture likelihood technique is used to estimate the probability distribution of the target values of the model.
What is the Mixtu
What is a Mixture of Softmaxes?
In deep learning, a mixture of softmaxes is a mathematical operation that involves combining multiple softmax functions together. The goal of this operation is to increase the expressiveness of the conditional probabilities we can model. This is important because traditional softmax functions suffer from a bottleneck that limits the complexity of the models we can create.
Why is the Traditional Softmax Limited?
The traditional softmax used in deep learning mod
Overview of Softmax
The Softmax function is commonly used in machine learning for multiclass classification. Its purpose is to transform a previous layer's output into a vector of probabilities. This allows us to determine the likelihood of a particular input belonging to a specific class.
How Does Softmax Work?
The Softmax function takes an input vector ($x$) and a weighting vector ($w$). It then calculates the probability that a given input belongs to a specific class (j).
Softmax works b
Sparsemax: A New Type of Activation Function with Sparse Probability Output
Activation functions are an essential component in deep learning models that allow for non-linear transformations between layers. One commonly used activation function is the Softmax, which is used to transform the output into normalized probabilities. However, it can often produce dense probabilities that are not computationally efficient and can emphasize the largest elements, diminishing the importance of the smaller