1 citations · 3 across the 4 of their papers we have counts for
5 papers
Probabilistic Attention for Interactive Segmentation
Prasad Gabbur, Manjot Bilkhu, Javier Movellan
We provide a probabilistic interpretation of attention and show that the standard dot-product attention in transformers is a special case of Maximum A Posteriori (MAP) inference. T…
Probabilistic Transformers
Javier R. Movellan, Prasad Gabbur
We show that Transformers are Maximum Posterior Probability estimators for Mixtures of Gaussian Models. This brings a probabilistic point of view to Transformers and suggests exten…
Deep Active Object Recognition by Joint Label and Action Prediction
Mohsen Malmir, Karan Sikka, Deborah Forster +3
An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performanc…
Variable and Fixed Interval Exponential Smoothing
Javier R. Movellan
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers fo…
Discriminately Decreasing Discriminability with Learned Image Filters
Jacob Whitehill, Javier Movellan
In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discrimi…