Method and system for accelerated attention operations in a machine learning model and differentiable point rendering using proximity attention

A system for accelerated attention operations for use in a machine learning model, the system comprising a processing unit and a data storage, the data storage comprising executable instructions that when executed by the processing unit cause to the system to:

• receive rows or columns of a weight matrix, an input vector, and columns of an identity matrix, where each row or column of the weight matrix is associated with a value vector;
• transform the rows or columns of the weight matrix and the input vector using an embedding function;
• perform a search to identify transformed rows or columns of the weight matrix that are to receive high post-activations based on each of the transformed input vector;
• determine post-activations using similarity of the rows or columns of the weight matrix that were returned by the search to the associated input vector as measured by a kernel function;
• and
• output the post-activations for operation of the machine learning model.

A processor-executed method for differentiable point rendering using proximity attention to learn a representation of a three-dimensional (3D) scene, the method comprising:

• receiving an input matrix derived from a set of images;
• initializing points in the 3D scene;
• extracting ray-to-point features using the points and the input matrix;
• aggregating the ray-to-point features to generate a feature map that represents surface-ray intersections;
• generating an image prediction, using a convolutional neural network, and taking the feature map as input;
• and
• outputting the image prediction as the representation of the 3D scene.