We revisit the Adaptive Stochastic Quantization (ASQ) problem and present algorithms that find optimal solutions with asymptotically improved time and space complexities.

If we consider for example the image pictured in Figure 1 on the left, we can easily describe its content by'what' we see - the building, sky and a flag.

Besides, it's worth noting that our MH-MoE is

Incomplete spatio-temporal data in the real world has spawned much research.

Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities.

The method begins with an iterative Gaussian cloud soft clustering module, offering structured temporal point cloud representations.

In this paper, we reconstruct naturalistic images directly from macaque brain signals using a convolutional neural network (CNN) based decoder.

In this work, we propose a first approach and benchmarks to tackle this challenge.