Heterogeneity, e.g., due to different types of layers or multiple sub-models, poses

This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called evidential deep learning (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function.

To evaluate VLMs' fine-grained understanding, existing benchmarks primarily focus on evaluating VLMs' capability

Furthermore, extensive analysis ( 4.3) on Spider2-V demonstrate that these strategies remarkably

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data coupled with the high expenses involved in sensor deployment results in notable data imbalances.

Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale.

The goal of multi-expert denoising is to increase the overall capacity of diffusion models while keeping an acceptable overhead.

Typically, existing models are trained and tested on the same dataset, leading to poor generalizability across variations in device location, device mounting orientation, and human activity type.

Empowering 3D Gaussian Splatting with generalization ability is appealing.