Existing systems mostly compress features at pixel level and ignore the characteristics of feature structure, which could be further exploited for more efficient compression.

Existing systems mostly compress features at pixel level and ignore the characteristics of feature structure, which could be further exploited for more efficient compression.

While 3DGaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a goodinitialization.

We assume, withoutlossofgenerality, that isdiagonal. The function m anditsderivativem0 arewell-definedandpositiveatz =0 (andhenceR ( ) is well-definedforall > ).

Adaptive parameterization is a means of increasing this flexibility and thereby increasing the model's capacity to learn non-linear patterns. We focus on the feed-forward layer, f(x):= φ(W x+b),for some activation functionφ: R 7 R. Define the pre-activation layer as a = A(x):= Wx+band denote byg(a):= φ(a)/athe activation effect ofφgivena, where divisioniselement-wise.

We show the universality of depth-2 group convolutional neural networks (GCNNs) inaunified and constructivemanner based ontheridgelet theory.

Given a query text, the similarity is determined bythemost similar prototype tofind correspondence inthevideo, which is termed text-adaptive matching.

Deep RL(DRL) tackles the curse of dimensionality due to large state spaces by utilizing a Deep Neural Network (DNN) to approximate the value function and/or the policy.

Novel view synthesis methods of this type have three common limitations.

In the meanwhile, multi-task RL is considered promising to enhance the generality of RL policies and improve the learning efficiency [4, 5, 6, 7, 8, 9, 10].