However, to obtain the desired effect, the step-size should be chosen sufficiently large, a task which is problem dependent andcanbedifficultinpractice.

To address the label scarcity problem in practice, a number of approaches apply the test-time adaptation setting to the domain of adapting VLMs to downstream tasks, as shown in Figure 1.

Convolutional Neural Networks (CNNs) are computation intensive, making their deployment on resource-constrained devices (e.g., Microcontrollers equipped with 2MB memory) challenging.

Experiments onthe ZJU-MoCap andAISTdatasets showthatourmethod significantly outperforms recent generalizable NeRF methods on unseen identities and poses.

The ability toleverage large-scale hardware parallelism has been one of the key1 enablers of the accelerated recent progress in machine learning.

The ability toleverage large-scale hardware parallelism has been one ofthe key enablers of the accelerated recent progress in machine learning.

More insights about the predictive GP posterior used for the contrastive expectation integrals are inthefirst section. Our coding idea is that the Python user only specifies a dictionary of models as input:models = {model1, model2, ..., modelK}.