Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server.

Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. With the aid of geometry guides either in the form of occupancy grids or proposal networks, the number of color neural network evaluations can be reduced from hundreds to dozens in the standard volume rendering framework.

Table 1 summarizes the models and datasets used in this work. ImageNet-1K Deng u. a. (2009): We use the subset of the ImageNet dataset containing DeepCAM Kurth u. a. (2018): DeepCAM dataset for image segmentation, which consists of Fractal-3K Kataoka u. a. (2022) A rendered dataset from the Visual Atom method Kataoka We also use the setting in Kataoka u. a. (2022) Table 2 shows the detail of our hyper-parameters. Specifically, We follow the guideline of'TorchVision' to train the ResNet-50 that uses the CosineLR To show the robustness of KAKURENBO, we also train ResNet-50 with different settings, e.g., ResNet-50 (A) setting, we follow the hyper-parameters reported in Goyal u. a. (2017). It is worth noting that KAKURENBO merely hides samples before the input pipeline. In this section, we present an analysis of the factors affecting KAKURENBO's performance, e.g., the The result shows that our method could dynamically hide the samples at each epoch.

This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of

The table ( right) shows 100 epoch results using best lr and wd values found at 50 epochs. ViT's patchify stem differs from the proposed convolutional stem in the type of convolution used and We investigate these factors next. The focus of this paper is studying the large, positive impact of changing ViT's default We use AdamW for all experiments. Figure 7 shows the results. The table ( right) shows 100 epoch results using optimal lr and wd values chosen from the 50 epoch runs.

We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks.

Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the standard method for achieving state-of-the-art performance on NLP benchmarks.