Dataset & Benchmarks: Confidence Box Plot for all status
llm时代，不需要任何数理基础，甚至不一定需要会写deep learning代码，你就可以中iclr，甚至可以拿oral 量...
This is an authors' implementation of the NIPS 2022 dataset and Benchmark Track Paper "A Comprehensive Study on Large Scale Graph Training: Benchmarking and Rethinking" in PyTorch. - VITA-Group/Large_Scale_GCN_Benchmarking
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This method acts as a regularizer similar to dropout [Srivastava et al., 2014], but doesn’t lose effectiveness when used with batch normalization [Ioffe and Szegedy, 2015] on ResNets. On the CIFAR-10 image dataset, applying stochastic depth to a ResNet with more than 1200 layers ...
This avoided a common problem of “multiple track” benchmarks in which no conclusion can be drawn because too few participants enter all tracks. To promote collaboration between researchers, reduce the level of anxiety, and let people explore various strategies (e.g. “pure” methods and “...
8.24 投稿datasetand benchmark track, 提交 official comment 三天了 reviewer 还没回复，怎么办......
Test the benchmark and baseline using the algorithm's definition file on small test inputs python run.py --neurips23track filter --algorithm faiss --dataset random-filter-s python run.py --neurips23track sparse --algorithm linscan --dataset sparse-small python run.py --neurips23track ood ...