Kento Nozawa's repositories
optuna-wandb
Example codes in the medium post titled "Optuna meets Weights and Biases."
pb-contrastive
#UAI2020 Codes for PAC-Bayesian Contrastive Unsupervised Representation Learning
gap-contrastive-and-supervised-losses
#ICML2022 Experimental codes of "On the Surrogate Gap between Contrastive and Supervised Losses"
Understanding-Negative-Samples
#NeurIPS2021 Codes for Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
optuna-examples
Examples for https://github.com/optuna/optuna
pytorch-OpCounter
Count the MACs / FLOPs of your PyTorch model.
pytorch-pfn-extras
Supplementary components to accelerate research and development in PyTorch
quickshift
A clustering algorithm that first finds the high-density regions (cluster-cores) of the data and then clusters the remaining points by hill-climbing. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by popular algorithm such as mean shift. (https://arxiv.org/abs/1805.07909)
scikit-learn
scikit-learn: machine learning in Python