nzw0301

Preferred Networks, Inc. / Preferred Elements, Inc.Japan

Pinned Repositories

gap-contrastive-and-supervised-losses
#ICML2022 Experimental codes of "On the Surrogate Gap between Contrastive and Supervised Losses"
Language:Python8 4 00
keras-examples
Language:Jupyter Notebook232 15 1287
lightLDA
fast sampling algorithm based on CGS
Language:Python50 4 116
optuna-wandb
Example codes in the medium post titled "Optuna meets Weights and Biases."
Language:Python20 2 21
pb-contrastive
#UAI2020 Codes for PAC-Bayesian Contrastive Unsupervised Representation Learning
Language:Python12 5 00
supervised_fasttext.pytorch
Language:Python4 1 80
TopicModels.jl
Language:Julia5 2 20
Understanding-Negative-Samples
#NeurIPS2021 Codes for Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Language:Jupyter Notebook8 2 10
word2vec4j
Language:Java4 3 32

nzw0301's Repositories

nzw0301/optuna-wandb
Example codes in the medium post titled "Optuna meets Weights and Biases."
Language:Python20 2 21
nzw0301/pb-contrastive
#UAI2020 Codes for PAC-Bayesian Contrastive Unsupervised Representation Learning
Language:Python12 5 00
nzw0301/gap-contrastive-and-supervised-losses
#ICML2022 Experimental codes of "On the Surrogate Gap between Contrastive and Supervised Losses"
Language:Python8 4 00
nzw0301/Understanding-Negative-Samples
#NeurIPS2021 Codes for Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Language:Jupyter Notebook8 2 10
nzw0301/optuna-examples
Examples for https://github.com/optuna/optuna
Language:Python1 1 0
nzw0301/aim
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Language:Python0 0
nzw0301/nzw0301.github.io
Language:TeX2 5
nzw0301/optuna
A hyperparameter optimization framework
Language:Python1 0
nzw0301/optuna-integration
Language:Python1 0
nzw0301/peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Language:Python1 0
nzw0301/play-faiss
Language:Jupyter Notebook2 0
nzw0301/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Language:C++1 0
nzw0301/pytorch-OpCounter
Count the MACs / FLOPs of your PyTorch model.
Language:Python1 0
nzw0301/pytorch-pfn-extras
Supplementary components to accelerate research and development in PyTorch
Language:Python1 0
nzw0301/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)
Language:C++1 0
nzw0301/refill
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers (EMNLP 2022)
Language:Python1 0
nzw0301/research_project_template
Language:Makefile1 0
nzw0301/scikit-learn
scikit-learn: machine learning in Python
Language:Python1 0
nzw0301/some-pytorch-func-set
Language:Python2 2
nzw0301/v119
Proceedings of ICML 2020
Language:TeX1 0
nzw0301/v12
NIPS Mini-Symposium on Causality in Time Series
Language:HTML1 0
nzw0301/v13
ACML 2010 Proceedings
Language:Ruby1 0
nzw0301/v14
Proceedings of the Learning to Rank Challenge
Language:CSS1 0
nzw0301/v15
AISTATS 2011 Proceedings
Language:Ruby1 0
nzw0301/v16
Active Learning and Experimental Design Workshop
Language:CSS1 0
nzw0301/v162
Proceedings of ICML 2022
Language:TeX1 0
nzw0301/v18
KDD Cup 2011
Language:CSS1 0
nzw0301/v202
Proceedings of ICML 2023
Language:TeX1 0
nzw0301/v48
ICML 2016 Proceedings
Language:Ruby1 0
nzw0301/v9
AISTATS 2010 Proceedings
Language:Ruby1 0