This document discusses clustering and anomaly detection in data science. It introduces the concept of clustering, which is grouping a set of data into clusters so that data within each cluster are more similar to each other than data in other clusters. The k-means clustering algorithm is described in detail, which works by iteratively assigning data to the closest cluster centroid and updating the centroids. Other clustering algorithms like k-medoids and hierarchical clustering are also briefly mentioned. The document then discusses how anomaly detection, which identifies outliers in data that differ from expected patterns, can be performed based on measuring distances between data points. Examples applications of anomaly detection are provided.
This document contains a summary of 3 papers on deep residual networks and squeeze-and-excitation networks:
1. Kaiming He et al. "Deep Residual Learning for Image Recognition" which introduced residual networks for image recognition.
2. Andreas Veit et al. "Residual Networks Behave Like Ensembles of Relatively Shallow Networks" which analyzed how residual networks behave like ensembles.
3. Jie Hu et al. "Squeeze-and-Excitation Networks" which introduced squeeze-and-excitation blocks to help convolutional networks learn channel dependencies.
The document also references the PyTorch ResNet implementation and provides URLs to the first and third papers. It contains non-English
ReAct: Synergizing Reasoning and Acting in Language Modelsharmonylab
公開URL:https://arxiv.org/abs/2210.03629
出典:Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao : ReAct: Synergizing Reasoning and Acting in Language Models, arXiv: 2210.03629 (2022)
概要:本論文では大規模言語モデルにおいて推論(Reasoning)と行動(Acting)を組合わせるprompt手法であるReActを提案した。知識集約型の推論タスク(QAタスク・事実検証タスク)と意思決定タスク(テキストゲーム、ウェブナビゲーション)でReActの性能を評価した。推論タスクにおいてReActはWikipedia APIとの対話によりChain of ThoughtのHallucinationを軽減し、意思決定タスクにおいては模倣学習や強化学習による手法を上回る性能を示した。
第6回 統計・機械学習若手シンポジウムの公演で使用したユーザーサイド情報検索システムについてのスライドです。
https://sites.google.com/view/statsmlsymposium21/
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) https://arxiv.org/abs/2105.12353
Retrieving Black-box Optimal Images from External Databases (WSDM 2022) https://arxiv.org/abs/2112.14921
This document discusses clustering and anomaly detection in data science. It introduces the concept of clustering, which is grouping a set of data into clusters so that data within each cluster are more similar to each other than data in other clusters. The k-means clustering algorithm is described in detail, which works by iteratively assigning data to the closest cluster centroid and updating the centroids. Other clustering algorithms like k-medoids and hierarchical clustering are also briefly mentioned. The document then discusses how anomaly detection, which identifies outliers in data that differ from expected patterns, can be performed based on measuring distances between data points. Examples applications of anomaly detection are provided.
This document contains a summary of 3 papers on deep residual networks and squeeze-and-excitation networks:
1. Kaiming He et al. "Deep Residual Learning for Image Recognition" which introduced residual networks for image recognition.
2. Andreas Veit et al. "Residual Networks Behave Like Ensembles of Relatively Shallow Networks" which analyzed how residual networks behave like ensembles.
3. Jie Hu et al. "Squeeze-and-Excitation Networks" which introduced squeeze-and-excitation blocks to help convolutional networks learn channel dependencies.
The document also references the PyTorch ResNet implementation and provides URLs to the first and third papers. It contains non-English
ReAct: Synergizing Reasoning and Acting in Language Modelsharmonylab
公開URL:https://arxiv.org/abs/2210.03629
出典:Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao : ReAct: Synergizing Reasoning and Acting in Language Models, arXiv: 2210.03629 (2022)
概要:本論文では大規模言語モデルにおいて推論(Reasoning)と行動(Acting)を組合わせるprompt手法であるReActを提案した。知識集約型の推論タスク(QAタスク・事実検証タスク)と意思決定タスク(テキストゲーム、ウェブナビゲーション)でReActの性能を評価した。推論タスクにおいてReActはWikipedia APIとの対話によりChain of ThoughtのHallucinationを軽減し、意思決定タスクにおいては模倣学習や強化学習による手法を上回る性能を示した。
第6回 統計・機械学習若手シンポジウムの公演で使用したユーザーサイド情報検索システムについてのスライドです。
https://sites.google.com/view/statsmlsymposium21/
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) https://arxiv.org/abs/2105.12353
Retrieving Black-box Optimal Images from External Databases (WSDM 2022) https://arxiv.org/abs/2112.14921
DLゼミ: MobileOne: An Improved One millisecond Mobile Backboneharmonylab
公開URL:https://openaccess.thecvf.com/content/CVPR2023/html/Vasu_MobileOne_An_Improved_One_Millisecond_Mobile_Backbone_CVPR_2023_paper.html
出典:Vasu, Pavan Kumar Anasosalu, et al.: MobileOne: An Improved One Millisecond Mobile Backbone, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
概要:モバイル端末向けのニューラルネットワークは多くの場合、FLOPsやパラメータ数で最適化されています。しかし、これらの最適化は実際のモバイルデバイスで実行した場合のネットワークの応答時間に相関しない場合があります。我々は昨今のニューラルネットワークの最適化のボトルネックを特定・分析し、その結果をもとにした新たな効率的なバックボーンMobileOneを設計しました。結果はMobileFormerと同等の性能を得ながら、38倍高速であり、最先端の効率性を達成しました。