Deep Learning, NLP, NMT, AI, ML
How to use A simple example. – uses: beiyuouo/[email protected] with: blog_rss_link: https://blog.bj-yan.top/index.xml douban_name: 179948994 A full example. name: Build Readme on: schedule: – cron: “0 0 * * *” workflow_dispatch: push:
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browse and watch anime (scrape from gogoanime) (wip) basically ani-cli but in python cuz python good demo dependencies installation pip install git+https://github.com/sleepntsheep/ani-cli usages or notes it take quite long to open mpv, i don’t know why too 💀 GitHub View Github
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Heterogeneous Star Graph Attention Network for Product Attributes Prediction This repository contains the official PyTorch implementation for ADVIE Paper “Heterogeneous Star Graph Attention Network for Product Attributes Prediction“.We also present the Alibaba Custermers Interaction Dataset used in this paper. Citation If you find this code or dataset is helpful, please kindly consider citing the following papers: Zhao, Xuejiao, et al. “Heterogeneous star graph attention network for product attributes prediction.” Advanced Engineering Informatics. 51, 101447. 2022.
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This repository provides the official PyTorch implementation of the ICRA 2021 paper: Adversarial Differentiable Data Augmentation for Autonomous SystemsAuthor: Manli Shu, Yu Shen, Ming C Lin, Tom Goldstein Environment The code has been tested on: python == 3.7.9 pytorch == 1.10.0 torchvision == 0.8.2 kornia == 0.6.2More dependencies can be found at ./requirements.txt Hardware requirements: The default training and testing setting requires 1 GPU. Data Datasets appeared in our paper can be downloaded/generated by following the directions in this page. […]
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This is a quick-and-dirty tool used to scrape bitcoin/bitcoin pull request andcommentary data. Each output/ folder contains comments.json: an aggregated list of both issue and review comments, in Github’soriginal format commits.json: a list of commit objects corresponding to the PR, in Github’soriginal format pr.json: the pull request object, in Github’s original format comments_abbrev.csv: abbreviated representation of each comment in CSV format pr_abbrev.csv: abbreviated representation of the PR in CSV format done: the datetime we retrieved the PR data Limitations Right […]
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Overview Yolov5, Yolor, mmdetection기반의 모델을 사용 (총 11개 모델 앙상블) 모든 모델은 학습 시 Pretrained Weight을 yolov5, yolor, mmdetection 및 swin transformer github로부터 받아서 사용 각 방식에 필요한 형태로 데이터의 format 변경 Train set과 Validation set을 나누어 진행 총 11개의 결과를 앙상블 detectors_casacde_rcnn_resnet50_multiscale, retinanet_swin-l, retinanet_swin-l_multiscale, retinanet_swin-t, atss_swin-l_multiscale, faster_rcnn-swin-l_multiscale, yolor_tta_multiscale, yolov5x, yolov5x_tta, yolov5x_tta_multiscale Weighted boxes fusion (WBF) 방식으로 앙상블 진행 (Iou threshold = 0.4) 모델에 관한 보다 자세한 내용은 /all_steps 폴더 내에 STEP2로 시작하는 .sh 스크립트들에 적힌 주석을 참고해주세요! 환경(env) 세팅 […]
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Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed using Python 3.6.9 on Ubuntu 18.04 LTS. Name Version Docker 20.10.8 Docker Compose 1.23.2 Python Package Knowledge Graph We have opened our knowledge graphs in releases. If you need to create a new knowledge graph, follow the instructions below: First, you need to install Neo4j 4.1.1 and its required Java version (Java SE 11). Install extra Python dependencies: pip install -r build_KG/requirements.txt […]
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This is the official implementation of the NeurIPS 2021 paper. Method VMAIL simultaneously learns a variational dynamics model and trains an on-policyadversarial imitation learning algorithm in the latent space using only model-basedrollouts. This allows for stable and sample efficient training, as well as zero-shotimitation learning by transfering the learned dynamics model Instructions Get dependencies: conda env create -f vmail.yml conda activate vmail cd robel_claw/robel pip install -e . To train agents for each environmnet download the expert data from the […]
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