• IDSIA/GoGePo: Official repository for the paper "Goal-Conditioned Generators of Deep Policies"

  Faccio, Francesco; Herrmann, Vincent; Ramesh, Aditya; Kirsch, Louis; Schmidhuber, Jürgen (Github, 2022-07-04) [Software]

  Official repository for the paper "Goal-Conditioned Generators of Deep Policies"
• yiqun-wang/HFS: Improved surface reconstruction using high-frequency details

  Wang, Yiqun; Skorokhodov, Ivan; Wonka, Peter (Github, 2022-06-15) [Software]

  Improved surface reconstruction using high-frequency details
• ruzayqat/unbiased_uld: Unbiased Estimation using the Underdamped Langevin Dynamics (ULD)

  Jasra, Ajay; Ruzayqat, Hamza Mahmoud; Chada, Neil Kumar (Github, 2022-06-14) [Software]

  Unbiased Estimation using the Underdamped Langevin Dynamics (ULD)
• MotasemAlfarra/federated-learning-with-pytorch:

  Alfarra, Motasem; Pérez, Juan C.; Shulgin, Egor; Richtarik, Peter; Ghanem, Bernard (Github, 2022-06-05) [Software]
• showlab/EgoVLP: [Arxiv2022] Egocentric Video-Language Pretraining

  Lin, Kevin Qinghong; Wang, Alex Jinpeng; Soldan, Mattia; Wray, Michael; Yan, Rui; Xu, Eric Zhongcong; Gao, Difei; Tu, Rongcheng; Zhao, Wenzhe; Kong, Weijie; Cai, Chengfei; Wang, Hongfa; Damen, Dima; Ghanem, Bernard; Liu, Wei; Shou, Mike Zheng (Github, 2022-05-31) [Software]

  [Arxiv2022] Egocentric Video-Language Pretraining
• guochengqian/PointNeXt: PyTorch repo for ``PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies''

  Qian, Guocheng; Li, Yuchen; Peng, Houwen; Mai, Jinjie; Hammoud, Hasan Abed Al Kader; Elhoseiny, Mohamed; Ghanem, Bernard (Github, 2022-05-21) [Software]

  PyTorch repo for ``PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies''
• iriscxy/Target-aware-RWG: Official code and data for SIGIR 2022 paper 'Target-aware Abstractive Related Work Generation with Contrastive Learning''

  Chen, Xiuying; Alamro, Hind; Li, Mingzhe; Gao, Shen; Yan, Rui; Gao, Xin; Zhang, Xiangliang (Github, 2022-04-27) [Software]

  Official code and data for SIGIR 2022 paper 'Target-aware Abstractive Related Work Generation with Contrastive Learning''
• kaust-csg-uaamg/uaamg:

  Shao, Han; Huang, Libo; Michels, Dominik L. (Github, 2022-04-21) [Software]
• DenisRustand/INLAjoint: Joint modeling multivariate longitudinal and time-to-event outcomes with INLA

  Rustand, Denis; Krainski, Elias T; Rue, Haavard (Github, 2022-03-30) [Software]

  Joint modeling multivariate longitudinal and time-to-event outcomes with INLA
• makamoa/hyplex:

  Makarenko, Maksim; Burguete-Lopez, A.; Wang, Qizhou; Getman, Fedor; Giancola, Silvio; Ghanem, Bernard; Fratalocchi, Andrea (Github, 2022-03-27) [Software]
• MarcoFusi1980/Tuscan_Vineyard_microbial_ecology:

  Marasco, Ramona; Alturkey, Hend; Fusi, Marco; Brandi, Michele; Ghiglieno, Isabella; Valenti, Leonardo; Daffonchio, Daniele (Github, 2022-03-11) [Software]
• msdueholm/MiDAS4:

  Dueholm, Morten Kam Dahl; Nierychlo, Marta; Andersen, Kasper Skytte; Rudkjøbing, Vibeke; Knutsson, Simon; Arriaga, Sonia; Bakke, Rune; Boon, Nico; Bux, Faizal; Christensson, Magnus; Chua, Adeline Seak May; Curtis, Thomas P.; Cytryn, Eddie; Erijman, Leonardo; Etchebehere, Claudia; Fatta-Kassinos, Despo; Frigon, Dominic; Garcia-Chaves, Maria Carolina; Gu, April Z.; Horn, Harald; Jenkins, David; Kreuzinger, Norbert; Kumari, Sheena; Lanham, Ana; Law, Yingyu; Leiknes, TorOve; Morgenroth, Eberhard; Muszyński, Adam; Petrovski, Steve; Pijuan, Maite; Pillai, Suraj Babu; Reis, Maria A.M.; Rong, Qi; Rossetti, Simona; Seviour, Robert; Tooker, Nick; Vainio, Pirjo; van Loosdrecht, Mark; Vikraman, R.; Wanner, Jiří; Weissbrodt, David; Wen, Xianghua; Zhang, Tong; Nielsen, Per H.; Albertsen, Mads; Nielsen, Per Halkjær (Github, 2022-03-03) [Software]
• lilv98/ABIN: Implementation for Joint Abductive and Inductive Neural Logical Reasoning

  Tang, Zhenwei; Pei, Shichao; Peng, Xi; Zhuang, Fuzhen; Zhang, Xiangliang; Hoehndorf, Robert (Github, 2022-02-11) [Software]

  Implementation for Joint Abductive and Inductive Neural Logical Reasoning
• lyijin/increased_incompatibility: Code and intermediate files to replicate analysis for a manuscript in review.

  Cziesielski, Maha Joana; Liew, Yi Jin; Cui, Guoxin; Aranda, Manuel (Github, 2022-02-09) [Software]

  Code and intermediate files to replicate analysis for a manuscript in review.
• Xinxinatg/RBP-TSTL:

  Peng, Xinxin; Wang, Xiaoyu; Guo, Yuming; Ge, Zongyuan; Li, Fuyi; Gao, Xin; Song, Jiangning (Github, 2022-01-27) [Software]
• mysteryresearcher/dasha:

  Tyurin, Alexander; Richtarik, Peter (Github, 2022-01-18) [Software]
• emadalibrahim/reaction_rate_prediction:

  Al Ibrahim, Emad; Farooq, Aamir (Github, 2022-01-12) [Software]
• universome/stylegan-v: Official implementation of StyleGAN-V [CVPR 2022]

  Skorokhodov, Ivan; Tulyakov, Sergey; Elhoseiny, Mohamed (Github, 2021-12-29) [Software]

  Official implementation of StyleGAN-V [CVPR 2022]
• Y-Tang99/GMSS2.0: GMSS2.0 is a software package for implementing stochastic ground motion simulation using finite-fault sources.

  Tang, Yuxiang (Github, 2021-11-20) [Software]

  GMSS2.0 is a software package for implementing stochastic ground motion simulation using finite-fault sources.
• 

  KNIME Workflow: Query Semantic Scholar API by DOI

  Balagtas, Carl; Grenz, Daryl M. (2021-11-18) [Software]

  This workflow retrieves results from the Semantic Scholar API by DOI and then saves the results to Excel. It can be used to identify which items in a list of DOIs have a record in Semantic Scholar and to also further check if the reference list from a document has been extracted by Semantic Scholar. The workflow also includes a section to go through the citations to an item in Semantic Scholar and check if they are self-citations or not.

View more