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Table of contents:

• Computational resources
• Good paper for sharing
  • Transfer learning

Computational resources

• Sheffield HPC:

  • Iceberg

  • ShARC

  Please contact Haiping for requesting access.

• Departmental Resources:

  • Big memory nodes

  • Nvidia DGX-1

  Please contact Twin Karmakharm ([email protected]) or RSE group ([email protected]) for requesting access.

• TALE Private Node on ShARC:

  • Starting an interactive session

    Type qrshx -P tale to get access to the node (node156) with default 2GB real memory allocation. Use top to check how busy it is (currently idle, so please use it).

    If more memory e.g., 16GB is needed for your job, type qrshx -P tale -q tale.q -l rmem=16G. Be sure to request enough resource for your job in the interactive session, otherwise, the job will be killed without any warning or error message and you will be dropped out of the TALE node.

  • Submitting batch jobs

    Batch Jobs can be submitted to the TALE private node by adding the -P tale -q tale.q parameters, for example:

    #!/bin/bash
    #$ -P tale
    #$ -q tale.q
    
    echo "Hello world"
    

    More about the command for configuring batch jobs can be found on https://www.sheffield.ac.uk/cics/research/hpc/sharc/batch

• Data Storage:

  TALE group shared storage folder: "tale2". Only TALE group members have the access.

  Access research storage: https://www.sheffield.ac.uk/cics/research-storage/using-research-storage

  Access "tale2" from Sheffield HPC: Shared folders are not visible but accessible from Sheffield HPC (ShARC or iceberg) by using this command: cd /shared/tale2/Shared

Good paper for sharing

Transfer learning

• Learning transferable features with deep adaptation networks (DAN) Long et al., in ICML, 2015. [Code]
• Transfer Feature Learning with Joint Distribution Adaptation (JDA) Long et al., in ICCV, 2013. [Code]
• Domain adaptation via transfer component analysis (TCA) Pan et al.., in IJCA, 2009.
• Boosting for transfer learning (TrAdaBoost) Dai et al., in ICML, 2007.

Network Embedding

Spectral Clustering for Higher-order Network Structures