Call for Papers

Recently, the success of deep learning in AI has attracted great attention from academia and industry. However, research shows that the performance of models in the wild is far from practical due to the lack of efficiency and robustness towards open-world data and scenarios. We welcome research contributions related to the following (but not limited to) topics:
  • Network quantization and binarization
  • Adversarial attacking deep learning systems
  • Neural architecture search (NAS)
  • Robust architectures against adversarial attacks
  • Hardware implementation and on-device deployment
  • Benchmark for evaluating model robustness
  • New methodologies and architectures for efficient and rubost deep learning