Browsing Department of Computational and Data Sciences (CDS) by Subject "Deep Neural Networks"

Now showing items 1-4 of 4

    • Efficient and Effective Algorithms for Improving the Robustness of Deep Neural Networks 

      Addepalli, Sravanti
      Deep Neural Networks achieve near-human performance on several benchmark datasets, yet they are not as robust as humans. Their success relies on the proximity of test samples to the distribution of training data, resulting ...

    • Learning Compact Architectures for Deep Neural Networks 

      Srinivas, Suraj (2018-05-22)
      Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just ...

    • Learning from Limited and Imperfect Data 

      Rangwani, Harsh
      Deep Neural Networks have demonstrated orders of magnitude improvement in capabilities over the years after AlexNet won the ImageNet challenge in 2012. One of the major reasons for this success is the availability of ...

    • Relating Representations in Deep Learning and the Brain 

      Jat, Sharmistha
      Deep Neural Networks (DNN) inspired by the human brain have redefined the state-of-the-art performance in AI during the past decade. Much of the research is still trying to understand and explain the function of these ...