publications

2023

1. CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks

   Mahmood Kalemati, Saeid Darvishi, and Somayyeh Koohi

   Communications biology, 2023

2015

1. U-net: Convolutional networks for biomedical image segmentation

   Olaf Ronneberger, Philipp Fischer, and Thomas Brox

   In International Conference on Medical image computing and computer-assisted intervention, 2015

   arXiv
2. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

   Kaiming He, Xiangyu Zhang, Shaoqing Ren, and 1 more author

   In Proceedings of the IEEE international conference on computer vision, 2015

   PDF
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

   Sergey Ioffe and Christian Szegedy

   In Proceedings of the 32nd International Conference on Machine Learning, 07–09 jul 2015

   Abs PDF

   Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.

2014

1. Adam: A method for stochastic optimization

   Diederik P Kingma

   arXiv preprint arXiv:1412.6980, 07–09 jul 2014

   arXiv
2. Dropout: a simple way to prevent neural networks from overfitting

   Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, and 2 more authors

   The journal of machine learning research, 07–09 jul 2014

   PDF

2009

1. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

   Honglak Lee, Roger Grosse, Rajesh Ranganath, and 1 more author

   In Proceedings of the 26th annual international conference on machine learning, 07–09 jul 2009

1977

1. Consistency in networks of relations

   Alan K Mackworth

   Artificial intelligence, 07–09 jul 1977