Neural Information Processing Systems http://nips.cc/

Contexts are crucial for action recognition in video. Current methods often mine contexts after extracting hierarchical local features and focus on their high-order encodings. This paper instead explores contexts as early as possible and leverages their evolutions for action recognition. In particular, we introduce a novel architecture called deep alternative neural network (DANN) stacking alternative layers. Each alternative layer consists of a volumetric convolutional layer followed by a recurrent layer.

Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet.

Optical Character Recognition (OCR) aims to recognize text in natural images.

Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This submission describes a novel autoencoder method, that uses unsupervised learning to configure a recurrent network to encode both the current and past states of an input. I am not a mathematician nor machine learning expert, and thus am not qualified to review the work for technical merit. However, I have extensive experience in neural network modeling, and thus appreciate both the objective and purported accomplishments: the ability to train a recurrent network to store input sequences in an efficient manner using non-supervised learning. The authors describe a mechanism that addresses the problem by breaking it into two stages -- autoencoding, and then optimization -- that are carried out over different times scales.