First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Paper For paper 1180: Deep Recursive Neural Networks for Compositionality in Language This paper introduces a new architecture -- deep recursive neural network (deep RNN) which is constructed by stacking multiple recursive layers. The authors evaluate the proposed model on the task of fine-grained sentiment classification. Clarity - In general, this paper is well written and pleasant to read. Quality - The paper seems technically sound.

However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest and most popular Graph Neural Networks (GNNs).

Such high-stakes applications require not only point predictions but also accurate quantification of predictive uncertainty, i.e. meaningful confidence values in addition to class predictions.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper proposes a model for solving discriminative tasks with video inputs. The model consists of two convolutional nets. The input to one net is an appearance frame. The input to the second net is a stack of densely computed optical flow features. Each pathway is trained separately to classify its input.

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Deep learning methods and applications continue to become more diverse.

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