Deep Neural Networks (DNNs) are powerful models that have ac hieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be us ed to map sequences to sequences. In this paper, we present a general end-to-end ap proach to sequence learning that makes minimal assumptions on the sequence str ucture. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LS TM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT -14 dataset, the translations p roduced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the L STM's BLEU score was penalized on out-of-vocabulary words.

In its original formulation EP was described in the case of real-time neuronal dynamics, which is computationally costly.

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

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

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper propose to use CNN to classify rhythms from EEG recordings. A dataset with 13 subjects is analyzed. Temporal and spatiotemporal (STFT) data representation are investigated. The paper is well written with a good review of the relevant literature.

W e investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures.

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

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1703" "Title:","How transferable are features in deep neural networks?" First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper aims to quantify the transferrability of features in deep neural networks, both in terms of the difference between source and target tasks and in terms of the depth of the features being transferred. To this end, the authors take an existing network (Krizhevsky et al. 2012), and performs generalization by fixing different layer depth and by transferring between different splits of the ImageNet dataset. I find the paper sufficiently interesting in the sense that, despite the many papers describing the success of feature transfer (e.g.