Deep neural networks have shown recent promise in many language-related tasks such as the modeling of conversations. We extend RNN-based sequence to sequence models to capture the long range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models are abl...

We propose a method for extracting very accurate masks of hands in egocentric views. Our method is based on a novel Deep Learning architecture: In contrast with current Deep Learning methods, we do not use upscaling layers applied to a low-dimensional representation of the input image. Instead, we extract features with convolutional layers and map them directly to a segmentation mask with a ful...

Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is further complicated as a result of variations in sample preparation. It is sh...

We interpret part of the experimental results of Shwartz-Ziv and Tishby [2017]. Inspired by these results, we established a conjecture of the dynamics of the machinary of deep neural network. This conjecture can be used to explain the counterpart result by Saxe et al. [2018].

This paper presents a method of genre classification using deep neural networks for the AcousticBrainz genre classification task of MediaEval 2017.

This paper describes our automatic speech recognition system for IWSLT2014 evaluation campaign. The system is based on weighted finite-state transducers and a combination of multiple subsystems which consists of four types of acoustic feature sets, four types of acoustic models, and Ngram and recurrent neural network language models. Compared with our system used in last year, we added addition...

Detecting ships in marine images is an essential problem in maritime surveillance systems. Although several types of deep neural networks have almost ubiquitously used for this purpose, but the performance of such networks greatly drops when they are exposed to low size and low contrast images which have been captured by passive monitoring systems. On the other hand factors such as sea waves, c...

Corresponding author details: Jie Fan, Accutar Biotechnology, 760 Parkside Ave, Room 213 Brooklyn, NY 11226, USA. [email protected] 1. Accutar Biotechnology, 760 parkside Ave, Room 213, Brooklyn, NY 11226, USA. 2. Laboratory of Cell Biology, Howard Hughes Medical Institute, The Rockefeller University, New York, New York 10065, USA 3. Accutar Biotechnology (Shanghai) Room 307, No. 6 Building...

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This paper is based on finding and extracting important key phrases (catchphrase) from a document from which the the document can be summarized. This is important as this will reduce time consumption in summarization of documents. This work is realizedwith the help of deep neural network to train anmodel for recognizing such important key phrases based on various calculated parameters.