Much work has been done in recognizing the semantics of sentences as well as semantic relationships between sentences. We applied some of these previous approaches to the space of question similarity. Our task was to create a classifier that given a pair of questions, attempted to predict whether or not the questions were asking the same question. Of the four models we attempted (bag of words, ...

This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a Ma...

The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi-dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen gre...

\Recurrent backprop" for learning to store information over extended time periods takes too long. The main reason is insuucient, decaying error back ow. We describe a novel, ee-cient \Long Short Term Memory" (LSTM) that overcomes this and related problems. Unlike previous approaches, LSTM can learn to bridge arbitrary time lags by enforcing constant error ow. Using gradient descent, LSTM explic...

Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especia...

A drug can affect the activity of other drugs, when administered together, in both synergistic or antagonistic ways. In one hand synergistic effects lead to improved therapeutic outcomes, antagonistic consequences can be life-threatening, leading to increased healthcare cost, or may even cause death. Thus, identification of unknown drug-drug interaction (DDI) is an important concern for efficie...

Long Short-Term Memory (LSTM) can learn algorithms for temporal pattern processing not learnable by alternative recurrent neural networks (RNNs) or other methods such as Hidden Markov Models (HMMs) and symbolic grammar learning (SGL). Here we present tasks involving arithmetic operations on continual input streams that even LSTM cannot solve. But an LSTM variant based on \forget gates," a recen...

This work briefly introduces the recurrent residual network which is a combination of the residual network and the long short term memory network(LSTM). The residual network is featured by residual blocks and the LSTM as a variant of RNN, is featured by the recurrent structure and long short term-memory cells. We modify the LSTM by adding residual links between nonadjacent layers. Experiments o...