We propose a novel architecture for natural language inference. On top of a traditional recurrent neural net with attention architecture, we add memory-based modules, residual connections, and richer word embeddings. With these, we are able to achieve 76.6% accuracy.

In this paper, we present a novel neural network architecture called M-net, which exploits the don't-care information in training multilayer feedforward neural networks. Our method takes advantage of the user's prior knowledge as well as the neural network's ability to learn from examples. The user's prior knowledge is encoded in the form of don't-care inputs to reduce the number of training pa...

The proposed neural architecture consists of an analytic lower net, and a synthetic upper net. This paper focuses on the upper net. The lower net performs a 2D multiresolution wavelet decomposition of an initial spectral representation to yield a multichannel representation of local frequency modulations at multiple scales. From this representation, the upper net synthesizes increasingly comple...

Although recurrent neural nets have been moderately successful in learning to emulate nite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

In this paper we derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularization parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative combinatorial search among the relevant subsets of an initial neural network architecture by emp...

Neural nets offer an approach to computation thatmimics biological nervous systems. Algorithms based on neural nets have been proposed to address speech recognition tasks which humans perlorm with little apparent effort. In this paper, neural net classifiers are described and compared with conventional classification algorithms. Perceptron classifiers trained with a new algorithm, called back p...

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. In this paper, we propose a Recurrent Co...