We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically

learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine learning techniques are being applied to new kinds of problems including knowledge discovery in databases, language processing, robot control, and combinatorial

A new protein fold recognition method is described which is both fast and reliable. The method uses a traditional sequence alignment algorithm to generate alignments which are then evaluated by a method derived from threading techniques. As a ®nal step, each threaded model is evaluated by a neural

, deferred compilation of uncommon cases and non-backtracking splitting using path objects, have improved compilation speed by more than an order of magnitude. SELF now compiles about as fast as an optimizing C compiler and runs at over half the speed of optimized C. This new level of performance may make

neural networks. The viewpoint is that of optimization: many methods can be cast in the language of optimization techniques, allowing the transfer to neural nets of detailed results about computational complexity and safety procedures to ensure convergence and to avoid numerical problems. The review

-evolution approachwithout loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications. Keywords: Neuro-Evolution, Reinforcement Learning, Genetic Algorithms, Neural Networks

Recent work has shown success in us-ing neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window. Our model is purely lexi-calized and can be integrated

This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC, 1 is operational and runs the back-propagation algorithm at a speed of 247 million connection updates per second (continuous weight update

The paper proposes the use of the orthogonal arrays for neural network learning. Learning can be seen as a search for the neural weights that give an optimal network performance. The search/optimization adopted here is inspired from methods based on Orthogonal Arrays (a special set of Latin Squares

Recurrent Neural Networks (RNNs) are very powerful sequence models that do not enjoy widespread use because it is extremely difficult to train them properly. Fortunately, recent advances in Hessian-free optimization have been able to overcome the difficulties associated with training RNNs, making