Recent Advances in Convolutional Neural Networks
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths
DyNet: The Dynamic Neural Network Toolkit
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its deriva
Deep Probabilistic Programming
We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same
Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-reduced variants obtained by eliminating combinations of the input signal, bias,
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Deep Learning for Computational Chemistry
Learning to Invert: Signal Recovery via Deep Convolutional Networks
Playtime Measurement with Survival Analysis
Heterogeneous domain adaptation: An unsupervised approach
AdaGAN: Boosting Generative Models
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification
Simplified Minimal Gated Unit Variations for Recurrent Neural Networks