Learning to Execute

17 Oct 20143 code implementations

Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.


Sequence Modeling via Segmentations

ICML 2017 2 code implementations

The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.


DeepCoder: Learning to Write Programs

7 Nov 20163 code implementations

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.


Financial Trading as a Game: A Deep Reinforcement Learning Approach

8 Jul 20181 code implementation

We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1.

Protein identification with deep learning: from abc to xyz

8 Oct 20171 code implementation

We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to reconstruct protein sequences from tandem mass spectrometry data.

Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction

Scientific Reports 2019 1 code implementation

In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes.


Real-time Power System State Estimation and Forecasting via Deep Neural Networks

15 Nov 20182 code implementations

To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring.


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