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.


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.

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.

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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