Search Results

Learning to Execute

6 code implementations17 Oct 2014

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

Learning to Execute

Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch

1 code implementation5 Feb 2018

Significance: This proof of concept study demonstrates the technical feasibility of a smartwatch device and supervised machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols.

Human-Computer Interaction I.2.1

DeepCoder: Learning to Write Programs

3 code implementations7 Nov 2016

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

Enumerative Search

Sequence Modeling via Segmentations

2 code implementations ICML 2017

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.

Segmentation speech-recognition +3

Financial Trading as a Game: A Deep Reinforcement Learning Approach

1 code implementation8 Jul 2018

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

reinforcement-learning Reinforcement Learning (RL)

Protein identification with deep learning: from abc to xyz

1 code implementation8 Oct 2017

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.

de novo peptide sequencing

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

1 code implementation29 Feb 2024

As the role of artificial intelligence becomes increasingly pivotal in modern society, the efficient training and deployment of deep neural networks have emerged as critical areas of focus.

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

1 code implementation Scientific Reports 2019

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.

Protein Secondary Structure Prediction

CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair

1 code implementation18 Jul 2020

To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages.

Ensemble Learning Machine Translation +3

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

3 code implementations15 Nov 2018

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

Rolling Shutter Correction Time Series Analysis