pathnet - Tensorflow Implementation of PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Understanding the current state of the art in Machine Reasoning

The abstraction from big data on English words of distributed vector representations computing simple semantic aspects has drawn interest from the computing community.

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In this post we give a brief overview of the DocNADE model, and provide a TensorFlow implementation.

Pointer networks are a variation of the sequence-to-sequence model with attention. Instead of translating one sequence into another, they yield a …

I know it’s a weird way to start a blog with a negative, but there was a wave of discussion in the last few days that I think serves as a good hook for some topics on which I’ve been thinking recently. It all started with a post in the Simply Stats blog by…

Evaluation code for various unsupervised automated metrics for Natural Language Generation. - Maluuba/nlg-eval

The paper "A simple neural network module for relational reasoning" is available here: https://arxiv.org/abs/1706.01427 Details on our Patreon page: https://...

How to build a Sound Classifier from scratch using Neural Networks. Recognizing day-to-day sounds using Artificial Intelligence. Learn more.

TCSVT2018 Pedestrian Alignment Network for Large-scale Person Re-identification - layumi/Pedestrian_Alignment

Deepeyeballers - Ushering in deep learning's foray into the netherworld of eyeballing

I experimented with generating faces of cats using Generative adversarial networks (GAN). I wanted to try DCGAN, WGAN and WGAN-GP in low and higher resolutions. I used the CAT dataset (yes this is …

В этом посте изложены две недавно опубликованные идеи, как ускорить процесс обучения глубоких нейронных сетей при увеличении точности предсказания. Предложенные (разными авторами) способы...

Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state.

The promise of adding state to neural networks is that…

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. quantities in the…

Метод BFGS , итерационный метод численной оптимизации, назван в честь его исследователей: B royden, F letcher, G oldfarb, S hanno. Относится к классу так называемых квазиньютоновских методов. В...

Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence…

Algorithms off the convex path.