AI Research

When humans generate novel neural architectures, they go through a surprisingly large amount of trial and error.

Most neural architectures for machine translation use an encoder-decoder model consisting of either convolutional or recurrent layers. The encoder layers map the input to a latent space and the decoder, in turn, uses this latent representation to map the inputs to the targets.

Over the past few years, neural networks have driven rapid improvements in accuracy and quality for natural language tasks.

A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce.

There are times when word vectors are initialized to lists of random numbers before a model is trained for a specific task, but it is also quite common to initialize the word vectors of a model with those obtained by running methods like word2vec, GloVe, or FastText.

The last few decades have witnessed a fundamental change in the challenge of taking in new information.

The rise of Machine Learning, Deep Learning, and Artificial Intelligence has already had a massive impact on the field of computer science.

Automatically generating captions for images has emerged as a prominent interdisciplinary research problem in both academia and industry.

LSTMs have become a basic building block for many deep NLP models.

Humans learn natural languages, such as English, starting from basic grammar to complex semantics in a single brain.