neural networks
A Biologically Plausible Learning Algorithm for Neural Networks
A "biological" learning algorithm for neural networks that learns in an unsupervised fashion and performs well in image classification.
Certifying Attack Resistance of Convolutional Neural Networks
Researchers from MIT and IBM propose an efficient and effective method for certifying attack resistance of convolutional neural networks to given input data.
NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI
NeuNetS uses AI to automatically synthesize deep neural networks faster and more easily than ever before, scaling up the deployment and adoption of AI.
Interpretability and Performance: Can the Same Model Achieve Both?
Interpretability and performance of a system are usually at odds with each other, as many of the best-performing models (viz. deep neural networks) are black box in nature. In our work, improving simple models, we try to bridge this gap by proposing a method to transfer information from a high-performing neural network to another model […]
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
Online social media has become one of the most important ways to communicate and exchange ideas. Unfortunately, the discourse is often crippled by abusive language that can have damaging effects on social media users. For instance, a recent survey by YouGov.uk discovered that, among the information employers can find online about job candidates, aggressive or […]
Open standards for deep learning to simplify development of neural networks
Among the various fields of exploration in artificial intelligence, deep learning is an exciting and increasingly important area of research that holds great potential for helping computers understand and extract meaning from data, e.g. deciphering images and sounds. To help further the creation and adoption of interoperable deep learning models, IBM joined the Open Neural […]