Distributed AI Research Collaboration

About Us

We are a multi-disciplinary team of scientists and engineers who like doing research for fun. Our only objective is to publish good machine learning research that is useful and interesting. Our collaborators include researchers from research institutions such as Google Brain, University of Oxford, and Vector Institute for Artificial Intelligence. Our published experiments and tools can be found on github.com/for-ai.


Publications by members of FOR.ai

Targeted Dropout

Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant. This makes it possible to compress neural networks without having a drastic effect on performance. We introduce targeted dropout, a strategy for post …


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Unsupervised Cipher Cracking Using Discrete GANs

Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of …

ICLR 2018

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Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network

Mazhar Shaikh, Ganesh Anand, Gagan Acharya, Abhijit Amrutkar, Varghese Alex, Ganapathy Krishnamurthi

Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu …

International MICCAI Brainlesion Workshop 2018

Towards Incremental Cylindrical Algebraic Decomposition in Maple

Alexander Imani Cowen-Rivers, Matthew England

Cylindrical Algebraic Decomposition (CAD) is an important tool within computational real algebraic geometry, capable of solving many problems for polynomial systems over the reals. It has long been studied by the Symbolic Computation community and has found recent interest in the Satisfiability …

FLOC 2018

GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language

Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder

Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming …

View all contributor publications

Where We Are

We’re a team with members spanning the globe, from Hyderabad to the Valley!

Join The Team

We're looking for individuals interested in contributing to cross-institutional research projects on machine learning using neural networks. We recruit students and industrial members globally. Our only requirement is a strong background in computer science, mathematics and statistics. Remote work is our modus operandi, although we regularly organize working sessions in cities with more than one member. If you're interested in deep learning and neural networks, and are well-versed in calculus, statistics, and software development then you're a perfect fit. The team is volunteer-run, but we do provide mentorship and authorship opportunities, and we have a rich set of resources and training infrastructure at our disposal!

Apply to join the team here!