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
RL: Generic reinforcement learning codebase in TensorFlow
Vast reinforcement learning (RL) research groups, such as DeepMind and OpenAI, have their internal (private) reinforcement learning codebases, which enable quick prototyping and comparing of ideas to many state-of-the-art (SOTA) methods. We argue the five fundamental properties of a sophisticated …
Journal of Open Source Software 2019| source code
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 …
NeurIPS 2018 CDNNRIA| source code
Unsupervised Cipher Cracking Using Discrete GANs
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| source code
Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
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
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 …
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!