MLT is presenting 2 workshop papers at the NeurIPS 2019 conference in Vancouver, Canada.
Meet Asir Saeed at the poster session at the NeurIPS Workshop Machine Learning for Creativity and Design.
Creative GANs for generating poems, lyrics, and metaphors
Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
Meet Anugraha Sinha at the poster session at the NeurIPS Workshop Machine Learning for the Developing World.
Quantized deep learning models on low-power edge devices for robotic systems
In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space. This serves as the fundamental building block for the original setup, a robotic system for farms or greenhouses aimed at a wide range of agricultural tasks. Deep learning on edge devices and its implications could have a substantial impact on farming systems in the developing world, leading not only to sustainable food production and income, but also increased data privacy and autonomy.