50 citations · 171 across the 14 of their papers we have counts for
5 papers · 1 filter
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations
Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson +1
Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied…
Designing Interpretable Approximations to Deep Reinforcement Learning
Nathan Dahlin, Krishna Chaitanya Kalagarla, Nikhil Naik +2
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such a…
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng, Alexander Trott, Sunil Srinivasa +4
Tackling real-world socio-economic challenges requires designing and testing economic policies. However, this is hard in practice, due to a lack of appropriate (micro-level) econom…
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque, Nikhil Naik, Junnan Li +2
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained us…
ProGen: Language Modeling for Protein Generation
Ali Madani, Bryan McCann, Nikhil Naik +5
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervi…