16 citations · 27 across the 8 of their papers we have counts for
5 papers · 1 filter
Status-quo policy gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri +4
Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instan…
A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks
Daniel Nichols, Siddharth Singh, Shu-Huai Lin +1
The field of deep learning has witnessed a remarkable shift towards extremely compute- and memory-intensive neural networks. These newer larger models have enabled researchers to a…
AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning
Siddharth Singh, Abhinav Bhatele
In the last few years, the memory requirements to train state-of-the-art neural networks have far exceeded the DRAM capacities of modern hardware accelerators. This has necessitate…
Domain Adaptation for Real-World Single View 3D Reconstruction
Brandon Leung, Siddharth Singh, Arik Horodniceanu
Deep learning-based object reconstruction algorithms have shown remarkable improvements over classical methods. However, supervised learning based methods perform poorly when the t…
Learning to Map for Active Semantic Goal Navigation
Georgios Georgakis, Bernadette Bucher, Karl Schmeckpeper +2
We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the sp…