1.6k citations · 6.3k across the 48 of their papers we have counts for
4 papers · 1 filter
Compositional Transformers for Scene Generation
Drew A. Hudson, C. Lawrence Zitnick
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural p…
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das +4
Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…
Generative Adversarial Transformers
Drew A. Hudson, C. Lawrence Zitnick
We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that ena…