Arash Vahdat

Arash Vahdat is a principal research scientist at NVIDIA research specializing in computer vision and machine learning. Before joining NVIDIA, he was a research scientist at D-Wave Systems where he worked on deep generative learning and weakly supervised learning. Prior to D-Wave, Arash was a research faculty member at Simon Fraser University (SFU), where he led research on deep video analysis and taught graduate-level courses on machine learning for big data. Arash obtained his Ph.D. and M.Sc. from SFU under Greg Mori’s supervision working on latent variable frameworks for visual analysis. His current areas of research include deep generative learning, representation learning, efficient neural networks, and probabilistic deep learning.

Posts by Arash Vahdat

Technical Walkthrough 1

Improving Diffusion Models as an Alternative To GANs, Part 2

Part 2 of this series reviews three recent techniques developed at NVIDIA for overcoming the slow sampling challenge in diffusion models. 16 MIN READ
Technical Walkthrough 1

Improving Diffusion Models as an Alternative To GANs, Part 1

Part 1 of this series introduces diffusion models as a powerful class for deep generative models and examines their trade-offs in addressing the generative learning trilemma. 8 MIN READ
Technical Walkthrough 0

Discovering GPU-friendly Deep Neural Networks with Unified Neural Architecture Search

After the first successes of deep learning, designing neural network architectures with desirable performance criteria for a given task (for example… 9 MIN READ