1.3k citations · 4.9k across the 150 of their papers we have counts for
13 papers · 1 filter
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk +1
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. Howeve…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
The Implicit Regularization of Ordinary Least Squares Ensembles
Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk
Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a si…
Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
Haoran You, Chaojian Li, Pengfei Xu +6
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comp…
Out-of-Distribution Detection Using Neural Rendering Generative Models
Yujia Huang, Sihui Dai, Tan Nguyen +2
Out-of-distribution (OoD) detection is a natural downstream task for deep generative models, due to their ability to learn the input probability distribution. There are mainly two…
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu +5
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widel…