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12 papers · 1 filter
A deep learning energy method for hyperelasticity and viscoelasticity
Diab W. Abueidda, Seid Koric, Rashid Abu Al-Rub +3
The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials. The pres…
Stein Variational Inference for Discrete Distributions
Jun Han, Fan Ding, Xianglong Liu +3
Gradient-based approximate inference methods, such as Stein variational gradient descent (SVGD), provide simple and general-purpose inference engines for differentiable continuous…
DLSpec: A Deep Learning Task Exchange Specification
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Deep Learning (DL) innovations are being introduced at a rapid pace. However, the current lack of standard specification of DL tasks makes sharing, running, reproducing, and compar…
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang +3
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The app…
Knowledge Flow: Improve Upon Your Teachers
Iou-Jen Liu, Jian Peng, Alexander G. Schwing
A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an i…
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed -…