Model complexity of deep learning: A survey
Abstract Model complexity is a fundamental problem in deep learning. In this paper, we
conduct a systematic overview of the latest studies on model complexity in deep learning …
conduct a systematic overview of the latest studies on model complexity in deep learning …
Probabilistic machine learning and artificial intelligence
Z Ghahramani - Nature, 2015 - nature.com
How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …
for understanding what learning is, and has therefore emerged as one of the principal …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov - Advances in neural information …, 2020 - proceedings.neurips.cc
The key distinguishing property of a Bayesian approach is marginalization, rather than using
a single setting of weights. Bayesian marginalization can particularly improve the accuracy …
a single setting of weights. Bayesian marginalization can particularly improve the accuracy …
Hidden physics models: Machine learning of nonlinear partial differential equations
M Raissi, GE Karniadakis - Journal of Computational Physics, 2018 - Elsevier
While there is currently a lot of enthusiasm about “big data”, useful data is usually “small”
and expensive to acquire. In this paper, we present a new paradigm of learning partial …
and expensive to acquire. In this paper, we present a new paradigm of learning partial …
[图书][B] Mathematics for machine learning
The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
Sensitivity and generalization in neural networks: an empirical study
In practice it is often found that large over-parameterized neural networks generalize better
than their smaller counterparts, an observation that appears to conflict with classical notions …
than their smaller counterparts, an observation that appears to conflict with classical notions …
Machine learning of linear differential equations using Gaussian processes
M Raissi, P Perdikaris, GE Karniadakis - Journal of Computational Physics, 2017 - Elsevier
This work leverages recent advances in probabilistic machine learning to discover
governing equations expressed by parametric linear operators. Such equations involve, but …
governing equations expressed by parametric linear operators. Such equations involve, but …
Deep kernel learning
We introduce scalable deep kernels, which combine the structural properties of deep
learning architectures with the non-parametric flexibility of kernel methods. Specifically, we …
learning architectures with the non-parametric flexibility of kernel methods. Specifically, we …
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
RR Griffiths, JM Hernández-Lobato - Chemical science, 2020 - pubs.rsc.org
Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …
properties. The original scheme, featuring Bayesian optimization over the latent space of a …