11,756 research outputs found
Matching Models Across Abstraction Levels with Gaussian Processes
Biological systems are often modelled at different levels of abstraction
depending on the particular aims/resources of a study. Such different models
often provide qualitatively concordant predictions over specific
parametrisations, but it is generally unclear whether model predictions are
quantitatively in agreement, and whether such agreement holds for different
parametrisations. Here we present a generally applicable statistical machine
learning methodology to automatically reconcile the predictions of different
models across abstraction levels. Our approach is based on defining a
correction map, a random function which modifies the output of a model in order
to match the statistics of the output of a different model of the same system.
We use two biological examples to give a proof-of-principle demonstration of
the methodology, and discuss its advantages and potential further applications.Comment: LNCS forma
Automated transition state theory calculations for high-throughput kinetics
A scarcity of known chemical kinetic parameters leads to the use of many
reaction rate estimates, which are not always sufficiently accurate, in the
construction of detailed kinetic models. To reduce the reliance on these
estimates and improve the accuracy of predictive kinetic models, we have
developed a high-throughput, fully automated, reaction rate calculation method,
AutoTST. The algorithm integrates automated saddle-point geometry search
methods and a canonical transition state theory kinetics calculator. The
automatically calculated reaction rates compare favorably to existing estimated
rates. Comparison against high level theoretical calculations show the new
automated method performs better than rate estimates when the estimate is made
by a poor analogy. The method will improve by accounting for internal rotor
contributions and by improving methods to determine molecular symmetry.Comment: 29 pages, 8 figure
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that
learns the underlying structure of voxelized 3D shapes in an unsupervised
fashion. Through the use of skip-connections, our model can successfully learn
and infer a latent, hierarchical representation of objects. Furthermore,
realistic 3D objects can be easily generated by sampling the VSL's latent
probabilistic manifold. We show that our generative model can be trained
end-to-end from 2D images to perform single image 3D model retrieval.
Experiments show, both quantitatively and qualitatively, the improved
generalization of our proposed model over a range of tasks, performing better
or comparable to various state-of-the-art alternatives.Comment: Accepted as oral presentation at International Conference on 3D
Vision (3DV), 201
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Adaptive Resonance Theory
SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378
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