9,171 research outputs found
Predictive Collective Variable Discovery with Deep Bayesian Models
Extending spatio-temporal scale limitations of models for complex atomistic
systems considered in biochemistry and materials science necessitates the
development of enhanced sampling methods. The potential acceleration in
exploring the configurational space by enhanced sampling methods depends on the
choice of collective variables (CVs). In this work, we formulate the discovery
of CVs as a Bayesian inference problem and consider the CVs as hidden
generators of the full-atomistic trajectory. The ability to generate samples of
the fine-scale atomistic configurations using limited training data allows us
to compute estimates of observables as well as our probabilistic confidence on
them. The methodology is based on emerging methodological advances in machine
learning and variational inference. The discovered CVs are related to
physicochemical properties which are essential for understanding mechanisms
especially in unexplored complex systems. We provide a quantitative assessment
of the CVs in terms of their predictive ability for alanine dipeptide (ALA-2)
and ALA-15 peptide
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Data-based discovery of effective, coarse-grained (CG) models of
high-dimensional dynamical systems presents a unique challenge in computational
physics and particularly in the context of multiscale problems. The present
paper offers a data-based, probablistic perspective that enables the
quantification of predictive uncertainties. One of the outstanding problems has
been the introduction of physical constraints in the probabilistic machine
learning objectives. The primary utility of such constraints stems from the
undisputed physical laws such as conservation of mass, energy etc. that they
represent. Furthermore and apart from leading to physically realistic
predictions, they can significantly reduce the requisite amount of training
data which for high-dimensional, multiscale systems are expensive to obtain
(Small Data regime). We formulate the coarse-graining process by employing a
probabilistic state-space model and account for the aforementioned equality
constraints as virtual observables in the associated densities. We demonstrate
how probabilistic inference tools can be employed to identify the
coarse-grained variables in combination with deep neural nets and their
evolution model without ever needing to define a fine-to-coarse (restriction)
projection and without needing time-derivatives of state variables.
Furthermore, it is capable of reconstructing the evolution of the full,
fine-scale system and therefore the observables of interest need not be
selected a priori. We demonstrate the efficacy of the proposed framework by
applying it to systems of interacting particles and an image-series of a
nonlinear pendulum
Uncertainty Aware AI ML: Why and How
This paper argues the need for research to realize uncertainty-aware
artificial intelligence and machine learning (AI\&ML) systems for decision
support by describing a number of motivating scenarios. Furthermore, the paper
defines uncertainty-awareness and lays out the challenges along with surveying
some promising research directions. A theoretical demonstration illustrates how
two emerging uncertainty-aware ML and AI technologies could be integrated and
be of value for a route planning operation.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and
Public Sector, Arlington, Virginia, US
Predictive Coarse-Graining
We propose a data-driven, coarse-graining formulation in the context of
equilibrium statistical mechanics. In contrast to existing techniques which are
based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a
probabilistic coarse-to-fine map. This corresponds to a directed probabilistic
model where the coarse variables play the role of latent generators of the fine
scale (all-atom) data. From an information-theoretic perspective, the framework
proposed provides an improvement upon the relative entropy method and is
capable of quantifying the uncertainty due to the information loss that
unavoidably takes place during the CG process. Furthermore, it can be readily
extended to a fully Bayesian model where various sources of uncertainties are
reflected in the posterior of the model parameters. The latter can be used to
produce not only point estimates of fine-scale reconstructions or macroscopic
observables, but more importantly, predictive posterior distributions on these
quantities. Predictive posterior distributions reflect the confidence of the
model as a function of the amount of data and the level of coarse-graining. The
issues of model complexity and model selection are seamlessly addressed by
employing a hierarchical prior that favors the discovery of sparse solutions,
revealing the most prominent features in the coarse-grained model. A flexible
and parallelizable Monte Carlo - Expectation-Maximization (MC-EM) scheme is
proposed for carrying out inference and learning tasks. A comparative
assessment of the proposed methodology is presented for a lattice spin system
and the SPC/E water model
Bayesian Modeling of Intersectional Fairness: The Variance of Bias
Intersectionality is a framework that analyzes how interlocking systems of
power and oppression affect individuals along overlapping dimensions including
race, gender, sexual orientation, class, and disability. Intersectionality
theory therefore implies it is important that fairness in artificial
intelligence systems be protected with regard to multi-dimensional protected
attributes. However, the measurement of fairness becomes statistically
challenging in the multi-dimensional setting due to data sparsity, which
increases rapidly in the number of dimensions, and in the values per dimension.
We present a Bayesian probabilistic modeling approach for the reliable,
data-efficient estimation of fairness with multi-dimensional protected
attributes, which we apply to two existing intersectional fairness metrics.
Experimental results on census data and the COMPAS criminal justice recidivism
dataset demonstrate the utility of our methodology, and show that Bayesian
methods are valuable for the modeling and measurement of fairness in an
intersectional context
Attribute-aware Collaborative Filtering: Survey and Classification
Attribute-aware CF models aims at rating prediction given not only the
historical rating from users to items, but also the information associated with
users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This
paper surveys works in the past decade developing attribute-aware CF systems,
and discovered that mathematically they can be classified into four different
categories. We provide the readers not only the high level mathematical
interpretation of the existing works in this area but also the mathematical
insight for each category of models. Finally we provide in-depth experiment
results comparing the effectiveness of the major works in each category
Inferring Complementary Products from Baskets and Browsing Sessions
Complementary products recommendation is an important problem in e-commerce.
Such recommendations increase the average order price and the number of
products in baskets. Complementary products are typically inferred from basket
data. In this study, we propose the BB2vec model. The BB2vec model learns
vector representations of products by analyzing jointly two types of data -
Baskets and Browsing sessions (visiting web pages of products). These vector
representations are used for making complementary products recommendation. The
proposed model alleviates the cold start problem by delivering better
recommendations for products having few or no purchases. We show that the
BB2vec model has better performance than other models which use only basket
data.Comment: Workshop on Intelligent Recommender Systems by Knowledge Transfer and
Learning (RecSysKTL'18
Stacking with Neural network for Cryptocurrency investment
Predicting the direction of assets have been an active area of study and a
difficult task. Machine learning models have been used to build robust models
to model the above task. Ensemble methods is one of them showing results better
than a single supervised method. In this paper, we have used generative and
discriminative classifiers to create the stack, particularly 3 generative and 6
discriminative classifiers and optimized over one-layer Neural Network to model
the direction of price cryptocurrencies. Features used are technical indicators
used are not limited to trend, momentum, volume, volatility indicators, and
sentiment analysis has also been used to gain useful insight combined with the
above features. For Cross-validation, Purged Walk forward cross-validation has
been used. In terms of accuracy, we have done a comparative analysis of the
performance of Ensemble method with Stacking and Ensemble method with blending.
We have also developed a methodology for combined features importance for the
stacked model. Important indicators are also identified based on feature
importance.Comment: 20 pages,7 figue
A Price Driven Hazard Approach to User Retention
Customer loyalty is crucial for internet services since retaining users of a
service to ensure the staying time of the service is of significance for
increasing revenue. It demands the retention of customers to be high enough to
meet the needs for yielding profit for the internet servers. Besides, the
growing of rich purchasing interaction feedback helps in uncovering the inner
mechanism of purchasing intent of the customers.
In this work, we exploit the rich interaction data of user to build a
customers retention evaluation model focusing on the return time of a user to a
product. Three aspects, namely the consilience between user and product, the
sensitivity of the user to price and the external influence the user might
receive, are promoted to effect the purchase intents, which are jointly modeled
by a probability model based on Cox's proportional hazard approach. The hazard
based model provides benefits in the dynamics in user retention and it can
conveniently incorporate covariates in the model. Extensive experiments on real
world purchasing data have demonstrated the superiority of the proposed model
over state-of-the-art algorithms.Comment: 11 page
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
New technologies have enabled the investigation of biology and human health
at an unprecedented scale and in multiple dimensions. These dimensions include
a myriad of properties describing genome, epigenome, transcriptome, microbiome,
phenotype, and lifestyle. No single data type, however, can capture the
complexity of all the factors relevant to understanding a phenomenon such as a
disease. Integrative methods that combine data from multiple technologies have
thus emerged as critical statistical and computational approaches. The key
challenge in developing such approaches is the identification of effective
models to provide a comprehensive and relevant systems view. An ideal method
can answer a biological or medical question, identifying important features and
predicting outcomes, by harnessing heterogeneous data across several dimensions
of biological variation. In this Review, we describe the principles of data
integration and discuss current methods and available implementations. We
provide examples of successful data integration in biology and medicine.
Finally, we discuss current challenges in biomedical integrative methods and
our perspective on the future development of the field
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