338 research outputs found
Stochastic simulation methods for structural reliability under mixed uncertainties
Uncertainty quantification (UQ) has been widely recognized as one of the most important, yet challenging task in both structural engineering and system engineering, and the current researches are mainly on the proper treatment of different types of uncertainties, resulting from either natural randomness or lack of information, in all related sub-problems of UQ such as uncertainty characterization, uncertainty propagation, sensitivity analysis, model updating, model validation, risk and reliability analysis, etc. It has been widely accepted that those uncertainties can be grouped as either aleatory uncertainty or epistemic uncertainty, depending on whether they are reducible or not. For dealing with the above challenge, many non-traditional uncertainty characterization models have been developed, and those models can be grouped as either imprecise probability models (e.g., probability-box model, evidence theory, second-order probability model and fuzzy probability model) or non-probabilistic models (e.g., interval/convex model and fuzzy set theory).
This thesis concerns the efficient numerical propagation of the three kinds of uncertainty characterization models, and for simplicity, the precise probability model, the distribution probability-box model, and the interval model are taken as examples. The target is to develop efficient numerical algorithms for learning the functional behavior of the probabilistic responses (e.g., response moments and failure probability) with respect to the epistemic parameters of model inputs, which is especially useful for making reliable decisions even when the available information on model inputs is imperfect.
To achieve the above target, my thesis presents three main developments for improving the Non-intrusive Imprecise Stochastic Simulation (NISS), which is a general methodology framework for propagating the imprecise probability models with only one stochastic simulation. The first development is on generalizing the NISS methods to the problems with inputs including both imprecise probability models and non-probability models. The algorithm is established by combining Bayes rule and kernel density estimation. The sensitivity indices of the epistemic parameters are produced as by-products. The NASA Langley UQ challenge is then successfully solved by using the generalized NISS method. The second development is to inject the classical line sampling to the NISS framework so as to substantially improve the efficiency of the algorithm for rare failure event analysis, and two strategies, based on different interpretations of line sampling, are developed. The first strategy is based on the hyperplane approximations, while the second-strategy is derived based on the one-dimensional integrals. Both strategies can be regarded as post-processing of the classical line sampling, while the results show that their resultant NISS estimators have different performance. The third development aims at further substantially improving the efficiency and suitability to highly nonlinear problems of line sampling, for complex structures and systems where one deterministic simulation may take hours. For doing this, the active learning strategy based on Gaussian process regression is embedded into the line sampling procedure for accurately estimating the interaction point for each sample line, with only a small number of deterministic simulations.
The above three developments have largely improved the suitability and efficiency of the NISS methods, especially for real-world engineering applications. The efficiency and effectiveness of those developments are clearly interpreted with toy examples and sufficiently demonstrated by real-world test examples in system engineering, civil engineering, and mechanical engineering
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference
Consistency is one of the major challenges faced by dialogue agents. A
human-like dialogue agent should not only respond naturally, but also maintain
a consistent persona. In this paper, we exploit the advantages of natural
language inference (NLI) technique to address the issue of generating persona
consistent dialogues. Different from existing work that re-ranks the retrieved
responses through an NLI model, we cast the task as a reinforcement learning
problem and propose to exploit the NLI signals from response-persona pairs as
rewards for the process of dialogue generation. Specifically, our generator
employs an attention-based encoder-decoder to generate persona-based responses.
Our evaluator consists of two components: an adversarially trained naturalness
module and an NLI based consistency module. Moreover, we use another
well-performed NLI model in the evaluation of persona-consistency. Experimental
results on both human and automatic metrics, including the model-based
consistency evaluation, demonstrate that the proposed approach outperforms
strong generative baselines, especially in the persona-consistency of generated
responses.Comment: AAAI20. Update code link
A Survey of Neural Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of
machine learning, yet coming with mutually exclusive advantages and
limitations. To bring the best of the two worlds, a variety of approaches are
proposed to integrate NNs and DTs explicitly or implicitly. In this survey,
these approaches are organized in a school which we term as neural trees (NTs).
This survey aims to present a comprehensive review of NTs and attempts to
identify how they enhance the model interpretability. We first propose a
thorough taxonomy of NTs that expresses the gradual integration and
co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their
interpretability and performance, and suggest possible solutions to the
remaining challenges. Finally, this survey concludes with a discussion about
other considerations like conditional computation and promising directions
towards this field. A list of papers reviewed in this survey, along with their
corresponding codes, is available at:
https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl
Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative
disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration.
There are no effective treatments for Alzheimer's disease at a late stage,
urging for early intervention. However, existing statistical inference
approaches of AD subtype identification ignore the pathological domain
knowledge, which could lead to ill-posed results that are sometimes
inconsistent with the essential neurological principles. Integrating systems
biology modeling with machine learning, we propose a novel pathology steered
stratification network (PSSN) that incorporates established domain knowledge in
AD pathology through a reaction-diffusion model, where we consider non-linear
interactions between major biomarkers and diffusion along brain structural
network. Trained on longitudinal multimodal neuroimaging data, the biological
model predicts long-term trajectories that capture individual progression
pattern, filling in the gaps between sparse imaging data available. A deep
predictive neural network is then built to exploit spatiotemporal dynamics,
link neurological examinations with clinical profiles, and generate subtype
assignment probability on an individual basis. We further identify an
evolutionary disease graph to quantify subtype transition probabilities through
extensive simulations. Our stratification achieves superior performance in both
inter-cluster heterogeneity and intra-cluster homogeneity of various clinical
scores. Applying our approach to enriched samples of aging populations, we
identify six subtypes spanning AD spectrum, where each subtype exhibits a
distinctive biomarker pattern that is consistent with its clinical outcome.
PSSN provides insights into pre-symptomatic diagnosis and practical guidance on
clinical treatments, which may be further generalized to other
neurodegenerative diseases
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