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Representation
Representation is an important step in building ML models. This chapter introduces how data items, classes and clusters are represented. It also...
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SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning
Probabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during...
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Context Autoencoder for Self-supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an...
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DeepAR-Attention probabilistic prediction for stock price series
Stock price prediction is a significant research domain, intersecting statistics, finance, and economics. Accurately forecasting stock price trends...
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Constraint-based debugging in probabilistic model checking
A counterexample in model checking is an error trace that represents a valuable tool for debugging. In Probabilistic Model Checking (PMC), the...
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Formal Models of Probabilistic Grammar
To enable computers to process natural languages, Chomsky’s context-free grammar is often used for rule-based syntactic parsing. Based on...
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Automated Sensitivity Analysis for Probabilistic Loops
We present an exact approach to analyze and quantify the sensitivity of higher moments of probabilistic loops with symbolic parameters, polynomial...
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Probabilistic Compositional Semantics, Purely
We provide a general framework for the integration of formal semantics with probabilistic reasoning. This framework is conservative, in the sense...
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Msap: multi-scale attention probabilistic network for underwater image enhancement network
Underwater image enhancement is a key technology for improving underwater image quality and enhancing visualization. Due to the light propagation...
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Logical-Probabilistic Modeling and Structural Analysis of Reconfigurable Systems Composed of Multifunctional Elements
AbstractMultifunctional elements are a special class of elements, the reliability model of which differs from the classical two-pole “on-off” model....
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Probabilistic Metric Learning
It was mentioned in Chap. 11 that metric learning can be divided into three types of learning—spectral,...
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Probabilistic time series forecasts with autoregressive transformation models
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a...
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Probabilistic Beliefs
Much of high-level AI research is concerned with the behavior of some putative agent, operating in a partially known environment. This agent is...
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Towards effective urban region-of-interest demand modeling via graph representation learning
Identifying the region’s functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due...
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Probabilistic Matrix Factorization for Visual Tracking
AbstractIn visual tracking, designing an effective and robust target appearances remains a challenging work due to variations of complicated target...
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Trusted 3D self-supervised representation learning with cross-modal settings
Cross-modal setting employing 2D images and 3D point clouds in self-supervised representation learning is proven to be an effective way to enhance...
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Probabilistic Interpretation of Traditional KWS Approaches
In this chapter traditional ideas and approaches forKWS are reviewed under the probabilistic framework proposed in Chapter 3. Both QbE and QbS...
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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems
Cognitive diagnosis is an important issue of intelligent education systems, which aims to estimate students’ proficiency on specific knowledge...
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Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval
We present a discriminative learning algorithm for the probabilistic estimation of two-dimensional probabilistic context-free grammars (2D-PCFG) for...
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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity...
