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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 69 for “"Probabilistic inference"”.
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Practical probabilistic inference
… manifestation of disease in a patient. Exact inference of posterior distributions over the disease nodes is extremely intractable using generic algorithms. Inference can be made much more efficient by exploiting the QMR-DT's unique structure. Indeed, tailor-made inference algorithms for the …
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Lifted First-Order Probabilistic Inference
… division in AI between logical symbolic and probabilistic reasoning approaches. While probabilistic models can deal well with inherent uncertainty in many real-world domains, they operate on a mostly propositional level. Logic systems, on the other hand, can deal with much richer …
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Sampling architectures for probabilistic inference
… focus on hardware accelerator architectures for inference on probabilistic graphical models, in particular for Markov random field (MRF) and for latent Dirichlet allocation (LDA). Our work focuses on inference via sampling methods, in particular, Markov chain Monte Carlo (MCMC) methods. Roughly …
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Composable probabilistic inference with BLAISE
… use those patterns to make accurate predictions. Probabilistic inference provides a unified, systematic framework for specifying and solving these problems. Recent work has demonstrated the great value of probabilistic models defined over complex, structured domains. However, our ability to …
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Inference Plans for Hybrid Probabilistic Inference
Advanced probabilistic programming languages (PPLs) use hybrid inference systems to combine symbolic exact inference and Monte Carlo sampling to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are represented …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
… of research on uncertainty quantification is probabilistic modelling which is concerned with capturing model uncertainty by placing a distribution over the models which can be marginalized at test-time. This is especially useful in underspecified models which can have diverse near-optimal …
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Typesafety for explicitly-coded probabilistic inference procedures
… that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually …
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Probabilistic Inference: Theory and Practice (Learning, Inductive, Logic, Synthesis)
… thesis presents a system and a methodology for probabilistic learning from examples.
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Probabilistic Inference for Inference Time Scaling of Language Models
… test-time compute. Existing deterministic inference-time scaling methods, usually with reward models, cast the task as a search problem, but suffer from a key limitation: early pruning. Due to inherently imperfect reward models, promising trajectories may be discarded prematurely, leading …
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High performance and error resilient probabilistic inference system for machine learning
… they are often dealt with using approximate inference algorithms on Markov random field (MRF) such as belief propagation (BP). However, this approximate inference is still computationally demanding, and thus custom hardware accelerators have been attractive for high performance and energy …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
… is its reliance on large datasets. In contrast, probabilistic inference excels in data-scarce settings but suffers from computational inefficiencies for high dimensional data and struggles to model structured data where representation learning is crucial. This thesis focuses on the synergies …
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Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles
Probabilistic inference and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly, across both applications the presented novel contributions share real-time inference of stochastic systems for …
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Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis
In this dissertation, we consider probabilistic inference problems on binary pairwise Gibbs random fields (BPW-GRFs), which belong to a class of Markov random fields with applications to a large variety of systems, including computer vision, statistical mechanics, modeling of neural functions, and …
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Composable inference metaprogramming using subproblems
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different base probabilistic inference algorithms to different …
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Cutting plane algorithms for variational inference in graphical models
… on the entropy, this gives a new variational inference algorithm for probabilistic inference in discrete Markov Random Fields (MRFs). Valid constraints are derived for the marginal polytope through a series of projections onto the cut polytope. Projecting onto a larger model gives an efficient …
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Belief propagation generative adversarial networks
… potentials in the graphical model; performing probabilistic inference in this graphical model then yields the marginal distribution. Message passing based on discrete variables keeps a table of local potential values, the size of which could be too big for natural images. We present a solution …
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Scalable Methodologies for Optimizing Over Probability Distributions
… applications, such as generative modeling and probabilistic inference, demand a new generation of methodologies for optimizing over the space of probability distributions, where the optimization variable represents a weighted population of potentially infinitely many points. Despite the …
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Using the language of thought
… high-level conceputal knowledge by performing probabilistic inference over a language of thought (Fodor 1975) - a space of symbolic and compositional mental representations sufficiently expressive to capture the meanings of human thoughts and utterances. These models and their associated …
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Mouse behavior recognition with the wisdom of crowd
… crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to …
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