Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 20 for “"distributed inference"”.
-
Augmented Human Machine Intelligence for Distributed Inference
… situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves.</p><p>In this dissertation, we …
-
Distributed Inference and Learning with Byzantine Data
… data. Although the area of statistical inference has been an active area of research in the</p> <p>past, distributed learning and inference in a networked setup with potentially unreliable components</p> <p>has only gained attention recently. The emergence of big and dirty data era …
-
Value of information based distributed inference and planning
… information. This thesis presents efficient distributed sensing and planning algorithms that improve resource planning efficiently by taking into account the obtainable Value of Information (VoI) and improve distributed sensing efficiency by ensuring agents only broadcast high value …
-
Distributed inference : combining variational inference with distributed computing
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using …
-
Statistical Methods for Cooperative and Distributed Inference in Wireless Networks
… theory (RMT) and belief propagation (BP) – in distributed inference problems in wireless communication networks. The term “distributed inference” denotes, in general, detection/estimation involving multiple network nodes (“sensors”) that collect physical measurements and communicate with each …
-
Resource efficient distributed inference of deep neural networks for Edge AI
… a unified framework for resource-efficient distributed inference of DNNs in Edge AI, built upon three tightly coupled research objectives: asynchronous split inference, adaptive batching for resource efficiency, and tensor compression at the client–server boundary. First, the study …
-
A hierarchical framework for constructing computationally efficient algorithms for distributed inference problems
… efficient algorithms for large-scale inference systems on system architectures with distributed autonomous agents. The principle of information-based computation is the underlying idea driving elements of this methodology. The methodology consists of a layered information processing …
-
Scalable Embedded Tiny Machine Learning (SETML): A General Framework for Embedded Distributed Inference
… and pruning. To facilitate machine learning inference on such devices, this work introduces Scalable Embedded Tiny Machine Learning (SETML), a general framework for distributed machine learning inference on microcontrollers. Furthermore, the framework is designed to be compatible with …
-
Probabilistic graphical models : distributed inference and learning models with small feedback vertex sets
… study of GGMs. The first problem is to perform inference or sampling when the graph structure and model parameters are given. For inference in graphs with cycles, loopy belief propagation (LBP) is a purely distributed algorithm, but it gives inaccurate variance estimates in general and often …
-
On the Design and Analysis of Secure Inference Networks
<p>Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that a global inference is made regarding the phenomenon-of-interest (PoI). In this dissertation, we address two types …
-
Truncated Bayesian nonparametrics
… size. We also require corresponding streaming, distributed inference algorithms that handle persistently growing datasets without slowing down over time. However, a key ingredient in streaming, distributed inference-an explicit representation of the latent variables used to statistically …
-
Distributed belief propagation and its generalizations for location-aware networks
… (GBP) and belief propagation (BP) algorithms for distributed inference. The concept of a network region graph is introduced, along with several approximation structures that can be distributed across a network. In this formulation, clustered region graphs are introduced to create a network …
-
CAPRI : a common architecture for distributed probabilistic Internet fault diagnosis
… Reasoning in the Internet (CAPRI) in which distributed, heterogeneous diagnostic agents efficiently conduct diagnostic tests and communicate observations, beliefs, and knowledge to probabilistically infer the cause of network failures. Unlike previous systems that can only diagnose a limited …
-
Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks
<p>Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate several issues related to distributed inference in sensor networks, emphasizing parameter estimation and target tracking with …
-
Approximate inference in Gaussian graphical models
The focus of this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to describe complex …
-
Non-parametric Bayesian models for structured output prediction
… a map-reduce implementation of a stochastic inference method designed for the infinite hidden Markov model, applied to a computational linguistics task, part-of-speech tagging. We show that mainstream map-reduce frameworks do not easily support highly iterative algorithms. The main …
-
Reliable Inference from Unreliable Agents
<p>Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze …
-
Towards efficient and reliable infrastructure for machine learning
… modern ML workloads. QLM improves efficiency of distributed inference for large language models by multiplexing interactive and batch requests. QLM leverages statistical properties of continuous batching to estimate request waiting times in queues and groups requests with similar performance …
-
Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence
… topic in recent years. Several issues related to inference using heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data.</p> <p>In centralized detection, where sensor …
-
Copula-based Multimodal Data Fusion for Inference with Dependent Observations
… heterogeneous data from multiple modalities for inference problems has been an attractive and important topic in recent years. There are several challenges in multi-modal fusion, such as data heterogeneity and data correlation. In this dissertation, we investigate inference problems with …