Autonomous systems are used in a wide range of domains from indoor utensils to autonomous robot surgeries and self-driving cars. Stereo vision cameras probably are the most flexible sensing way in these systems since ...
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ISBN:
(纸本)9781538666890
Autonomous systems are used in a wide range of domains from indoor utensils to autonomous robot surgeries and self-driving cars. Stereo vision cameras probably are the most flexible sensing way in these systems since they can extract depth, luminance, color, and shape information. However, stereo vision based applications suffer from huge image sizes and computational complexity leading system to higher power consumption. To tackle these challenges, in the first step, GIMME2 stereo vision system [1] is employed. GIMME2 is a high-throughput and cost efficient FPGA-based stereo vision embedded system. In the next step, we present a framework for designing an optimized Deep Convolutional neuralnetwork (DCNN) for time constraint applications and/or limited resource budget platforms. Our framework tries to automatically generate a highly robust DCNN architecture for image data receiving from stereo vision cameras. Our proposed framework takes advantage of a multi-objective evolutionary optimization approach to design a near-optimal networkarchitecture for both the accuracy and network size objectives. Unlike recent works aiming to generate a highly accurate network, we also considered the network size parameters to build a highly compact architecture. After designing a robust network, our proposed framework maps generated network on a multi/many core heterogeneous System-on-Chip (SoC). In addition, we have integrated our framework to the GIMME2 processing pipeline such that it can also estimate the distance of detected objects. The generated network by our framework offers up to 24x compression rate while losing only 5% accuracy compare to the best result on the CIFAR-10 dataset.
Graphs provide powerful representations for statistical modeling of interrelated variables (observed or latent) in a broad range of machine learning applications. Examples include learning and inference based on the d...
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ISBN:
(纸本)9781450356565
Graphs provide powerful representations for statistical modeling of interrelated variables (observed or latent) in a broad range of machine learning applications. Examples include learning and inference based on the dependency structures among words, documents, topics, users, items, web sites, and more. How to best leverage such dependency structures from multiple graphs with massive and heterogeneous types of nodes and relations has posed grand challenges to machine learning theory and algorithms. This talk presents our recent work in this direction focusing on three significant tasks, including 1) a novel framework for fusing multiple heterogeneous graphs into a unified product graph to enable semi-supervised multi-relational learning, 2) the first algorithmic solution for imposing analogical structures in graph-based entity/relation embedding, and 3) a new formulation of neuralarchitecturesearch as a graph topology optimization problem, with simple yet powerful algorithms that automatically discover high-performing convolutional neuralarchitectures on image recognition benchmarks, and reduce the computational cost over state-of-the-art non-differentiable techniques by several orders of magnitude.
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