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Found 13 papers, 3 papers with code
A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
Electricity consumption forecasting has vital importance for the energy planning of a country.
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.
An Empirical Study of Low Precision Quantization for TinyML
In this study, we focus on post-training quantization (PTQ) algorithms that quantize a model to low-bit (less than 8-bit) precision with only a small set of calibration data and benchmark them on different tinyML use cases.
End-to-end Adaptive Distributed Training on PaddlePaddle
The experiments demonstrate that our framework can satisfy various requirements from the diversity of applications and the heterogeneity of resources with highly competitive performance.
Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm
Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions.
Novel Co-variant Feature Point Matching Based on Gaussian Mixture Model
In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Radiative Transport Based Flame Volume Reconstruction from Videos
The approach includes an economical data capture technique using inexpensive CCD cameras.
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
Deep joint rain and haze removal from single images
On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil.
MSR-net:Low-light Image Enhancement Using Deep Convolutional Network
In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.
