Efficient Neural Network
71 papers with code • 0 benchmarks • 0 datasets
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Efficient Neural Network Analysis with Sum-of-Infeasibilities
NeuralNetworkVerification/Marabou
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Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation.
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network
HSE-asavchenko/face-emotion-recognition
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IEEE Transactions on Affective Computing 2022
It is shown that the resulting facial features can be used for fast simultaneous prediction of students’ engagement levels (from disengaged to highly engaged), individual emotions (happy, sad, etc.,) and group-level affect (positive, neutral or negative).
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure.
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
ywolfeee/lapjax
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Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers.
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication.
Deep Learning for Single Image Super-Resolution: A Brief Review
ryanhalabi/starcraft_super_resolution
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Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification.
Efficient Neural Network Compression
The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.
