On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,786 papers · 148 categories

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7 results for “MobileNet-v2”

Real-time Person Re-identification at the Edge: A Mixed Precision Approachcs.LG

MobileNet-v2 improves re-ID on edge devices with mixed precision.

problem Real-time person re-identification in edge devices with limited resources.

method Mixed precision training (FP32 and FP16) for MobileNet-v2 on edge nodes.

result Mixed precision training improves inference throughput and reduces power consumption.

person re-identification MobileNet-v2 mixed precision real-time edge devices

2019-08-19abs ↗pdf ↗

Advanced Capsule Networks via Context Awarenesscs.LG

Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we…

2019-03-18abs ↗pdf ↗

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantizationcs.LG

BitPruning learns optimal bitlengths for neural networks to balance accuracy and efficiency.

problem Finding the minimum bitlength for neural network accuracy.

method A training method that penalizes large bitlengths and minimizes other quantifiable criteria.

result The method learns efficient representations while maintaining accuracy, reducing bitlengths by 3.76 bits on average per layer.

quantization neural networks bitlength accuracy efficiency

2020-02-08abs ↗pdf ↗

Causal Explanations of Image Misclassificationscs.LG

Study identifies and mitigates causes of image misclassifications in CNN models.

problem Improving model interpretability and accuracy in image classification.

method Trained six CNN architectures on CIFAR-10, used conditional confusion matrices and misclassification networks to identify morphological similarity and non-essential information interference as causes of misclassification. Developed a method to reduce misclassifications by erasing pixels within top 5% saliency map bounding boxes.

result Identified two causes of misclassification: morphological similarity and non-essential information interference, and developed a method to reduce misclassifications caused by the latter.

image classification CNN models misclassification interpretability saliency map

2020-06-28abs ↗pdf ↗

Synaptic Strength For Convolutional Neural Networkcs.LG

Convolutional Neural Networks(CNNs) are both computation and memory intensive which hindered their deployment in mobile devices. Inspired by the relevant concept in neural science literature, we propose Synaptic Pruning: a data-driven method to prune connections between input and output feature maps with a newly propos…

2018-11-06abs ↗pdf ↗

FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architectureeess.SP

Paper proposes FTT-NAS to create fault-tolerant CNNs for edge devices.

problem Faults in edge devices affect deep learning applications.

method Formalized fault models, implemented FTT-NAS, incorporated FTT.

result Discovered CNNs outperform baseline architectures with fault tolerance.

Fault-tolerant CNNs Edge devices Deep learning Fault models

2020-03-20abs ↗pdf ↗

Adaptive Low-Rank Factorization to regularize shallow and deep neural networkscs.LG

AdaptiveLRF adapts low-rank factorization for neural networks to improve generalization without sacrificing accuracy.

problem Overfitting in neural networks, especially in shallow and deep models.

method Adaptive Low-Rank Factorization (LRF) applied to neural network layers based on their complexity.

result AdaptiveLRF improves generalization without significantly decreasing training speed or accuracy.

AdaptiveLRF Neural Networks Overfitting Low-Rank Factorization Generalization

2020-05-05abs ↗pdf ↗