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Found 6 papers, 1 papers with code
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference
DNNs are widely used but face significant computational costs due to matrix multiplications, especially from data movement between the memory and processing units.
Quantum Circuit Fidelity Improvement with Long Short-Term Memory Networks
By using this metric, we can observe the evolution of fidelity with time as the QC interacts with its external environment.
Addressing Gap between Training Data and Deployed Environment by On-Device Learning
This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments.
A Base Model Selection Methodology for Efficient Fine-Tuning
The experimental results reveal that one of the combined metrics is well correlated with fine-tuned accuracy in a variety of network structure and our method has a good potential to reduce the burden of CNN training.
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices
However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i. e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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Generation High resolution 3D model from natural language by Generative Adversarial Network
We present a method of generating high resolution 3D shapes from natural language descriptions.
