convolutional-neural-networks

Radio-frequency convolutional neural networks (RF-CNNs) repurpose existing wireless communication hardware for efficient AI inference on edge devices, demonstrating deep CNN performance with significant energy savings.

This paper provides a systematic review of AI and deep learning applications, specifically convolutional neural networks with data augmentation, for early lung cancer detection through image analysis. It analyzes 96 articles, highlighting both the potential and the challenges in clinical implementation.

This paper conducts a trajectory-level investigation of PGD attacks on CNNs trained on Fashion-MNIST, showing that while a robustness hierarchy exists, trajectory metrics like loss evolution and gradient alignment do not independently measure robustness, with steps-to-failure providing clearer separation.

This paper replicates a study on AI efficiency assessment using FLOPs, validates that raw FLOPs are not a suitable metric for execution time on newer hardware, and emphasizes the need for complete replication packages in research.

This preliminary study applies a fully convolutional neural network to automatically detect neutron resonances in transmission spectra, achieving ~93% classification accuracy but failing to generalize to unseen isotopes. The authors suggest future work with larger datasets and physics-informed features.

Proposes a decoupled training strategy that adapts normalization layers and uses precomputed features to reduce overhead in transfer learning, achieving competitive accuracy with significantly reduced training time and energy consumption.

This paper introduces a technique to disentangle a single convolutional neuron in Inceptionv1 by analyzing Hadamard products, revealing clean monosemantic clusters (cars, cats, dogs) and also low-valued clusters (letters, faces) with distributed weights as evidence of gradient descent behavior.

An article exploring the implementation of convolutional neural networks using the APL programming language, from 2019.

A blog post listing the 8 best convolutional neural network resources, including courses from Udacity, deeplearning.ai, Datacamp, and Udemy.

This paper presents a physics-informed convolutional encoder–decoder network to predict pore-scale velocity fields from porous media geometry, and demonstrates that using network predictions to initialize Lattice-Boltzmann simulations accelerates convergence in over 90% of cases.