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ConvGRUAutoencoder combines convolutional layers with gated recurrent units to compress and reconstruct video sequences, enabling unsupervised learning from video without human labeling.
This paper proposes lightweight convolutional neural networks for detecting FPV drones using time-domain rasterized RF signals, eliminating frequency-domain preprocessing and achieving high accuracy with low computational cost, suitable for embedded systems.
Proposes a quantum-inspired hybrid classical-quantum framework for image classification using a mixture of experts, demonstrating improved performance and reduced failure rate on MNIST and Fashion-MNIST datasets.
This paper introduces a learnable channel-class assignment mechanism for forward-only convolutional neural networks, combined with entropy and orthogonality regularization and a loss-aware layer contribution strategy. The method achieves state-of-the-art performance among forward-forward algorithms on CIFAR-10, CIFAR-100, and Tiny-ImageNet, significantly narrowing the gap with backpropagation.
OpenAI trained 9 agents on the CoinRun environment with varying numbers of training levels to quantify generalization in reinforcement learning, finding substantial overfitting even with 16,000 training levels and that IMPALA-CNN architectures generalize significantly better than Nature-CNN baselines.