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Found 6 papers, 1 papers with code
Precision, Stability, and Generalization: A Comprehensive Assessment of RNNs learnability capability for Classifying Counter and Dyck Languages
However, our research challenges this notion by demonstrating that RNNs primarily operate as state machines, where their linguistic capabilities are heavily influenced by the precision of their embeddings and the strategies used for sampling negative examples.
On the Computational Complexity and Formal Hierarchy of Second Order Recurrent Neural Networks
In this work, we extend the theoretical foundation for the $2^{nd}$-order recurrent network ($2^{nd}$ RNN) and prove there exists a class of a $2^{nd}$ RNN that is Turing-complete with bounded time.
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder
In light of this, we propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends, an approach we call Neural JPEG.
An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems
Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark.
Extractive Research Slide Generation Using Windowed Labeling Ranking
Presentation slides describing the content of scientific and technical papers are an efficient and effective way to present that work.
OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas
A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout.
