Inspirational Machine Learning Papers from Other Fields
(a very brief selection of the flood of research in vision/voice/etc)
Both of these works on CLDNNs are an excellent example of how learning from raw time series waveforms, building recurrent time series representations, and then mapping to supervised targets can be done with a CLDNN architecture.
A powerful method for end to end training of localization networks in the image domain by fitting 2D Affine transform parameters through a regression task prior to the discriminative task.
Generative models for acoustic time-series
Google’s WaveNet model for time series acoustic voice synthesis
Some impressive results on learned image compression schemes.
Radio Machine Learning Papers
An in depth overview of communications system and sensing applications of ML to comms.
Communication System PHY Learning
The fundamentals of posing an autoencoder as a communications system.
Work looking into the error correction decoding capacity of deep neural networks.
Efforts to scale learned decoding techniques for Polar Codes to larger block sizes!
RNN Decoding comparisons to BP decoders along with interesting complexity comparisons.
Modulation Recognition from a supervised learning standpoint.
Using a similar CNN classifier technique to identify 802.11 PHY variants over the air quite successfully.
Scaling RF labeling beyond supervised datasets.
Using DNNs on top of well regarded moment based features for modulation classification.
Radio Structure Learning
Unsupervised structure learning from radio datasets
Augmenting learning models with just-enough expert knowledge of the physical world.