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aalto-dl-for-physical-layer

An Introduction to Deep Learning for the Physical Layer vs End-to-End Learning of Communications Systems Without a Channel Model

aofp

Codes of Approximated Oracle Filter Pruning

bdn-refremv

channel-pruning-using-thinet-lasso-

compression

Data compression in TensorFlow

darknet-modified

A modified darknet for different precision tests

dqn_pytorch

Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch

dynamic_jscc

Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control"

flops-counter.pytorch

Flops counter for convolutional networks in pytorch framework

gr-eventstream

gr-eventstream is a set of GNU Radio blocks for creating precisely timed events and either inserting them into, or extracting them from normal data-streams precisely. It allows for the definition of high speed time-synchronous c++ burst event handlers, as well as bridging to standard GNU Radio Async PDU messages with precise timing easily.

grouplasso

hpc

hpc_lecture

hpc_lecture_2021

img_autoencoder

learning-deep-learning-from-scratch

coding work of deep-learning-from-scratch from Oreilly https://github.com/oreilly-japan/deep-learning-from-scratch

mika2019

repository for MIKA2019

modulatedautoencoder

Variable rate with MAE

ofdm-gan

one_hot_autoencoder

pytorch-gdn

PyTorch implementation of the Generalized divisive normalization non-linearity layer

rnp_feature

skipnet

Code for SkipNet: Learning Dynamic Routing in Convolutional Networks (ECCV 2018)

static_dynamic_rl_pruning

testrepo