* Make it easier to find sample apps for different workload types
* Add READMEs for sample apps the didn't have any
* Update readmes with experiment results where applicable

* summary_graph.py:
  * Change ONNX op.uniqueName() to op.debugName()
  * Removed scope-naming workaround which isn't needed in PyTorch 1.3
* Tests:
  * Naming of trace entries changed in 1.3. Fixed SummaryGraph unit
    test that checked that
  * Adjusted expected values in full_flow_tests
  * Adjusted tolerance in test_sim_bn_fold
  * Filter some new warnings

Remove monthly updates sections (Oct, Nov 2018)

add citation and links to published code

add citation

add citation

Added explicit thank you mention for Cadene

Added support for saving the randomly initialized network before
starting training; and added an implmentation showing how to extract
a (winning) lottery ticket from the prestine network, and the
pruned network.

*An implementation of AMC (the previous implementation
 code has moved to a new location under 
/distiller/examples/auto_compression/amc.  AMC is aligned
with the ‘master’ branch of Coach.
*compress_classifier.py is refactored.  The base code moved
to /distiller/apputils/image_classifier.py.  Further refactoring
will follow.
We want to provide a simple and small API to the basic features of
a classifier-compression application.
This will help applications that want to use the make features of a
classifier-compression application, without the standard training
regiment.
AMC is one example of a stand-alone application that needs to leverage
the capabilities of a classifier-compression application, but is currently
coupled to `compress_classifier.py`.
`multi-finetune.py` is another example.
* ranked_structures_pruner.py:
** Added support for grouping channels/filters
Sometimes we want to prune a group of structures: e.g. groups of
8-channels.  This feature does not force the groups to be adjacent,
so it is more like a set of structures.  E.g. in the case of pruning
channels from a 64-channels convolution, grouped by 8 channels, we 
will prune exactly one of 0/8/16/24/32/40/48/56 channels.  I.e. 
always a multiple of 8-channels, excluding the set of all 64 channels.
** Added FMReconstructionChannelPruner – this is channel
pruning using L1-magnitude to rank and select channels to
remove, and feature-map reconstruction to improve the
resilience to the pruning.
* Added a script to run multiple instances of an 
experiment, in different processes:
 examples/classifier_compression/multi-run.py
* Set the seed value even when not specified by the command-line
arguments, so that we can try and recreate the session.
* Added pruning ranking noise -
Ranking noise introduces Gaussian noise when ranking channels/filters
using Lp-norm.  The noise is introduced using the epsilon-greedy
methodology, where ranking using exact Lp-norm is considered greedy.
* Added configurable rounding of pruning level: choose whether to 
Round up/down when rounding the number of structures to prune 
(rounding is always to an integer).  

Added 2 new citations.

* "Net-aware quantization" - using the term coined in
  https://arxiv.org/abs/1811.09886. (section 3.2.2).
  Refers to considering sequences of modules when quantizing. This 
  isn't exactly layer fusion - we modify activation stats prior to
  setting quantization parameters, to make sure that when a module
  is followed by certain activation functions, only the relevant
  ranges are quantized. We do this for:
    * ReLU - Clip all negative values
    * Tanh / Sigmoid - Clip according to the (approximated) saturation
      values for these functions. We use [-4, 4] for tanh and [-6, 6]
      for sigmoid.

* Perform batch-norm folding before post-training quantization.
  Batch-norm parameters are folded into the parameters of the previous
  layer and the BN layer is replaced with an identity module.

* Both BN folding and "net-aware" are now automatically executed
  in PostTrainLinearQuantizer (details of this change below)

* BN folding enabled by new generic mechanism to "fuse" module
  sequences (at the Python API level)
    * First module in sequence is replaced/modified by a user-provided
      function, rest of moudles replaced with nn.Identity

* Quantizer changes:
  * Optionally create adjacency map during prepare_model
  * Subclasses may enforce adjacency map creation
  * Refatcoring: Replace _prepare_model_impl with pre and post
    override-able "callbacks", so core functionality is always executed

* PostTrainLinearQuantizer Changes:
  * Enforce creation of adjacency map. This means users must now pass a
    dummy input to PostTrainLinearQuantizer.prepare_model
  * Before module replacement - Apply BN folding and stats updates according
    to net-aware quantization

* Updated the language model quantization tutorial to reflect the new
  functionality

* Updated the image classification post-train quantization samples
  (command line and YAML)

* Other changes:
  * Distller LSTM implementation:
    Replace the ModuleList for cells with a plain list. The PyTorch trace
    mechanism doesn't "see" ModuleList objects, it only sees the 
    contained modules. This means that the "scopeName" of these modules
    isn't complete, which makes it impossible to match op names in 
    SummaryGraph to modules in the Python model.
  * ActivationStatsCollector: Ignore nn.Identity modules

* PyTorch 1.1.0 now required
  - Moved other dependencies to up-to-date versions as well
* Adapt LR scheduler to PyTorch 1.1 API changes:
  - Change lr_scheduler.step() calls to succeed validate calls,
    during training
  - Pass to lr_scheduler.step() caller both loss and top1
    (Resolves issue #240)
* Adapt thinning for PyTorch 1.1 semantic changes
  - **KNOWN ISSUE**: When a thinning recipe is applied, in certain
    cases PyTorch displays this warning:
    "UserWarning: non-inplace resize is deprecated".
    To be fixed later
* SummaryGraph: Workaround for new scope name issue from PyTorch 1.1.0
* Adapt to updated PyTest version:
  - Stop using deprecated 'message' parameter of pytest.raises(),
    use pytest.fail() instead
  - Make sure only a single test case per pytest.raises context
* Move PyTorch version check to root __init__.py 
  - This means the version each checked when Distiller is first
    imported. A RuntimeError is raised if the version is wrong.
* Updates to parameter_histograms notebook:
  - Replace deprecated normed argument with density
  - Add sparsity rate to plot title
  - Load model in CPU

Added two new citations

Added new paper and community citations and changed the section name to *Community*.
Added Bar Elharar and Lev Zlotnik to the authors list.

Some links have changed with the latest version of mkdocs.
This closes issues #280 and #65 (reopened).

- Fix broken link
- Add non-cuda9 installation instructions for Pytorch

Added a link to the FAQ wiki page.

Not backward compatible - re-installation is required

* Fixes for PyTorch==1.0.0
* Refactoring folder structure
* Update installation section in docs

Added recent Distiller citations

Minor command line fix in the post training example

* Asymmetric post-training quantization (only symmetric supported so until now)
* Quantization aware training for range-based (min-max) symmetric and asymmetric quantization
* Per-channel quantization support in both training and post-training
* Added tests and examples
* Updated documentation

Changed the description of the feature-set.
Updating the README a little bit since a lot has changed since we released 6 months ago.  Still a lot to add/remove/change.

fix issue #65: Missing link in the README file

* Implemented as a Policy
* Integrated in image classification sample
* Updated docs and README

Add a link to the Wiki