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Winning Model Documentation

Name: Gábor Melis

Location: Country: Hungary Postal code: 2049 City: Diósd Address: Éva u. 9

Email: Web:

Competition: Higgs Boson Machine Learning Challenge


The model is a bag of 70 dropout neural networks produced by 2-fold stratified cross-validations repeated 35 on random shuffles of the training data.

Features Selection / Extraction

All features were normalized to have zero mean, stddev 1. The *-phi features were dropped, because they increased overfitting a lot. A couple of invariant and transverse mass based features were added (see ENCODE-EXTRA-MASS in the code) but they didn't bring huge gains. The same can be said of the extra modulus feature and the features based on the absolute value of differences of pri_{tau,lep,met}_phi (see ENCODE-EXTRA-MODULI and ENCODE-EXTRA-RADIAN, respectively). Features with long tails were log transformed. I tried XGboost and got 3.75+ AMS in local CV with it. With the Cake features, that improved to 3.80+. In the end, I decided not to use the Cake features for fear of non-reproducability and insufficient testing although even the bag of neural networks plus bag of xgboost models seemed to benefit about 0.005.

Modeling Techniques and Training

Every neural network in the bag predicts the probability of an example being Signal. The probabilities predicted by networks in the bag were simply averaged and the top 15.2% of the examples were predicted to be S. This cutoff value was hand selected based on the AMS vs cutoff curve in cross-validation.

Each neural network had three hidden layers of 600 neurons each and a 2 neuron softmax output layer corresponding to the Signal and Background classes. The network was trained with cross-entropy loss and backpropagation.

The hidden layers had max-channel activation functions with a group size of 3. This means that in each group of 3 neurons, only the one with max activation fired and the rest were zeroed. See papers

'Compete to Compute' by Srivastava et al.

'From Maxout to Channel-Out: Encoding Information on Sparse
Pathways' by Qi Wang and Joseph Jaja

According to cross-validation results, max-channel worked a bit better than maxout which was in turn significantly better than rectified linear units.

In addition to regularization with dropout with the usual rate of 0.5, an L1 penalty of 0.000005 and an L2 penalty of 0.00005 was applied to the input-to-first-hidden-layer weights. Each neuron in the first hidden layer was connected only to 10 inputs (sampled with replacement before training started).

Code Description

Everything is in Common Lisp, except for some shell scripts. The most important files:

src/higgs-boson.lisp -- general data structures, evaluation src/csv.lisp -- reading/writing the csv files src/bpn.lisp -- CV bagging of backprop networks src/encoder.lisp -- normalization, extra features src/main.lisp -- gluing everything together -- installation guide doc/ -- this document rumcajsz -- prebuilt binary configure -- script to set up build environment and file locations SETTINGS -- the file produced by configure

The code is way larger than to be possibly described here in any detail. I cleaned it up, removed much dead code and added some comments.

The other files and directories are only relevant to development:

Makefile build/ -- build scripts *.asd -- project definition quicklisp/ -- Common Lisp libraries submission/ -- default location for generated submissions test/ -- test sources data/ -- default location for data files model/ -- default location for saved models xgboost-scripts/ -- scripts to train and run xgboost models


See the separate README file.

How To Generate the Solution

See the separate README file.

Additional Comments and Observations

The master plan was to breed features with an evolutionary algorithm but this didn't work. Ensembling with xgboost seemed a bit better in cross-validation but at the chosen cutoff scored a bit lower on the private leaderboard. There were many other things that didn't work, among them:

  • Finer grained ensembling based on various proxy losses.

  • Nesterov momentum (increased overfitting).

  • Direct optimization of the AMS (overfitting).

  • Optimization by AUC (much slower, worse results).

  • Pseudo labeling.

The key to this competition has been finding a reliable way to measure performance and avoiding overfitting. My local CV indicated 3.85ish AMS so I'm not sure how well that worked. All in all, I feel that the datasets were way too small for this contest due to the choice of AMS. The AMS vs cutoff curves of private test data would be great to see, but even they may indicate lottery taking place.

Simple Features and Methods

To save time one can train about 8-16 neural networks as opposed to the 70 that went into the winning submission.


See cv-ams-vs-cutoff.png for how the AMS varies against the cutoff threshold.



Improving neural networks by preventing co-adaptation of feature detectors


'Compete to Compute' by Srivastava et al.

'From Maxout to Channel-Out: Encoding Information on Sparse
Pathways' by Qi Wang and Joseph Jaja
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