[selectron]

For image competitions you are right. Neural networks are often in winning teams ensembles, but they require a lot more work than something like xgboost (gradient-boosted decision trees). For a dataset that isn't image processing or NLP, xgboost is in general much more widely used than neural nets. Neural nets suffer from the amount of computing resources and knowledge needed to apply them, though given infinite knowledge and computing power they are probably on par with or better than xgboost. And if you need to analyze an image they are great.