AI Technology & Industry Review
In a new paper How to Boost Any Loss Function, a Google research team provides a constructive, formal answer, demonstrating that any loss function can be optimized with boosting.
Waymo and Google researchers’ new paper A Polynomial Expansion Perspective of Classification Loss Functions presents PolyLoss, a novel and simple framework that redesigns loss functions as a linear combination of polynomial functions that can be tailored to different target tasks and datasets.
Synced tech analyst reviews the thesis “Tree Boosting With XGBoost – Why Does XGBoost Win ‘Every’ Machine Learning Competition”, which investigates how XGBoost differs from traditional MART, and XGBoost’s superiority in machine learning competition.
Instead of investigating what steps the brain takes to process information, the authors of this paper link brain activity directly to the learning process.
Softmax loss has become a standard build-in loss function for a lot of mathematical tools like TensorFlow, Torch and Caffe. It is mainly used for classification and has its advantages and disadvantages, the latter of which is the focus of this paper.
New autoencoder-like generative network, called Adversarial Generator-Encoder Networks (AGE Network), does not have any discriminators, which makes the entire architecture much simpler than some recently-proposed GANs, but with nearly the same-level performance
This paper proposes a deep generative model for face completion, which can directly generate facial components for the missing regions of a face image.