In this paper, we propose AutoPalette, a framework that minimizes color redundancy at the individual image and overall dataset levels, respectively. At the image level, we employ a palette network, a specialized neural network, to dynamically allocate colors from a reduced color space to each pixel. The palette network identifies essential areas in synthetic images for model training and consequently assigns more unique colors to them. At the dataset level, we develop a color-guided initialization strategy to minimize redundancy among images.

Neural Information Processing Systems http://nips.cc/

We contend that utilizing existing relational databases is a promising approach to construct a benchmark that has both merits.

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters.

Neural Information Processing Systems http://nips.cc/

We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic)programming, and(iv)(deep)learningfromexamples.

Neural Information Processing Systems http://nips.cc/