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Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning rate free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms. 

This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset.Instead of generic and commonly used representations trained on the ImageNet classification task, our approach employs acombination of networks trained for specialized tasks such as scene recognition, person activity classification, and attributeprediction. We also present a method for localizing phrases from candidate answers in order to provide spatial support forfeature extraction. We map each of these features, together with candidate answers, to a joint embedding space throughnormalized canonical correlation analysis (nCCA). Finally, we solve an optimization problem to learn to combine scoresfrom nCCA models trained on multiple cues to select the best answer. Extensive experimental results show a significantimprovement over the previous state of the art and confirm that answering questions from a wide range of types benefits fromexamining a variety of image cues and carefully choosing the spatial support for feature extraction.