State-of-the-art methods of attribute detection from faces almost always assume the presence of a full, unoccluded face. Hence, their performance degrades for partially visible and occluded faces. In this paper, we introduce SPLITFACE, a deep convolutional neural network-based method that is explicitly designed to perform attribute detection in partially occluded faces. Taking several facial se...

Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points. We verify the good performance of the BC-SVM using several...

In a We use statistical mechanics to study generalization in large committee machines. For an architecture with nonoverlapping receptive fields a replica calculation yields the generalization error in the limit of a large number of hidden units. For continuous weights the generalization error falls off asymptotically inversely proportional to Q, the number of training examples per weight. For b...

In this paper we use various methods for multiple neural network combination in tasks of prepositional phrase at tachment. Experiments with aggregation functions such as unweighted and weighted average, OWA operator, Choquet integral and stacked generalization demonstrate that combining multiple networks improve the estimation of each individual neural network. Using the Ratnaparkhi data set (t...

Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances more sustainable processes. The current wave of machine intelligence is revolutionizing all data-rich disciplines. Machine has emerged as a potential game-changer reaction exploration synthesis novel molecules materials. Herein,...