Abstract: A hierarchical cluster analyzer identifies clusters in a big data set by identifying topological structure without distance-based metrics. The hierarchical cluster analyzer stochastically partitions the big data set to create pseudo-partitions of the big data set. The stochastic partitioning may be implemented with a random forest classifier that uses ensemble techniques to reduce variance and prevent overfitting. The hierarchical cluster analyzer implements random intersection leaves (RIL), a data mining technique that grows an intersection tree by intersecting candidate sets generated from the pseudo-partitions. The hierarchical cluster analyzer updates an association matrix according to co-occurrences of data points within each leaf node of the intersection tree. These co-occurring data points exhibit a high degree of similarity, which is recorded in the association matrix. A hierarchy of clusters may then be formed by finding community structure in the association matrix.

Abstract: The present disclosure provides therapeutic combinations, which provide a synergistic effect in treating a cancer. The present disclosure provides methods of treatment including administration of the combinations, and uses of the combinations, e.g., for treatment of cancer.

Abstract: A feature sub-network trainer improves robustness of interpretability of a deep neural network (DNN) by increasing the likelihood that the DNN will converge to a global minimum of a cost function of the DNN. After determining a plurality of correctly classified examples of a pre-trained DNN, the trainer extracts from the pre-trained DNN a feature sub-network that includes an input layer of the DNN and one or more subsequent sparsely-connected layers of the DNN. The trainer averages output signals from the sub-network to form an average representation of each class identifiable by the DNN. The trainer relabels each correctly classified example with the appropriate average representation, and then trains the feature sub-network with the relabeled examples. In one demonstration, the feature sub-network trainer improved classification accuracy of a seven-layer convolutional neural network, trained with two thousand examples, from 75% to 83% by reusing the training examples.

Abstract: A hierarchical cluster analyzer identifies clusters in a big data set by identifying topological structure without distance-based metrics. The hierarchical cluster analyzer stochastically partitions the big data set to create pseudo-partitions of the big data set. The stochastic partitioning may be implemented with a random forest classifier that uses ensemble techniques to reduce variance and prevent overfitting. The hierarchical cluster analyzer implements random intersection leaves (RIL), a data mining technique that grows an intersection tree by intersecting candidate sets generated from the pseudo-partitions. The hierarchical cluster analyzer updates an association matrix according to co-occurrences of data points within each leaf node of the intersection tree. These co-occurring data points exhibit a high degree of similarity, which is recorded in the association matrix. A hierarchy of clusters may then be formed by finding community structure in the association matrix.