In this work, we propose a novel and surprisingly simple routing strategy called self-routing, where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore, but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR10, SVHN, and SmallNORB showthat the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.

We introduce a principled approach for unsupervised structure learning of deep neural networks.

Howe updated 3] using isunkno internal predictions prediction) 4-7].

CP decomposition compresses an input tensor into a sum of rank-one components, and Tucker decomposition approximates an input tensor by a core tensor multiplied by matrices. To date, matrix and tensor decomposition has been extensively analyzed, and there are a number of variations of such decomposition (Kolda and Bader, 2009), where the common goal is to approximate a given tensor by a smaller number of components, or parameters,inanefficientmanner. However, despite the recent advances of decomposition techniques, a learning theory that can systematically define decomposition for any order tensors including vectors and matrices is still under development. Moreover, it is well known that CP and Tucker tensor decomposition include non-convex optimization and that the global convergence is not guaranteed.

As such, unimodal distributions cannot fully capture statistics of convolutional activations, which will limit performance of deep CNNs.

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

Objects are composed of a set of geometrically organized parts. We introducean unsupervised capsule autoencoder ( SCAE), which explicitly uses geometric relationships between parts toreason about objects. Since these relationships do not depend on the viewpoint, our model is robust to viewpoint changes.

Forsuch models, one particularly elegant approach is that ofpassive-aggressive learning[3]. In this framework, a model isonly updated when itfails to classify an example correctly with high confidence.

For the constructed knowledge graph, we use randomly initialized embeddingsH(0) = {v1,v2,...,vNKG} RNKG d to represent all node features. Table 2shows that all variants with different number ofretrieved instructionsNP can consistently outperform the baseline model, which proves the effectiveness of our approach in retrieving the working experience to boost the Patient Instruction generation. Asaresult, givenanewmale/female patient at61years old,wewillmatchmale/female patients in the age-group 55 <= Age < 70 in the training data to generate the PIs.