Fool me once and for all: You have no idea what you are doing wrong! – We propose a method for recovering the structure of neural networks from the random search of the input. The learned neural networks show a pattern that is consistent with a number of previous work, namely, that a given neural network contains a series of good or bad edges. We use the recently developed deep learning-based recurrent neural networks (RNNs) to learn this pattern over a sequence of randomly selected neural networks. To recover the data structure, we use the regularization of the RNNs. For model recovery, we apply a linear regression to the underlying data and use the normalization procedure to compute the network structure. We compare the recovered network with the prior knowledge on the data structure and compare it to the neural network training on a number of data sets. We find that the prior knowledge on the data structure does not have a strong impact on the recovery performance, and the recovery method will have to be based on only the training set.
We explore the problem of learning object labels over a set of video frames: the video frames represent a semantic graph or an interactive representation of the frame-level information that each neuron in a video stream generates. One of the most significant challenges in this field has been the lack of an effective way to annotate this visual representation. We present a novel approach that is able to recognize objects without annotating labels. The goal of our method is to learn a joint embedding strategy that is able to recognize objects without annotating labels. In other words, a video frame is a representation of the semantic graph, not a set of labels. We show how this can be achieved using the knowledge learned by the embedding strategy and how a video frame is a set of embeddings with a rich language of object labels. We show that our method is more robust than existing embedding strategies to label objects that are not annotated labels. Our method is based on a deep supervision mechanism, which is used to annotate individual labels. Empirical results show the effectiveness of our method compared to the state of the art.
Efficient Sparse Subspace Clustering via Semi-Supervised Learning
Neural Fisher Discriminant Analysis
Fool me once and for all: You have no idea what you are doing wrong!
Fast Partition Learning for Partially Observed Graphs
Learning Visual Coding with a Discriminative Stack Convolutional Neural NetworkWe explore the problem of learning object labels over a set of video frames: the video frames represent a semantic graph or an interactive representation of the frame-level information that each neuron in a video stream generates. One of the most significant challenges in this field has been the lack of an effective way to annotate this visual representation. We present a novel approach that is able to recognize objects without annotating labels. The goal of our method is to learn a joint embedding strategy that is able to recognize objects without annotating labels. In other words, a video frame is a representation of the semantic graph, not a set of labels. We show how this can be achieved using the knowledge learned by the embedding strategy and how a video frame is a set of embeddings with a rich language of object labels. We show that our method is more robust than existing embedding strategies to label objects that are not annotated labels. Our method is based on a deep supervision mechanism, which is used to annotate individual labels. Empirical results show the effectiveness of our method compared to the state of the art.