The Label Complexity of Mixed-Initiative Classifier Training Jina Suh Microsoft, Xiaojin Zhu University of Wisconsin, Saleema Amershi Microsoft Paper Abstract Mixed-initiative classifier training, where the human teacher can choose which items to label or to label items chosen by the computer, has enjoyed empirical success.
157 Learning a grammar (visual or linguistic) from training data would be equivalent to restricting the system to commonsense reasoning that operates on concepts in terms of grammatical production rules and is a basic goal of both human language acquisition 161 and.
Typically, neurons are organized in layers.
Then, we propose a new smooth and convex loss function which is the sparsemax analogue of the logistic loss.Are based on the (unsupervised) learning of multiple levels of features or representations of the data.Then, we infer a learning algorithm and perform experiments on real data.This theoretical framework also connects SGD to modern scalable inference algorithms; we analyze the recently proposed stochastic gradient Fisher scoring under this perspective.We introduce a new framework that allows the objective to be a more general function of the number of errors at each vertex (for example, we may wish to minimize the number of errors at the worst vertex) and provide a rounding algorithm which converts."A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion" (PDF).
In Proceedings of the International Conference on Machine Learning, icml 2006 : 369376.
We propose a convex problem to incorporate side information in robust PCA and show that the low rank matrix can be exactly recovered via the proposed method under certain conditions.
Persistent RNNs: Stashing Recurrent Weights On-Chip Greg Diamos Baidu USA, Inc., Shubho Sengupta Baidu USA, Inc., Bryan Catanzaro winamp 5.666 build 3516 pro Baidu USA, Inc., Mike Chrzanowski Baidu USA, Inc., Adam Coates, Erich Elsen Baidu USA, Inc., Jesse Engel Baidu USA, Inc., Awni Hannun Baidu USA, Inc., Sanjeev Satheesh.82 83 Other key techniques in this field are negative sampling 111 and word embedding.We demonstrate superior empirical performance on both multi-linear multi-task learning and spatio-temporal applications.By assuming that the clairvoyant moves slowly (i.e., the minimizers change slowly we present several improved variation-based upper bounds of the dynamic regret under the true and noisy gradient feedback, which are it optimal in light of the presented lower bounds.In particular, we provide an improved approximation guarantee for the greedy algorithm which we show is tight up to a constant factor, and present the first distributed implementation with provable approximation factors.A b Heck,.; Konig,.; Sonmez,.; Weintraub,.Given massive multiway data, traditional methods are often too slow to operate on or suffer from memory bottleneck.Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster already in the 1950s, but these results have not inspired practical algorithms.Workshop on Biologically Inspired Approaches to Advanced Information Technology, Bio-adit 2004, Lausanne, Switzerland.We show that it performs well both on synthetic data and neural language models with large output spaces.