fast_ml fast_ml-2013 fast_ml-2013-46 knowledge-graph by maker-knowledge-mining
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Introduction: Recently Rob Zinkov published his selection of interesting-looking NIPS papers . Inspired by this, we list some more. Rob seems to like Bayesian stuff, we’re more into neural networks. If you feel like browsing, Andrej Karpathy has a page with all NIPS 2013 papers . They are categorized by topics discovered by running LDA. When you see an interesting paper, you can discover ones ranked similiar by TF-IDF. Here’s what we found. Understanding Dropout Pierre Baldi, Peter J. Sadowski Dropout is a relatively new algorithm for training neural networks which relies on stochastically dropping out neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characte
sentIndex sentText sentNum sentScore
1 We show that they reach state-of-the-art performance for recurrent networks in character-level language modelling when trained with simple stochastic gradient descent. [sent-19, score-0.617]
2 RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle We introduce RNADE, a new model for joint density estimation of real-valued vectors. [sent-21, score-0.448]
3 Our model calculates the density of a datapoint as the product of one-dimensional conditionals modeled using mixture density networks with shared parameters. [sent-22, score-0.491]
4 We compare the performance of RNADE on several datasets of heterogeneous and perceptual data, finding it outperforms mixture models in all but one case. [sent-25, score-0.743]
5 We describe experiments on real-world datasets which sometimes show improvements of several orders of magnitude over the classical algorithm. [sent-35, score-0.515]
6 The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. [sent-39, score-0.654]
7 Using Dirichlet process mixture models for image clustering and denoising, we demonstrate major improvements in robustness and accuracy. [sent-48, score-0.53]
8 We propose a simple and scalable new approach to learning word embeddings based on training log-bilinear models with noise-contrastive estimation. [sent-51, score-0.597]
9 We also investigate several model types and find that the embeddings learned by the simpler models perform at least as well as those learned by the more complex ones. [sent-55, score-0.713]
10 Learning Stochastic Feedforward Neural Networks Yichuan Tang, Ruslan Salakhutdinov Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression and classification tasks. [sent-56, score-0.51]
11 As regressors, MLPs model the conditional distribution of the predictor variables Y given the input variables X. [sent-57, score-0.464]
12 In this paper, we propose a stochastic feedforward network with hidden layers having both deterministic and stochastic variables. [sent-64, score-0.553]
13 We demonstrate the superiority of our model to conditional Restricted Boltzmann Machines and Mixture Density Networks on synthetic datasets and on modeling facial expressions. [sent-66, score-0.451]
14 Corrado, Jeff Dean The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. [sent-69, score-0.477]
15 We show that XNV consistently outperforms a state-of-the-art algorithm for semi-supervised learning: substantially improving predictive performance and reducing the variability of performance on a wide variety of real-world datasets, whilst also reducing runtime by orders of magnitude. [sent-82, score-0.524]
16 Unfortunately, such models are difficult to train because inference over latent variables must be performed concurrently with parameter optimization—creating a highly non-convex problem. [sent-84, score-0.66]
17 Instead of proposing another local training method, we develop a convex relaxation of hidden-layer conditional models that admits global training. [sent-85, score-0.494]
18 Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti We propose an approximate inference algorithm for continuous time Gaussian-Markov process models with both discrete and continuous time likelihoods. [sent-88, score-1.821]
19 We show that the continuous time limit of the expectation propagation algorithm exists and results in a hybrid fixed point iteration consisting of (1) expectation propagation updates for the discrete time terms and (2) variational updates for the continuous time term. [sent-89, score-0.685]
20 This approach extends the classical Kalman-Bucy smoothing procedure to non-Gaussian observations, enabling continuous-time inference in a variety of models, including spiking neuronal models (state-space models with point process observations) and box likelihood models. [sent-91, score-0.805]
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Introduction: Recently Rob Zinkov published his selection of interesting-looking NIPS papers . Inspired by this, we list some more. Rob seems to like Bayesian stuff, we’re more into neural networks. If you feel like browsing, Andrej Karpathy has a page with all NIPS 2013 papers . They are categorized by topics discovered by running LDA. When you see an interesting paper, you can discover ones ranked similiar by TF-IDF. Here’s what we found. Understanding Dropout Pierre Baldi, Peter J. Sadowski Dropout is a relatively new algorithm for training neural networks which relies on stochastically dropping out neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characte
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Introduction: As usual, there’s an interesting competition at Kaggle: The Black Box. It’s connected to ICML 2013 Workshop on Challenges in Representation Learning, held by the deep learning guys from Montreal. There are a couple benchmarks for this competition and the best one is unusually hard to beat 1 - only less than a fourth of those taking part managed to do so. We’re among them. Here’s how. The key ingredient in our success is a recently developed secret Stanford technology for deep unsupervised learning: sparse filtering by Jiquan Ngiam et al. Actually, it’s not secret. It’s available at Github , and has one or two very appealling properties. Let us explain. The main idea of deep unsupervised learning, as we understand it, is feature extraction. One of the most common applications is in multimedia. The reason for that is that multimedia tasks, for example object recognition, are easy for humans, but difficult for computers 2 . Geoff Hinton from Toronto talks about two ends
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