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The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization | https://openreview.net/forum?id=i87JIQTAnB8AQ | [
"Hugo Van hamme"
] | Poster | null | Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative en... | [] | null | 60 | 1301.3389 | title_snapshot | [
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Complexity of Representation and Inference in Compositional Models with
Part Sharing | https://openreview.net/forum?id=ZhGJ9KQlXi9jk | [
"Alan Yuille",
"Roozbeh Mottaghi"
] | Oral | null | This paper describes serial and parallel compositional models of multiple objects with part sharing. Objects are built by part-subpart compositions and expressed in terms of a hierarchical dictionary of object parts. These parts are represented on lattices of decreasing sizes which yield an executive summary descriptio... | [] | null | 34 | 1301.3560 | title_snapshot | [
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Indoor Semantic Segmentation using depth information | https://openreview.net/forum?id=ttnAE7vaATtaK | [
"Camille Couprie",
"Clement Farabet",
"Laurent Najman",
"Yann LeCun"
] | Oral | null | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. ... | [] | null | 40 | 1301.3572 | title_snapshot | [
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Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines | https://openreview.net/forum?id=OpvgONa-3WODz | [
"Guillaume Desjardins",
"Razvan Pascanu",
"Aaron Courville",
"Yoshua Bengio"
] | Poster | null | This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitely storing the natural g... | [] | null | 65 | 1301.3545 | title_snapshot | [
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Local Component Analysis | https://openreview.net/forum?id=mLr3In-nbamNu | [
"Nicolas Le Roux",
"Francis Bach"
] | Poster | null | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | [] | null | 64 | 1109.0093 | title_snapshot | [
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Discriminative Recurrent Sparse Auto-Encoders | https://openreview.net/forum?id=aJh-lFL2dFJ21 | [
"Jason Rolfe",
"Yann LeCun"
] | Oral | null | We present the discriminative recurrent sparse auto-encoder model, which consists of an encoder whose hidden layer is recurrent, and two linear decoders, one to reconstruct the input, and one to predict the output. The hidden layer is composed of rectified linear units (ReLU) and is subject to a sparsity penalty. The n... | [] | null | 51 | 1301.3775 | title_snapshot | [
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Training Neural Networks with Stochastic Hessian-Free Optimization | https://openreview.net/forum?id=tFbuFKWX3MFC8 | [
"Ryan Kiros"
] | Poster | null | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | [] | null | 48 | 1301.3641 | title_snapshot | [
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Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals | https://openreview.net/forum?id=4eEO5rd6xSevQ | [
"Sebastian Hitziger",
"Maureen Clerc",
"Alexandre Gramfort",
"Sandrine Saillet",
"Christian Bénar",
"Théodore Papadopoulo"
] | Poster | null | Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of informati... | [] | null | 33 | 1301.3611 | title_snapshot | [
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The Neural Representation Benchmark and its Evaluation on Brain and
Machine | https://openreview.net/forum?id=7hXs7GzQHo-QK | [
"Charles Cadieu",
"Ha Hong",
"Dan Yamins",
"Nicolas Pinto",
"Najib J. Majaj",
"James J. DiCarlo"
] | Oral | null | A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible... | [] | null | 32 | 1301.3530 | title_snapshot | [
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Feature grouping from spatially constrained multiplicative interaction | https://openreview.net/forum?id=4UGuUZWZmi4Ze | [
"Felix Bauer",
"Roland Memisevic"
] | Oral | null | We present a feature learning model that learns to encode relationships between images. The model is defined as a Gated Boltzmann Machine, which is constrained such that hidden units that are nearby in space can gate each other's connections. We show how frequency/orientation 'columns' as well as topographic filter map... | [] | null | 4 | 1301.3391 | title_snapshot | [
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Barnes-Hut-SNE | https://openreview.net/forum?id=eQWJec0ursynH | [
"Laurens van der Maaten"
] | Oral | null | The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N^2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data object... | [] | null | 19 | 1301.3342 | title_snapshot | [
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Information Theoretic Learning with Infinitely Divisible Kernels | https://openreview.net/forum?id=-AIqBI4_qZAQ1 | [
"Luis Gonzalo Sánchez",
"Jose C. Principe"
] | Oral | null | In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matrices based on Renyi's entropy definition and examine some key properties of this functional that lead to the concept of infinite divisibility.... | [] | null | 23 | 1301.3551 | title_snapshot | [
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Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | https://openreview.net/forum?id=l_PClqDdLb5Bp | [
"Matthew Zeiler",
"Rob Fergus"
] | Oral | null | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | [] | null | 14 | 1301.3557 | title_snapshot | [
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Adaptive learning rates and parallelization for stochastic, sparse,
non-smooth gradients | https://openreview.net/forum?id=7IOAIAx1AiEYC | [
"Tom Schaul",
"Yann LeCun"
] | Poster | null | Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in non-stati... | [] | null | 13 | 1301.3764 | title_snapshot | [
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Block Coordinate Descent for Sparse NMF | https://openreview.net/forum?id=G0OapcfeK3g_R | [
"Vamsi Potluru",
"Sergey M. Plis",
"Jonathan Le Roux",
"Barak A. Pearlmutter",
"Vince D. Calhoun",
"Thomas P. Hayes"
] | Poster | null | Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms, such as L$_1$... | [] | null | 24 | 1301.3527 | title_snapshot | [
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Cutting Recursive Autoencoder Trees | https://openreview.net/forum?id=6s2YsOZPYcb8N | [
"Christian Scheible",
"Hinrich Schuetze"
] | Poster | null | Deep Learning models enjoy considerable success in Natural Language Processing. While deep architectures produce useful representations that lead to improvements in various tasks, they are often difficult to interpret. This makes the analysis of learned structures particularly difficult. We therefore have to rely on em... | [] | null | 52 | 1301.2811 | title_snapshot | [
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Discrete Restricted Boltzmann Machines | https://openreview.net/forum?id=ttxM6DQKghdOi | [
"Guido F. Montufar",
"Jason Morton"
] | Oral | null | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | [] | null | 59 | 1301.3529 | title_snapshot | [
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Herded Gibbs Sampling | https://openreview.net/forum?id=2LzIDWSabfLe9 | [
"Luke Bornn",
"Yutian Chen",
"Nando de Freitas",
"Maya Baya",
"Jing Fang",
"Max Welling"
] | Oral | null | The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an $O(1/T)$ convergence rate for models with independent variables and for ful... | [] | null | 2 | 1301.4168 | title_snapshot | [
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Knowledge Matters: Importance of Prior Information for Optimization | https://openreview.net/forum?id=SSnY462CYz1Cu | [
"Çağlar Gülçehre",
"Yoshua Bengio"
] | Oral | null | We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via... | [] | null | 36 | 1301.4083 | title_snapshot | [
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Efficient Learning of Domain-invariant Image Representations | https://openreview.net/forum?id=BBIbj9w8Lvj8F | [
"Judy Hoffman",
"Erik Rodner",
"Jeff Donahue",
"Kate Saenko",
"Trevor Darrell"
] | Oral | null | We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifi... | [] | null | 8 | 1301.3224 | title_snapshot | [
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0.... |
Feature Learning in Deep Neural Networks - A Study on Speech Recognition
Tasks | https://openreview.net/forum?id=kk_XkMO0-dP8W | [
"Dong Yu",
"Mike Seltzer",
"Jinyu Li",
"Jui-Ting Huang",
"Frank Seide"
] | Oral | null | Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. D... | [] | null | 43 | 1301.3605 | title_judge | [
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... |
What Regularized Auto-Encoders Learn from the Data Generating Distribution | https://openreview.net/forum?id=-4IA4WgNAy4Wx | [
"Guillaume Alain",
"Yoshua Bengio"
] | Oral | null | What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous intuitive observations by showing that minimizing a particular form of regulari... | [] | null | 6 | 1211.4246 | title_snapshot | [
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0.... |
Saturating Auto-Encoder | https://openreview.net/forum?id=yGgjGkkbeFSbt | [
"Ross Goroshin",
"Yann LeCun"
] | Poster | null | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | [] | null | 39 | null | null | [
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-0.00... |
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