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Unsupervised Representation Learning with Deep Convolutional Generative
Adversarial Networks | cs.LG cs.CV | In recent years, supervised learning with convolutional networks (CNNs) has
seen huge adoption in computer vision applications. Comparatively, unsupervised
learning with CNNs has received less attention. In this work we hope to help
bridge the gap between the success of CNNs for supervised learning and
unsupervised learning. We introduce a class of CNNs called deep convolutional
generative adversarial networks (DCGANs), that have certain architectural
constraints, and demonstrate that they are a strong candidate for unsupervised
learning. Training on various image datasets, we show convincing evidence that
our deep convolutional adversarial pair learns a hierarchy of representations
from object parts to scenes in both the generator and discriminator.
Additionally, we use the learned features for novel tasks - demonstrating their
applicability as general image representations.
| Alec Radford, Luke Metz, and Soumith Chintala | null | 1511.06434 | null | null |
Comparative Study of Deep Learning Software Frameworks | cs.LG | Deep learning methods have resulted in significant performance improvements
in several application domains and as such several software frameworks have
been developed to facilitate their implementation. This paper presents a
comparative study of five deep learning frameworks, namely Caffe, Neon,
TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware
utilization, and speed. The study is performed on several types of deep
learning architectures and we evaluate the performance of the above frameworks
when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia
Titan X) settings. The speed performance metrics used here include the gradient
computation time, which is important during the training phase of deep
networks, and the forward time, which is important from the deployment
perspective of trained networks. For convolutional networks, we also report how
each of these frameworks support various convolutional algorithms and their
corresponding performance. From our experiments, we observe that Theano and
Torch are the most easily extensible frameworks. We observe that Torch is best
suited for any deep architecture on CPU, followed by Theano. It also achieves
the best performance on the GPU for large convolutional and fully connected
networks, followed closely by Neon. Theano achieves the best performance on GPU
for training and deployment of LSTM networks. Caffe is the easiest for
evaluating the performance of standard deep architectures. Finally, TensorFlow
is a very flexible framework, similar to Theano, but its performance is
currently not competitive compared to the other studied frameworks.
| Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah | null | 1511.06435 | null | null |
A convnet for non-maximum suppression | cs.CV cs.LG | Non-maximum suppression (NMS) is used in virtually all state-of-the-art
object detection pipelines. While essential object detection ingredients such
as features, classifiers, and proposal methods have been extensively researched
surprisingly little work has aimed to systematically address NMS. The de-facto
standard for NMS is based on greedy clustering with a fixed distance threshold,
which forces to trade-off recall versus precision. We propose a convnet
designed to perform NMS of a given set of detections. We report experiments on
a synthetic setup, and results on crowded pedestrian detection scenes. Our
approach overcomes the intrinsic limitations of greedy NMS, obtaining better
recall and precision.
| Jan Hosang, Rodrigo Benenson, Bernt Schiele | null | 1511.06437 | null | null |
Towards Principled Unsupervised Learning | cs.LG | General unsupervised learning is a long-standing conceptual problem in
machine learning. Supervised learning is successful because it can be solved by
the minimization of the training error cost function. Unsupervised learning is
not as successful, because the unsupervised objective may be unrelated to the
supervised task of interest. For an example, density modelling and
reconstruction have often been used for unsupervised learning, but they did not
produced the sought-after performance gains, because they have no knowledge of
the supervised tasks.
In this paper, we present an unsupervised cost function which we name the
Output Distribution Matching (ODM) cost, which measures a divergence between
the distribution of predictions and distributions of labels. The ODM cost is
appealing because it is consistent with the supervised cost in the following
sense: a perfect supervised classifier is also perfect according to the ODM
cost. Therefore, by aggressively optimizing the ODM cost, we are almost
guaranteed to improve our supervised performance whenever the space of possible
predictions is exponentially large.
We demonstrate that the ODM cost works well on number of small and
semi-artificial datasets using no (or almost no) labelled training cases.
Finally, we show that the ODM cost can be used for one-shot domain adaptation,
which allows the model to classify inputs that differ from the input
distribution in significant ways without the need for prior exposure to the new
domain.
| Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim
Lillicrap, Oriol Vinyals | null | 1511.06440 | null | null |
Fast Metric Learning For Deep Neural Networks | cs.LG cs.CV stat.ML | Similarity metrics are a core component of many information retrieval and
machine learning systems. In this work we propose a method capable of learning
a similarity metric from data equipped with a binary relation. By considering
only the similarity constraints, and initially ignoring the features, we are
able to learn target vectors for each instance using one of several
appropriately designed loss functions. A regression model can then be
constructed that maps novel feature vectors to the same target vector space,
resulting in a feature extractor that computes vectors for which a predefined
metric is a meaningful measure of similarity. We present results on both
multiclass and multi-label classification datasets that demonstrate
considerably faster convergence, as well as higher accuracy on the majority of
the intrinsic evaluation tasks and all extrinsic evaluation tasks.
| Henry Gouk, Bernhard Pfahringer, Michael Cree | null | 1511.06442 | null | null |
Neural Network Matrix Factorization | cs.LG stat.ML | Data often comes in the form of an array or matrix. Matrix factorization
techniques attempt to recover missing or corrupted entries by assuming that the
matrix can be written as the product of two low-rank matrices. In other words,
matrix factorization approximates the entries of the matrix by a simple, fixed
function---namely, the inner product---acting on the latent feature vectors for
the corresponding row and column. Here we consider replacing the inner product
by an arbitrary function that we learn from the data at the same time as we
learn the latent feature vectors. In particular, we replace the inner product
by a multi-layer feed-forward neural network, and learn by alternating between
optimizing the network for fixed latent features, and optimizing the latent
features for a fixed network. The resulting approach---which we call neural
network matrix factorization or NNMF, for short---dominates standard low-rank
techniques on a suite of benchmark but is dominated by some recent proposals
that take advantage of the graph features. Given the vast range of
architectures, activation functions, regularizers, and optimization techniques
that could be used within the NNMF framework, it seems likely the true
potential of the approach has yet to be reached.
| Gintare Karolina Dziugaite and Daniel M. Roy | null | 1511.06443 | null | null |
Universal halting times in optimization and machine learning | cs.LG math.NA math.PR | The authors present empirical distributions for the halting time (measured by
the number of iterations to reach a given accuracy) of optimization algorithms
applied to two random systems: spin glasses and deep learning. Given an
algorithm, which we take to be both the optimization routine and the form of
the random landscape, the fluctuations of the halting time follow a
distribution that, after centering and scaling, remains unchanged even when the
distribution on the landscape is changed. We observe two qualitative classes: A
Gumbel-like distribution that appears in Google searches, human decision times,
the QR eigenvalue algorithm and spin glasses, and a Gaussian-like distribution
that appears in conjugate gradient method, deep network with MNIST input data
and deep network with random input data. This empirical evidence suggests
presence of a class of distributions for which the halting time is independent
of the underlying distribution under some conditions.
| Levent Sagun, Thomas Trogdon and Yann LeCun | 10.1090/qam/1483 | 1511.06444 | null | null |
Learning Representations from EEG with Deep Recurrent-Convolutional
Neural Networks | cs.LG cs.CV | One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.
| Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella | null | 1511.06448 | null | null |
Learning to decompose for object detection and instance segmentation | cs.CV cs.LG | Although deep convolutional neural networks(CNNs) have achieved remarkable
results on object detection and segmentation, pre- and post-processing steps
such as region proposals and non-maximum suppression(NMS), have been required.
These steps result in high computational complexity and sensitivity to
hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel
end-to-end trainable deep neural network architecture, which consists of
convolutional and recurrent layers, that generates the correct number of object
instances and their bounding boxes (or segmentation masks) given an image,
using only a single network evaluation without any pre- or post-processing
steps. We have tested on detecting digits in multi-digit images synthesized
using MNIST, automatically segmenting digits in these images, and detecting
cars in the KITTI benchmark dataset. The proposed approach outperforms a strong
CNN baseline on the synthesized digits datasets and shows promising results on
KITTI car detection.
| Eunbyung Park, Alexander C. Berg | null | 1511.06449 | null | null |
Deep Metric Learning via Lifted Structured Feature Embedding | cs.CV cs.LG | Learning the distance metric between pairs of examples is of great importance
for learning and visual recognition. With the remarkable success from the state
of the art convolutional neural networks, recent works have shown promising
results on discriminatively training the networks to learn semantic feature
embeddings where similar examples are mapped close to each other and dissimilar
examples are mapped farther apart. In this paper, we describe an algorithm for
taking full advantage of the training batches in the neural network training by
lifting the vector of pairwise distances within the batch to the matrix of
pairwise distances. This step enables the algorithm to learn the state of the
art feature embedding by optimizing a novel structured prediction objective on
the lifted problem. Additionally, we collected Online Products dataset: 120k
images of 23k classes of online products for metric learning. Our experiments
on the CUB-200-2011, CARS196, and Online Products datasets demonstrate
significant improvement over existing deep feature embedding methods on all
experimented embedding sizes with the GoogLeNet network.
| Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese | null | 1511.06452 | null | null |
Variational Auto-encoded Deep Gaussian Processes | cs.LG stat.ML | We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformulation is that it prevents the proliferation of variational parameters
which otherwise grow linearly in proportion to the sample size. We derive a new
formulation of the variational lower bound that allows us to distribute most of
the computation in a way that enables to handle datasets of the size of
mainstream deep learning tasks. We show the efficacy of the method on a variety
of challenges including deep unsupervised learning and deep Bayesian
optimization.
| Zhenwen Dai, Andreas Damianou, Javier Gonz\'alez, Neil Lawrence | null | 1511.06455 | null | null |
Task Loss Estimation for Sequence Prediction | cs.LG | Often, the performance on a supervised machine learning task is evaluated
with a emph{task loss} function that cannot be optimized directly. Examples of
such loss functions include the classification error, the edit distance and the
BLEU score. A common workaround for this problem is to instead optimize a
emph{surrogate loss} function, such as for instance cross-entropy or hinge
loss. In order for this remedy to be effective, it is important to ensure that
minimization of the surrogate loss results in minimization of the task loss, a
condition that we call emph{consistency with the task loss}. In this work, we
propose another method for deriving differentiable surrogate losses that
provably meet this requirement. We focus on the broad class of models that
define a score for every input-output pair. Our idea is that this score can be
interpreted as an estimate of the task loss, and that the estimation error may
be used as a consistent surrogate loss. A distinct feature of such an approach
is that it defines the desirable value of the score for every input-output
pair. We use this property to design specialized surrogate losses for
Encoder-Decoder models often used for sequence prediction tasks. In our
experiment, we benchmark on the task of speech recognition. Using a new
surrogate loss instead of cross-entropy to train an Encoder-Decoder speech
recognizer brings a significant ~13% relative improvement in terms of Character
Error Rate (CER) in the case when no extra corpora are used for language
modeling.
| Dzmitry Bahdanau, Dmitriy Serdyuk, Phil\'emon Brakel, Nan Rosemary Ke,
Jan Chorowski, Aaron Courville, Yoshua Bengio | null | 1511.06456 | null | null |
DOC: Deep OCclusion Estimation From a Single Image | cs.CV cs.LG | Recovering the occlusion relationships between objects is a fundamental human
visual ability which yields important information about the 3D world. In this
paper we propose a deep network architecture, called DOC, which acts on a
single image, detects object boundaries and estimates the border ownership
(i.e. which side of the boundary is foreground and which is background). We
represent occlusion relations by a binary edge map, to indicate the object
boundary, and an occlusion orientation variable which is tangential to the
boundary and whose direction specifies border ownership by a left-hand rule. We
train two related deep convolutional neural networks, called DOC, which exploit
local and non-local image cues to estimate this representation and hence
recover occlusion relations. In order to train and test DOC we construct a
large-scale instance occlusion boundary dataset using PASCAL VOC images, which
we call the PASCAL instance occlusion dataset (PIOD). This contains 10,000
images and hence is two orders of magnitude larger than existing occlusion
datasets for outdoor images. We test two variants of DOC on PIOD and on the
BSDS occlusion dataset and show they outperform state-of-the-art methods.
Finally, we perform numerous experiments investigating multiple settings of DOC
and transfer between BSDS and PIOD, which provides more insights for further
study of occlusion estimation.
| Peng Wang and Alan Yuille | null | 1511.06457 | null | null |
Bayesian inference via rejection filtering | cs.LG quant-ph stat.ML | We provide a method for approximating Bayesian inference using rejection
sampling. We not only make the process efficient, but also dramatically reduce
the memory required relative to conventional methods by combining rejection
sampling with particle filtering. We also provide an approximate form of
rejection sampling that makes rejection filtering tractable in cases where
exact rejection sampling is not efficient. Finally, we present several
numerical examples of rejection filtering that show its ability to track time
dependent parameters in online settings and also benchmark its performance on
MNIST classification problems.
| Nathan Wiebe, Christopher Granade, Ashish Kapoor, Krysta M Svore | null | 1511.06458 | null | null |
Unitary Evolution Recurrent Neural Networks | cs.LG cs.NE stat.ML | Recurrent neural networks (RNNs) are notoriously difficult to train. When the
eigenvalues of the hidden to hidden weight matrix deviate from absolute value
1, optimization becomes difficult due to the well studied issue of vanishing
and exploding gradients, especially when trying to learn long-term
dependencies. To circumvent this problem, we propose a new architecture that
learns a unitary weight matrix, with eigenvalues of absolute value exactly 1.
The challenge we address is that of parametrizing unitary matrices in a way
that does not require expensive computations (such as eigendecomposition) after
each weight update. We construct an expressive unitary weight matrix by
composing several structured matrices that act as building blocks with
parameters to be learned. Optimization with this parameterization becomes
feasible only when considering hidden states in the complex domain. We
demonstrate the potential of this architecture by achieving state of the art
results in several hard tasks involving very long-term dependencies.
| Martin Arjovsky, Amar Shah, Yoshua Bengio | null | 1511.06464 | null | null |
On Binary Embedding using Circulant Matrices | cs.DS cs.LG | Binary embeddings provide efficient and powerful ways to perform operations
on large scale data. However binary embedding typically requires long codes in
order to preserve the discriminative power of the input space. Thus binary
coding methods traditionally suffer from high computation and storage costs in
such a scenario. To address this problem, we propose Circulant Binary Embedding
(CBE) which generates binary codes by projecting the data with a circulant
matrix. The circulant structure allows us to use Fast Fourier Transform
algorithms to speed up the computation. For obtaining $k$-bit binary codes from
$d$-dimensional data, this improves the time complexity from $O(dk)$ to
$O(d\log{d})$, and the space complexity from $O(dk)$ to $O(d)$.
We study two settings, which differ in the way we choose the parameters of
the circulant matrix. In the first, the parameters are chosen randomly and in
the second, the parameters are learned using the data. For randomized CBE, we
give a theoretical analysis comparing it with binary embedding using an
unstructured random projection matrix. The challenge here is to show that the
dependencies in the entries of the circulant matrix do not lead to a loss in
performance. In the second setting, we design a novel time-frequency
alternating optimization to learn data-dependent circulant projections, which
alternatively minimizes the objective in original and Fourier domains. In both
the settings, we show by extensive experiments that the CBE approach gives much
better performance than the state-of-the-art approaches if we fix a running
time, and provides much faster computation with negligible performance
degradation if we fix the number of bits in the embedding.
| Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu
Chang | null | 1511.06480 | null | null |
Variance Reduction in SGD by Distributed Importance Sampling | stat.ML cs.LG | Humans are able to accelerate their learning by selecting training materials
that are the most informative and at the appropriate level of difficulty. We
propose a framework for distributing deep learning in which one set of workers
search for the most informative examples in parallel while a single worker
updates the model on examples selected by importance sampling. This leads the
model to update using an unbiased estimate of the gradient which also has
minimum variance when the sampling proposal is proportional to the L2-norm of
the gradient. We show experimentally that this method reduces gradient variance
even in a context where the cost of synchronization across machines cannot be
ignored, and where the factors for importance sampling are not updated
instantly across the training set.
| Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville,
Yoshua Bengio | null | 1511.06481 | null | null |
On the energy landscape of deep networks | cs.LG | We introduce "AnnealSGD", a regularized stochastic gradient descent algorithm
motivated by an analysis of the energy landscape of a particular class of deep
networks with sparse random weights. The loss function of such networks can be
approximated by the Hamiltonian of a spherical spin glass with Gaussian
coupling. While different from currently-popular architectures such as
convolutional ones, spin glasses are amenable to analysis, which provides
insights on the topology of the loss function and motivates algorithms to
minimize it. Specifically, we show that a regularization term akin to a
magnetic field can be modulated with a single scalar parameter to transition
the loss function from a complex, non-convex landscape with exponentially many
local minima, to a phase with a polynomial number of minima, all the way down
to a trivial landscape with a unique minimum. AnnealSGD starts training in the
relaxed polynomial regime and gradually tightens the regularization parameter
to steer the energy towards the original exponential regime. Even for
convolutional neural networks, which are quite unlike sparse random networks,
we empirically show that AnnealSGD improves the generalization error using
competitive baselines on MNIST and CIFAR-10.
| Pratik Chaudhari, Stefano Soatto | null | 1511.06485 | null | null |
Resiliency of Deep Neural Networks under Quantization | cs.LG cs.NE | The complexity of deep neural network algorithms for hardware implementation
can be much lowered by optimizing the word-length of weights and signals.
Direct quantization of floating-point weights, however, does not show good
performance when the number of bits assigned is small. Retraining of quantized
networks has been developed to relieve this problem. In this work, the effects
of retraining are analyzed for a feedforward deep neural network (FFDNN) and a
convolutional neural network (CNN). The network complexity is controlled to
know their effects on the resiliency of quantized networks by retraining. The
complexity of the FFDNN is controlled by varying the unit size in each hidden
layer and the number of layers, while that of the CNN is done by modifying the
feature map configuration. We find that the performance gap between the
floating-point and the retrain-based ternary (+1, 0, -1) weight neural networks
exists with a fair amount in 'complexity limited' networks, but the discrepancy
almost vanishes in fully complex networks whose capability is limited by the
training data, rather than by the number of connections. This research shows
that highly complex DNNs have the capability of absorbing the effects of severe
weight quantization through retraining, but connection limited networks are
less resilient. This paper also presents the effective compression ratio to
guide the trade-off between the network size and the precision when the
hardware resource is limited.
| Wonyong Sung, Sungho Shin, Kyuyeon Hwang | null | 1511.06488 | null | null |
The Variational Gaussian Process | stat.ML cs.LG cs.NE stat.CO | Variational inference is a powerful tool for approximate inference, and it
has been recently applied for representation learning with deep generative
models. We develop the variational Gaussian process (VGP), a Bayesian
nonparametric variational family, which adapts its shape to match complex
posterior distributions. The VGP generates approximate posterior samples by
generating latent inputs and warping them through random non-linear mappings;
the distribution over random mappings is learned during inference, enabling the
transformed outputs to adapt to varying complexity. We prove a universal
approximation theorem for the VGP, demonstrating its representative power for
learning any model. For inference we present a variational objective inspired
by auto-encoders and perform black box inference over a wide class of models.
The VGP achieves new state-of-the-art results for unsupervised learning,
inferring models such as the deep latent Gaussian model and the recently
proposed DRAW.
| Dustin Tran, Rajesh Ranganath, David M. Blei | null | 1511.06499 | null | null |
Integrating Deep Features for Material Recognition | cs.CV cs.LG | We propose a method for integration of features extracted using deep
representations of Convolutional Neural Networks (CNNs) each of which is
learned using a different image dataset of objects and materials for material
recognition. Given a set of representations of multiple pre-trained CNNs, we
first compute activations of features using the representations on the images
to select a set of samples which are best represented by the features. Then, we
measure the uncertainty of the features by computing the entropy of class
distributions for each sample set. Finally, we compute the contribution of each
feature to representation of classes for feature selection and integration. We
examine the proposed method on three benchmark datasets for material
recognition. Experimental results show that the proposed method achieves
state-of-the-art performance by integrating deep features. Additionally, we
introduce a new material dataset called EFMD by extending Flickr Material
Database (FMD). By the employment of the EFMD with transfer learning for
updating the learned CNN models, we achieve 84.0%+/-1.8% accuracy on the FMD
dataset which is close to human performance that is 84.9%.
| Yan Zhang, Mete Ozay, Xing Liu, Takayuki Okatani | null | 1511.06522 | null | null |
Compression of Deep Convolutional Neural Networks for Fast and Low Power
Mobile Applications | cs.CV cs.LG | Although the latest high-end smartphone has powerful CPU and GPU, running
deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet
classification on mobile devices is challenging. To deploy deep CNNs on mobile
devices, we present a simple and effective scheme to compress the entire CNN,
which we call one-shot whole network compression. The proposed scheme consists
of three steps: (1) rank selection with variational Bayesian matrix
factorization, (2) Tucker decomposition on kernel tensor, and (3) fine-tuning
to recover accumulated loss of accuracy, and each step can be easily
implemented using publicly available tools. We demonstrate the effectiveness of
the proposed scheme by testing the performance of various compressed CNNs
(AlexNet, VGGS, GoogLeNet, and VGG-16) on the smartphone. Significant
reductions in model size, runtime, and energy consumption are obtained, at the
cost of small loss in accuracy. In addition, we address the important
implementation level issue on 1?1 convolution, which is a key operation of
inception module of GoogLeNet as well as CNNs compressed by our proposed
scheme.
| Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang,
Dongjun Shin | null | 1511.06530 | null | null |
Dueling Network Architectures for Deep Reinforcement Learning | cs.LG | In recent years there have been many successes of using deep representations
in reinforcement learning. Still, many of these applications use conventional
architectures, such as convolutional networks, LSTMs, or auto-encoders. In this
paper, we present a new neural network architecture for model-free
reinforcement learning. Our dueling network represents two separate estimators:
one for the state value function and one for the state-dependent action
advantage function. The main benefit of this factoring is to generalize
learning across actions without imposing any change to the underlying
reinforcement learning algorithm. Our results show that this architecture leads
to better policy evaluation in the presence of many similar-valued actions.
Moreover, the dueling architecture enables our RL agent to outperform the
state-of-the-art on the Atari 2600 domain.
| Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot,
Nando de Freitas | null | 1511.06581 | null | null |
Exponential Natural Particle Filter | cs.LG cs.NE cs.RO | Particle Filter algorithm (PF) suffers from some problems such as the loss of
particle diversity, the need for large number of particles, and the costly
selection of the importance density functions. In this paper, a novel
Exponential Natural Particle Filter (xNPF) is introduced to solve the above
problems. In this approach, a state transitional probability with the use of
natural gradient learning is proposed which balances exploration and
exploitation more robustly. The results show that xNPF converges much closer to
the true target states than the other state of the art particle filter.
| Ghazal Zand, Mojtaba Taherkhani, Reza Safabakhsh | null | 1511.06603 | null | null |
Data Representation and Compression Using Linear-Programming
Approximations | cs.LG | We propose `Dracula', a new framework for unsupervised feature selection from
sequential data such as text. Dracula learns a dictionary of $n$-grams that
efficiently compresses a given corpus and recursively compresses its own
dictionary; in effect, Dracula is a `deep' extension of Compressive Feature
Learning. It requires solving a binary linear program that may be relaxed to a
linear program. Both problems exhibit considerable structure, their solution
paths are well behaved, and we identify parameters which control the depth and
diversity of the dictionary. We also discuss how to derive features from the
compressed documents and show that while certain unregularized linear models
are invariant to the structure of the compressed dictionary, this structure may
be used to regularize learning. Experiments are presented that demonstrate the
efficacy of Dracula's features.
| Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie | null | 1511.06606 | null | null |
Recurrent Gaussian Processes | cs.LG stat.ML | We define Recurrent Gaussian Processes (RGP) models, a general family of
Bayesian nonparametric models with recurrent GP priors which are able to learn
dynamical patterns from sequential data. Similar to Recurrent Neural Networks
(RNNs), RGPs can have different formulations for their internal states,
distinct inference methods and be extended with deep structures. In such
context, we propose a novel deep RGP model whose autoregressive states are
latent, thereby performing representation and dynamical learning
simultaneously. To fully exploit the Bayesian nature of the RGP model we
develop the Recurrent Variational Bayes (REVARB) framework, which enables
efficient inference and strong regularization through coherent propagation of
uncertainty across the RGP layers and states. We also introduce a RGP extension
where variational parameters are greatly reduced by being reparametrized
through RNN-based sequential recognition models. We apply our model to the
tasks of nonlinear system identification and human motion modeling. The
promising obtained results indicate that our RGP model maintains its highly
flexibility while being able to avoid overfitting and being applicable even
when larger datasets are not available.
| C\'esar Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy
Forth, Guilherme A. Barreto, Neil D. Lawrence | null | 1511.06644 | null | null |
Recurrent Semi-supervised Classification and Constrained Adversarial
Generation with Motion Capture Data | cs.CV cs.LG | We explore recurrent encoder multi-decoder neural network architectures for
semi-supervised sequence classification and reconstruction. We find that the
use of multiple reconstruction modules helps models generalize in a
classification task when only a small amount of labeled data is available,
which is often the case in practice. Such models provide useful high-level
representations of motions allowing clustering, searching and faster labeling
of new sequences. We also propose a new, realistic partitioning of a
well-known, high quality motion-capture dataset for better evaluations. We
further explore a novel formulation for future-predicting decoders based on
conditional recurrent generative adversarial networks, for which we propose
both soft and hard constraints for transition generation derived from desired
physical properties of synthesized future movements and desired animation
goals. We find that using such constraints allow to stabilize the training of
recurrent adversarial architectures for animation generation.
| F\'elix G. Harvey, Julien Roy, David Kanaa, Christopher Pal | null | 1511.06653 | null | null |
Modeling the Temporal Nature of Human Behavior for Demographics
Prediction | cs.LG | Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predicting demographic information from mobile phone metadata.
Previous work focused on creating increasingly advanced features to be modeled
with standard machine learning algorithms. We here instead model the raw mobile
phone metadata directly using deep learning, exploiting the temporal nature of
the patterns in the data. From high-level assumptions we design a data
representation and convolutional network architecture for modeling patterns
within a week. We then examine three strategies for aggregating patterns across
weeks and show that our method reaches state-of-the-art accuracy on both age
and gender prediction using only the temporal modality in mobile metadata. We
finally validate our method on low activity users and evaluate the modeling
assumptions.
| Bjarke Felbo, P{\aa}l Sunds{\o}y, Alex 'Sandy' Pentland, Sune Lehmann,
Yves-Alexandre de Montjoye | null | 1511.06660 | null | null |
L1 logistic regression as a feature selection step for training stable
classification trees for the prediction of severity criteria in imported
malaria | cs.LG q-bio.QM stat.AP | Multivariate classification methods using explanatory and predictive models
are necessary for characterizing subgroups of patients according to their risk
profiles. Popular methods include logistic regression and classification trees
with performances that vary according to the nature and the characteristics of
the dataset. In the context of imported malaria, we aimed at classifying
severity criteria based on a heterogeneous patient population. We investigated
these approaches by implementing two different strategies: L1 logistic
regression (L1LR) that models a single global solution and classification trees
that model multiple local solutions corresponding to discriminant subregions of
the feature space. For each strategy, we built a standard model, and a sparser
version of it. As an alternative to pruning, we explore a promising approach
that first constrains the tree model with an L1LR-based feature selection, an
approach we called L1LR-Tree. The objective is to decrease its vulnerability to
small data variations by removing variables corresponding to unstable local
phenomena. Our study is twofold: i) from a methodological perspective comparing
the performances and the stability of the three previous methods, i.e L1LR,
classification trees and L1LR-Tree, for the classification of severe forms of
imported malaria, and ii) from an applied perspective improving the actual
classification of severe forms of imported malaria by identifying more
personalized profiles predictive of several clinical criteria based on
variables dismissed for the clinical definition of the disease. The main
methodological results show that the combined method L1LR-Tree builds sparse
and stable models that significantly predicts the different severity criteria
and outperforms all the other methods in terms of accuracy.
| Luca Talenti, Margaux Luck, Anastasia Yartseva, Nicolas Argy, Sandrine
Houz\'e and Cecilia Damon | null | 1511.06663 | null | null |
Top-k Multiclass SVM | stat.ML cs.CV cs.LG | Class ambiguity is typical in image classification problems with a large
number of classes. When classes are difficult to discriminate, it makes sense
to allow k guesses and evaluate classifiers based on the top-k error instead of
the standard zero-one loss. We propose top-k multiclass SVM as a direct method
to optimize for top-k performance. Our generalization of the well-known
multiclass SVM is based on a tight convex upper bound of the top-k error. We
propose a fast optimization scheme based on an efficient projection onto the
top-k simplex, which is of its own interest. Experiments on five datasets show
consistent improvements in top-k accuracy compared to various baselines.
| Maksim Lapin, Matthias Hein and Bernt Schiele | null | 1511.06683 | null | null |
Top-N recommendations from expressive recommender systems | cs.LG stat.ML | Normalized nonnegative models assign probability distributions to users and
random variables to items; see [Stark, 2015]. Rating an item is regarded as
sampling the random variable assigned to the item with respect to the
distribution assigned to the user who rates the item. Models of that kind are
highly expressive. For instance, using normalized nonnegative models we can
understand users' preferences as mixtures of interpretable user stereotypes,
and we can arrange properties of users and items in a hierarchical manner.
These features would not be useful if the predictive power of normalized
nonnegative models was poor. Thus, we analyze here the performance of
normalized nonnegative models for top-N recommendation and observe that their
performance matches the performance of methods like PureSVD which was
introduced in [Cremonesi et al., 2010]. We conclude that normalized nonnegative
models not only provide accurate recommendations but they also deliver (for
free) representations that are interpretable. We deepen the discussion of
normalized nonnegative models by providing further theoretical insights. In
particular, we introduce total variational distance as an operational
similarity measure, we discover scenarios where normalized nonnegative models
yield unique representations of users and items, we prove that the inference of
optimal normalized nonnegative models is NP-hard and finally, we discuss the
relationship between normalized nonnegative models and nonnegative matrix
factorization.
| Cyril Stark | null | 1511.06718 | null | null |
Scalable Gradient-Based Tuning of Continuous Regularization
Hyperparameters | cs.LG | Hyperparameter selection generally relies on running multiple full training
trials, with selection based on validation set performance. We propose a
gradient-based approach for locally adjusting hyperparameters during training
of the model. Hyperparameters are adjusted so as to make the model parameter
gradients, and hence updates, more advantageous for the validation cost. We
explore the approach for tuning regularization hyperparameters and find that in
experiments on MNIST, SVHN and CIFAR-10, the resulting regularization levels
are within the optimal regions. The additional computational cost depends on
how frequently the hyperparameters are trained, but the tested scheme adds only
30% computational overhead regardless of the model size. Since the method is
significantly less computationally demanding compared to similar gradient-based
approaches to hyperparameter optimization, and consistently finds good
hyperparameter values, it can be a useful tool for training neural network
models.
| Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko | null | 1511.06727 | null | null |
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised
Learning | cs.CV cs.AI cs.LG | We propose a method for hand pose estimation based on a deep regressor
trained on two different kinds of input. Raw depth data is fused with an
intermediate representation in the form of a segmentation of the hand into
parts. This intermediate representation contains important topological
information and provides useful cues for reasoning about joint locations. The
mapping from raw depth to segmentation maps is learned in a
semi/weakly-supervised way from two different datasets: (i) a synthetic dataset
created through a rendering pipeline including densely labeled ground truth
(pixelwise segmentations); and (ii) a dataset with real images for which ground
truth joint positions are available, but not dense segmentations. Loss for
training on real images is generated from a patch-wise restoration process,
which aligns tentative segmentation maps with a large dictionary of synthetic
poses. The underlying premise is that the domain shift between synthetic and
real data is smaller in the intermediate representation, where labels carry
geometric and topological meaning, than in the raw input domain. Experiments on
the NYU dataset show that the proposed training method decreases error on
joints over direct regression of joints from depth data by 15.7%.
| Natalia Neverova, Christian Wolf, Florian Nebout, Graham Taylor | null | 1511.06728 | null | null |
Sequence Level Training with Recurrent Neural Networks | cs.LG cs.CL | Many natural language processing applications use language models to generate
text. These models are typically trained to predict the next word in a
sequence, given the previous words and some context such as an image. However,
at test time the model is expected to generate the entire sequence from
scratch. This discrepancy makes generation brittle, as errors may accumulate
along the way. We address this issue by proposing a novel sequence level
training algorithm that directly optimizes the metric used at test time, such
as BLEU or ROUGE. On three different tasks, our approach outperforms several
strong baselines for greedy generation. The method is also competitive when
these baselines employ beam search, while being several times faster.
| Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba | null | 1511.06732 | null | null |
Training CNNs with Low-Rank Filters for Efficient Image Classification | cs.CV cs.LG cs.NE | We propose a new method for creating computationally efficient convolutional
neural networks (CNNs) by using low-rank representations of convolutional
filters. Rather than approximating filters in previously-trained networks with
more efficient versions, we learn a set of small basis filters from scratch;
during training, the network learns to combine these basis filters into more
complex filters that are discriminative for image classification. To train such
networks, a novel weight initialization scheme is used. This allows effective
initialization of connection weights in convolutional layers composed of groups
of differently-shaped filters. We validate our approach by applying it to
several existing CNN architectures and training these networks from scratch
using the CIFAR, ILSVRC and MIT Places datasets. Our results show similar or
higher accuracy than conventional CNNs with much less compute. Applying our
method to an improved version of VGG-11 network using global max-pooling, we
achieve comparable validation accuracy using 41% less compute and only 24% of
the original VGG-11 model parameters; another variant of our method gives a 1
percentage point increase in accuracy over our improved VGG-11 model, giving a
top-5 center-crop validation accuracy of 89.7% while reducing computation by
16% relative to the original VGG-11 model. Applying our method to the GoogLeNet
architecture for ILSVRC, we achieved comparable accuracy with 26% less compute
and 41% fewer model parameters. Applying our method to a near state-of-the-art
network for CIFAR, we achieved comparable accuracy with 46% less compute and
55% fewer parameters.
| Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla,
Antonio Criminisi | null | 1511.06744 | null | null |
Images Don't Lie: Transferring Deep Visual Semantic Features to
Large-Scale Multimodal Learning to Rank | cs.CV cs.LG | Search is at the heart of modern e-commerce. As a result, the task of ranking
search results automatically (learning to rank) is a multibillion dollar
machine learning problem. Traditional models optimize over a few
hand-constructed features based on the item's text. In this paper, we introduce
a multimodal learning to rank model that combines these traditional features
with visual semantic features transferred from a deep convolutional neural
network. In a large scale experiment using data from the online marketplace
Etsy, we verify that moving to a multimodal representation significantly
improves ranking quality. We show how image features can capture fine-grained
style information not available in a text-only representation. In addition, we
show concrete examples of how image information can successfully disentangle
pairs of highly different items that are ranked similarly by a text-only model.
| Corey Lynch, Kamelia Aryafar, Josh Attenberg | null | 1511.06746 | null | null |
Data-Dependent Path Normalization in Neural Networks | cs.LG | We propose a unified framework for neural net normalization, regularization
and optimization, which includes Path-SGD and Batch-Normalization and
interpolates between them across two different dimensions. Through this
framework we investigate issue of invariance of the optimization, data
dependence and the connection with natural gradients.
| Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro | null | 1511.06747 | null | null |
Adding Gradient Noise Improves Learning for Very Deep Networks | stat.ML cs.LG | Deep feedforward and recurrent networks have achieved impressive results in
many perception and language processing applications. This success is partially
attributed to architectural innovations such as convolutional and long
short-term memory networks. The main motivation for these architectural
innovations is that they capture better domain knowledge, and importantly are
easier to optimize than more basic architectures. Recently, more complex
architectures such as Neural Turing Machines and Memory Networks have been
proposed for tasks including question answering and general computation,
creating a new set of optimization challenges. In this paper, we discuss a
low-overhead and easy-to-implement technique of adding gradient noise which we
find to be surprisingly effective when training these very deep architectures.
The technique not only helps to avoid overfitting, but also can result in lower
training loss. This method alone allows a fully-connected 20-layer deep network
to be trained with standard gradient descent, even starting from a poor
initialization. We see consistent improvements for many complex models,
including a 72% relative reduction in error rate over a carefully-tuned
baseline on a challenging question-answering task, and a doubling of the number
of accurate binary multiplication models learned across 7,000 random restarts.
We encourage further application of this technique to additional complex modern
architectures.
| Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz
Kaiser, Karol Kurach, James Martens | null | 1511.06807 | null | null |
Learning visual groups from co-occurrences in space and time | cs.LG cs.CV | We propose a self-supervised framework that learns to group visual entities
based on their rate of co-occurrence in space and time. To model statistical
dependencies between the entities, we set up a simple binary classification
problem in which the goal is to predict if two visual primitives occur in the
same spatial or temporal context. We apply this framework to three domains:
learning patch affinities from spatial adjacency in images, learning frame
affinities from temporal adjacency in videos, and learning photo affinities
from geospatial proximity in image collections. We demonstrate that in each
case the learned affinities uncover meaningful semantic groupings. From patch
affinities we generate object proposals that are competitive with
state-of-the-art supervised methods. From frame affinities we generate movie
scene segmentations that correlate well with DVD chapter structure. Finally,
from geospatial affinities we learn groups that relate well to semantic place
categories.
| Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson | null | 1511.06811 | null | null |
GradNets: Dynamic Interpolation Between Neural Architectures | cs.LG cs.NE | In machine learning, there is a fundamental trade-off between ease of
optimization and expressive power. Neural Networks, in particular, have
enormous expressive power and yet are notoriously challenging to train. The
nature of that optimization challenge changes over the course of learning.
Traditionally in deep learning, one makes a static trade-off between the needs
of early and late optimization. In this paper, we investigate a novel
framework, GradNets, for dynamically adapting architectures during training to
get the benefits of both. For example, we can gradually transition from linear
to non-linear networks, deterministic to stochastic computation, shallow to
deep architectures, or even simple downsampling to fully differentiable
attention mechanisms. Benefits include increased accuracy, easier convergence
with more complex architectures, solutions to test-time execution of batch
normalization, and the ability to train networks of up to 200 layers.
| Diogo Almeida, Nate Sauder | null | 1511.06827 | null | null |
Online Sequence Training of Recurrent Neural Networks with Connectionist
Temporal Classification | cs.LG cs.NE | Connectionist temporal classification (CTC) based supervised sequence
training of recurrent neural networks (RNNs) has shown great success in many
machine learning areas including end-to-end speech and handwritten character
recognition. For the CTC training, however, it is required to unroll (or
unfold) the RNN by the length of an input sequence. This unrolling requires a
lot of memory and hinders a small footprint implementation of online learning
or adaptation. Furthermore, the length of training sequences is usually not
uniform, which makes parallel training with multiple sequences inefficient on
shared memory models such as graphics processing units (GPUs). In this work, we
introduce an expectation-maximization (EM) based online CTC algorithm that
enables unidirectional RNNs to learn sequences that are longer than the amount
of unrolling. The RNNs can also be trained to process an infinitely long input
sequence without pre-segmentation or external reset. Moreover, the proposed
approach allows efficient parallel training on GPUs. For evaluation, phoneme
recognition and end-to-end speech recognition examples are presented on the
TIMIT and Wall Street Journal (WSJ) corpora, respectively. Our online model
achieves 20.7% phoneme error rate (PER) on the very long input sequence that is
generated by concatenating all 192 utterances in the TIMIT core test set. On
WSJ, a network can be trained with only 64 times of unrolling while sacrificing
4.5% relative word error rate (WER).
| Kyuyeon Hwang, Wonyong Sung | null | 1511.06841 | null | null |
Unsupervised learning of object semantic parts from internal states of
CNNs by population encoding | cs.LG cs.CV | We address the key question of how object part representations can be found
from the internal states of CNNs that are trained for high-level tasks, such as
object classification. This work provides a new unsupervised method to learn
semantic parts and gives new understanding of the internal representations of
CNNs. Our technique is based on the hypothesis that semantic parts are
represented by populations of neurons rather than by single filters. We propose
a clustering technique to extract part representations, which we call Visual
Concepts. We show that visual concepts are semantically coherent in that they
represent semantic parts, and visually coherent in that corresponding image
patches appear very similar. Also, visual concepts provide full spatial
coverage of the parts of an object, rather than a few sparse parts as is
typically found in keypoint annotations. Furthermore, We treat single visual
concept as part detector and evaluate it for keypoint detection using the
PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart
dataset. The experiments demonstrate that visual concepts can be used to detect
parts. We also show that some visual concepts respond to several semantic
parts, provided these parts are visually similar. Thus visual concepts have the
essential properties: semantic meaning and detection capability. Note that our
ImageNetPart dataset gives rich part annotations which cover the whole object,
making it useful for other part-related applications.
| Jianyu Wang, Zhishuai Zhang, Cihang Xie, Vittal Premachandran, Alan
Yuille | null | 1511.06855 | null | null |
Data-dependent Initializations of Convolutional Neural Networks | cs.CV cs.LG | Convolutional Neural Networks spread through computer vision like a wildfire,
impacting almost all visual tasks imaginable. Despite this, few researchers
dare to train their models from scratch. Most work builds on one of a handful
of ImageNet pre-trained models, and fine-tunes or adapts these for specific
tasks. This is in large part due to the difficulty of properly initializing
these networks from scratch. A small miscalibration of the initial weights
leads to vanishing or exploding gradients, as well as poor convergence
properties. In this work we present a fast and simple data-dependent
initialization procedure, that sets the weights of a network such that all
units in the network train at roughly the same rate, avoiding vanishing or
exploding gradients. Our initialization matches the current state-of-the-art
unsupervised or self-supervised pre-training methods on standard computer
vision tasks, such as image classification and object detection, while being
roughly three orders of magnitude faster. When combined with pre-training
methods, our initialization significantly outperforms prior work, narrowing the
gap between supervised and unsupervised pre-training.
| Philipp Kr\"ahenb\"uhl, Carl Doersch, Jeff Donahue, Trevor Darrell | null | 1511.06856 | null | null |
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical
Auto-Zoom Net | cs.CV cs.LG | Parsing articulated objects, e.g. humans and animals, into semantic parts
(e.g. body, head and arms, etc.) from natural images is a challenging and
fundamental problem for computer vision. A big difficulty is the large
variability of scale and location for objects and their corresponding parts.
Even limited mistakes in estimating scale and location will degrade the parsing
output and cause errors in boundary details. To tackle these difficulties, we
propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which
adapts to the local scales of objects and parts. HAZN is a sequence of two
"Auto-Zoom Net" (AZNs), each employing fully convolutional networks that
perform two tasks: (1) predict the locations and scales of object instances
(the first AZN) or their parts (the second AZN); (2) estimate the part scores
for predicted object instance or part regions. Our model can adaptively "zoom"
(resize) predicted image regions into their proper scales to refine the
parsing.
We conduct extensive experiments over the PASCAL part datasets on humans,
horses, and cows. For humans, our approach significantly outperforms the
state-of-the-arts by 5% mIOU and is especially better at segmenting small
instances and small parts. We obtain similar improvements for parsing cows and
horses over alternative methods. In summary, our strategy of first zooming into
objects and then zooming into parts is very effective. It also enables us to
process different regions of the image at different scales adaptively so that,
for example, we do not need to waste computational resources scaling the entire
image.
| Fangting Xia, Peng Wang, Liang-Chieh Chen, Alan L. Yuille | null | 1511.06881 | null | null |
Gaussian Process Planning with Lipschitz Continuous Reward Functions:
Towards Unifying Bayesian Optimization, Active Learning, and Beyond | stat.ML cs.AI cs.LG cs.RO | This paper presents a novel nonmyopic adaptive Gaussian process planning
(GPP) framework endowed with a general class of Lipschitz continuous reward
functions that can unify some active learning/sensing and Bayesian optimization
criteria and offer practitioners some flexibility to specify their desired
choices for defining new tasks/problems. In particular, it utilizes a
principled Bayesian sequential decision problem framework for jointly and
naturally optimizing the exploration-exploitation trade-off. In general, the
resulting induced GPP policy cannot be derived exactly due to an uncountable
set of candidate observations. A key contribution of our work here thus lies in
exploiting the Lipschitz continuity of the reward functions to solve for a
nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real
time, we further propose an asymptotically optimal, branch-and-bound anytime
variant of epsilon-GPP with performance guarantee. We empirically demonstrate
the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian
optimization and an energy harvesting task.
| Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet | null | 1511.06890 | null | null |
Near-Optimal Active Learning of Multi-Output Gaussian Processes | stat.ML cs.AI cs.LG | This paper addresses the problem of active learning of a multi-output
Gaussian process (MOGP) model representing multiple types of coexisting
correlated environmental phenomena. In contrast to existing works, our active
learning problem involves selecting not just the most informative sampling
locations to be observed but also the types of measurements at each selected
location for minimizing the predictive uncertainty (i.e., posterior joint
entropy) of a target phenomenon of interest given a sampling budget.
Unfortunately, such an entropy criterion scales poorly in the numbers of
candidate sampling locations and selected observations when optimized. To
resolve this issue, we first exploit a structure common to sparse MOGP models
for deriving a novel active learning criterion. Then, we exploit a relaxed form
of submodularity property of our new criterion for devising a polynomial-time
approximation algorithm that guarantees a constant-factor approximation of that
achieved by the optimal set of selected observations. Empirical evaluation on
real-world datasets shows that our proposed approach outperforms existing
algorithms for active learning of MOGP and single-output GP models.
| Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli | null | 1511.06891 | null | null |
BlackOut: Speeding up Recurrent Neural Network Language Models With Very
Large Vocabularies | cs.LG cs.CL cs.NE stat.ML | We propose BlackOut, an approximation algorithm to efficiently train massive
recurrent neural network language models (RNNLMs) with million word
vocabularies. BlackOut is motivated by using a discriminative loss, and we
describe a new sampling strategy which significantly reduces computation while
improving stability, sample efficiency, and rate of convergence. One way to
understand BlackOut is to view it as an extension of the DropOut strategy to
the output layer, wherein we use a discriminative training loss and a weighted
sampling scheme. We also establish close connections between BlackOut,
importance sampling, and noise contrastive estimation (NCE). Our experiments,
on the recently released one billion word language modeling benchmark,
demonstrate scalability and accuracy of BlackOut; we outperform the
state-of-the art, and achieve the lowest perplexity scores on this dataset.
Moreover, unlike other established methods which typically require GPUs or CPU
clusters, we show that a carefully implemented version of BlackOut requires
only 1-10 days on a single machine to train a RNNLM with a million word
vocabulary and billions of parameters on one billion words. Although we
describe BlackOut in the context of RNNLM training, it can be used to any
networks with large softmax output layers.
| Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson
and Pradeep Dubey | null | 1511.06909 | null | null |
ICU Patient Deterioration prediction: a Data-Mining Approach | cs.CY cs.LG | A huge amount of medical data is generated every day, which presents a
challenge in analysing these data. The obvious solution to this challenge is to
reduce the amount of data without information loss. Dimension reduction is
considered the most popular approach for reducing data size and also to reduce
noise and redundancies in data. In this paper, we investigate the effect of
feature selection in improving the prediction of patient deterioration in ICUs.
We consider lab tests as features. Thus, choosing a subset of features would
mean choosing the most important lab tests to perform. If the number of tests
can be reduced by identifying the most important tests, then we could also
identify the redundant tests. By omitting the redundant tests, observation time
could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses
state-ofthe- art feature selection for predicting ICU patient deterioration
using the medical lab results. We apply our technique on the publicly available
MIMIC-II database and show the effectiveness of the feature selection. We also
provide a detailed analysis of the best features identified by our approach.
| Noura AlNuaimi, Mohammad M Masud and Farhan Mohammed | 10.5121/csit.2015.51517 | 1511.06910 | null | null |
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems | cs.CL cs.LG | A long-term goal of machine learning is to build intelligent conversational
agents. One recent popular approach is to train end-to-end models on a large
amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals
& Le, 2015; Shang et al., 2015). However, this approach leaves many questions
unanswered as an understanding of the precise successes and shortcomings of
each model is hard to assess. A contrasting recent proposal are the bAbI tasks
(Weston et al., 2015b) which are synthetic data that measure the ability of
learning machines at various reasoning tasks over toy language. Unfortunately,
those tests are very small and hence may encourage methods that do not scale.
In this work, we propose a suite of new tasks of a much larger scale that
attempt to bridge the gap between the two regimes. Choosing the domain of
movies, we provide tasks that test the ability of models to answer factual
questions (utilizing OMDB), provide personalization (utilizing MovieLens),
carry short conversations about the two, and finally to perform on natural
dialogs from Reddit. We provide a dataset covering 75k movie entities and with
3.5M training examples. We present results of various models on these tasks,
and evaluate their performance.
| Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra,
Alexander Miller, Arthur Szlam, Jason Weston | null | 1511.06931 | null | null |
Session-based Recommendations with Recurrent Neural Networks | cs.LG cs.IR cs.NE | We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base
recommendations only on short session-based data (e.g. a small sportsware
website) instead of long user histories (as in the case of Netflix). In this
situation the frequently praised matrix factorization approaches are not
accurate. This problem is usually overcome in practice by resorting to
item-to-item recommendations, i.e. recommending similar items. We argue that by
modeling the whole session, more accurate recommendations can be provided. We
therefore propose an RNN-based approach for session-based recommendations. Our
approach also considers practical aspects of the task and introduces several
modifications to classic RNNs such as a ranking loss function that make it more
viable for this specific problem. Experimental results on two data-sets show
marked improvements over widely used approaches.
| Bal\'azs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos
Tikk | null | 1511.06939 | null | null |
Gradual DropIn of Layers to Train Very Deep Neural Networks | cs.NE cs.CV cs.LG | We introduce the concept of dynamically growing a neural network during
training. In particular, an untrainable deep network starts as a trainable
shallow network and newly added layers are slowly, organically added during
training, thereby increasing the network's depth. This is accomplished by a new
layer, which we call DropIn. The DropIn layer starts by passing the output from
a previous layer (effectively skipping over the newly added layers), then
increasingly including units from the new layers for both feedforward and
backpropagation. We show that deep networks, which are untrainable with
conventional methods, will converge with DropIn layers interspersed in the
architecture. In addition, we demonstrate that DropIn provides regularization
during training in an analogous way as dropout. Experiments are described with
the MNIST dataset and various expanded LeNet architectures, CIFAR-10 dataset
with its architecture expanded from 3 to 11 layers, and on the ImageNet dataset
with the AlexNet architecture expanded to 13 layers and the VGG 16-layer
architecture.
| Leslie N. Smith, Emily M. Hand, Timothy Doster | null | 1511.06951 | null | null |
On the Linear Algebraic Structure of Distributed Word Representations | cs.CL cs.LG | In this work, we leverage the linear algebraic structure of distributed word
representations to automatically extend knowledge bases and allow a machine to
learn new facts about the world. Our goal is to extract structured facts from
corpora in a simpler manner, without applying classifiers or patterns, and
using only the co-occurrence statistics of words. We demonstrate that the
linear algebraic structure of word embeddings can be used to reduce data
requirements for methods of learning facts. In particular, we demonstrate that
words belonging to a common category, or pairs of words satisfying a certain
relation, form a low-rank subspace in the projected space. We compute a basis
for this low-rank subspace using singular value decomposition (SVD), then use
this basis to discover new facts and to fit vectors for less frequent words
which we do not yet have vectors for.
| Lisa Seung-Yeon Lee | null | 1511.06961 | null | null |
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and
Denoising Autoencoders | cs.LG | Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and
the Deep Hybrid Denoising Auto-encoder, are proposed for handling
semi-supervised learning problems. The models combine experts that model
relevant distributions at different levels of abstraction to improve overall
predictive performance on discriminative tasks. Theoretical motivations and
algorithms for joint learning for each are presented. We apply the new models
to the domain of data-streams in work towards life-long learning. The proposed
architectures show improved performance compared to a pseudo-labeled, drop-out
rectifier network.
| Alexander G. Ororbia II, C. Lee Giles, David Reitter | null | 1511.06964 | null | null |
End-to-end Learning of Action Detection from Frame Glimpses in Videos | cs.CV cs.LG | In this work we introduce a fully end-to-end approach for action detection in
videos that learns to directly predict the temporal bounds of actions. Our
intuition is that the process of detecting actions is naturally one of
observation and refinement: observing moments in video, and refining hypotheses
about when an action is occurring. Based on this insight, we formulate our
model as a recurrent neural network-based agent that interacts with a video
over time. The agent observes video frames and decides both where to look next
and when to emit a prediction. Since backpropagation is not adequate in this
non-differentiable setting, we use REINFORCE to learn the agent's decision
policy. Our model achieves state-of-the-art results on the THUMOS'14 and
ActivityNet datasets while observing only a fraction (2% or less) of the video
frames.
| Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei | null | 1511.06984 | null | null |
Anvaya: An Algorithm and Case-Study on Improving the Goodness of
Software Process Models generated by Mining Event-Log Data in Issue Tracking
System | cs.SE cs.LG | Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware
Information Systems (PAIS) generating event-logs during the life-cycle of a bug
report. Process Mining consists of mining event logs generated from PAIS for
process model discovery, conformance and enhancement. We apply process map
discovery techniques to mine event trace data generated from ITS of open source
Firefox browser project to generate and study process models. Bug life-cycle
consists of diversity and variance. Therefore, the process models generated
from the event-logs are spaghetti-like with large number of edges,
inter-connections and nodes. Such models are complex to analyse and difficult
to comprehend by a process analyst. We improve the Goodness (fitness and
structural complexity) of the process models by splitting the event-log into
homogeneous subsets by clustering structurally similar traces. We adapt the
K-Medoid clustering algorithm with two different distance metrics: Longest
Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the
goodness of the process models generated from the clusters using complexity and
fitness metrics. We study back-forth \& self-loops, bug reopening, and
bottleneck in the clusters obtained and show that clustering enables better
analysis. We also propose an algorithm to automate the clustering process -the
algorithm takes as input the event log and returns the best cluster set.
| Prerna Juneja, Divya Kundra, Ashish Sureka | null | 1511.07023 | null | null |
Detecting Road Surface Wetness from Audio: A Deep Learning Approach | cs.LG cs.NE cs.SD | We introduce a recurrent neural network architecture for automated road
surface wetness detection from audio of tire-surface interaction. The
robustness of our approach is evaluated on 785,826 bins of audio that span an
extensive range of vehicle speeds, noises from the environment, road surface
types, and pavement conditions including international roughness index (IRI)
values from 25 in/mi to 1400 in/mi. The training and evaluation of the model
are performed on different roads to minimize the impact of environmental and
other external factors on the accuracy of the classification. We achieve an
unweighted average recall (UAR) of 93.2% across all vehicle speeds including 0
mph. The classifier still works at 0 mph because the discriminating signal is
present in the sound of other vehicles driving by.
| Irman Abdi\'c, Lex Fridman, Erik Marchi, Daniel E Brown, William
Angell, Bryan Reimer, Bj\"orn Schuller | null | 1511.07035 | null | null |
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation | cs.CV cs.LG | We propose a structured prediction architecture, which exploits the local
generic features extracted by Convolutional Neural Networks and the capacity of
Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed
architecture, called ReSeg, is based on the recently introduced ReNet model for
image classification. We modify and extend it to perform the more challenging
task of semantic segmentation. Each ReNet layer is composed of four RNN that
sweep the image horizontally and vertically in both directions, encoding
patches or activations, and providing relevant global information. Moreover,
ReNet layers are stacked on top of pre-trained convolutional layers, benefiting
from generic local features. Upsampling layers follow ReNet layers to recover
the original image resolution in the final predictions. The proposed ReSeg
architecture is efficient, flexible and suitable for a variety of semantic
segmentation tasks. We evaluate ReSeg on several widely-used semantic
segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving
state-of-the-art performance. Results show that ReSeg can act as a suitable
architecture for semantic segmentation tasks, and may have further applications
in other structured prediction problems. The source code and model
hyperparameters are available on https://github.com/fvisin/reseg.
| Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner,
Kyunghyun Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville | null | 1511.07053 | null | null |
Multiple--Instance Learning: Christoffel Function Approach to
Distribution Regression Problem | cs.LG | A two--step Christoffel function based solution is proposed to distribution
regression problem. On the first step, to model distribution of observations
inside a bag, build Christoffel function for each bag of observations. Then, on
the second step, build outcome variable Christoffel function, but use the bag's
Christoffel function value at given point as the weight for the bag's outcome.
The approach allows the result to be obtained in closed form and then to be
evaluated numerically. While most of existing approaches minimize some kind an
error between outcome and prediction, the proposed approach is conceptually
different, because it uses Christoffel function for knowledge representation,
what is conceptually equivalent working with probabilities only. To receive
possible outcomes and their probabilities Gauss quadrature for second--step
measure can be built, then the nodes give possible outcomes and normalized
weights -- outcome probabilities. A library providing numerically stable
polynomial basis for these calculations is available, what make the proposed
approach practical.
| Vladislav Gennadievich Malyshkin | null | 1511.07085 | null | null |
On the Generalization Error Bounds of Neural Networks under
Diversity-Inducing Mutual Angular Regularization | cs.LG | Recently diversity-inducing regularization methods for latent variable models
(LVMs), which encourage the components in LVMs to be diverse, have been studied
to address several issues involved in latent variable modeling: (1) how to
capture long-tail patterns underlying data; (2) how to reduce model complexity
without sacrificing expressivity; (3) how to improve the interpretability of
learned patterns. While the effectiveness of diversity-inducing regularizers
such as the mutual angular regularizer has been demonstrated empirically, a
rigorous theoretical analysis of them is still missing. In this paper, we aim
to bridge this gap and analyze how the mutual angular regularizer (MAR) affects
the generalization performance of supervised LVMs. We use neural network (NN)
as a model instance to carry out the study and the analysis shows that
increasing the diversity of hidden units in NN would reduce estimation error
and increase approximation error. In addition to theoretical analysis, we also
present empirical study which demonstrates that the MAR can greatly improve the
performance of NN and the empirical observations are in accordance with the
theoretical analysis.
| Pengtao Xie, Yuntian Deng, Eric Xing | null | 1511.07110 | null | null |
Cascading Denoising Auto-Encoder as a Deep Directed Generative Model | cs.LG | Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE)
become gener-ative models as a density estimator. However,in practice, the
framework suffers from a mixingproblem in the MCMC sampling process and
nodirect method to estimate the test log-likelihood.We consider a directed
model with an stochas-tic identity mapping (simple corruption pro-cess) as an
inference model and a DAE as agenerative model. By cascading these mod-els, we
propose Cascading Denoising Auto-Encoders(CDAE) which can generate samples
ofdata distribution from tractable prior distributionunder the assumption that
probabilistic distribu-tion of corrupted data approaches tractable
priordistribution as the level of corruption increases.This work tries to
answer two questions. On theone hand, can deep directed models be success-fully
trained without intractable posterior infer-ence and difficult optimization of
very deep neu-ral networks in inference and generative mod-els? These are
unavoidable when recent suc-cessful directed model like VAE (Kingma &Welling,
2014) is trained on complex dataset likereal images. On the other hand, can
DAEs getclean samples of data distribution from heavilycorrupted samples which
can be considered oftractable prior distribution far from data mani-fold?
so-called global denoising scheme.Our results show positive responses of
thesequestions and this work can provide fairly simpleframework for generative
models of very com-plex dataset.
| Dong-Hyun Lee | null | 1511.07118 | null | null |
What Happened to My Dog in That Network: Unraveling Top-down Generators
in Convolutional Neural Networks | cs.NE cs.CV cs.LG stat.ML | Top-down information plays a central role in human perception, but plays
relatively little role in many current state-of-the-art deep networks, such as
Convolutional Neural Networks (CNNs). This work seeks to explore a path by
which top-down information can have a direct impact within current deep
networks. We explore this path by learning and using "generators" corresponding
to the network internal effects of three types of transformation (each a
restriction of a general affine transformation): rotation, scaling, and
translation. We demonstrate how these learned generators can be used to
transfer top-down information to novel settings, as mediated by the "feature
flows" that the transformations (and the associated generators) correspond to
inside the network. Specifically, we explore three aspects: 1) using generators
as part of a method for synthesizing transformed images --- given a previously
unseen image, produce versions of that image corresponding to one or more
specified transformations, 2) "zero-shot learning" --- when provided with a
feature flow corresponding to the effect of a transformation of unknown amount,
leverage learned generators as part of a method by which to perform an accurate
categorization of the amount of transformation, even for amounts never observed
during training, and 3) (inside-CNN) "data augmentation" --- improve the
classification performance of an existing network by using the learned
generators to directly provide additional training "inside the CNN".
| Patrick W. Gallagher, Shuai Tang, Zhuowen Tu | null | 1511.07125 | null | null |
Parallel Predictive Entropy Search for Batch Global Optimization of
Expensive Objective Functions | cs.LG stat.ML | We develop parallel predictive entropy search (PPES), a novel algorithm for
Bayesian optimization of expensive black-box objective functions. At each
iteration, PPES aims to select a batch of points which will maximize the
information gain about the global maximizer of the objective. Well known
strategies exist for suggesting a single evaluation point based on previous
observations, while far fewer are known for selecting batches of points to
evaluate in parallel. The few batch selection schemes that have been studied
all resort to greedy methods to compute an optimal batch. To the best of our
knowledge, PPES is the first non-greedy batch Bayesian optimization strategy.
We demonstrate the benefit of this approach in optimization performance on both
synthetic and real world applications, including problems in machine learning,
rocket science and robotics.
| Amar Shah, Zoubin Ghahramani | null | 1511.07130 | null | null |
A PAC Approach to Application-Specific Algorithm Selection | cs.LG cs.DS | The best algorithm for a computational problem generally depends on the
"relevant inputs," a concept that depends on the application domain and often
defies formal articulation. While there is a large literature on empirical
approaches to selecting the best algorithm for a given application domain,
there has been surprisingly little theoretical analysis of the problem.
This paper adapts concepts from statistical and online learning theory to
reason about application-specific algorithm selection. Our models capture
several state-of-the-art empirical and theoretical approaches to the problem,
ranging from self-improving algorithms to empirical performance models, and our
results identify conditions under which these approaches are guaranteed to
perform well. We present one framework that models algorithm selection as a
statistical learning problem, and our work here shows that dimension notions
from statistical learning theory, historically used to measure the complexity
of classes of binary- and real-valued functions, are relevant in a much broader
algorithmic context. We also study the online version of the algorithm
selection problem, and give possibility and impossibility results for the
existence of no-regret learning algorithms.
| Rishi Gupta and Tim Roughgarden | null | 1511.07147 | null | null |
Noisy Submodular Maximization via Adaptive Sampling with Applications to
Crowdsourced Image Collection Summarization | cs.AI cs.LG stat.ML | We address the problem of maximizing an unknown submodular function that can
only be accessed via noisy evaluations. Our work is motivated by the task of
summarizing content, e.g., image collections, by leveraging users' feedback in
form of clicks or ratings. For summarization tasks with the goal of maximizing
coverage and diversity, submodular set functions are a natural choice. When the
underlying submodular function is unknown, users' feedback can provide noisy
evaluations of the function that we seek to maximize. We provide a generic
algorithm -- \submM{} -- for maximizing an unknown submodular function under
cardinality constraints. This algorithm makes use of a novel exploration module
-- \blbox{} -- that proposes good elements based on adaptively sampling noisy
function evaluations. \blbox{} is able to accommodate different kinds of
observation models such as value queries and pairwise comparisons. We provide
PAC-style guarantees on the quality and sampling cost of the solution obtained
by \submM{}. We demonstrate the effectiveness of our approach in an
interactive, crowdsourced image collection summarization application.
| Adish Singla, Sebastian Tschiatschek, Andreas Krause | null | 1511.07211 | null | null |
NetVLAD: CNN architecture for weakly supervised place recognition | cs.CV cs.LG | We tackle the problem of large scale visual place recognition, where the task
is to quickly and accurately recognize the location of a given query
photograph. We present the following three principal contributions. First, we
develop a convolutional neural network (CNN) architecture that is trainable in
an end-to-end manner directly for the place recognition task. The main
component of this architecture, NetVLAD, is a new generalized VLAD layer,
inspired by the "Vector of Locally Aggregated Descriptors" image representation
commonly used in image retrieval. The layer is readily pluggable into any CNN
architecture and amenable to training via backpropagation. Second, we develop a
training procedure, based on a new weakly supervised ranking loss, to learn
parameters of the architecture in an end-to-end manner from images depicting
the same places over time downloaded from Google Street View Time Machine.
Finally, we show that the proposed architecture significantly outperforms
non-learnt image representations and off-the-shelf CNN descriptors on two
challenging place recognition benchmarks, and improves over current
state-of-the-art compact image representations on standard image retrieval
benchmarks.
| Relja Arandjelovi\'c, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef
Sivic | null | 1511.07247 | null | null |
Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score
Sampling | cs.DS cs.LG | We present a new algorithm for finding a near optimal low-rank approximation
of a matrix $A$ in $O(nnz(A))$ time. Our method is based on a recursive
sampling scheme for computing a representative subset of $A$'s columns, which
is then used to find a low-rank approximation.
This approach differs substantially from prior $O(nnz(A))$ time algorithms,
which are all based on fast Johnson-Lindenstrauss random projections. It
matches the guarantees of these methods while offering a number of advantages.
Not only are sampling algorithms faster for sparse and structured data, but
they can also be applied in settings where random projections cannot. For
example, we give new single-pass streaming algorithms for the column subset
selection and projection-cost preserving sample problems. Our method has also
been used to give the fastest algorithms for provably approximating kernel
matrices [MM16].
| Michael B. Cohen, Cameron Musco, Christopher Musco | null | 1511.07263 | null | null |
Learning Simple Algorithms from Examples | cs.AI cs.LG | We present an approach for learning simple algorithms such as copying,
multi-digit addition and single digit multiplication directly from examples.
Our framework consists of a set of interfaces, accessed by a controller.
Typical interfaces are 1-D tapes or 2-D grids that hold the input and output
data. For the controller, we explore a range of neural network-based models
which vary in their ability to abstract the underlying algorithm from training
instances and generalize to test examples with many thousands of digits. The
controller is trained using $Q$-learning with several enhancements and we show
that the bottleneck is in the capabilities of the controller rather than in the
search incurred by $Q$-learning.
| Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus | null | 1511.07275 | null | null |
Fast and Accurate Deep Network Learning by Exponential Linear Units
(ELUs) | cs.LG | We introduce the "exponential linear unit" (ELU) which speeds up learning in
deep neural networks and leads to higher classification accuracies. Like
rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs
(PReLUs), ELUs alleviate the vanishing gradient problem via the identity for
positive values. However, ELUs have improved learning characteristics compared
to the units with other activation functions. In contrast to ReLUs, ELUs have
negative values which allows them to push mean unit activations closer to zero
like batch normalization but with lower computational complexity. Mean shifts
toward zero speed up learning by bringing the normal gradient closer to the
unit natural gradient because of a reduced bias shift effect. While LReLUs and
PReLUs have negative values, too, they do not ensure a noise-robust
deactivation state. ELUs saturate to a negative value with smaller inputs and
thereby decrease the forward propagated variation and information. Therefore,
ELUs code the degree of presence of particular phenomena in the input, while
they do not quantitatively model the degree of their absence. In experiments,
ELUs lead not only to faster learning, but also to significantly better
generalization performance than ReLUs and LReLUs on networks with more than 5
layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with
batch normalization while batch normalization does not improve ELU networks.
ELU networks are among the top 10 reported CIFAR-10 results and yield the best
published result on CIFAR-100, without resorting to multi-view evaluation or
model averaging. On ImageNet, ELU networks considerably speed up learning
compared to a ReLU network with the same architecture, obtaining less than 10%
classification error for a single crop, single model network.
| Djork-Arn\'e Clevert, Thomas Unterthiner, Sepp Hochreiter | null | 1511.07289 | null | null |
Sparse Recovery via Partial Regularization: Models, Theory and
Algorithms | math.OC cs.IT cs.LG math.IT stat.ME stat.ML | In the context of sparse recovery, it is known that most of existing
regularizers such as $\ell_1$ suffer from some bias incurred by some leading
entries (in magnitude) of the associated vector. To neutralize this bias, we
propose a class of models with partial regularizers for recovering a sparse
solution of a linear system. We show that every local minimizer of these models
is sufficiently sparse or the magnitude of all its nonzero entries is above a
uniform constant depending only on the data of the linear system. Moreover, for
a class of partial regularizers, any global minimizer of these models is a
sparsest solution to the linear system. We also establish some sufficient
conditions for local or global recovery of the sparsest solution to the linear
system, among which one of the conditions is weaker than the best known
restricted isometry property (RIP) condition for sparse recovery by $\ell_1$.
In addition, a first-order feasible augmented Lagrangian (FAL) method is
proposed for solving these models, in which each subproblem is solved by a
nonmonotone proximal gradient (NPG) method. Despite the complication of the
partial regularizers, we show that each proximal subproblem in NPG can be
solved as a certain number of one-dimensional optimization problems, which
usually have a closed-form solution. We also show that any accumulation point
of the sequence generated by FAL is a first-order stationary point of the
models. Numerical results on compressed sensing and sparse logistic regression
demonstrate that the proposed models substantially outperform the widely used
ones in the literature in terms of solution quality.
| Zhaosong Lu and Xiaorui Li | null | 1511.07293 | null | null |
Modular Autoencoders for Ensemble Feature Extraction | cs.LG | We introduce the concept of a Modular Autoencoder (MAE), capable of learning
a set of diverse but complementary representations from unlabelled data, that
can later be used for supervised tasks. The learning of the representations is
controlled by a trade off parameter, and we show on six benchmark datasets the
optimum lies between two extremes: a set of smaller, independent autoencoders
each with low capacity, versus a single monolithic encoding, outperforming an
appropriate baseline. In the present paper we explore the special case of
linear MAE, and derive an SVD-based algorithm which converges several orders of
magnitude faster than gradient descent.
| Henry W J Reeve and Gavin Brown | null | 1511.07340 | null | null |
Interpretable Two-level Boolean Rule Learning for Classification | cs.LG cs.AI | This paper proposes algorithms for learning two-level Boolean rules in
Conjunctive Normal Form (CNF, i.e. AND-of-ORs) or Disjunctive Normal Form (DNF,
i.e. OR-of-ANDs) as a type of human-interpretable classification model, aiming
for a favorable trade-off between the classification accuracy and the
simplicity of the rule. Two formulations are proposed. The first is an integer
program whose objective function is a combination of the total number of errors
and the total number of features used in the rule. We generalize a previously
proposed linear programming (LP) relaxation from one-level to two-level rules.
The second formulation replaces the 0-1 classification error with the Hamming
distance from the current two-level rule to the closest rule that correctly
classifies a sample. Based on this second formulation, block coordinate descent
and alternating minimization algorithms are developed. Experiments show that
the two-level rules can yield noticeably better performance than one-level
rules due to their dramatically larger modeling capacity, and the two
algorithms based on the Hamming distance formulation are generally superior to
the other two-level rule learning methods in our comparison. A proposed
approach to binarize any fractional values in the optimal solutions of LP
relaxations is also shown to be effective.
| Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov | null | 1511.07361 | null | null |
Pushing the Boundaries of Boundary Detection using Deep Learning | cs.CV cs.LG | In this work we show that adapting Deep Convolutional Neural Network training
to the task of boundary detection can result in substantial improvements over
the current state-of-the-art in boundary detection.
Our contributions consist firstly in combining a careful design of the loss
for boundary detection training, a multi-resolution architecture and training
with external data to improve the detection accuracy of the current state of
the art. When measured on the standard Berkeley Segmentation Dataset, we
improve theoptimal dataset scale F-measure from 0.780 to 0.808 - while human
performance is at 0.803. We further improve performance to 0.813 by combining
deep learning with grouping, integrating the Normalized Cuts technique within a
deep network.
We also examine the potential of our boundary detector in conjunction with
the task of semantic segmentation and demonstrate clear improvements over
state-of-the-art systems. Our detector is fully integrated in the popular Caffe
framework and processes a 320x420 image in less than a second.
| Iasonas Kokkinos | null | 1511.07386 | null | null |
MazeBase: A Sandbox for Learning from Games | cs.LG cs.AI cs.NE | This paper introduces MazeBase: an environment for simple 2D games, designed
as a sandbox for machine learning approaches to reasoning and planning. Within
it, we create 10 simple games embodying a range of algorithmic tasks (e.g.
if-then statements or set negation). A variety of neural models (fully
connected, convolutional network, memory network) are deployed via
reinforcement learning on these games, with and without a procedurally
generated curriculum. Despite the tasks' simplicity, the performance of the
models is far from optimal, suggesting directions for future development. We
also demonstrate the versatility of MazeBase by using it to emulate small
combat scenarios from StarCraft. Models trained on the MazeBase version can be
directly applied to StarCraft, where they consistently beat the in-game AI.
| Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith
Chintala, Rob Fergus | null | 1511.07401 | null | null |
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior | cs.CV cs.LG | Current practice in convolutional neural networks (CNN) remains largely
bottom-up and the role of top-down process in CNN for pattern analysis and
visual inference is not very clear. In this paper, we propose a new method for
structured labeling by developing convolutional pseudo-prior (ConvPP) on the
ground-truth labels. Our method has several interesting properties: (1)
compared with classical machine learning algorithms like CRFs and Structural
SVM, ConvPP automatically learns rich convolutional kernels to capture both
short- and long- range contexts; (2) compared with cascade classifiers like
Auto-Context, ConvPP avoids the iterative steps of learning a series of
discriminative classifiers and automatically learns contextual configurations;
(3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP
learns convolution in the labeling space with much improved modeling capability
and less manual specification; (4) compared with Bayesian models like MRFs,
ConvPP capitalizes on the rich representation power of convolution by
automatically learning priors built on convolutional filters. We accomplish our
task using pseudo-likelihood approximation to the prior under a novel
fixed-point network structure that facilitates an end-to-end learning process.
We show state-of-the-art results on sequential labeling and image labeling
benchmarks.
| Saining Xie, Xun Huang and Zhuowen Tu | null | 1511.07409 | null | null |
Weak Convergence Properties of Constrained Emphatic Temporal-difference
Learning with Constant and Slowly Diminishing Stepsize | cs.LG | We consider the emphatic temporal-difference (TD) algorithm, ETD($\lambda$),
for learning the value functions of stationary policies in a discounted, finite
state and action Markov decision process. The ETD($\lambda$) algorithm was
recently proposed by Sutton, Mahmood, and White to solve a long-standing
divergence problem of the standard TD algorithm when it is applied to
off-policy training, where data from an exploratory policy are used to evaluate
other policies of interest. The almost sure convergence of ETD($\lambda$) has
been proved in our recent work under general off-policy training conditions,
but for a narrow range of diminishing stepsize. In this paper we present
convergence results for constrained versions of ETD($\lambda$) with constant
stepsize and with diminishing stepsize from a broad range. Our results
characterize the asymptotic behavior of the trajectory of iterates produced by
those algorithms, and are derived by combining key properties of ETD($\lambda$)
with powerful convergence theorems from the weak convergence methods in
stochastic approximation theory. For the case of constant stepsize, in addition
to analyzing the behavior of the algorithms in the limit as the stepsize
parameter approaches zero, we also analyze their behavior for a fixed stepsize
and bound the deviations of their averaged iterates from the desired solution.
These results are obtained by exploiting the weak Feller property of the Markov
chains associated with the algorithms, and by using ergodic theorems for weak
Feller Markov chains, in conjunction with the convergence results we get from
the weak convergence methods. Besides ETD($\lambda$), our analysis also applies
to the off-policy TD($\lambda$) algorithm, when the divergence issue is avoided
by setting $\lambda$ sufficiently large.
| Huizhen Yu | null | 1511.07471 | null | null |
Constrained Structured Regression with Convolutional Neural Networks | cs.CV cs.LG | Convolutional Neural Networks (CNNs) have recently emerged as the dominant
model in computer vision. If provided with enough training data, they predict
almost any visual quantity. In a discrete setting, such as classification, CNNs
are not only able to predict a label but often predict a confidence in the form
of a probability distribution over the output space. In continuous regression
tasks, such a probability estimate is often lacking. We present a regression
framework which models the output distribution of neural networks. This output
distribution allows us to infer the most likely labeling following a set of
physical or modeling constraints. These constraints capture the intricate
interplay between different input and output variables, and complement the
output of a CNN. However, they may not hold everywhere. Our setup further
allows to learn a confidence with which a constraint holds, in the form of a
distribution of the constrain satisfaction. We evaluate our approach on the
problem of intrinsic image decomposition, and show that constrained structured
regression significantly increases the state-of-the-art.
| Deepak Pathak, Philipp Kr\"ahenb\"uhl, Stella X. Yu, Trevor Darrell | null | 1511.07497 | null | null |
The Limitations of Deep Learning in Adversarial Settings | cs.CR cs.LG cs.NE stat.ML | Deep learning takes advantage of large datasets and computationally efficient
training algorithms to outperform other approaches at various machine learning
tasks. However, imperfections in the training phase of deep neural networks
make them vulnerable to adversarial samples: inputs crafted by adversaries with
the intent of causing deep neural networks to misclassify. In this work, we
formalize the space of adversaries against deep neural networks (DNNs) and
introduce a novel class of algorithms to craft adversarial samples based on a
precise understanding of the mapping between inputs and outputs of DNNs. In an
application to computer vision, we show that our algorithms can reliably
produce samples correctly classified by human subjects but misclassified in
specific targets by a DNN with a 97% adversarial success rate while only
modifying on average 4.02% of the input features per sample. We then evaluate
the vulnerability of different sample classes to adversarial perturbations by
defining a hardness measure. Finally, we describe preliminary work outlining
defenses against adversarial samples by defining a predictive measure of
distance between a benign input and a target classification.
| Nicolas Papernot and Patrick McDaniel and Somesh Jha and Matt
Fredrikson and Z. Berkay Celik and Ananthram Swami | null | 1511.07528 | null | null |
Convergent Learning: Do different neural networks learn the same
representations? | cs.LG cs.NE | Recent success in training deep neural networks have prompted active
investigation into the features learned on their intermediate layers. Such
research is difficult because it requires making sense of non-linear
computations performed by millions of parameters, but valuable because it
increases our ability to understand current models and create improved versions
of them. In this paper we investigate the extent to which neural networks
exhibit what we call convergent learning, which is when the representations
learned by multiple nets converge to a set of features which are either
individually similar between networks or where subsets of features span similar
low-dimensional spaces. We propose a specific method of probing
representations: training multiple networks and then comparing and contrasting
their individual, learned representations at the level of neurons or groups of
neurons. We begin research into this question using three techniques to
approximately align different neural networks on a feature level: a bipartite
matching approach that makes one-to-one assignments between neurons, a sparse
prediction approach that finds one-to-many mappings, and a spectral clustering
approach that finds many-to-many mappings. This initial investigation reveals a
few previously unknown properties of neural networks, and we argue that future
research into the question of convergent learning will yield many more. The
insights described here include (1) that some features are learned reliably in
multiple networks, yet other features are not consistently learned; (2) that
units learn to span low-dimensional subspaces and, while these subspaces are
common to multiple networks, the specific basis vectors learned are not; (3)
that the representation codes show evidence of being a mix between a local code
and slightly, but not fully, distributed codes across multiple units.
| Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson and John Hopcroft | null | 1511.07543 | null | null |
Transductive Log Opinion Pool of Gaussian Process Experts | cs.LG stat.ML | We introduce a framework for analyzing transductive combination of Gaussian
process (GP) experts, where independently trained GP experts are combined in a
way that depends on test point location, in order to scale GPs to big data. The
framework provides some theoretical justification for the generalized product
of GP experts (gPoE-GP) which was previously shown to work well in practice but
lacks theoretical basis. Based on the proposed framework, an improvement over
gPoE-GP is introduced and empirically validated.
| Yanshuai Cao, David J. Fleet | null | 1511.07551 | null | null |
DenseCap: Fully Convolutional Localization Networks for Dense Captioning | cs.CV cs.LG | We introduce the dense captioning task, which requires a computer vision
system to both localize and describe salient regions in images in natural
language. The dense captioning task generalizes object detection when the
descriptions consist of a single word, and Image Captioning when one predicted
region covers the full image. To address the localization and description task
jointly we propose a Fully Convolutional Localization Network (FCLN)
architecture that processes an image with a single, efficient forward pass,
requires no external regions proposals, and can be trained end-to-end with a
single round of optimization. The architecture is composed of a Convolutional
Network, a novel dense localization layer, and Recurrent Neural Network
language model that generates the label sequences. We evaluate our network on
the Visual Genome dataset, which comprises 94,000 images and 4,100,000
region-grounded captions. We observe both speed and accuracy improvements over
baselines based on current state of the art approaches in both generation and
retrieval settings.
| Justin Johnson and Andrej Karpathy and Li Fei-Fei | null | 1511.07571 | null | null |
Picking a Conveyor Clean by an Autonomously Learning Robot | cs.RO cs.CV cs.LG | We present a research picking prototype related to our company's industrial
waste sorting application. The goal of the prototype is to be as autonomous as
possible and it both calibrates itself and improves its picking with minimal
human intervention. The system learns to pick objects better based on a
feedback sensor in its gripper and uses machine learning to choosing the best
proposal from a random sample produced by simple hard-coded geometric models.
We show experimentally the system improving its picking autonomously by
measuring the pick success rate as function of time. We also show how this
system can pick a conveyor belt clean, depositing 70 out of 80 objects in a
difficult to manipulate pile of novel objects into the correct chute. We
discuss potential improvements and next steps in this direction.
| Janne V. Kujala, Tuomas J. Lukka, and Harri Holopainen | null | 1511.07608 | null | null |
LocNet: Improving Localization Accuracy for Object Detection | cs.CV cs.LG cs.NE | We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditional probabilities to each row and column of this region,
where these probabilities provide useful information regarding the location of
the boundaries of the object inside the search region and allow the accurate
inference of the object bounding box under a simple probabilistic framework.
For implementing our localization model, we make use of a convolutional
neural network architecture that is properly adapted for this task, called
LocNet. We show experimentally that LocNet achieves a very significant
improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and
that it can be very easily coupled with recent state-of-the-art object
detection systems, helping them to boost their performance. Finally, we
demonstrate that our detection approach can achieve high detection accuracy
even when it is given as input a set of sliding windows, thus proving that it
is independent of box proposal methods.
| Spyros Gidaris, Nikos Komodakis | null | 1511.07763 | null | null |
Generalized Conjugate Gradient Methods for $\ell_1$ Regularized Convex
Quadratic Programming with Finite Convergence | math.OC cs.LG math.NA stat.CO stat.ML | The conjugate gradient (CG) method is an efficient iterative method for
solving large-scale strongly convex quadratic programming (QP). In this paper
we propose some generalized CG (GCG) methods for solving the
$\ell_1$-regularized (possibly not strongly) convex QP that terminate at an
optimal solution in a finite number of iterations. At each iteration, our
methods first identify a face of an orthant and then either perform an exact
line search along the direction of the negative projected minimum-norm
subgradient of the objective function or execute a CG subroutine that conducts
a sequence of CG iterations until a CG iterate crosses the boundary of this
face or an approximate minimizer of over this face or a subface is found. We
determine which type of step should be taken by comparing the magnitude of some
components of the minimum-norm subgradient of the objective function to that of
its rest components. Our analysis on finite convergence of these methods makes
use of an error bound result and some key properties of the aforementioned
exact line search and the CG subroutine. We also show that the proposed methods
are capable of finding an approximate solution of the problem by allowing some
inexactness on the execution of the CG subroutine. The overall arithmetic
operation cost of our GCG methods for finding an $\epsilon$-optimal solution
depends on $\epsilon$ in $O(\log(1/\epsilon))$, which is superior to the
accelerated proximal gradient method [2,23] that depends on $\epsilon$ in
$O(1/\sqrt{\epsilon})$. In addition, our GCG methods can be extended
straightforwardly to solve box-constrained convex QP with finite convergence.
Numerical results demonstrate that our methods are very favorable for solving
ill-conditioned problems.
| Zhaosong Lu and Xiaojun Chen | null | 1511.07837 | null | null |
Dynamic Capacity Networks | cs.LG cs.NE | We introduce the Dynamic Capacity Network (DCN), a neural network that can
adaptively assign its capacity across different portions of the input data.
This is achieved by combining modules of two types: low-capacity sub-networks
and high-capacity sub-networks. The low-capacity sub-networks are applied
across most of the input, but also provide a guide to select a few portions of
the input on which to apply the high-capacity sub-networks. The selection is
made using a novel gradient-based attention mechanism, that efficiently
identifies input regions for which the DCN's output is most sensitive and to
which we should devote more capacity. We focus our empirical evaluation on the
Cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are
able to drastically reduce the number of computations, compared to traditional
convolutional neural networks, while maintaining similar or even better
performance.
| Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo
Larochelle, Aaron Courville | null | 1511.07838 | null | null |
Private Posterior distributions from Variational approximations | stat.ML cs.CR cs.LG | Privacy preserving mechanisms such as differential privacy inject additional
randomness in the form of noise in the data, beyond the sampling mechanism.
Ignoring this additional noise can lead to inaccurate and invalid inferences.
In this paper, we incorporate the privacy mechanism explicitly into the
likelihood function by treating the original data as missing, with an end goal
of estimating posterior distributions over model parameters. This leads to a
principled way of performing valid statistical inference using private data,
however, the corresponding likelihoods are intractable. In this paper, we
derive fast and accurate variational approximations to tackle such intractable
likelihoods that arise due to privacy. We focus on estimating posterior
distributions of parameters of the naive Bayes log-linear model, where the
sufficient statistics of this model are shared using a differentially private
interface. Using a simulation study, we show that the posterior approximations
outperform the naive method of ignoring the noise addition mechanism.
| Vishesh Karwa and Dan Kifer and Aleksandra B. Slavkovi\'c | null | 1511.07896 | null | null |
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single
Agent Case | stat.ML cs.LG cs.MA | In this work and the supporting Part II, we examine the performance of
stochastic sub-gradient learning strategies under weaker conditions than
usually considered in the literature. The new conditions are shown to be
automatically satisfied by several important cases of interest including SVM,
LASSO, and Total-Variation denoising formulations. In comparison, these
problems do not satisfy the traditional assumptions used in prior analyses and,
therefore, conclusions derived from these earlier treatments are not directly
applicable to these problems. The results in this article establish that
stochastic sub-gradient strategies can attain linear convergence rates, as
opposed to sub-linear rates, to the steady-state regime. A realizable
exponential-weighting procedure is employed to smooth the intermediate iterates
and guarantee useful performance bounds in terms of convergence rate and
excessive risk performance. Part I of this work focuses on single-agent
scenarios, which are common in stand-alone learning applications, while Part II
extends the analysis to networked learners. The theoretical conclusions are
illustrated by several examples and simulations, including comparisons with the
FISTA procedure.
| Bicheng Ying and Ali H. Sayed | null | 1511.07902 | null | null |
Context-aware CNNs for person head detection | cs.CV cs.LG | Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.
| Tuan-Hung Vu, Anton Osokin, Ivan Laptev | null | 1511.07917 | null | null |
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests | cs.LG | Early diagnosis of treatable diseases is essential for improving healthcare,
and many diseases' onsets are predictable from annual lab tests and their
temporal trends. We introduce a multi-resolution convolutional neural network
for early detection of multiple diseases from irregularly measured sparse lab
values. Our novel architecture takes as input both an imputed version of the
data and a binary observation matrix. For imputing the temporal sparse
observations, we develop a flexible, fast to train method for differentiable
multivariate kernel regression. Our experiments on data from 298K individuals
over 8 years, 18 common lab measurements, and 171 diseases show that the
temporal signatures learned via convolution are significantly more predictive
than baselines commonly used for early disease diagnosis.
| Narges Razavian, David Sontag | null | 1511.07938 | null | null |
Learning Halfspaces and Neural Networks with Random Initialization | cs.LG | We study non-convex empirical risk minimization for learning halfspaces and
neural networks. For loss functions that are $L$-Lipschitz continuous, we
present algorithms to learn halfspaces and multi-layer neural networks that
achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is
polynomial in the input dimension $d$ and the sample size $n$, but exponential
in the quantity $(L/\epsilon^2)\log(L/\epsilon)$. These algorithms run multiple
rounds of random initialization followed by arbitrary optimization steps. We
further show that if the data is separable by some neural network with constant
margin $\gamma>0$, then there is a polynomial-time algorithm for learning a
neural network that separates the training data with margin $\Omega(\gamma)$.
As a consequence, the algorithm achieves arbitrary generalization error
$\epsilon>0$ with ${\rm poly}(d,1/\epsilon)$ sample and time complexity. We
establish the same learnability result when the labels are randomly flipped
with probability $\eta<1/2$.
| Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan | null | 1511.07948 | null | null |
Exploring Correlation between Labels to improve Multi-Label
Classification | cs.LG cs.SI | This paper attempts multi-label classification by extending the idea of
independent binary classification models for each output label, and exploring
how the inherent correlation between output labels can be used to improve
predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models
were constructed, with SVM giving the best results: an improvement of 12.9\%
over binary models was achieved for hold out cross validation by augmenting
with pairwise correlation probabilities of the labels.
| Amit Garg, Jonathan Noyola, Romil Verma, Ashutosh Saxena, Aditya Jami | null | 1511.07953 | null | null |
MOOCs Meet Measurement Theory: A Topic-Modelling Approach | cs.LG cs.CY | This paper adapts topic models to the psychometric testing of MOOC students
based on their online forum postings. Measurement theory from education and
psychology provides statistical models for quantifying a person's attainment of
intangible attributes such as attitudes, abilities or intelligence. Such models
infer latent skill levels by relating them to individuals' observed responses
on a series of items such as quiz questions. The set of items can be used to
measure a latent skill if individuals' responses on them conform to a Guttman
scale. Such well-scaled items differentiate between individuals and inferred
levels span the entire range from most basic to the advanced. In practice,
education researchers manually devise items (quiz questions) while optimising
well-scaled conformance. Due to the costly nature and expert requirements of
this process, psychometric testing has found limited use in everyday teaching.
We aim to develop usable measurement models for highly-instrumented MOOC
delivery platforms, by using participation in automatically-extracted online
forum topics as items. The challenge is to formalise the Guttman scale
educational constraint and incorporate it into topic models. To favour topics
that automatically conform to a Guttman scale, we introduce a novel
regularisation into non-negative matrix factorisation-based topic modelling. We
demonstrate the suitability of our approach with both quantitative experiments
on three Coursera MOOCs, and with a qualitative survey of topic
interpretability on two MOOCs by domain expert interviews.
| Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra
Milligan, Jeffrey Chan | null | 1511.07961 | null | null |
Learning with Memory Embeddings | cs.AI cs.CL cs.LG | Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Latent variable
models are well suited to deal with the high dimensionality and sparsity of
typical knowledge graphs. In recent publications the embedding models were
extended to also consider time evolutions, time patterns and subsymbolic
representations. In this paper we map embedding models, which were developed
purely as solutions to technical problems for modelling temporal knowledge
graphs, to various cognitive memory functions, in particular to semantic and
concept memory, episodic memory, sensory memory, short-term memory, and working
memory. We discuss learning, query answering, the path from sensory input to
semantic decoding, and the relationship between episodic memory and semantic
memory. We introduce a number of hypotheses on human memory that can be derived
from the developed mathematical models.
| Volker Tresp and Crist\'obal Esteban and Yinchong Yang and Stephan
Baier and Denis Krompa{\ss} | null | 1511.07972 | null | null |
Learning to detect video events from zero or very few video examples | cs.LG cs.CV | In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training samples together with a small number of ``related'' videos. For
learning only from an event's textual description, we first identify a general
learning framework and then study the impact of different design choices for
various stages of this framework. For additionally learning from example
videos, when true positive training samples are scarce, we employ an extension
of the Support Vector Machine that allows us to exploit ``related'' event
videos by automatically introducing different weights for subsets of the videos
in the overall training set. Experimental evaluations performed on the
large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness
of the proposed methods.
| Christos Tzelepis, Damianos Galanopoulos, Vasileios Mezaris, Ioannis
Patras | 10.1016/j.imavis.2015.09.005 | 1511.08032 | null | null |
Relaxed Majorization-Minimization for Non-smooth and Non-convex
Optimization | math.OC cs.LG cs.NA | We propose a new majorization-minimization (MM) method for non-smooth and
non-convex programs, which is general enough to include the existing MM
methods. Besides the local majorization condition, we only require that the
difference between the directional derivatives of the objective function and
its surrogate function vanishes when the number of iterations approaches
infinity, which is a very weak condition. So our method can use a surrogate
function that directly approximates the non-smooth objective function. In
comparison, all the existing MM methods construct the surrogate function by
approximating the smooth component of the objective function. We apply our
relaxed MM methods to the robust matrix factorization (RMF) problem with
different regularizations, where our locally majorant algorithm shows
advantages over the state-of-the-art approaches for RMF. This is the first
algorithm for RMF ensuring, without extra assumptions, that any limit point of
the iterates is a stationary point.
| Chen Xu, Zhouchen Lin, Zhenyu Zhao, Hongbin Zha | null | 1511.08062 | null | null |
Strategic Dialogue Management via Deep Reinforcement Learning | cs.AI cs.LG | Artificially intelligent agents equipped with strategic skills that can
negotiate during their interactions with other natural or artificial agents are
still underdeveloped. This paper describes a successful application of Deep
Reinforcement Learning (DRL) for training intelligent agents with strategic
conversational skills, in a situated dialogue setting. Previous studies have
modelled the behaviour of strategic agents using supervised learning and
traditional reinforcement learning techniques, the latter using tabular
representations or learning with linear function approximation. In this study,
we apply DRL with a high-dimensional state space to the strategic board game of
Settlers of Catan---where players can offer resources in exchange for others
and they can also reply to offers made by other players. Our experimental
results report that the DRL-based learnt policies significantly outperformed
several baselines including random, rule-based, and supervised-based
behaviours. The DRL-based policy has a 53% win rate versus 3 automated players
(`bots'), whereas a supervised player trained on a dialogue corpus in this
setting achieved only 27%, versus the same 3 bots. This result supports the
claim that DRL is a promising framework for training dialogue systems, and
strategic agents with negotiation abilities.
| Heriberto Cuay\'ahuitl, Simon Keizer, Oliver Lemon | null | 1511.08099 | null | null |
Unifying Decision Trees Split Criteria Using Tsallis Entropy | stat.ML cs.AI cs.LG | The construction of efficient and effective decision trees remains a key
topic in machine learning because of their simplicity and flexibility. A lot of
heuristic algorithms have been proposed to construct near-optimal decision
trees. ID3, C4.5 and CART are classical decision tree algorithms and the split
criteria they used are Shannon entropy, Gain Ratio and Gini index respectively.
All the split criteria seem to be independent, actually, they can be unified in
a Tsallis entropy framework. Tsallis entropy is a generalization of Shannon
entropy and provides a new approach to enhance decision trees' performance with
an adjustable parameter $q$. In this paper, a Tsallis Entropy Criterion (TEC)
algorithm is proposed to unify Shannon entropy, Gain Ratio and Gini index,
which generalizes the split criteria of decision trees. More importantly, we
reveal the relations between Tsallis entropy with different $q$ and other split
criteria. Experimental results on UCI data sets indicate that the TEC algorithm
achieves statistically significant improvement over the classical algorithms.
| Yisen Wang, Chaobing Song, Shu-Tao Xia | null | 1511.08136 | null | null |
Towards Universal Paraphrastic Sentence Embeddings | cs.CL cs.LG | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings based on supervision from the Paraphrase Database (Ganitkevitch et
al., 2013). We compare six compositional architectures, evaluating them on
annotated textual similarity datasets drawn both from the same distribution as
the training data and from a wide range of other domains. We find that the most
complex architectures, such as long short-term memory (LSTM) recurrent neural
networks, perform best on the in-domain data. However, in out-of-domain
scenarios, simple architectures such as word averaging vastly outperform LSTMs.
Our simplest averaging model is even competitive with systems tuned for the
particular tasks while also being extremely efficient and easy to use.
In order to better understand how these architectures compare, we conduct
further experiments on three supervised NLP tasks: sentence similarity,
entailment, and sentiment classification. We again find that the word averaging
models perform well for sentence similarity and entailment, outperforming
LSTMs. However, on sentiment classification, we find that the LSTM performs
very strongly-even recording new state-of-the-art performance on the Stanford
Sentiment Treebank.
We then demonstrate how to combine our pretrained sentence embeddings with
these supervised tasks, using them both as a prior and as a black box feature
extractor. This leads to performance rivaling the state of the art on the SICK
similarity and entailment tasks. We release all of our resources to the
research community with the hope that they can serve as the new baseline for
further work on universal sentence embeddings.
| John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu | null | 1511.08198 | null | null |
Neural GPUs Learn Algorithms | cs.LG cs.NE | Learning an algorithm from examples is a fundamental problem that has been
widely studied. Recently it has been addressed using neural networks, in
particular by Neural Turing Machines (NTMs). These are fully differentiable
computers that use backpropagation to learn their own programming. Despite
their appeal NTMs have a weakness that is caused by their sequential nature:
they are not parallel and are are hard to train due to their large depth when
unfolded.
We present a neural network architecture to address this problem: the Neural
GPU. It is based on a type of convolutional gated recurrent unit and, like the
NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly
parallel which makes it easier to train and efficient to run.
An essential property of algorithms is their ability to handle inputs of
arbitrary size. We show that the Neural GPU can be trained on short instances
of an algorithmic task and successfully generalize to long instances. We
verified it on a number of tasks including long addition and long
multiplication of numbers represented in binary. We train the Neural GPU on
numbers with upto 20 bits and observe no errors whatsoever while testing it,
even on much longer numbers.
To achieve these results we introduce a technique for training deep recurrent
networks: parameter sharing relaxation. We also found a small amount of dropout
and gradient noise to have a large positive effect on learning and
generalization.
| {\L}ukasz Kaiser and Ilya Sutskever | null | 1511.08228 | null | null |
Hierarchical classification of e-commerce related social media | cs.SI cs.CL cs.IR cs.LG | In this paper, we attempt to classify tweets into root categories of the
Amazon browse node hierarchy using a set of tweets with browse node ID labels,
a much larger set of tweets without labels, and a set of Amazon reviews.
Examining twitter data presents unique challenges in that the samples are short
(under 140 characters) and often contain misspellings or abbreviations that are
trivial for a human to decipher but difficult for a computer to parse. A
variety of query and document expansion techniques are implemented in an effort
to improve information retrieval to modest success.
| Matthew Long, Aditya Jami, Ashutosh Saxena | null | 1511.08299 | null | null |
Named Entity Recognition with Bidirectional LSTM-CNNs | cs.CL cs.LG cs.NE | Named entity recognition is a challenging task that has traditionally
required large amounts of knowledge in the form of feature engineering and
lexicons to achieve high performance. In this paper, we present a novel neural
network architecture that automatically detects word- and character-level
features using a hybrid bidirectional LSTM and CNN architecture, eliminating
the need for most feature engineering. We also propose a novel method of
encoding partial lexicon matches in neural networks and compare it to existing
approaches. Extensive evaluation shows that, given only tokenized text and
publicly available word embeddings, our system is competitive on the CoNLL-2003
dataset and surpasses the previously reported state of the art performance on
the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed
from publicly-available sources, we establish new state of the art performance
with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing
systems that employ heavy feature engineering, proprietary lexicons, and rich
entity linking information.
| Jason P.C. Chiu and Eric Nichols | null | 1511.08308 | null | null |
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