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Boosted Zero-Shot Learning with Semantic Correlation Regularization | cs.LG | We study zero-shot learning (ZSL) as a transfer learning problem, and focus
on the two key aspects of ZSL, model effectiveness and model adaptation. For
effective modeling, we adopt the boosting strategy to learn a zero-shot
classifier from weak models to a strong model. For adaptable knowledge
transfer, we devise a Semantic Correlation Regularization (SCR) approach to
regularize the boosted model to be consistent with the inter-class semantic
correlations. With SCR embedded in the boosting objective, and with a
self-controlled sample selection for learning robustness, we propose a unified
framework, Boosted Zero-shot classification with Semantic Correlation
Regularization (BZ-SCR). By balancing the SCR-regularized boosted model
selection and the self-controlled sample selection, BZ-SCR is capable of
capturing both discriminative and adaptable feature-to-class semantic
alignments, while ensuring the reliability and adaptability of the learned
samples. The experiments on two ZSL datasets show the superiority of BZ-SCR
over the state-of-the-arts.
| Te Pi, Xi Li, Zhongfei (Mark) Zhang | null | 1707.08008 | null | null |
A Simple Exponential Family Framework for Zero-Shot Learning | cs.LG cs.CV stat.ML | We present a simple generative framework for learning to predict previously
unseen classes, based on estimating class-attribute-gated class-conditional
distributions. We model each class-conditional distribution as an exponential
family distribution and the parameters of the distribution of each seen/unseen
class are defined as functions of the respective observed class attributes.
These functions can be learned using only the seen class data and can be used
to predict the parameters of the class-conditional distribution of each unseen
class. Unlike most existing methods for zero-shot learning that represent
classes as fixed embeddings in some vector space, our generative model
naturally represents each class as a probability distribution. It is simple to
implement and also allows leveraging additional unlabeled data from unseen
classes to improve the estimates of their class-conditional distributions using
transductive/semi-supervised learning. Moreover, it extends seamlessly to
few-shot learning by easily updating these distributions when provided with a
small number of additional labelled examples from unseen classes. Through a
comprehensive set of experiments on several benchmark data sets, we demonstrate
the efficacy of our framework.
| Vinay Kumar Verma and Piyush Rai | null | 1707.0804 | null | null |
Learning to Singulate Objects using a Push Proposal Network | cs.RO cs.LG cs.NE | Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.de
| Andreas Eitel, Nico Hauff and Wolfram Burgard | null | 1707.08101 | null | null |
Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting | cs.LG | The paper presents a spatio-temporal wind speed forecasting algorithm using
Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated
by recent advances in renewable energy integration and smart grids, we apply
our proposed algorithm for wind speed forecasting. Renewable energy resources
(wind and solar)are random in nature and, thus, their integration is
facilitated with accurate short-term forecasts. In our proposed framework, we
model the spatiotemporal information by a graph whose nodes are data generating
entities and its edges basically model how these nodes are interacting with
each other. One of the main contributions of our work is the fact that we
obtain forecasts of all nodes of the graph at the same time based on one
framework. Results of a case study on recorded time series data from a
collection of wind mills in the north-east of the U.S. show that the proposed
DL-based forecasting algorithm significantly improves the short-term forecasts
compared to a set of widely-used benchmarks models.
| Amir Ghaderi, Borhan M. Sanandaji, Faezeh Ghaderi | null | 1707.0811 | null | null |
A Survey on Multi-Task Learning | cs.LG cs.AI | Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL from the perspective of algorithmic modeling,
applications and theoretical analyses. For algorithmic modeling, we give a
definition of MTL and then classify different MTL algorithms into five
categories, including feature learning approach, low-rank approach, task
clustering approach, task relation learning approach and decomposition approach
as well as discussing the characteristics of each approach. In order to improve
the performance of learning tasks further, MTL can be combined with other
learning paradigms including semi-supervised learning, active learning,
unsupervised learning, reinforcement learning, multi-view learning and
graphical models. When the number of tasks is large or the data dimensionality
is high, we review online, parallel and distributed MTL models as well as
dimensionality reduction and feature hashing to reveal their computational and
storage advantages. Many real-world applications use MTL to boost their
performance and we review representative works in this paper. Finally, we
present theoretical analyses and discuss several future directions for MTL.
| Yu Zhang and Qiang Yang | null | 1707.08114 | null | null |
Proxy Non-Discrimination in Data-Driven Systems | cs.CY cs.LG | Machine learnt systems inherit biases against protected classes, historically
disparaged groups, from training data. Usually, these biases are not explicit,
they rely on subtle correlations discovered by training algorithms, and are
therefore difficult to detect. We formalize proxy discrimination in data-driven
systems, a class of properties indicative of bias, as the presence of protected
class correlates that have causal influence on the system's output. We evaluate
an implementation on a corpus of social datasets, demonstrating how to validate
systems against these properties and to repair violations where they occur.
| Anupam Datta, Matt Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen | null | 1707.0812 | null | null |
On The Robustness of a Neural Network | stat.ML cs.AI cs.DC cs.LG cs.NE | With the development of neural networks based machine learning and their
usage in mission critical applications, voices are rising against the
\textit{black box} aspect of neural networks as it becomes crucial to
understand their limits and capabilities. With the rise of neuromorphic
hardware, it is even more critical to understand how a neural network, as a
distributed system, tolerates the failures of its computing nodes, neurons, and
its communication channels, synapses. Experimentally assessing the robustness
of neural networks involves the quixotic venture of testing all the possible
failures, on all the possible inputs, which ultimately hits a combinatorial
explosion for the first, and the impossibility to gather all the possible
inputs for the second.
In this paper, we prove an upper bound on the expected error of the output
when a subset of neurons crashes. This bound involves dependencies on the
network parameters that can be seen as being too pessimistic in the average
case. It involves a polynomial dependency on the Lipschitz coefficient of the
neurons activation function, and an exponential dependency on the depth of the
layer where a failure occurs. We back up our theoretical results with
experiments illustrating the extent to which our prediction matches the
dependencies between the network parameters and robustness. Our results show
that the robustness of neural networks to the average crash can be estimated
without the need to neither test the network on all failure configurations, nor
access the training set used to train the network, both of which are
practically impossible requirements.
| El Mahdi El Mhamdi, Rachid Guerraoui, Sebastien Rouault | null | 1707.08167 | null | null |
Efficient Low Rank Tensor Ring Completion | cs.LG cs.IT math.IT | Using the matrix product state (MPS) representation of the recently proposed
tensor ring decompositions, in this paper we propose a tensor completion
algorithm, which is an alternating minimization algorithm that alternates over
the factors in the MPS representation. This development is motivated in part by
the success of matrix completion algorithms that alternate over the (low-rank)
factors. In this paper, we propose a spectral initialization for the tensor
ring completion algorithm and analyze the computational complexity of the
proposed algorithm. We numerically compare it with existing methods that employ
a low rank tensor train approximation for data completion and show that our
method outperforms the existing ones for a variety of real computer vision
settings, and thus demonstrate the improved expressive power of tensor ring as
compared to tensor train.
| Wenqi Wang and Vaneet Aggarwal and Shuchin Aeron | null | 1707.08184 | null | null |
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation
Functions in Quasi-Recurrent Neural Networks | cs.CL cs.LG cs.NE | In this paper, we introduce a novel type of Rectified Linear Unit (ReLU),
called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an
unbounded positive and negative image, can be used as a drop-in replacement for
a tanh activation function in the recurrent step of Quasi-Recurrent Neural
Networks (QRNNs) (Bradbury et al. (2017)). Similar to ReLUs, DReLUs are less
prone to the vanishing gradient problem, they are noise robust, and they induce
sparse activations.
We independently reproduce the QRNN experiments of Bradbury et al. (2017) and
compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long
Short-Term Memory networks (LSTMs) on sentiment classification and word-level
language modeling. Additionally, we evaluate on character-level language
modeling, showing that we are able to stack up to eight QRNN layers with
DReLUs, thus making it possible to improve the current state-of-the-art in
character-level language modeling over shallow architectures based on LSTMs.
| Fr\'ederic Godin, Jonas Degrave, Joni Dambre, Wesley De Neve | 10.1016/j.patrec.2018.09.006 | 1707.08214 | null | null |
SLEEPNET: Automated Sleep Staging System via Deep Learning | cs.LG | Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect
50-70 million adults in the United States (Hillman et al., 2006). Overnight
polysomnography (PSG), including brain monitoring using electroencephalography
(EEG), is a central component of the diagnostic evaluation for sleep disorders.
While PSG is conventionally performed by trained technologists, the recent rise
of powerful neural network learning algorithms combined with large
physiological datasets offers the possibility of automation, potentially making
expert-level sleep analysis more widely available. We propose SLEEPNET (Sleep
EEG neural network), a deployed annotation tool for sleep staging. SLEEPNET
uses a deep recurrent neural network trained on the largest sleep physiology
database assembled to date, consisting of PSGs from over 10,000 patients from
the Massachusetts General Hospital (MGH) Sleep Laboratory. SLEEPNET achieves
human-level annotation performance on an independent test set of 1,000 EEGs,
with an average accuracy of 85.76% and algorithm-expert inter-rater agreement
(IRA) of kappa = 79.46%, comparable to expert-expert IRA.
| Siddharth Biswal, Joshua Kulas, Haoqi Sun, Balaji Goparaju, M Brandon
Westover, Matt T Bianchi, Jimeng Sun | null | 1707.08262 | null | null |
Dragon: A Computation Graph Virtual Machine Based Deep Learning
Framework | cs.SE cs.LG cs.MS cs.NE | Deep Learning has made a great progress for these years. However, it is still
difficult to master the implement of various models because different
researchers may release their code based on different frameworks or interfaces.
In this paper, we proposed a computation graph based framework which only aims
to introduce well-known interfaces. It will help a lot when reproducing a newly
model or transplanting models that were implemented by other frameworks.
Additionally, we implement numerous recent models covering both Computer Vision
and Nature Language Processing. We demonstrate that our framework will not
suffer from model-starving because it is much easier to make full use of the
works that are already done.
| Ting Pan | null | 1707.08265 | null | null |
MMGAN: Manifold Matching Generative Adversarial Network | cs.LG | It is well-known that GANs are difficult to train, and several different
techniques have been proposed in order to stabilize their training. In this
paper, we propose a novel training method called manifold-matching, and a new
GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
representing the vector representations of real and fake images. If these two
manifolds match, it means that real and fake images are statistically
identical. To assist the manifold-matching task, we also use i) kernel tricks
to find better manifold structures, ii) moving-averaged manifolds across
mini-batches, and iii) a regularizer based on correlation matrix to suppress
mode collapse.
We conduct in-depth experiments with three image datasets and compare with
several state-of-the-art GAN models. 32.4% of images generated by the proposed
MMGAN are recognized as fake images during our user study (16% enhancement
compared to other state-of-the-art model). MMGAN achieved an unsupervised
inception score of 7.8 for CIFAR-10.
| Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee,
Tanmoy Chakraborty, Jaegul Choo, Hongkyu Park, Youngmin Kim | null | 1707.08273 | null | null |
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal
Activity Data | cs.HC cs.LG | One of the main benefits of a wrist-worn computer is its ability to collect a
variety of physiological data in a minimally intrusive manner. Among these
data, electrodermal activity (EDA) is readily collected and provides a window
into a person's emotional and sympathetic responses. EDA data collected using a
wearable wristband are easily influenced by motion artifacts (MAs) that may
significantly distort the data and degrade the quality of analyses performed on
the data if not identified and removed. Prior work has demonstrated that MAs
can be successfully detected using supervised machine learning algorithms on a
small data set collected in a lab setting. In this paper, we demonstrate that
unsupervised learning algorithms perform competitively with supervised
algorithms for detecting MAs on EDA data collected in both a lab-based setting
and a real-world setting comprising about 23 hours of data. We also find,
somewhat surprisingly, that incorporating accelerometer data as well as EDA
improves detection accuracy only slightly for supervised algorithms and
significantly degrades the accuracy of unsupervised algorithms.
| Yuning Zhang, Maysam Haghdan, and Kevin S. Xu | null | 1707.08287 | null | null |
Graph-Based Classification of Omnidirectional Images | cs.CV cs.LG | Omnidirectional cameras are widely used in such areas as robotics and virtual
reality as they provide a wide field of view. Their images are often processed
with classical methods, which might unfortunately lead to non-optimal solutions
as these methods are designed for planar images that have different geometrical
properties than omnidirectional ones. In this paper we study image
classification task by taking into account the specific geometry of
omnidirectional cameras with graph-based representations. In particular, we
extend deep learning architectures to data on graphs; we propose a principled
way of graph construction such that convolutional filters respond similarly for
the same pattern on different positions of the image regardless of lens
distortions. Our experiments show that the proposed method outperforms current
techniques for the omnidirectional image classification problem.
| Renata Khasanova and Pascal Frossard | null | 1707.08301 | null | null |
Tensor Regression Networks | cs.LG | Convolutional neural networks typically consist of many convolutional layers
followed by one or more fully connected layers. While convolutional layers map
between high-order activation tensors, the fully connected layers operate on
flattened activation vectors. Despite empirical success, this approach has
notable drawbacks. Flattening followed by fully connected layers discards
multilinear structure in the activations and requires many parameters. We
address these problems by incorporating tensor algebraic operations that
preserve multilinear structure at every layer. First, we introduce Tensor
Contraction Layers (TCLs) that reduce the dimensionality of their input while
preserving their multilinear structure using tensor contraction. Next, we
introduce Tensor Regression Layers (TRLs), which express outputs through a
low-rank multilinear mapping from a high-order activation tensor to an output
tensor of arbitrary order. We learn the contraction and regression factors
end-to-end, and produce accurate nets with fewer parameters. Additionally, our
layers regularize networks by imposing low-rank constraints on the activations
(TCL) and regression weights (TRL). Experiments on ImageNet show that, applied
to VGG and ResNet architectures, TCLs and TRLs reduce the number of parameters
compared to fully connected layers by more than 65% while maintaining or
increasing accuracy. In addition to the space savings, our approach's ability
to leverage topological structure can be crucial for structured data such as
MRI. In particular, we demonstrate significant performance improvements over
comparable architectures on three tasks associated with the UK Biobank dataset.
| Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna,
Tommaso Furlanello, Anima Anandkumar | null | 1707.08308 | null | null |
Learning Sparse Representations in Reinforcement Learning with Sparse
Coding | cs.AI cs.LG stat.ML | A variety of representation learning approaches have been investigated for
reinforcement learning; much less attention, however, has been given to
investigating the utility of sparse coding. Outside of reinforcement learning,
sparse coding representations have been widely used, with non-convex objectives
that result in discriminative representations. In this work, we develop a
supervised sparse coding objective for policy evaluation. Despite the
non-convexity of this objective, we prove that all local minima are global
minima, making the approach amenable to simple optimization strategies. We
empirically show that it is key to use a supervised objective, rather than the
more straightforward unsupervised sparse coding approach. We compare the
learned representations to a canonical fixed sparse representation, called
tile-coding, demonstrating that the sparse coding representation outperforms a
wide variety of tilecoding representations.
| Lei Le, Raksha Kumaraswamy, Martha White | null | 1707.08316 | null | null |
Asymmetric Deep Supervised Hashing | cs.LG stat.ML | Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications.
| Qing-Yuan Jiang, Wu-Jun Li | null | 1707.08325 | null | null |
General Latent Feature Modeling for Data Exploration Tasks | stat.ML cs.LG | This paper introduces a general Bayesian non- parametric latent feature model
suitable to per- form automatic exploratory analysis of heterogeneous datasets,
where the attributes describing each object can be either discrete, continuous
or mixed variables. The proposed model presents several important properties.
First, it accounts for heterogeneous data while can be inferred in linear time
with respect to the number of objects and attributes. Second, its Bayesian
nonparametric nature allows us to automatically infer the model complexity from
the data, i.e., the number of features necessary to capture the latent
structure in the data. Third, the latent features in the model are
binary-valued variables, easing the interpretability of the obtained latent
features in data exploration tasks.
| Isabel Valera, Melanie F. Pradier and Zoubin Ghahramani | null | 1707.08352 | null | null |
Updating Singular Value Decomposition for Rank One Matrix Perturbation | cs.LG math.NA | An efficient Singular Value Decomposition (SVD) algorithm is an important
tool for distributed and streaming computation in big data problems. It is
observed that update of singular vectors of a rank-1 perturbed matrix is
similar to a Cauchy matrix-vector product. With this observation, in this
paper, we present an efficient method for updating Singular Value Decomposition
of rank-1 perturbed matrix in $O(n^2 \ \text{log}(\frac{1}{\epsilon}))$ time.
The method uses Fast Multipole Method (FMM) for updating singular vectors in
$O(n \ \text{log} (\frac{1}{\epsilon}))$ time, where $\epsilon$ is the
precision of computation.
| Ratnik Gandhi and Amoli Rajgor | null | 1707.08369 | null | null |
Prediction of amino acid side chain conformation using a deep neural
network | q-bio.BM cs.LG stat.ML | A deep neural network based architecture was constructed to predict amino
acid side chain conformation with unprecedented accuracy. Amino acid side chain
conformation prediction is essential for protein homology modeling and protein
design. Current widely-adopted methods use physics-based energy functions to
evaluate side chain conformation. Here, using a deep neural network
architecture without physics-based assumptions, we have demonstrated that side
chain conformation prediction accuracy can be improved by more than 25%,
especially for aromatic residues compared with current standard methods. More
strikingly, the prediction method presented here is robust enough to identify
individual conformational outliers from high resolution structures in a protein
data bank without providing its structural factors. We envisage that our amino
acid side chain predictor could be used as a quality check step for future
protein structure model validation and many other potential applications such
as side chain assignment in Cryo-electron microscopy, crystallography model
auto-building, protein folding and small molecule ligand docking.
| Ke Liu (1), Xiangyan Sun (3), Jun Ma (3), Zhenyu Zhou (3), Qilin Dong
(4), Shengwen Peng (3), Junqiu Wu (3), Suocheng Tan (3), G\"unter Blobel (2),
and Jie Fan (1) ((1) Accutar Biotechnology, (2) Laboratory of Cell Biology,
Howard Hughes Medical Institute, The Rockefeller University (3) Accutar
Biotechnology (Shanghai), (4) Fudan University) | null | 1707.08381 | null | null |
Non-Stationary Bandits with Habituation and Recovery Dynamics | math.OC cs.LG | Many settings involve sequential decision-making where a set of actions can
be chosen at each time step, each action provides a stochastic reward, and the
distribution for the reward of each action is initially unknown. However,
frequent selection of a specific action may reduce its expected reward, while
abstaining from choosing an action may cause its expected reward to increase.
Such non-stationary phenomena are observed in many real world settings such as
personalized healthcare-adherence improving interventions and targeted online
advertising. Though finding an optimal policy for general models with
non-stationarity is PSPACE-complete, we propose and analyze a new class of
models called ROGUE (Reducing or Gaining Unknown Efficacy) bandits, which we
show in this paper can capture these phenomena and are amenable to the design
of effective policies. We first present a consistent maximum likelihood
estimator for the parameters of these models. Next, we construct finite sample
concentration bounds that lead to an upper confidence bound policy called the
ROGUE Upper Confidence Bound (ROGUE-UCB) algorithm. We prove that under proper
conditions the ROGUE-UCB algorithm achieves logarithmic in time regret, unlike
existing algorithms which result in linear regret. We conclude with a numerical
experiment using real data from a personalized healthcare-adherence improving
intervention to increase physical activity. In this intervention, the goal is
to optimize the selection of messages (e.g., confidence increasing vs.
knowledge increasing) to send to each individual each day to increase adherence
and physical activity. Our results show that ROGUE-UCB performs better in terms
of regret and average reward as compared to state of the art algorithms, and
the use of ROGUE-UCB increases daily step counts by roughly 1,000 steps a day
(about a half-mile more of walking) as compared to other algorithms.
| Yonatan Mintz, Anil Aswani, Philip Kaminsky, Elena Flowers, Yoshimi
Fukuoka | null | 1707.08423 | null | null |
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning | stat.ML cs.AI cs.LG | Domain adaptation is an important open problem in deep reinforcement learning
(RL). In many scenarios of interest data is hard to obtain, so agents may learn
a source policy in a setting where data is readily available, with the hope
that it generalises well to the target domain. We propose a new multi-stage RL
agent, DARLA (DisentAngled Representation Learning Agent), which learns to see
before learning to act. DARLA's vision is based on learning a disentangled
representation of the observed environment. Once DARLA can see, it is able to
acquire source policies that are robust to many domain shifts - even with no
access to the target domain. DARLA significantly outperforms conventional
baselines in zero-shot domain adaptation scenarios, an effect that holds across
a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms
(DQN, A3C and EC).
| Irina Higgins, Arka Pal, Andrei A. Rusu, Loic Matthey, Christopher P
Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander
Lerchner | null | 1707.08475 | null | null |
TensorLayer: A Versatile Library for Efficient Deep Learning Development | cs.LG cs.DC stat.ML | Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.
| Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao
Yu, Yike Guo | 10.1145/3123266.3129391 | 1707.08551 | null | null |
A Robust Multi-Batch L-BFGS Method for Machine Learning | math.OC cs.LG stat.ML | This paper describes an implementation of the L-BFGS method designed to deal
with two adversarial situations. The first occurs in distributed computing
environments where some of the computational nodes devoted to the evaluation of
the function and gradient are unable to return results on time. A similar
challenge occurs in a multi-batch approach in which the data points used to
compute function and gradients are purposely changed at each iteration to
accelerate the learning process. Difficulties arise because L-BFGS employs
gradient differences to update the Hessian approximations, and when these
gradients are computed using different data points the updating process can be
unstable. This paper shows how to perform stable quasi-Newton updating in the
multi-batch setting, studies the convergence properties for both convex and
nonconvex functions, and illustrates the behavior of the algorithm in a
distributed computing platform on binary classification logistic regression and
neural network training problems that arise in machine learning.
| Albert S. Berahas and Martin Tak\'a\v{c} | null | 1707.08552 | null | null |
Direct Load Control of Thermostatically Controlled Loads Based on Sparse
Observations Using Deep Reinforcement Learning | cs.LG | This paper considers a demand response agent that must find a near-optimal
sequence of decisions based on sparse observations of its environment.
Extracting a relevant set of features from these observations is a challenging
task and may require substantial domain knowledge. One way to tackle this
problem is to store sequences of past observations and actions in the state
vector, making it high dimensional, and apply techniques from deep learning.
This paper investigates the capabilities of different deep learning techniques,
such as convolutional neural networks and recurrent neural networks, to extract
relevant features for finding near-optimal policies for a residential heating
system and electric water heater that are hindered by sparse observations. Our
simulation results indicate that in this specific scenario, feeding sequences
of time-series to an LSTM network, which is a specific type of recurrent neural
network, achieved a higher performance than stacking these time-series in the
input of a convolutional neural network or deep neural network.
| Frederik Ruelens, Bert J. Claessens, Peter Vrancx, Fred Spiessens, and
Geert Deconinck | 10.17775/CSEEJPES.2019.00590 | 1707.08553 | null | null |
Video Highlight Prediction Using Audience Chat Reactions | cs.CL cs.AI cs.CV cs.LG cs.MM | Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and traditional Chinese. We present a novel dataset based on League of
Legends championships recorded from North American and Taiwanese Twitch.tv
channels (will be released for further research), and demonstrate strong
results on these using multimodal, character-level CNN-RNN model architectures.
| Cheng-Yang Fu, Joon Lee, Mohit Bansal, Alexander C. Berg | null | 1707.08559 | null | null |
Quantum machine learning: a classical perspective | quant-ph cs.LG stat.ML | Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.
| Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo,
Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig | 10.1098/rspa.2017.0551 | 1707.08561 | null | null |
Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition
in Unmodified Smartphones | cs.HC cs.LG cs.NI | This paper introduces Wisture, a new online machine learning solution for
recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on
the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term
Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic
induction. Unlike other Wi-Fi based gesture recognition methods, the proposed
method does not require a modification of the smartphone hardware or the
operating system, and performs the gesture recognition without interfering with
the normal operation of other smartphone applications.
We discuss the characteristics of Wisture, and conduct extensive experiments
to compare its performance against state-of-the-art machine learning solutions
in terms of both accuracy and time efficiency. The experiments include a set of
different scenarios in terms of both spatial setup and traffic between the
smartphone and Wi-Fi access points (AP). The results show that Wisture achieves
an online recognition accuracy of up to 94% (average 78%) in detecting and
classifying three hand gestures.
| Mohamed Abudulaziz Ali Haseeb and Ramviyas Parasuraman | null | 1707.08569 | null | null |
Self-organized Hierarchical Softmax | cs.CL cs.LG | We propose a new self-organizing hierarchical softmax formulation for
neural-network-based language models over large vocabularies. Instead of using
a predefined hierarchical structure, our approach is capable of learning word
clusters with clear syntactical and semantic meaning during the language model
training process. We provide experiments on standard benchmarks for language
modeling and sentence compression tasks. We find that this approach is as fast
as other efficient softmax approximations, while achieving comparable or even
better performance relative to similar full softmax models.
| Yikang Shen, Shawn Tan, Chrisopher Pal and Aaron Courville | null | 1707.08588 | null | null |
Guiding Reinforcement Learning Exploration Using Natural Language | cs.AI cs.CL cs.LG stat.ML | In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action information. We then use this learned model to guide agent
exploration using a modified version of policy shaping to make it more
effective at learning in unseen environments. We evaluate this technique using
the popular arcade game, Frogger, under ideal and non-ideal conditions. This
evaluation shows that our modified policy shaping algorithm improves over a
Q-learning agent as well as a baseline version of policy shaping.
| Brent Harrison, Upol Ehsan, Mark O. Riedl | null | 1707.08616 | null | null |
Multi-Robot Transfer Learning: A Dynamical System Perspective | cs.RO cs.LG cs.SY | Multi-robot transfer learning allows a robot to use data generated by a
second, similar robot to improve its own behavior. The potential advantages are
reducing the time of training and the unavoidable risks that exist during the
training phase. Transfer learning algorithms aim to find an optimal transfer
map between different robots. In this paper, we investigate, through a
theoretical study of single-input single-output (SISO) systems, the properties
of such optimal transfer maps. We first show that the optimal transfer learning
map is, in general, a dynamic system. The main contribution of the paper is to
provide an algorithm for determining the properties of this optimal dynamic map
including its order and regressors (i.e., the variables it depends on). The
proposed algorithm does not require detailed knowledge of the robots' dynamics,
but relies on basic system properties easily obtainable through simple
experimental tests. We validate the proposed algorithm experimentally through
an example of transfer learning between two different quadrotor platforms.
Experimental results show that an optimal dynamic map, with correct properties
obtained from our proposed algorithm, achieves 60-70% reduction of transfer
learning error compared to the cases when the data is directly transferred or
transferred using an optimal static map.
| Mohamed K. Helwa, Angela P. Schoellig | null | 1707.08689 | null | null |
Learning audio sequence representations for acoustic event
classification | cs.SD cs.LG | Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a `hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame level. In this article, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and an RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC.
| Zixing Zhang, Ding Liu, Jing Han, Kun Qian, Bj\"orn Schuller | null | 1707.08729 | null | null |
A Downsampled Variant of ImageNet as an Alternative to the CIFAR
datasets | cs.CV cs.LG | The original ImageNet dataset is a popular large-scale benchmark for training
Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm
design, architecture search, and hyperparameter tuning) on the original dataset
might be prohibitive, we propose to consider a downsampled version of ImageNet.
In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet,
our proposed ImageNet32$\times$32 (and its variants ImageNet64$\times$64 and
ImageNet16$\times$16) contains exactly the same number of classes and images as
ImageNet, with the only difference that the images are downsampled to
32$\times$32 pixels per image (64$\times$64 and 16$\times$16 pixels for the
variants, respectively). Experiments on these downsampled variants are
dramatically faster than on the original ImageNet and the characteristics of
the downsampled datasets with respect to optimal hyperparameters appear to
remain similar. The proposed datasets and scripts to reproduce our results are
available at http://image-net.org/download-images and
https://github.com/PatrykChrabaszcz/Imagenet32_Scripts
| Patryk Chrabaszcz, Ilya Loshchilov and Frank Hutter | null | 1707.08819 | null | null |
Max K-armed bandit: On the ExtremeHunter algorithm and beyond | stat.ML cs.LG | This paper is devoted to the study of the max K-armed bandit problem, which
consists in sequentially allocating resources in order to detect extreme
values. Our contribution is twofold. We first significantly refine the analysis
of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and
next propose an alternative approach, showing that, remarkably, Extreme Bandits
can be reduced to a classical version of the bandit problem to a certain
extent. Beyond the formal analysis, these two approaches are compared through
numerical experiments.
| Mastane Achab, Stephan Cl\'emen\c{c}on, Aur\'elien Garivier, Anne
Sabourin, Claire Vernade | null | 1707.0882 | null | null |
Detecting and Explaining Causes From Text For a Time Series Event | cs.CL cs.AI cs.LG | Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.
| Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, Eduard Hovy | null | 1707.08852 | null | null |
Algorithms for Approximate Subtropical Matrix Factorization | cs.LG | Matrix factorization methods are important tools in data mining and analysis.
They can be used for many tasks, ranging from dimensionality reduction to
visualization. In this paper we concentrate on the use of matrix factorizations
for finding patterns from the data. Rather than using the standard algebra --
and the summation of the rank-1 components to build the approximation of the
original matrix -- we use the subtropical algebra, which is an algebra over the
nonnegative real values with the summation replaced by the maximum operator.
Subtropical matrix factorizations allow "winner-takes-it-all" interpretations
of the rank-1 components, revealing different structure than the normal
(nonnegative) factorizations. We study the complexity and sparsity of the
factorizations, and present a framework for finding low-rank subtropical
factorizations. We present two specific algorithms, called Capricorn and
Cancer, that are part of our framework. They can be used with data that has
been corrupted with different types of noise, and with different error metrics,
including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon
divergence. Our experiments show that the algorithms perform well on data that
has subtropical structure, and that they can find factorizations that are both
sparse and easy to interpret.
| Sanjar Karaev and Pauli Miettinen | null | 1707.08872 | null | null |
Robust Physical-World Attacks on Deep Learning Models | cs.CR cs.LG | Recent studies show that the state-of-the-art deep neural networks (DNNs) are
vulnerable to adversarial examples, resulting from small-magnitude
perturbations added to the input. Given that that emerging physical systems are
using DNNs in safety-critical situations, adversarial examples could mislead
these systems and cause dangerous situations.Therefore, understanding
adversarial examples in the physical world is an important step towards
developing resilient learning algorithms. We propose a general attack
algorithm,Robust Physical Perturbations (RP2), to generate robust visual
adversarial perturbations under different physical conditions. Using the
real-world case of road sign classification, we show that adversarial examples
generated using RP2 achieve high targeted misclassification rates against
standard-architecture road sign classifiers in the physical world under various
environmental conditions, including viewpoints. Due to the current lack of a
standardized testing method, we propose a two-stage evaluation methodology for
robust physical adversarial examples consisting of lab and field tests. Using
this methodology, we evaluate the efficacy of physical adversarial
manipulations on real objects. Witha perturbation in the form of only black and
white stickers,we attack a real stop sign, causing targeted misclassification
in 100% of the images obtained in lab settings, and in 84.8%of the captured
video frames obtained on a moving vehicle(field test) for the target
classifier.
| Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati,
Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song | null | 1707.08945 | null | null |
Bandit Convex Optimization for Scalable and Dynamic IoT Management | cs.LG cs.NI | The present paper deals with online convex optimization involving both
time-varying loss functions, and time-varying constraints. The loss functions
are not fully accessible to the learner, and instead only the function values
(a.k.a. bandit feedback) are revealed at queried points. The constraints are
revealed after making decisions, and can be instantaneously violated, yet they
must be satisfied in the long term. This setting fits nicely the emerging
online network tasks such as fog computing in the Internet-of-Things (IoT),
where online decisions must flexibly adapt to the changing user preferences
(loss functions), and the temporally unpredictable availability of resources
(constraints). Tailored for such human-in-the-loop systems where the loss
functions are hard to model, a family of bandit online saddle-point (BanSaP)
schemes are developed, which adaptively adjust the online operations based on
(possibly multiple) bandit feedback of the loss functions, and the changing
environment. Performance here is assessed by: i) dynamic regret that
generalizes the widely used static regret; and, ii) fit that captures the
accumulated amount of constraint violations. Specifically, BanSaP is proved to
simultaneously yield sub-linear dynamic regret and fit, provided that the best
dynamic solutions vary slowly over time. Numerical tests in fog computation
offloading tasks corroborate that our proposed BanSaP approach offers
competitive performance relative to existing approaches that are based on
gradient feedback.
| Tianyi Chen and Georgios B. Giannakis | 10.1109/JIOT.2018.2839563 | 1707.0906 | null | null |
Deep Kernels for Optimizing Locomotion Controllers | cs.RO cs.LG | Sample efficiency is important when optimizing parameters of locomotion
controllers, since hardware experiments are time consuming and expensive.
Bayesian Optimization, a sample-efficient optimization framework, has recently
been widely applied to address this problem, but further improvements in sample
efficiency are needed for practical applicability to real-world robots and
high-dimensional controllers. To address this, prior work has proposed using
domain expertise for constructing custom distance metrics for locomotion. In
this work we show how to learn such a distance metric automatically. We use a
neural network to learn an informed distance metric from data obtained in
high-fidelity simulations. We conduct experiments on two different controllers
and robot architectures. First, we demonstrate improvement in sample efficiency
when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We
then conduct simulation experiments to optimize a 16-dimensional controller for
a 7-link robot model and obtain significant improvements even when optimizing
in perturbed environments. This demonstrates that our approach is able to
enhance sample efficiency for two different controllers, hence is a fitting
candidate for further experiments on hardware in the future.
| Rika Antonova, Akshara Rai, Christopher G. Atkeson | null | 1707.09062 | null | null |
An Open Source C++ Implementation of Multi-Threaded Gaussian Mixture
Models, k-Means and Expectation Maximisation | cs.MS cs.LG | Modelling of multivariate densities is a core component in many signal
processing, pattern recognition and machine learning applications. The
modelling is often done via Gaussian mixture models (GMMs), which use
computationally expensive and potentially unstable training algorithms. We
provide an overview of a fast and robust implementation of GMMs in the C++
language, employing multi-threaded versions of the Expectation Maximisation
(EM) and k-means training algorithms. Multi-threading is achieved through
reformulation of the EM and k-means algorithms into a MapReduce-like framework.
Furthermore, the implementation uses several techniques to improve numerical
stability and modelling accuracy. We demonstrate that the multi-threaded
implementation achieves a speedup of an order of magnitude on a recent 16 core
machine, and that it can achieve higher modelling accuracy than a previously
well-established publically accessible implementation. The multi-threaded
implementation is included as a user-friendly class in recent releases of the
open source Armadillo C++ linear algebra library. The library is provided under
the permissive Apache~2.0 license, allowing unencumbered use in commercial
products.
| Conrad Sanderson, Ryan Curtin | 10.1109/ICSPCS.2017.8270510 | 1707.09094 | null | null |
Counterfactual Learning from Bandit Feedback under Deterministic
Logging: A Case Study in Statistical Machine Translation | stat.ML cs.CL cs.LG | The goal of counterfactual learning for statistical machine translation (SMT)
is to optimize a target SMT system from logged data that consist of user
feedback to translations that were predicted by another, historic SMT system. A
challenge arises by the fact that risk-averse commercial SMT systems
deterministically log the most probable translation. The lack of sufficient
exploration of the SMT output space seemingly contradicts the theoretical
requirements for counterfactual learning. We show that counterfactual learning
from deterministic bandit logs is possible nevertheless by smoothing out
deterministic components in learning. This can be achieved by additive and
multiplicative control variates that avoid degenerate behavior in empirical
risk minimization. Our simulation experiments show improvements of up to 2 BLEU
points by counterfactual learning from deterministic bandit feedback.
| Carolin Lawrence, Artem Sokolov, Stefan Riezler | null | 1707.09118 | null | null |
Efficient Algorithms for Non-convex Isotonic Regression through
Submodular Optimization | cs.LG stat.ML | We consider the minimization of submodular functions subject to ordering
constraints. We show that this optimization problem can be cast as a convex
optimization problem on a space of uni-dimensional measures, with ordering
constraints corresponding to first-order stochastic dominance. We propose new
discretization schemes that lead to simple and efficient algorithms based on
zero-th, first, or higher order oracles; these algorithms also lead to
improvements without isotonic constraints. Finally, our experiments show that
non-convex loss functions can be much more robust to outliers for isotonic
regression, while still leading to an efficient optimization problem.
| Francis Bach (SIERRA) | null | 1707.09157 | null | null |
A Survey of Learning in Multiagent Environments: Dealing with
Non-Stationarity | cs.MA cs.LG | The key challenge in multiagent learning is learning a best response to the
behaviour of other agents, which may be non-stationary: if the other agents
adapt their strategy as well, the learning target moves. Disparate streams of
research have approached non-stationarity from several angles, which make a
variety of implicit assumptions that make it hard to keep an overview of the
state of the art and to validate the innovation and significance of new works.
This survey presents a coherent overview of work that addresses
opponent-induced non-stationarity with tools from game theory, reinforcement
learning and multi-armed bandits. Further, we reflect on the principle
approaches how algorithms model and cope with this non-stationarity, arriving
at a new framework and five categories (in increasing order of sophistication):
ignore, forget, respond to target models, learn models, and theory of mind. A
wide range of state-of-the-art algorithms is classified into a taxonomy, using
these categories and key characteristics of the environment (e.g.,
observability) and adaptation behaviour of the opponents (e.g., smooth,
abrupt). To clarify even further we present illustrative variations of one
domain, contrasting the strengths and limitations of each category. Finally, we
discuss in which environments the different approaches yield most merit, and
point to promising avenues of future research.
| Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag and Enrique Munoz
de Cote | null | 1707.09183 | null | null |
Data-Driven Stochastic Robust Optimization: A General Computational
Framework and Algorithm for Optimization under Uncertainty in the Big Data
Era | cs.LG cs.AI cs.SY math.OC | A novel data-driven stochastic robust optimization (DDSRO) framework is
proposed for optimization under uncertainty leveraging labeled multi-class
uncertainty data. Uncertainty data in large datasets are often collected from
various conditions, which are encoded by class labels. Machine learning methods
including Dirichlet process mixture model and maximum likelihood estimation are
employed for uncertainty modeling. A DDSRO framework is further proposed based
on the data-driven uncertainty model through a bi-level optimization structure.
The outer optimization problem follows a two-stage stochastic programming
approach to optimize the expected objective across different data classes;
adaptive robust optimization is nested as the inner problem to ensure the
robustness of the solution while maintaining computational tractability. A
decomposition-based algorithm is further developed to solve the resulting
multi-level optimization problem efficiently. Case studies on process network
design and planning are presented to demonstrate the applicability of the
proposed framework and algorithm.
| Chao Ning and Fengqi You | 10.1016/j.compchemeng.2017.12.015 | 1707.09198 | null | null |
Recurrent Ladder Networks | cs.NE cs.AI cs.LG stat.ML | We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The architecture shows close-to-optimal results on temporal modeling
of video data, competitive results on music modeling, and improved perceptual
grouping based on higher order abstractions, such as stochastic textures and
motion cues. We present results for fully supervised, semi-supervised, and
unsupervised tasks. The results suggest that the proposed architecture and
principles are powerful tools for learning a hierarchy of abstractions,
learning iterative inference and handling temporal information.
| Isabeau Pr\'emont-Schwarz, Alexander Ilin, Tele Hotloo Hao, Antti
Rasmus, Rinu Boney, Harri Valpola | null | 1707.09219 | null | null |
Human Pose Forecasting via Deep Markov Models | cs.CV cs.LG | Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging due to the difficulty of estimating how long a
person continues an activity. To this end, our contributions are threefold: (i)
we propose a generative framework for poses using variational autoencoders
based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a
pose-based action classifier, which we argue better reflects the subjective
quality of pose forecasts than distance in coordinate space; (iii) last, for
evaluation of the new model, we introduce a 480,000-frame video dataset called
Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling
and disassembling furniture. We demonstrate promising results for our approach
on both Ikea FA and the existing NTU RGB+D dataset.
| Sam Toyer, Anoop Cherian, Tengda Han, Stephen Gould | null | 1707.0924 | null | null |
Generator Reversal | stat.ML cs.LG | We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we propose instead to use more flexible code distributions. These
distributions are estimated non-parametrically by reversing the generator map
during training. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
| Yannic Kilcher, Aur\'elien Lucchi, Thomas Hofmann | null | 1707.09241 | null | null |
A Fourier-invariant method for locating point-masses and computing their
attributes | stat.OT cs.LG math.NA | Motivated by the interest of observing the growth of cancer cells among
normal living cells and exploring how galaxies and stars are truly formed, the
objective of this paper is to introduce a rigorous and effective method for
counting point-masses, determining their spatial locations, and computing their
attributes. Based on computation of Hermite moments that are Fourier-invariant,
our approach facilitates the processing of both spatial and Fourier data in any
dimension.
| Charles K. Chui, Hrushikesh N. Mhaskar | null | 1707.09319 | null | null |
Improved Face Detection and Alignment using Cascade Deep Convolutional
Network | cs.CV cs.LG | Real-world face detection and alignment demand an advanced discriminative
model to address challenges by pose, lighting and expression. Illuminated by
the deep learning algorithm, some convolutional neural networks based face
detection and alignment methods have been proposed. Recent studies have
utilized the relation between face detection and alignment to make models
computationally efficiency, however they ignore the connection between each
cascade CNNs. In this paper, we propose an structure to propose higher quality
training data for End-to-End cascade network training, which give computers
more space to automatic adjust weight parameter and accelerate convergence.
Experiments demonstrate considerable improvement over existing detection and
alignment models.
| Weilin Cong, Sanyuan Zhao, Hui Tian, Jianbing Shen | null | 1707.09364 | null | null |
Face Deidentification with Generative Deep Neural Networks | cs.CV cs.AI cs.LG | Face deidentification is an active topic amongst privacy and security
researchers. Early deidentification methods relying on image blurring or
pixelization were replaced in recent years with techniques based on formal
anonymity models that provide privacy guaranties and at the same time aim at
retaining certain characteristics of the data even after deidentification. The
latter aspect is particularly important, as it allows to exploit the
deidentified data in applications for which identity information is irrelevant.
In this work we present a novel face deidentification pipeline, which ensures
anonymity by synthesizing artificial surrogate faces using generative neural
networks (GNNs). The generated faces are used to deidentify subjects in images
or video, while preserving non-identity-related aspects of the data and
consequently enabling data utilization. Since generative networks are very
adaptive and can utilize a diverse set of parameters (pertaining to the
appearance of the generated output in terms of facial expressions, gender,
race, etc.), they represent a natural choice for the problem of face
deidentification. To demonstrate the feasibility of our approach, we perform
experiments using automated recognition tools and human annotators. Our results
show that the recognition performance on deidentified images is close to
chance, suggesting that the deidentification process based on GNNs is highly
effective.
| Bla\v{z} Meden, Refik Can Mall{\i}, Sebastjan Fabijan, Haz{\i}m Kemal
Ekenel, Vitomir \v{S}truc, Peter Peer | 10.1049/iet-spr.2017.0049 | 1707.09376 | null | null |
The Topology of Statistical Verifiability | cs.LG cs.AI math.PR | Topological models of empirical and formal inquiry are increasingly
prevalent. They have emerged in such diverse fields as domain theory [1, 16],
formal learning theory [18], epistemology and philosophy of science [10, 15, 8,
9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open
sets are typically interpreted as hypotheses deductively verifiable by true
propositional information that rules out relevant possibilities. However, in
statistical data analysis, one routinely receives random samples logically
compatible with every statistical hypothesis. We bridge the gap between
propositional and statistical data by solving for the unique topology on
probability measures in which the open sets are exactly the statistically
verifiable hypotheses. Furthermore, we extend that result to a topological
characterization of learnability in the limit from statistical data.
| Konstantin Genin (Carnegie Mellon University), Kevin T. Kelly
(Carnegie Mellon University) | 10.4204/EPTCS.251.17 | 1707.09378 | null | null |
Inverse Reinforcement Learning in Large State Spaces via Function
Approximation | cs.LG cs.RO | This paper introduces a new method for inverse reinforcement learning in
large-scale and high-dimensional state spaces. To avoid solving the
computationally expensive reinforcement learning problems in reward learning,
we propose a function approximation method to ensure that the Bellman
Optimality Equation always holds, and then estimate a function to maximize the
likelihood of the observed motion. The time complexity of the proposed method
is linearly proportional to the cardinality of the action set, thus it can
handle large state spaces efficiently. We test the proposed method in a
simulated environment, and show that it is more accurate than existing methods
and significantly better in scalability. We also show that the proposed method
can extend many existing methods to high-dimensional state spaces. We then
apply the method to evaluating the effect of rehabilitative stimulations on
patients with spinal cord injuries based on the observed patient motions.
| Kun Li, Joel W. Burdick | null | 1707.09394 | null | null |
Photographic Image Synthesis with Cascaded Refinement Networks | cs.CV cs.AI cs.GR cs.LG | We present an approach to synthesizing photographic images conditioned on
semantic layouts. Given a semantic label map, our approach produces an image
with photographic appearance that conforms to the input layout. The approach
thus functions as a rendering engine that takes a two-dimensional semantic
specification of the scene and produces a corresponding photographic image.
Unlike recent and contemporaneous work, our approach does not rely on
adversarial training. We show that photographic images can be synthesized from
semantic layouts by a single feedforward network with appropriate structure,
trained end-to-end with a direct regression objective. The presented approach
scales seamlessly to high resolutions; we demonstrate this by synthesizing
photographic images at 2-megapixel resolution, the full resolution of our
training data. Extensive perceptual experiments on datasets of outdoor and
indoor scenes demonstrate that images synthesized by the presented approach are
considerably more realistic than alternative approaches. The results are shown
in the supplementary video at https://youtu.be/0fhUJT21-bs
| Qifeng Chen and Vladlen Koltun | null | 1707.09405 | null | null |
Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia
with Deep Learning Pose Estimation | cs.CV cs.LG | Objective: To apply deep learning pose estimation algorithms for vision-based
assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine
participants with Parkinson's disease (PD) and LID completed a levodopa
infusion protocol, where symptoms were assessed at regular intervals using the
Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating
Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used
to extract movement trajectories from videos of PD assessments. Features of the
movement trajectories were used to detect and estimate the severity of
parkinsonism and LID using random forest. Communication and drinking tasks were
used to assess LID, while leg agility and toe tapping tasks were used to assess
parkinsonism. Feature sets from tasks were also combined to predict total
UDysRS and UPDRS Part III scores. Results: For LID, the communication task
yielded the best results for dyskinesia (severity estimation: r = 0.661,
detection: AUC = 0.930). For parkinsonism, leg agility had better results for
severity estimation (r = 0.618), while toe tapping was better for detection
(AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741
and 0.530, respectively. Conclusion: This paper presents the first application
of deep learning for vision-based assessment of parkinsonism and LID and
demonstrates promising performance for the future translation of deep learning
to PD clinical practices. Significance: The proposed system provides insight
into the potential of computer vision and deep learning for clinical
application in PD.
| Michael H. Li, Tiago A. Mestre, Susan H. Fox, Babak Taati | 10.1186/s12984-018-0446-z | 1707.09416 | null | null |
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of
Class Imbalance and Concept Drift (LPCICD'17) | cs.LG | With the wide application of machine learning algorithms to the real world,
class imbalance and concept drift have become crucial learning issues. Class
imbalance happens when the data categories are not equally represented, i.e.,
at least one category is minority compared to other categories. It can cause
learning bias towards the majority class and poor generalization. Concept drift
is a change in the underlying distribution of the problem, and is a significant
issue specially when learning from data streams. It requires learners to be
adaptive to dynamic changes.
Class imbalance and concept drift can significantly hinder predictive
performance, and the problem becomes particularly challenging when they occur
simultaneously. This challenge arises from the fact that one problem can affect
the treatment of the other. For example, drift detection algorithms based on
the traditional classification error may be sensitive to the imbalanced degree
and become less effective; and class imbalance techniques need to be adaptive
to changing imbalance rates, otherwise the class receiving the preferential
treatment may not be the correct minority class at the current moment.
Therefore, the mutual effect of class imbalance and concept drift should be
considered during algorithm design.
The aim of this workshop is to bring together researchers from the areas of
class imbalance learning and concept drift in order to encourage discussions
and new collaborations on solving the combined issue of class imbalance and
concept drift. It provides a forum for international researchers and
practitioners to share and discuss their original work on addressing new
challenges and research issues in class imbalance learning, concept drift, and
the combined issues of class imbalance and concept drift. The proceedings
include 8 papers on these topics.
| Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao | null | 1707.09425 | null | null |
A unified method for super-resolution recovery and real exponential-sum
separation | math.NA cs.LG | In this paper, motivated by diffraction of traveling light waves, a simple
mathematical model is proposed, both for the multivariate super-resolution
problem and the problem of blind-source separation of real-valued exponential
sums. This model facilitates the development of a unified theory and a unified
solution of both problems in this paper. Our consideration of the
super-resolution problem is aimed at applications to fluorescence microscopy
and observational astronomy, and the motivation for our consideration of the
second problem is the current need of extracting multivariate exponential
features in magnetic resonance spectroscopy (MRS) for the neurologist and
radiologist as well as for providing a mathematical tool for isotope separation
in Nuclear Chemistry. The unified method introduced in this paper can be easily
realized by processing only finitely many data, sampled at locations that are
not necessarily prescribed in advance, with computational scheme consisting
only of matrix - vector multiplication, peak finding, and clustering.
| Charles K. Chui, Hrushikesh N. Mhaskar | null | 1707.09428 | null | null |
Human in the Loop: Interactive Passive Automata Learning via
Evidence-Driven State-Merging Algorithms | stat.ML cs.LG | We present an interactive version of an evidence-driven state-merging (EDSM)
algorithm for learning variants of finite state automata. Learning these
automata often amounts to recovering or reverse engineering the model
generating the data despite noisy, incomplete, or imperfectly sampled data
sources rather than optimizing a purely numeric target function. Domain
expertise and human knowledge about the target domain can guide this process,
and typically is captured in parameter settings. Often, domain expertise is
subconscious and not expressed explicitly. Directly interacting with the
learning algorithm makes it easier to utilize this knowledge effectively.
| Christian A. Hammerschmidt, Radu State, Sicco Verwer | null | 1707.0943 | null | null |
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes | cs.CV cs.LG | During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.
| Yang Zhang, Philip David, Boqing Gong | 10.1109/ICCV.2017.223 | 1707.09465 | null | null |
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform | stat.ML cs.LG | Recurrent Neural Networks (RNNs) are designed to handle sequential data but
suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent
Neural Networks (uRNNs) have been used to address this issue and in some cases,
exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose
a simpler and novel update scheme to maintain orthogonal recurrent weight
matrices without using complex valued matrices. This is done by parametrizing
with a skew-symmetric matrix using the Cayley transform. Such a parametrization
is unable to represent matrices with negative one eigenvalues, but this
limitation is overcome by scaling the recurrent weight matrix by a diagonal
matrix consisting of ones and negative ones. The proposed training scheme
involves a straightforward gradient calculation and update step. In several
experiments, the proposed scaled Cayley orthogonal recurrent neural network
(scoRNN) achieves superior results with fewer trainable parameters than other
unitary RNNs.
| Kyle Helfrich, Devin Willmott, Qiang Ye | null | 1707.0952 | null | null |
MLBench: How Good Are Machine Learning Clouds for Binary Classification
Tasks on Structured Data? | cs.DC cs.LG stat.ML | We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement.
| Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang | null | 1707.09562 | null | null |
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for
Neural Networks | cs.LG | We present a generalization bound for feedforward neural networks in terms of
the product of the spectral norm of the layers and the Frobenius norm of the
weights. The generalization bound is derived using a PAC-Bayes analysis.
| Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro | null | 1707.09564 | null | null |
Deep Potential Molecular Dynamics: a scalable model with the accuracy of
quantum mechanics | physics.comp-ph cs.LG physics.chem-ph | We introduce a scheme for molecular simulations, the Deep Potential Molecular
Dynamics (DeePMD) method, based on a many-body potential and interatomic forces
generated by a carefully crafted deep neural network trained with ab initio
data. The neural network model preserves all the natural symmetries in the
problem. It is "first principle-based" in the sense that there are no ad hoc
components aside from the network model. We show that the proposed scheme
provides an efficient and accurate protocol in a variety of systems, including
bulk materials and molecules. In all these cases, DeePMD gives results that are
essentially indistinguishable from the original data, at a cost that scales
linearly with system size.
| Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E | 10.1103/PhysRevLett.120.143001 | 1707.09571 | null | null |
Towards Visual Explanations for Convolutional Neural Networks via Input
Resampling | cs.LG stat.ML | The predictive power of neural networks often costs model interpretability.
Several techniques have been developed for explaining model outputs in terms of
input features; however, it is difficult to translate such interpretations into
actionable insight. Here, we propose a framework to analyze predictions in
terms of the model's internal features by inspecting information flow through
the network. Given a trained network and a test image, we select neurons by two
metrics, both measured over a set of images created by perturbations to the
input image: (1) magnitude of the correlation between the neuron activation and
the network output and (2) precision of the neuron activation. We show that the
former metric selects neurons that exert large influence over the network
output while the latter metric selects neurons that activate on generalizable
features. By comparing the sets of neurons selected by these two metrics, our
framework suggests a way to investigate the internal attention mechanisms of
convolutional neural networks.
| Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie
Rosenthal, Manuela Veloso | null | 1707.09641 | null | null |
Model-Free Renewable Scenario Generation Using Generative Adversarial
Networks | cs.LG cs.SY math.OC | Scenario generation is an important step in the operation and planning of
power systems with high renewable penetrations. In this work, we proposed a
data-driven approach for scenario generation using generative adversarial
networks, which is based on two interconnected deep neural networks. Compared
with existing methods based on probabilistic models that are often hard to
scale or sample from, our method is data-driven, and captures renewable energy
production patterns in both temporal and spatial dimensions for a large number
of correlated resources. For validation, we use wind and solar times-series
data from NREL integration data sets. We demonstrate that the proposed method
is able to generate realistic wind and photovoltaic power profiles with full
diversity of behaviors. We also illustrate how to generate scenarios based on
different conditions of interest by using labeled data during training. For
example, scenarios can be conditioned on weather events~(e.g. high wind day) or
time of the year~(e,g. solar generation for a day in July). Because of the
feedforward nature of the neural networks, scenarios can be generated extremely
efficiently without sophisticated sampling techniques.
| Yize Chen and Yishen Wang and Daniel Kirschen and Baosen Zhang | null | 1707.09676 | null | null |
Learning to Match | cs.LG | Outsourcing tasks to previously unknown parties is becoming more common. One
specific such problem involves matching a set of workers to a set of tasks.
Even if the latter have precise requirements, the quality of individual workers
is usually unknown. The problem is thus a version of matching under
uncertainty. We believe that this type of problem is going to be increasingly
important.
When the problem involves only a single skill or type of job, it is
essentially a type of bandit problem, and can be solved with standard
algorithms. However, we develop an algorithm that can perform matching for
workers with multiple skills hired for multiple jobs with multiple
requirements. We perform an experimental evaluation in both single-task and
multi-task problems, comparing with the bounded $\epsilon$-first algorithm, as
well as an oracle that knows the true skills of workers. One of the algorithms
we developed gives results approaching 85\% of oracle's performance. We invite
the community to take a closer look at this problem and develop real-world
benchmarks.
| Philip Ekman, Sebastian Bellevik, Christos Dimitrakakis, Aristide
Tossou | null | 1707.09678 | null | null |
Taming Non-stationary Bandits: A Bayesian Approach | stat.ML cs.LG | We consider the multi armed bandit problem in non-stationary environments.
Based on the Bayesian method, we propose a variant of Thompson Sampling which
can be used in both rested and restless bandit scenarios. Applying discounting
to the parameters of prior distribution, we describe a way to systematically
reduce the effect of past observations. Further, we derive the exact expression
for the probability of picking sub-optimal arms. By increasing the exploitative
value of Bayes' samples, we also provide an optimistic version of the
algorithm. Extensive empirical analysis is conducted under various scenarios to
validate the utility of proposed algorithms. A comparison study with various
state-of-the-arm algorithms is also included.
| Vishnu Raj and Sheetal Kalyani | null | 1707.09727 | null | null |
Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement
Learning Approach | cs.IT cs.LG cs.NI math.IT | With the advent of the 5th generation of wireless standards and an increasing
demand for higher throughput, methods to improve the spectral efficiency of
wireless systems have become very important. In the context of cognitive radio,
a substantial increase in throughput is possible if the secondary user can make
smart decisions regarding which channel to sense and when or how often to
sense. Here, we propose an algorithm to not only select a channel for data
transmission but also to predict how long the channel will remain unoccupied so
that the time spent on channel sensing can be minimized. Our algorithm learns
in two stages - a reinforcement learning approach for channel selection and a
Bayesian approach to determine the optimal duration for which sensing can be
skipped. Comparisons with other learning methods are provided through extensive
simulations. We show that the number of sensing is minimized with negligible
increase in primary interference; this implies that lesser energy is spent by
the secondary user in sensing and also higher throughput is achieved by saving
on sensing.
| Vishnu Raj, Irene Dias, Thulasi Tholeti and Sheetal Kalyani | 10.1109/JSTSP.2018.2798920 | 1707.09792 | null | null |
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning | cs.LG | Few-shot learning is challenging for learning algorithms that learn each task
in isolation and from scratch. In contrast, meta-learning learns from many
related tasks a meta-learner that can learn a new task more accurately and
faster with fewer examples, where the choice of meta-learners is crucial. In
this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner
that can initialize and adapt any differentiable learner in just one step, on
both supervised learning and reinforcement learning. Compared to the popular
meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and
can be learned more efficiently. Compared to the latest meta-learner MAML,
Meta-SGD has a much higher capacity by learning to learn not just the learner
initialization, but also the learner update direction and learning rate, all in
a single meta-learning process. Meta-SGD shows highly competitive performance
for few-shot learning on regression, classification, and reinforcement
learning.
| Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li | null | 1707.09835 | null | null |
Feature Extraction via Recurrent Random Deep Ensembles and its
Application in Gruop-level Happiness Estimation | cs.CV cs.LG | This paper presents a novel ensemble framework to extract highly
discriminative feature representation of image and its application for
group-level happpiness intensity prediction in wild. In order to generate
enough diversity of decisions, n convolutional neural networks are trained by
bootstrapping the training set and extract n features for each image from them.
A recurrent neural network (RNN) is then used to remember which network
extracts better feature and generate the final feature representation for one
individual image. Several group emotion models (GEM) are used to aggregate face
fea- tures in a group and use parameter-optimized support vector regressor
(SVR) to get the final results. Through extensive experiments, the great
effectiveness of the proposed recurrent random deep ensembles (RRDE) is
demonstrated in both structural and decisional ways. The best result yields a
0.55 root-mean-square error (RMSE) on validation set of HAPPEI dataset,
significantly better than the baseline of 0.78.
| Shitao Tang, Yichen Pan | null | 1707.09871 | null | null |
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM
Recurrent Neural Networks | cs.CV cs.LG | The outstanding pattern recognition performance of deep learning brings new
vitality to the synthetic aperture radar (SAR) automatic target recognition
(ATR). However, there is a limitation in current deep learning based ATR
solution that each learning process only handle one SAR image, namely learning
the static scattering information, while missing the space-varying information.
It is obvious that multi-aspect joint recognition introduced space-varying
scattering information should improve the classification accuracy and
robustness. In this paper, a novel multi-aspect-aware method is proposed to
achieve this idea through the bidirectional Long Short-Term Memory (LSTM)
recurrent neural networks based space-varying scattering information learning.
Specifically, we first select different aspect images to generate the
multi-aspect space-varying image sequences. Then, the Gabor filter and
three-patch local binary pattern (TPLBP) are progressively implemented to
extract a comprehensive spatial features, followed by dimensionality reduction
with the Multi-layer Perceptron (MLP) network. Finally, we design a
bidirectional LSTM recurrent neural network to learn the multi-aspect features
with further integrating the softmax classifier to achieve target recognition.
Experimental results demonstrate that the proposed method can achieve 99.9%
accuracy for 10-class recognition. Besides, its anti-noise and anti-confusion
performance are also better than the conventional deep learning based methods.
| Fan Zhang, Chen Hu, Qiang Yin, Wei Li, Hengchao Li and Wen Hong | 10.1109/ACCESS.2017.2773363 | 1707.09875 | null | null |
Quantum Privacy-Preserving Perceptron | quant-ph cs.CR cs.LG | With the extensive applications of machine learning, the issue of private or
sensitive data in the training examples becomes more and more serious: during
the training process, personal information or habits may be disclosed to
unexpected persons or organisations, which can cause serious privacy problems
or even financial loss. In this paper, we present a quantum privacy-preserving
algorithm for machine learning with perceptron. There are mainly two steps to
protect original training examples. Firstly when checking the current
classifier, quantum tests are employed to detect data user's possible
dishonesty. Secondly when updating the current classifier, private random noise
is used to protect the original data. The advantages of our algorithm are: (1)
it protects training examples better than the known classical methods; (2) it
requires no quantum database and thus is easy to implement.
| Shenggang Ying, Mingsheng Ying, Yuan Feng | null | 1707.09893 | null | null |
A breakthrough in Speech emotion recognition using Deep Retinal
Convolution Neural Networks | cs.SD cs.LG | Speech emotion recognition (SER) is to study the formation and change of
speaker's emotional state from the speech signal perspective, so as to make the
interaction between human and computer more intelligent. SER is a challenging
task that has encountered the problem of less training data and low prediction
accuracy. Here we propose a data augmentation algorithm based on the imaging
principle of the retina and convex lens, to acquire the different sizes of
spectrogram and increase the amount of training data by changing the distance
between the spectrogram and the convex lens. Meanwhile, with the help of deep
learning to get the high-level features, we propose the Deep Retinal
Convolution Neural Networks (DRCNNs) for SER and achieve the average accuracy
over 99%. The experimental results indicate that DRCNNs outperforms the
previous studies in terms of both the number of emotions and the accuracy of
recognition. Predictably, our results will dramatically improve human-computer
interaction.
| Yafeng Niu, Dongsheng Zou, Yadong Niu, Zhongshi He, Hua Tan | null | 1707.09917 | null | null |
Learning Neural Network Classifiers with Low Model Complexity | cs.LG | Modern neural network architectures for large-scale learning tasks have
substantially higher model complexities, which makes understanding, visualizing
and training these architectures difficult. Recent contributions to deep
learning techniques have focused on architectural modifications to improve
parameter efficiency and performance. In this paper, we derive a continuous and
differentiable error functional for a neural network that minimizes its
empirical error as well as a measure of the model complexity. The latter
measure is obtained by deriving a differentiable upper bound on the
Vapnik-Chervonenkis (VC) dimension of the classifier layer of a class of deep
networks. Using standard backpropagation, we realize a training rule that tries
to minimize the error on training samples, while improving generalization by
keeping the model complexity low. We demonstrate the effectiveness of our
formulation (the Low Complexity Neural Network - LCNN) across several deep
learning algorithms, and a variety of large benchmark datasets. We show that
hidden layer neurons in the resultant networks learn features that are crisp,
and in the case of image datasets, quantitatively sharper. Our proposed
approach yields benefits across a wide range of architectures, in comparison to
and in conjunction with methods such as Dropout and Batch Normalization, and
our results strongly suggest that deep learning techniques can benefit from
model complexity control methods such as the LCNN learning rule.
| Jayadeva, Himanshu Pant, Mayank Sharma, Abhimanyu Dubey, Sumit Soman,
Suraj Tripathi, Sai Guruju and Nihal Goalla | null | 1707.09933 | null | null |
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet
Residual Network | stat.ML cs.AI cs.CV cs.LG | Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally expensive. To address this problem, we recently proposed a
deep convolutional neural network (CNN) for low-dose X-ray CT and won the
second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the
texture were not fully recovered. To address this problem, here we propose a
novel framelet-based denoising algorithm using wavelet residual network which
synergistically combines the expressive power of deep learning and the
performance guarantee from the framelet-based denoising algorithms. The new
algorithms were inspired by the recent interpretation of the deep convolutional
neural network (CNN) as a cascaded convolution framelet signal representation.
Extensive experimental results confirm that the proposed networks have
significantly improved performance and preserves the detail texture of the
original images.
| Eunhee Kang, Jaejun Yoo, and Jong Chul Ye | null | 1707.09938 | null | null |
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking | stat.ML cs.IT cs.LG math.IT math.ST stat.TH | This paper is concerned with the problem of top-$K$ ranking from pairwise
comparisons. Given a collection of $n$ items and a few pairwise comparisons
across them, one wishes to identify the set of $K$ items that receive the
highest ranks. To tackle this problem, we adopt the logistic parametric model
--- the Bradley-Terry-Luce model, where each item is assigned a latent
preference score, and where the outcome of each pairwise comparison depends
solely on the relative scores of the two items involved. Recent works have made
significant progress towards characterizing the performance (e.g. the mean
square error for estimating the scores) of several classical methods, including
the spectral method and the maximum likelihood estimator (MLE). However, where
they stand regarding top-$K$ ranking remains unsettled.
We demonstrate that under a natural random sampling model, the spectral
method alone, or the regularized MLE alone, is minimax optimal in terms of the
sample complexity --- the number of paired comparisons needed to ensure exact
top-$K$ identification, for the fixed dynamic range regime. This is
accomplished via optimal control of the entrywise error of the score estimates.
We complement our theoretical studies by numerical experiments, confirming that
both methods yield low entrywise errors for estimating the underlying scores.
Our theory is established via a novel leave-one-out trick, which proves
effective for analyzing both iterative and non-iterative procedures. Along the
way, we derive an elementary eigenvector perturbation bound for probability
transition matrices, which parallels the Davis-Kahan $\sin\Theta$ theorem for
symmetric matrices. This also allows us to close the gap between the $\ell_2$
error upper bound for the spectral method and the minimax lower limit.
| Yuxin Chen, Jianqing Fan, Cong Ma, Kaizheng Wang | 10.1214/18-AOS1745 | 1707.09971 | null | null |
Which Distribution Distances are Sublinearly Testable? | cs.DS cs.IT cs.LG math.IT math.ST stat.TH | Given samples from an unknown distribution $p$ and a description of a
distribution $q$, are $p$ and $q$ close or far? This question of "identity
testing" has received significant attention in the case of testing whether $p$
and $q$ are equal or far in total variation distance. However, in recent work,
the following questions have been been critical to solving problems at the
frontiers of distribution testing:
-Alternative Distances: Can we test whether $p$ and $q$ are far in other
distances, say Hellinger?
-Tolerance: Can we test when $p$ and $q$ are close, rather than equal? And if
so, close in which distances?
Motivated by these questions, we characterize the complexity of distribution
testing under a variety of distances, including total variation, $\ell_2$,
Hellinger, Kullback-Leibler, and $\chi^2$. For each pair of distances $d_1$ and
$d_2$, we study the complexity of testing if $p$ and $q$ are close in $d_1$
versus far in $d_2$, with a focus on identifying which problems allow strongly
sublinear testers (i.e., those with complexity $O(n^{1 - \gamma})$ for some
$\gamma > 0$ where $n$ is the size of the support of the distributions $p$ and
$q$). We provide matching upper and lower bounds for each case. We also study
these questions in the case where we only have samples from $q$ (equivalence
testing), showing qualitative differences from identity testing in terms of
when tolerance can be achieved. Our algorithms fall into the classical paradigm
of $\chi^2$-statistics, but require crucial changes to handle the challenges
introduced by each distance we consider. Finally, we survey other recent
results in an attempt to serve as a reference for the complexity of various
distribution testing problems.
| Constantinos Daskalakis, Gautam Kamath, John Wright | null | 1708.00002 | null | null |
Interpretable Active Learning | stat.ML cs.LG | Active learning has long been a topic of study in machine learning. However,
as increasingly complex and opaque models have become standard practice, the
process of active learning, too, has become more opaque. There has been little
investigation into interpreting what specific trends and patterns an active
learning strategy may be exploring. This work expands on the Local
Interpretable Model-agnostic Explanations framework (LIME) to provide
explanations for active learning recommendations. We demonstrate how LIME can
be used to generate locally faithful explanations for an active learning
strategy, and how these explanations can be used to understand how different
models and datasets explore a problem space over time. In order to quantify the
per-subgroup differences in how an active learning strategy queries spatial
regions, we introduce a notion of uncertainty bias (based on disparate impact)
to measure the discrepancy in the confidence for a model's predictions between
one subgroup and another. Using the uncertainty bias measure, we show that our
query explanations accurately reflect the subgroup focus of the active learning
queries, allowing for an interpretable explanation of what is being learned as
points with similar sources of uncertainty have their uncertainty bias
resolved. We demonstrate that this technique can be applied to track
uncertainty bias over user-defined clusters or automatically generated clusters
based on the source of uncertainty.
| Richard L. Phillips, Kyu Hyun Chang and Sorelle A. Friedler | null | 1708.00049 | null | null |
Streaming Architecture for Large-Scale Quantized Neural Networks on an
FPGA-Based Dataflow Platform | cs.CV cs.AR cs.LG | Deep neural networks (DNNs) are used by different applications that are
executed on a range of computer architectures, from IoT devices to
supercomputers. The footprint of these networks is huge as well as their
computational and communication needs. In order to ease the pressure on
resources, research indicates that in many cases a low precision representation
(1-2 bit per parameter) of weights and other parameters can achieve similar
accuracy while requiring less resources. Using quantized values enables the use
of FPGAs to run NNs, since FPGAs are well fitted to these primitives; e.g.,
FPGAs provide efficient support for bitwise operations and can work with
arbitrary-precision representation of numbers.
This paper presents a new streaming architecture for running QNNs on FPGAs.
The proposed architecture scales out better than alternatives, allowing us to
take advantage of systems with multiple FPGAs. We also included support for
skip connections, that are used in state-of-the art NNs, and shown that our
architecture allows to add those connections almost for free. All this allowed
us to implement an 18-layer ResNet for 224x224 images classification, achieving
57.5% top-1 accuracy.
In addition, we implemented a full-sized quantized AlexNet. In contrast to
previous works, we use 2-bit activations instead of 1-bit ones, which improves
AlexNet's top-1 accuracy from 41.8% to 51.03% for the ImageNet classification.
Both AlexNet and ResNet can handle 1000-class real-time classification on an
FPGA.
Our implementation of ResNet-18 consumes 5x less power and is 4x slower for
ImageNet, when compared to the same NN on the latest Nvidia GPUs. Smaller NNs,
that fit a single FPGA, are running faster then on GPUs on small (32x32)
inputs, while consuming up to 20x less energy and power.
| Chaim Baskin, Natan Liss, Evgenii Zheltonozhskii, Alex M. Bronshtein,
Avi Mendelson | 10.1109/IPDPSW.2018.00032 | 1708.00052 | null | null |
Asymptotically optimal private estimation under mean square loss | math.ST cs.IT cs.LG math.IT stat.TH | We consider the minimax estimation problem of a discrete distribution with
support size $k$ under locally differential privacy constraints. A
privatization scheme is applied to each raw sample independently, and we need
to estimate the distribution of the raw samples from the privatized samples. A
positive number $\epsilon$ measures the privacy level of a privatization
scheme.
In our previous work (arXiv:1702.00610), we proposed a family of new
privatization schemes and the corresponding estimator. We also proved that our
scheme and estimator are order optimal in the regime $e^{\epsilon} \ll k$ under
both $\ell_2^2$ and $\ell_1$ loss. In other words, for a large number of
samples the worst-case estimation loss of our scheme was shown to differ from
the optimal value by at most a constant factor. In this paper, we eliminate
this gap by showing asymptotic optimality of the proposed scheme and estimator
under the $\ell_2^2$ (mean square) loss. More precisely, we show that for any
$k$ and $\epsilon,$ the ratio between the worst-case estimation loss of our
scheme and the optimal value approaches $1$ as the number of samples tends to
infinity.
| Min Ye and Alexander Barg | null | 1708.00059 | null | null |
Time-Dependent Representation for Neural Event Sequence Prediction | cs.LG | Existing sequence prediction methods are mostly concerned with
time-independent sequences, in which the actual time span between events is
irrelevant and the distance between events is simply the difference between
their order positions in the sequence. While this time-independent view of
sequences is applicable for data such as natural languages, e.g., dealing with
words in a sentence, it is inappropriate and inefficient for many real world
events that are observed and collected at unequally spaced points of time as
they naturally arise, e.g., when a person goes to a grocery store or makes a
phone call. The time span between events can carry important information about
the sequence dependence of human behaviors. In this work, we propose a set of
methods for using time in sequence prediction. Because neural sequence models
such as RNN are more amenable for handling token-like input, we propose two
methods for time-dependent event representation, based on the intuition on how
time is tokenized in everyday life and previous work on embedding
contextualization. We also introduce two methods for using next event duration
as regularization for training a sequence prediction model. We discuss these
methods based on recurrent neural nets. We evaluate these methods as well as
baseline models on five datasets that resemble a variety of sequence prediction
tasks. The experiments revealed that the proposed methods offer accuracy gain
over baseline models in a range of settings.
| Yang Li, Nan Du, Samy Bengio | null | 1708.00065 | null | null |
Learning Robust Representations for Computer Vision | stat.ML cs.CV cs.LG | Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.
| Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy and
Jayaraman Jayaraman Thiagarajan | null | 1708.00069 | null | null |
Efficient Regret Minimization in Non-Convex Games | cs.LG cs.GT stat.ML | We consider regret minimization in repeated games with non-convex loss
functions. Minimizing the standard notion of regret is computationally
intractable. Thus, we define a natural notion of regret which permits efficient
optimization and generalizes offline guarantees for convergence to an
approximate local optimum. We give gradient-based methods that achieve optimal
regret, which in turn guarantee convergence to equilibrium in this framework.
| Elad Hazan, Karan Singh, Cyril Zhang | null | 1708.00075 | null | null |
Bayesian Sparsification of Recurrent Neural Networks | stat.ML cs.CL cs.LG | Recurrent neural networks show state-of-the-art results in many text analysis
tasks but often require a lot of memory to store their weights. Recently
proposed Sparse Variational Dropout eliminates the majority of the weights in a
feed-forward neural network without significant loss of quality. We apply this
technique to sparsify recurrent neural networks. To account for recurrent
specifics we also rely on Binary Variational Dropout for RNN. We report 99.5%
sparsity level on sentiment analysis task without a quality drop and up to 87%
sparsity level on language modeling task with slight loss of accuracy.
| Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov | null | 1708.00077 | null | null |
Learning Algorithms for Active Learning | cs.LG | We introduce a model that learns active learning algorithms via metalearning.
For a distribution of related tasks, our model jointly learns: a data
representation, an item selection heuristic, and a method for constructing
prediction functions from labeled training sets. Our model uses the item
selection heuristic to gather labeled training sets from which to construct
prediction functions. Using the Omniglot and MovieLens datasets, we test our
model in synthetic and practical settings.
| Philip Bachman, Alessandro Sordoni and Adam Trischler | null | 1708.00088 | null | null |
Advantages and Limitations of using Successor Features for Transfer in
Reinforcement Learning | cs.AI cs.LG stat.ML | One question central to Reinforcement Learning is how to learn a feature
representation that supports algorithm scaling and re-use of learned
information from different tasks. Successor Features approach this problem by
learning a feature representation that satisfies a temporal constraint. We
present an implementation of an approach that decouples the feature
representation from the reward function, making it suitable for transferring
knowledge between domains. We then assess the advantages and limitations of
using Successor Features for transfer.
| Lucas Lehnert, Stefanie Tellex, and Michael L. Littman | null | 1708.00102 | null | null |
Learned in Translation: Contextualized Word Vectors | cs.CL cs.AI cs.LG | Computer vision has benefited from initializing multiple deep layers with
weights pretrained on large supervised training sets like ImageNet. Natural
language processing (NLP) typically sees initialization of only the lowest
layer of deep models with pretrained word vectors. In this paper, we use a deep
LSTM encoder from an attentional sequence-to-sequence model trained for machine
translation (MT) to contextualize word vectors. We show that adding these
context vectors (CoVe) improves performance over using only unsupervised word
and character vectors on a wide variety of common NLP tasks: sentiment analysis
(SST, IMDb), question classification (TREC), entailment (SNLI), and question
answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe
improves performance of our baseline models to the state of the art.
| Bryan McCann, James Bradbury, Caiming Xiong and Richard Socher | null | 1708.00107 | null | null |
A Continuous Relaxation of Beam Search for End-to-end Training of Neural
Sequence Models | cs.LG cs.CL cs.NE | Beam search is a desirable choice of test-time decoding algorithm for neural
sequence models because it potentially avoids search errors made by simpler
greedy methods. However, typical cross entropy training procedures for these
models do not directly consider the behaviour of the final decoding method. As
a result, for cross-entropy trained models, beam decoding can sometimes yield
reduced test performance when compared with greedy decoding. In order to train
models that can more effectively make use of beam search, we propose a new
training procedure that focuses on the final loss metric (e.g. Hamming loss)
evaluated on the output of beam search. While well-defined, this "direct loss"
objective is itself discontinuous and thus difficult to optimize. Hence, in our
approach, we form a sub-differentiable surrogate objective by introducing a
novel continuous approximation of the beam search decoding procedure. In
experiments, we show that optimizing this new training objective yields
substantially better results on two sequence tasks (Named Entity Recognition
and CCG Supertagging) when compared with both cross entropy trained greedy
decoding and cross entropy trained beam decoding baselines.
| Kartik Goyal, Graham Neubig, Chris Dyer and Taylor Berg-Kirkpatrick | null | 1708.00111 | null | null |
Retrofitting Distributional Embeddings to Knowledge Graphs with
Functional Relations | stat.ML cs.CL cs.LG | Knowledge graphs are a versatile framework to encode richly structured data
relationships, but it can be challenging to combine these graphs with
unstructured data. Methods for retrofitting pre-trained entity representations
to the structure of a knowledge graph typically assume that entities are
embedded in a connected space and that relations imply similarity. However,
useful knowledge graphs often contain diverse entities and relations (with
potentially disjoint underlying corpora) which do not accord with these
assumptions. To overcome these limitations, we present Functional Retrofitting,
a framework that generalizes current retrofitting methods by explicitly
modeling pairwise relations. Our framework can directly incorporate a variety
of pairwise penalty functions previously developed for knowledge graph
completion. Further, it allows users to encode, learn, and extract information
about relation semantics. We present both linear and neural instantiations of
the framework. Functional Retrofitting significantly outperforms existing
retrofitting methods on complex knowledge graphs and loses no accuracy on
simpler graphs (in which relations do imply similarity). Finally, we
demonstrate the utility of the framework by predicting new drug--disease
treatment pairs in a large, complex health knowledge graph.
| Benjamin J. Lengerich, Andrew L. Maas, Christopher Potts | null | 1708.00112 | null | null |
Compiling Deep Learning Models for Custom Hardware Accelerators | cs.DC cs.LG | Convolutional neural networks (CNNs) are the core of most state-of-the-art
deep learning algorithms specialized for object detection and classification.
CNNs are both computationally complex and embarrassingly parallel. Two
properties that leave room for potential software and hardware optimizations
for embedded systems. Given a programmable hardware accelerator with a CNN
oriented custom instructions set, the compiler's task is to exploit the
hardware's full potential, while abiding with the hardware constraints and
maintaining generality to run different CNN models with varying workload
properties. Snowflake is an efficient and scalable hardware accelerator
implemented on programmable logic devices. It implements a control pipeline for
a custom instruction set. The goal of this paper is to present Snowflake's
compiler that generates machine level instructions from Torch7 model
description files. The main software design points explored in this work are:
model structure parsing, CNN workload breakdown, loop rearrangement for memory
bandwidth optimizations and memory access balancing. The performance achieved
by compiler generated instructions matches against hand optimized code for
convolution layers. Generated instructions also efficiently execute AlexNet and
ResNet18 inference on Snowflake. Snowflake with $256$ processing units was
synthesized on Xilinx's Zynq XC7Z045 FPGA. At $250$ MHz, AlexNet achieved in
$93.6$ frames/s and $1.2$ GB/s of off-chip memory bandwidth, and $21.4$
frames/s and $2.2$ GB/s for ResNet18. Total on-chip power is $5$ W.
| Andre Xian Ming Chang, Aliasger Zaidy, Vinayak Gokhale, Eugenio
Culurciello | null | 1708.00117 | null | null |
Predicting Session Length in Media Streaming | cs.IR cs.LG | Session length is a very important aspect in determining a user's
satisfaction with a media streaming service. Being able to predict how long a
session will last can be of great use for various downstream tasks, such as
recommendations and ad scheduling. Most of the related literature on user
interaction duration has focused on dwell time for websites, usually in the
context of approximating post-click satisfaction either in search results, or
display ads. In this work we present the first analysis of session length in a
mobile-focused online service, using a real world data-set from a major music
streaming service. We use survival analysis techniques to show that the
characteristics of the length distributions can differ significantly between
users, and use gradient boosted trees with appropriate objectives to predict
the length of a session using only information available at its beginning. Our
evaluation on real world data illustrates that our proposed technique
outperforms the considered baseline.
| Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, Valery Rashkov | 10.1145/3077136.3080695 | 1708.0013 | null | null |
Grounding Language for Transfer in Deep Reinforcement Learning | cs.CL cs.AI cs.LG | In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.
| Karthik Narasimhan, Regina Barzilay and Tommi Jaakkola | null | 1708.00133 | null | null |
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers | cs.LG cs.AI stat.ML | Low-rank modeling has many important applications in computer vision and
machine learning. While the matrix rank is often approximated by the convex
nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
better empirical performance. However, the resulting optimization problem is
much more challenging. Recent state-of-the-art requires an expensive full SVD
in each iteration. In this paper, we show that for many commonly-used nonconvex
low-rank regularizers, a cutoff can be derived to automatically threshold the
singular values obtained from the proximal operator. This allows such operator
being efficiently approximated by power method. Based on it, we develop a
proximal gradient algorithm (and its accelerated variant) with inexact proximal
splitting and prove that a convergence rate of O(1/T) where T is the number of
iterations is guaranteed. Furthermore, we show the proposed algorithm can be
well parallelized, which achieves nearly linear speedup w.r.t the number of
threads. Extensive experiments are performed on matrix completion and robust
principal component analysis, which shows a significant speedup over the
state-of-the-art. Moreover, the matrix solution obtained is more accurate and
has a lower rank than that of the nuclear norm regularizer.
| Quanming Yao, James T.Kwok, Taifeng Wang and Tie-Yan Liu | null | 1708.00146 | null | null |
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis | cs.LG cs.CV stat.ML | Traditional Recurrent Neural Networks assume vectorized data as inputs.
However many data from modern science and technology come in certain structures
such as tensorial time series data. To apply the recurrent neural networks for
this type of data, a vectorisation process is necessary, while such a
vectorisation leads to the loss of the precise information of the spatial or
longitudinal dimensions. In addition, such a vectorized data is not an optimum
solution for learning the representation of the longitudinal data. In this
paper, we propose a new variant of tensorial neural networks which directly
take tensorial time series data as inputs. We call this new variant as
Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor
Tucker decomposition.
| Mingyuan Bai, Boyan Zhang, and Junbin Gao | null | 1708.00185 | null | null |
Video Object Segmentation with Re-identification | cs.CV cs.LG | Conventional video segmentation methods often rely on temporal continuity to
propagate masks. Such an assumption suffers from issues like drifting and
inability to handle large displacement. To overcome these issues, we formulate
an effective mechanism to prevent the target from being lost via adaptive
object re-identification. Specifically, our Video Object Segmentation with
Re-identification (VS-ReID) model includes a mask propagation module and a ReID
module. The former module produces an initial probability map by flow warping
while the latter module retrieves missing instances by adaptive matching. With
these two modules iteratively applied, our VS-ReID records a global mean
(Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017
DAVIS Challenge.
| Xiaoxiao Li, Yuankai Qi, Zhe Wang, Kai Chen, Ziwei Liu, Jianping Shi,
Ping Luo, Xiaoou Tang, Chen Change Loy | null | 1708.00197 | null | null |
Deep Asymmetric Multi-task Feature Learning | cs.LG stat.ML | We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can
learn deep representations shared across multiple tasks while effectively
preventing negative transfer that may happen in the feature sharing process.
Specifically, we introduce an asymmetric autoencoder term that allows reliable
predictors for the easy tasks to have high contribution to the feature learning
while suppressing the influences of unreliable predictors for more difficult
tasks. This allows the learning of less noisy representations, and enables
unreliable predictors to exploit knowledge from the reliable predictors via the
shared latent features. Such asymmetric knowledge transfer through shared
features is also more scalable and efficient than inter-task asymmetric
transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for
multitask learning and image classification, on which it significantly
outperforms existing symmetric and asymmetric multitask learning models, by
effectively preventing negative transfer in deep feature learning.
| Hae Beom Lee, Eunho Yang, Sung Ju Hwang | null | 1708.0026 | null | null |
SenGen: Sentence Generating Neural Variational Topic Model | cs.CL cs.LG stat.ML | We present a new topic model that generates documents by sampling a topic for
one whole sentence at a time, and generating the words in the sentence using an
RNN decoder that is conditioned on the topic of the sentence. We argue that
this novel formalism will help us not only visualize and model the topical
discourse structure in a document better, but also potentially lead to more
interpretable topics since we can now illustrate topics by sampling
representative sentences instead of bag of words or phrases. We present a
variational auto-encoder approach for learning in which we use a factorized
variational encoder that independently models the posterior over topical
mixture vectors of documents using a feed-forward network, and the posterior
over topic assignments to sentences using an RNN. Our preliminary experiments
on two different datasets indicate early promise, but also expose many
challenges that remain to be addressed.
| Ramesh Nallapati, Igor Melnyk, Abhishek Kumar and Bowen Zhou | null | 1708.00308 | null | null |
Attend and Predict: Understanding Gene Regulation by Selective Attention
on Chromatin | cs.LG cs.AI cs.NE | The past decade has seen a revolution in genomic technologies that enable a
flood of genome-wide profiling of chromatin marks. Recent literature tried to
understand gene regulation by predicting gene expression from large-scale
chromatin measurements. Two fundamental challenges exist for such learning
tasks: (1) genome-wide chromatin signals are spatially structured,
high-dimensional and highly modular; and (2) the core aim is to understand what
are the relevant factors and how they work together? Previous studies either
failed to model complex dependencies among input signals or relied on separate
feature analysis to explain the decisions. This paper presents an
attention-based deep learning approach; we call AttentiveChrome, that uses a
unified architecture to model and to interpret dependencies among chromatin
factors for controlling gene regulation. AttentiveChrome uses a hierarchy of
multiple Long short-term memory (LSTM) modules to encode the input signals and
to model how various chromatin marks cooperate automatically. AttentiveChrome
trains two levels of attention jointly with the target prediction, enabling it
to attend differentially to relevant marks and to locate important positions
per mark. We evaluate the model across 56 different cell types (tasks) in
human. Not only is the proposed architecture more accurate, but its attention
scores also provide a better interpretation than state-of-the-art feature
visualization methods such as saliency map.
Code and data are shared at www.deepchrome.org
| Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi | null | 1708.00339 | null | null |
Learning the kernel matrix by resampling | cs.LG | In this abstract paper, we introduce a new kernel learning method by a
nonparametric density estimator. The estimator consists of a group of
k-centroids clusterings. Each clustering randomly selects data points with
randomly selected features as its centroids, and learns a one-hot encoder by
one-nearest-neighbor optimization. The estimator generates a sparse
representation for each data point. Then, we construct a nonlinear kernel
matrix from the sparse representation of data. One major advantage of the
proposed kernel method is that it is relatively insensitive to its free
parameters, and therefore, it can produce reasonable results without parameter
tuning. Another advantage is that it is simple. We conjecture that the proposed
method can find its applications in many learning tasks or methods where sparse
representation or kernel matrix is explored. In this preliminary study, we have
applied the kernel matrix to spectral clustering. Our experimental results
demonstrate that the kernel generated by the proposed method outperforms the
well-tuned Gaussian RBF kernel. This abstract paper is used to protect the
idea, full versions will be updated later.
| Xiao-Lei Zhang | null | 1708.00365 | null | null |
Active Learning for Convolutional Neural Networks: A Core-Set Approach | stat.ML cs.CV cs.LG | Convolutional neural networks (CNNs) have been successfully applied to many
recognition and learning tasks using a universal recipe; training a deep model
on a very large dataset of supervised examples. However, this approach is
rather restrictive in practice since collecting a large set of labeled images
is very expensive. One way to ease this problem is coming up with smart ways
for choosing images to be labelled from a very large collection (ie. active
learning).
Our empirical study suggests that many of the active learning heuristics in
the literature are not effective when applied to CNNs in batch setting.
Inspired by these limitations, we define the problem of active learning as
core-set selection, ie. choosing set of points such that a model learned over
the selected subset is competitive for the remaining data points. We further
present a theoretical result characterizing the performance of any selected
subset using the geometry of the datapoints. As an active learning algorithm,
we choose the subset which is expected to yield best result according to our
characterization. Our experiments show that the proposed method significantly
outperforms existing approaches in image classification experiments by a large
margin.
| Ozan Sener, Silvio Savarese | null | 1708.00489 | null | null |
Car sharing through the data analysis lens | cs.CY cs.DB cs.LG | Car sharing is one the pillars of a smart transportation infrastructure, as
it is expected to reduce traffic congestion, parking demands and pollution in
our cities. From the point of view of demand modelling, car sharing is a weak
signal in the city landscape: only a small percentage of the population uses
it, and thus it is difficult to study reliably with traditional techniques such
as households travel diaries. In this work, we depart from these traditional
approaches and we rely on web-based, digital records about vehicle availability
in 10 European cities for one of the major active car sharing operators. We
discuss how vehicles are used, what are the main characteristics of car sharing
trips, whether events happening in certain areas are predictable or not, and
how the spatio-temporal information about vehicle availability can be used to
infer how different zones in a city are used by customers. We conclude the
paper by presenting a direct application of the analysis of the dataset, aimed
at identifying where to locate maintenance facilities within the car sharing
operational area.
| Chiara Boldrini, Raffaele Bruno and Haitam Laarabi | null | 1708.00497 | null | null |
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