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Hemingway: Modeling Distributed Optimization Algorithms | cs.DC cs.AI cs.LG | Distributed optimization algorithms are widely used in many industrial
machine learning applications. However choosing the appropriate algorithm and
cluster size is often difficult for users as the performance and convergence
rate of optimization algorithms vary with the size of the cluster. In this
paper we make the case for an ML-optimizer that can select the appropriate
algorithm and cluster size to use for a given problem. To do this we propose
building two models: one that captures the system level characteristics of how
computation, communication change as we increase cluster sizes and another that
captures how convergence rates change with cluster sizes. We present
preliminary results from our prototype implementation called Hemingway and
discuss some of the challenges involved in developing such a system.
| Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez | null | 1702.05865 | null | null |
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in
Neural Networks | cs.LG cs.AI stat.ML | Traditionally, multi-layer neural networks use dot product between the output
vector of previous layer and the incoming weight vector as the input to
activation function. The result of dot product is unbounded, thus increases the
risk of large variance. Large variance of neuron makes the model sensitive to
the change of input distribution, thus results in poor generalization, and
aggravates the internal covariate shift which slows down the training. To bound
dot product and decrease the variance, we propose to use cosine similarity or
centered cosine similarity (Pearson Correlation Coefficient) instead of dot
product in neural networks, which we call cosine normalization. We compare
cosine normalization with batch, weight and layer normalization in
fully-connected neural networks as well as convolutional networks on the data
sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN. Experiments show that
cosine normalization achieves better performance than other normalization
techniques.
| Chunjie Luo, Jianfeng Zhan, Lei Wang, Qiang Yang | null | 1702.0587 | null | null |
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield
Networks with Arbitrary Priors | cond-mat.dis-nn cs.LG physics.data-an stat.ML | Restricted Boltzmann Machines are described by the Gibbs measure of a
bipartite spin glass, which in turn corresponds to the one of a generalised
Hopfield network. This equivalence allows us to characterise the state of these
systems in terms of retrieval capabilities, both at low and high load. We study
the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as
the pattern (i.e. weight) distribution and spin (i.e. unit) priors vary
smoothly from Gaussian real variables to Boolean discrete variables. Our
analysis shows that the presence of a retrieval phase is robust and not
peculiar to the standard Hopfield model with Boolean patterns. The retrieval
region is larger when the pattern entries and retrieval units get more peaked
and, conversely, when the hidden units acquire a broader prior and therefore
have a stronger response to high fields. Moreover, at low load retrieval always
exists below some critical temperature, for every pattern distribution ranging
from the Boolean to the Gaussian case.
| Adriano Barra, Giuseppe Genovese, Peter Sollich, Daniele Tantari | 10.1103/PhysRevE.97.022310 | 1702.05882 | null | null |
The importance of stain normalization in colorectal tissue
classification with convolutional networks | cs.CV cs.LG | The development of reliable imaging biomarkers for the analysis of colorectal
cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images
requires an accurate and reproducible classification of the main tissue
components in the image. In this paper, we propose a system for CRC tissue
classification based on convolutional networks (ConvNets). We investigate the
importance of stain normalization in tissue classification of CRC tissue
samples in H&E-stained images. Furthermore, we report the performance of
ConvNets on a cohort of rectal cancer samples and on an independent publicly
available dataset of colorectal H&E images.
| Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel
Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris
Nagtegaal, Jeroen van der Laak | null | 1702.05931 | null | null |
Wages of wins: could an amateur make money from match outcome
predictions? | stat.AP cs.LG | Evaluating the accuracies of models for match outcome predictions is nice and
well but in the end the real proof is in the money to be made by betting. To
evaluate the question whether the models developed by us could be used easily
to make money via sports betting, we evaluate three cases: NCAAB post-season,
NBA season, and NFL season, and find that it is possible yet not without its
pitfalls. In particular, we illustrate that high accuracy does not
automatically equal high pay-out, by looking at the type of match-ups that are
predicted correctly by different models.
| Albrecht Zimmermann | null | 1702.05982 | null | null |
Generating Adversarial Malware Examples for Black-Box Attacks Based on
GAN | cs.LG cs.CR | Machine learning has been used to detect new malware in recent years, while
malware authors have strong motivation to attack such algorithms. Malware
authors usually have no access to the detailed structures and parameters of the
machine learning models used by malware detection systems, and therefore they
can only perform black-box attacks. This paper proposes a generative
adversarial network (GAN) based algorithm named MalGAN to generate adversarial
malware examples, which are able to bypass black-box machine learning based
detection models. MalGAN uses a substitute detector to fit the black-box
malware detection system. A generative network is trained to minimize the
generated adversarial examples' malicious probabilities predicted by the
substitute detector. The superiority of MalGAN over traditional gradient based
adversarial example generation algorithms is that MalGAN is able to decrease
the detection rate to nearly zero and make the retraining based defensive
method against adversarial examples hard to work.
| Weiwei Hu and Ying Tan | null | 1702.05983 | null | null |
An Extended Framework for Marginalized Domain Adaptation | cs.CV cs.LG | We propose an extended framework for marginalized domain adaptation, aimed at
addressing unsupervised, supervised and semi-supervised scenarios. We argue
that the denoising principle should be extended to explicitly promote
domain-invariant features as well as help the classification task. Therefore we
propose to jointly learn the data auto-encoders and the target classifiers.
First, in order to make the denoised features domain-invariant, we propose a
domain regularization that may be either a domain prediction loss or a maximum
mean discrepancy between the source and target data. The noise marginalization
in this case is reduced to solving the linear matrix system $AX=B$ which has a
closed-form solution. Second, in order to help the classification, we include a
class regularization term. Adding this component reduces the learning problem
to solving a Sylvester linear matrix equation $AX+BX=C$, for which an efficient
iterative procedure exists as well. We did an extensive study to assess how
these regularization terms improve the baseline performance in the three domain
adaptation scenarios and present experimental results on two image and one text
benchmark datasets, conventionally used for validating domain adaptation
methods. We report our findings and comparison with state-of-the-art methods.
| Gabriela Csurka, Boris Chidlovski, Stephane Clinchant and Sophia
Michel | null | 1702.05993 | null | null |
Learning to Multi-Task by Active Sampling | cs.NE cs.LG | One of the long-standing challenges in Artificial Intelligence for learning
goal-directed behavior is to build a single agent which can solve multiple
tasks. Recent progress in multi-task learning for goal-directed sequential
problems has been in the form of distillation based learning wherein a student
network learns from multiple task-specific expert networks by mimicking the
task-specific policies of the expert networks. While such approaches offer a
promising solution to the multi-task learning problem, they require supervision
from large expert networks which require extensive data and computation time
for training. In this work, we propose an efficient multi-task learning
framework which solves multiple goal-directed tasks in an on-line setup without
the need for expert supervision. Our work uses active learning principles to
achieve multi-task learning by sampling the harder tasks more than the easier
ones. We propose three distinct models under our active sampling framework. An
adaptive method with extremely competitive multi-tasking performance. A
UCB-based meta-learner which casts the problem of picking the next task to
train on as a multi-armed bandit problem. A meta-learning method that casts the
next-task picking problem as a full Reinforcement Learning problem and uses
actor critic methods for optimizing the multi-tasking performance directly. We
demonstrate results in the Atari 2600 domain on seven multi-tasking instances:
three 6-task instances, one 8-task instance, two 12-task instances and one
21-task instance.
| Sahil Sharma, Ashutosh Jha, Parikshit Hegde, Balaraman Ravindran | null | 1702.06053 | null | null |
Learning to Repeat: Fine Grained Action Repetition for Deep
Reinforcement Learning | cs.LG cs.AI cs.NE | Reinforcement Learning algorithms can learn complex behavioral patterns for
sequential decision making tasks wherein an agent interacts with an environment
and acquires feedback in the form of rewards sampled from it. Traditionally,
such algorithms make decisions, i.e., select actions to execute, at every
single time step of the agent-environment interactions. In this paper, we
propose a novel framework, Fine Grained Action Repetition (FiGAR), which
enables the agent to decide the action as well as the time scale of repeating
it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm
which maintains an explicit policy estimate by enabling temporal abstractions
in the action space. We empirically demonstrate the efficacy of our framework
by showing performance improvements on top of three policy search algorithms in
different domains: Asynchronous Advantage Actor Critic in the Atari 2600
domain, Trust Region Policy Optimization in Mujoco domain and Deep
Deterministic Policy Gradients in the TORCS car racing domain.
| Sahil Sharma, Aravind Srinivas, Balaraman Ravindran | null | 1702.06054 | null | null |
Learning Non-Discriminatory Predictors | cs.LG | We consider learning a predictor which is non-discriminatory with respect to
a "protected attribute" according to the notion of "equalized odds" proposed by
Hardt et al. [2016]. We study the problem of learning such a non-discriminatory
predictor from a finite training set, both statistically and computationally.
We show that a post-hoc correction approach, as suggested by Hardt et al, can
be highly suboptimal, present a nearly-optimal statistical procedure, argue
that the associated computational problem is intractable, and suggest a second
moment relaxation of the non-discrimination definition for which learning is
tractable.
| Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan
Srebro | null | 1702.06081 | null | null |
Label Distribution Learning Forests | cs.LG cs.CV | Label distribution learning (LDL) is a general learning framework, which
assigns to an instance a distribution over a set of labels rather than a single
label or multiple labels. Current LDL methods have either restricted
assumptions on the expression form of the label distribution or limitations in
representation learning, e.g., to learn deep features in an end-to-end manner.
This paper presents label distribution learning forests (LDLFs) - a novel label
distribution learning algorithm based on differentiable decision trees, which
have several advantages: 1) Decision trees have the potential to model any
general form of label distributions by a mixture of leaf node predictions. 2)
The learning of differentiable decision trees can be combined with
representation learning. We define a distribution-based loss function for a
forest, enabling all the trees to be learned jointly, and show that an update
function for leaf node predictions, which guarantees a strict decrease of the
loss function, can be derived by variational bounding. The effectiveness of the
proposed LDLFs is verified on several LDL tasks and a computer vision
application, showing significant improvements to the state-of-the-art LDL
methods.
| Wei Shen, Kai Zhao, Yilu Guo, Alan Yuille | null | 1702.06086 | null | null |
An Attention-Based Deep Net for Learning to Rank | cs.LG | In information retrieval, learning to rank constructs a machine-based ranking
model which given a query, sorts the search results by their degree of
relevance or importance to the query. Neural networks have been successfully
applied to this problem, and in this paper, we propose an attention-based deep
neural network which better incorporates different embeddings of the queries
and search results with an attention-based mechanism. This model also applies a
decoder mechanism to learn the ranks of the search results in a listwise
fashion. The embeddings are trained with convolutional neural networks or the
word2vec model. We demonstrate the performance of this model with image
retrieval and text querying data sets.
| Baiyang Wang, Diego Klabjan | null | 1702.06106 | null | null |
On the Consistency of $k$-means++ algorithm | cs.LG | We prove in this paper that the expected value of the objective function of
the $k$-means++ algorithm for samples converges to population expected value.
As $k$-means++, for samples, provides with constant factor approximation for
$k$-means objectives, such an approximation can be achieved for the population
with increase of the sample size.
This result is of potential practical relevance when one is considering using
subsampling when clustering large data sets (large data bases).
| Mieczys{\l}aw A. K{\l}opotek | null | 1702.0612 | null | null |
Developing a comprehensive framework for multimodal feature extraction | cs.CV cs.IR cs.LG cs.MM | Feature extraction is a critical component of many applied data science
workflows. In recent years, rapid advances in artificial intelligence and
machine learning have led to an explosion of feature extraction tools and
services that allow data scientists to cheaply and effectively annotate their
data along a vast array of dimensions---ranging from detecting faces in images
to analyzing the sentiment expressed in coherent text. Unfortunately, the
proliferation of powerful feature extraction services has been mirrored by a
corresponding expansion in the number of distinct interfaces to feature
extraction services. In a world where nearly every new service has its own API,
documentation, and/or client library, data scientists who need to combine
diverse features obtained from multiple sources are often forced to write and
maintain ever more elaborate feature extraction pipelines. To address this
challenge, we introduce a new open-source framework for comprehensive
multimodal feature extraction. Pliers is an open-source Python package that
supports standardized annotation of diverse data types (video, images, audio,
and text), and is expressly with both ease-of-use and extensibility in mind.
Users can apply a wide range of pre-existing feature extraction tools to their
data in just a few lines of Python code, and can also easily add their own
custom extractors by writing modular classes. A graph-based API enables rapid
development of complex feature extraction pipelines that output results in a
single, standardized format. We describe the package's architecture, detail its
major advantages over previous feature extraction toolboxes, and use a sample
application to a large functional MRI dataset to illustrate how pliers can
significantly reduce the time and effort required to construct sophisticated
feature extraction workflows while increasing code clarity and maintainability.
| Quinten McNamara, Alejandro de la Vega, and Tal Yarkoni | null | 1702.06151 | null | null |
Bayesian Boolean Matrix Factorisation | stat.ML cs.LG cs.NA q-bio.GN q-bio.QM stat.ME | Boolean matrix factorisation aims to decompose a binary data matrix into an
approximate Boolean product of two low rank, binary matrices: one containing
meaningful patterns, the other quantifying how the observations can be
expressed as a combination of these patterns. We introduce the OrMachine, a
probabilistic generative model for Boolean matrix factorisation and derive a
Metropolised Gibbs sampler that facilitates efficient parallel posterior
inference. On real world and simulated data, our method outperforms all
currently existing approaches for Boolean matrix factorisation and completion.
This is the first method to provide full posterior inference for Boolean Matrix
factorisation which is relevant in applications, e.g. for controlling false
positive rates in collaborative filtering and, crucially, improves the
interpretability of the inferred patterns. The proposed algorithm scales to
large datasets as we demonstrate by analysing single cell gene expression data
in 1.3 million mouse brain cells across 11 thousand genes on commodity
hardware.
| Tammo Rukat and Chris C. Holmes and Michalis K. Titsias and
Christopher Yau | null | 1702.06166 | null | null |
Structured signal recovery from quadratic measurements: Breaking sample
complexity barriers via nonconvex optimization | cs.LG cs.IT math.FA math.IT math.OC stat.ML | This paper concerns the problem of recovering an unknown but structured
signal $x \in R^n$ from $m$ quadratic measurements of the form
$y_r=|<a_r,x>|^2$ for $r=1,2,...,m$. We focus on the under-determined setting
where the number of measurements is significantly smaller than the dimension of
the signal ($m<<n$). We formulate the recovery problem as a nonconvex
optimization problem where prior structural information about the signal is
enforced through constrains on the optimization variables. We prove that
projected gradient descent, when initialized in a neighborhood of the desired
signal, converges to the unknown signal at a linear rate. These results hold
for any constraint set (convex or nonconvex) providing convergence guarantees
to the global optimum even when the objective function and constraint set is
nonconvex. Furthermore, these results hold with a number of measurements that
is only a constant factor away from the minimal number of measurements required
to uniquely identify the unknown signal. Our results provide the first provably
tractable algorithm for this data-poor regime, breaking local sample complexity
barriers that have emerged in recent literature. In a companion paper we
demonstrate favorable properties for the optimization problem that may enable
similar results to continue to hold more globally (over the entire ambient
space). Collectively these two papers utilize and develop powerful tools for
uniform convergence of empirical processes that may have broader implications
for rigorous understanding of constrained nonconvex optimization heuristics.
The mathematical results in this paper also pave the way for a new generation
of data-driven phase-less imaging systems that can utilize prior information to
significantly reduce acquisition time and enhance image reconstruction,
enabling nano-scale imaging at unprecedented speeds and resolutions.
| Mahdi Soltanolkotabi | null | 1702.06175 | null | null |
Survey of reasoning using Neural networks | cs.LG cs.AI cs.NE | Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small memory contexts, but as context becomes larger than a threshold, it is
difficult to use them. The Solution is to use large external memory. Still, it
poses many challenges like, how to train neural networks for discrete memory
representation, how to describe long term dependencies in sequential data etc.
Most prominent neural architectures for such tasks are Memory networks:
inference components combined with long term memory and Neural Turing Machines:
neural networks using external memory resources. Also, additional techniques
like attention mechanism, end to end gradient descent on discrete memory
representation are needed to support these solutions. Preliminary results of
above neural architectures on simple algorithms (sorting, copying) and Question
Answering (based on story, dialogs) application are comparable with the state
of the art. In this paper, I explain these architectures (in general), the
additional techniques used and the results of their application.
| Amit Sahu | null | 1702.06186 | null | null |
Filtering Tweets for Social Unrest | cs.CL cs.IR cs.LG stat.ML | Since the events of the Arab Spring, there has been increased interest in
using social media to anticipate social unrest. While efforts have been made
toward automated unrest prediction, we focus on filtering the vast volume of
tweets to identify tweets relevant to unrest, which can be provided to
downstream users for further analysis. We train a supervised classifier that is
able to label Arabic language tweets as relevant to unrest with high
reliability. We examine the relationship between training data size and
performance and investigate ways to optimize the model building process while
minimizing cost. We also explore how confidence thresholds can be set to
achieve desired levels of performance.
| Alan Mishler, Kevin Wonus, Wendy Chambers and Michael Bloodgood | 10.1109/ICSC.2017.75 | 1702.06216 | null | null |
Determination of hysteresis in finite-state random walks using Bayesian
cross validation | stat.ME cs.LG physics.data-an q-bio.QM | Consider the problem of modeling hysteresis for finite-state random walks
using higher-order Markov chains. This Letter introduces a Bayesian framework
to determine, from data, the number of prior states of recent history upon
which a trajectory is statistically dependent. The general recommendation is to
use leave-one-out cross validation, using an easily-computable formula that is
provided in closed form. Importantly, Bayes factors using flat model priors are
biased in favor of too-complex a model (more hysteresis) when a large amount of
data is present and the Akaike information criterion (AIC) is biased in favor
of too-sparse a model (less hysteresis) when few data are present.
| Joshua C. Chang | null | 1702.06221 | null | null |
Beating the World's Best at Super Smash Bros. with Deep Reinforcement
Learning | cs.LG cs.AI | There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of RL tasks, from Atari games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning, that learn to play from experience with minimal knowledge of the
specific domain of interest. In this work, we will investigate the performance
of these methods on Super Smash Bros. Melee (SSBM), a popular console fighting
game. The SSBM environment has complex dynamics and partial observability,
making it challenging for human and machine alike. The multi-player aspect
poses an additional challenge, as the vast majority of recent advances in RL
have focused on single-agent environments. Nonetheless, we will show that it is
possible to train agents that are competitive against and even surpass human
professionals, a new result for the multi-player video game setting.
| Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum | null | 1702.0623 | null | null |
Exact tensor completion with sum-of-squares | cs.LG cs.CC cs.DS cs.IT math.IT stat.ML | We obtain the first polynomial-time algorithm for exact tensor completion
that improves over the bound implied by reduction to matrix completion. The
algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal
components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed
entries of the tensor. This bound improves over the previous best one of
$r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound
also matches the best known results for the easier problem of approximate
tensor completion (Barak & Moitra, 2015).
Our algorithm and analysis extends seminal results for exact matrix
completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares
method. The main technical challenge is to show that a small number of randomly
chosen monomials are enough to construct a degree-3 polynomial with precisely
planted orthogonal global optima over the sphere and that this fact can be
certified within the sum-of-squares proof system.
| Aaron Potechin, David Steurer | null | 1702.06237 | null | null |
Sample Efficient Policy Search for Optimal Stopping Domains | cs.AI cs.LG | Optimal stopping problems consider the question of deciding when to stop an
observation-generating process in order to maximize a return. We examine the
problem of simultaneously learning and planning in such domains, when data is
collected directly from the environment. We propose GFSE, a simple and flexible
model-free policy search method that reuses data for sample efficiency by
leveraging problem structure. We bound the sample complexity of our approach to
guarantee uniform convergence of policy value estimates, tightening existing
PAC bounds to achieve logarithmic dependence on horizon length for our setting.
We also examine the benefit of our method against prevalent model-based and
model-free approaches on 3 domains taken from diverse fields.
| Karan Goel, Christoph Dann and Emma Brunskill | null | 1702.06238 | null | null |
SAR: Semantic Analysis for Recommendation | cs.IR cs.LG | Recommendation system is a common demand in daily life and matrix completion
is a widely adopted technique for this task. However, most matrix completion
methods lack semantic interpretation and usually result in weak-semantic
recommendations. To this end, this paper proposes a $S$emantic $A$nalysis
approach for $R$ecommendation systems $(SAR)$, which applies a two-level
hierarchical generative process that assigns semantic properties and categories
for user and item. $SAR$ learns semantic representations of users/items merely
from user ratings on items, which offers a new path to recommendation by
semantic matching with the learned representations. Extensive experiments
demonstrate $SAR$ outperforms other state-of-the-art baselines substantially.
| Han Xiao, Lian Meng | null | 1702.06247 | null | null |
Memory and Communication Efficient Distributed Stochastic Optimization
with Minibatch-Prox | cs.LG | We present and analyze an approach for distributed stochastic optimization
which is statistically optimal and achieves near-linear speedups (up to
logarithmic factors). Our approach allows a communication-memory tradeoff, with
either logarithmic communication but linear memory, or polynomial communication
and a corresponding polynomial reduction in required memory. This
communication-memory tradeoff is achieved through minibatch-prox iterations
(minibatch passive-aggressive updates), where a subproblem on a minibatch is
solved at each iteration. We provide a novel analysis for such a minibatch-prox
procedure which achieves the statistical optimal rate regardless of minibatch
size and smoothness, thus significantly improving on prior work.
| Jialei Wang, Weiran Wang, Nathan Srebro | null | 1702.06269 | null | null |
On the (Statistical) Detection of Adversarial Examples | cs.CR cs.LG stat.ML | Machine Learning (ML) models are applied in a variety of tasks such as
network intrusion detection or Malware classification. Yet, these models are
vulnerable to a class of malicious inputs known as adversarial examples. These
are slightly perturbed inputs that are classified incorrectly by the ML model.
The mitigation of these adversarial inputs remains an open problem. As a step
towards understanding adversarial examples, we show that they are not drawn
from the same distribution than the original data, and can thus be detected
using statistical tests. Using thus knowledge, we introduce a complimentary
approach to identify specific inputs that are adversarial. Specifically, we
augment our ML model with an additional output, in which the model is trained
to classify all adversarial inputs. We evaluate our approach on multiple
adversarial example crafting methods (including the fast gradient sign and
saliency map methods) with several datasets. The statistical test flags sample
sets containing adversarial inputs confidently at sample sizes between 10 and
100 data points. Furthermore, our augmented model either detects adversarial
examples as outliers with high accuracy (> 80%) or increases the adversary's
cost - the perturbation added - by more than 150%. In this way, we show that
statistical properties of adversarial examples are essential to their
detection.
| Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes,
Patrick McDaniel | null | 1702.0628 | null | null |
Convolutional Recurrent Neural Networks for Polyphonic Sound Event
Detection | cs.LG cs.SD | Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.
| Emre \c{C}ak{\i}r, Giambattista Parascandolo, Toni Heittola, Heikki
Huttunen and Tuomas Virtanen | 10.1109/TASLP.2017.2690575 | 1702.06286 | null | null |
Convolution Aware Initialization | cs.LG stat.ML | Initialization of parameters in deep neural networks has been shown to have a
big impact on the performance of the networks (Mishkin & Matas, 2015). The
initialization scheme devised by He et al, allowed convolution activations to
carry a constrained mean which allowed deep networks to be trained effectively
(He et al., 2015a). Orthogonal initializations and more generally orthogonal
matrices in standard recurrent networks have been proved to eradicate the
vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of
current initialization schemes do not take fully into account the intrinsic
structure of the convolution operator. Using the duality of the Fourier
transform and the convolution operator, Convolution Aware Initialization builds
orthogonal filters in the Fourier space, and using the inverse Fourier
transform represents them in the standard space. With Convolution Aware
Initialization we noticed not only higher accuracy and lower loss, but faster
convergence. We achieve new state of the art on the CIFAR10 dataset, and
achieve close to state of the art on various other tasks.
| Armen Aghajanyan | null | 1702.06295 | null | null |
Towards a Common Implementation of Reinforcement Learning for Multiple
Robotic Tasks | cs.AI cs.LG cs.RO | Mobile robots are increasingly being employed for performing complex tasks in
dynamic environments. Reinforcement learning (RL) methods are recognized to be
promising for specifying such tasks in a relatively simple manner. However, the
strong dependency between the learning method and the task to learn is a
well-known problem that restricts practical implementations of RL in robotics,
often requiring major modifications of parameters and adding other techniques
for each particular task. In this paper we present a practical core
implementation of RL which enables the learning process for multiple robotic
tasks with minimal per-task tuning or none. Based on value iteration methods,
this implementation includes a novel approach for action selection, called
Q-biased softmax regression (QBIASSR), which avoids poor performance of the
learning process when the robot reaches new unexplored states. Our approach
takes advantage of the structure of the state space by attending the physical
variables involved (e.g., distances to obstacles, X,Y,{\theta} pose, etc.),
thus experienced sets of states may favor the decision-making process of
unexplored or rarely-explored states. This improvement has a relevant role in
reducing the tuning of the algorithm for particular tasks. Experiments with
real and simulated robots, performed with the software framework also
introduced here, show that our implementation is effectively able to learn
different robotic tasks without tuning the learning method. Results also
suggest that the combination of true online SARSA({\lambda}) with QBIASSR can
outperform the existing RL core algorithms in low-dimensional robotic tasks.
| Angel Mart\'inez-Tenor, Juan Antonio Fern\'andez-Madrigal, Ana
Cruz-Mart\'in and Javier Gonz\'alez-Jim\'enez | 10.1016/j.eswa.2017.11.011 | 1702.06329 | null | null |
Fast rates for online learning in Linearly Solvable Markov Decision
Processes | cs.LG math.OC stat.ML | We study the problem of online learning in a class of Markov decision
processes known as linearly solvable MDPs. In the stationary version of this
problem, a learner interacts with its environment by directly controlling the
state transitions, attempting to balance a fixed state-dependent cost and a
certain smooth cost penalizing extreme control inputs. In the current paper, we
consider an online setting where the state costs may change arbitrarily between
consecutive rounds, and the learner only observes the costs at the end of each
respective round. We are interested in constructing algorithms for the learner
that guarantee small regret against the best stationary control policy chosen
in full knowledge of the cost sequence. Our main result is showing that the
smoothness of the control cost enables the simple algorithm of following the
leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly
improving on the best known regret bound of order $T^{3/4}$ for this setting.
| Gergely Neu and Vicen\c{c} G\'omez | null | 1702.06341 | null | null |
Scalable Demand-Aware Recommendation | cs.LG | Recommendation for e-commerce with a mix of durable and nondurable goods has
characteristics that distinguish it from the well-studied media recommendation
problem. The demand for items is a combined effect of form utility and time
utility, i.e., a product must both be intrinsically appealing to a consumer and
the time must be right for purchase. In particular for durable goods, time
utility is a function of inter-purchase duration within product category
because consumers are unlikely to purchase two items in the same category in
close temporal succession. Moreover, purchase data, in contrast to ratings
data, is implicit with non-purchases not necessarily indicating dislike.
Together, these issues give rise to the positive-unlabeled demand-aware
recommendation problem that we pose via joint low-rank tensor completion and
product category inter-purchase duration vector estimation. We further relax
this problem and propose a highly scalable alternating minimization approach
with which we can solve problems with millions of users and millions of items
in a single thread. We also show superior prediction accuracies on multiple
real-world data sets.
| Jinfeng Yi, Cho-Jui Hsieh, Kush Varshney, Lijun Zhang, Yao Li | null | 1702.06347 | null | null |
Interpreting Outliers: Localized Logistic Regression for Density Ratio
Estimation | stat.ML cs.LG | We propose an inlier-based outlier detection method capable of both
identifying the outliers and explaining why they are outliers, by identifying
the outlier-specific features. Specifically, we employ an inlier-based outlier
detection criterion, which uses the ratio of inlier and test probability
densities as a measure of plausibility of being an outlier. For estimating the
density ratio function, we propose a localized logistic regression algorithm.
Thanks to the locality of the model, variable selection can be
outlier-specific, and will help interpret why points are outliers in a
high-dimensional space. Through synthetic experiments, we show that the
proposed algorithm can successfully detect the important features for outliers.
Moreover, we show that the proposed algorithm tends to outperform existing
algorithms in benchmark datasets.
| Makoto Yamada, Song Liu, Samuel Kaski | null | 1702.06354 | null | null |
Negative-Unlabeled Tensor Factorization for Location Category Inference
from Highly Inaccurate Mobility Data | cs.LG | Identifying significant location categories visited by mobile users is the
key to a variety of applications. This is an extremely challenging task due to
the possible deviation between the estimated location coordinate and the actual
location, which could be on the order of kilometers. To estimate the actual
location category more precisely, we propose a novel tensor factorization
framework, through several key observations including the intrinsic
correlations between users, to infer the most likely location categories within
the location uncertainty circle. In addition, the proposed algorithm can also
predict where users are even in the absence of location information. In order
to efficiently solve the proposed framework, we propose a parameter-free and
scalable optimization algorithm by effectively exploring the sparse and
low-rank structure of the tensor. Our empirical studies show that the proposed
algorithm is both efficient and effective: it can solve problems with millions
of users and billions of location updates, and also provides superior
prediction accuracies on real-world location updates and check-in data sets.
| Jinfeng Yi, Qi Lei, Wesley Gifford, Ji Liu, Junchi Yan | null | 1702.06362 | null | null |
Causal Inference on Multivariate and Mixed-Type Data | stat.ML cs.LG | Given data over the joint distribution of two random variables $X$ and $Y$,
we consider the problem of inferring the most likely causal direction between
$X$ and $Y$. In particular, we consider the general case where both $X$ and $Y$
may be univariate or multivariate, and of the same or mixed data types. We take
an information theoretic approach, based on Kolmogorov complexity, from which
it follows that first describing the data over cause and then that of effect
given cause is shorter than the reverse direction.
The ideal score is not computable, but can be approximated through the
Minimum Description Length (MDL) principle. Based on MDL, we propose two
scores, one for when both $X$ and $Y$ are of the same single data type, and one
for when they are mixed-type. We model dependencies between $X$ and $Y$ using
classification and regression trees. As inferring the optimal model is NP-hard,
we propose Crack, a fast greedy algorithm to determine the most likely causal
direction directly from the data.
Empirical evaluation on a wide range of data shows that Crack reliably, and
with high accuracy, infers the correct causal direction on both univariate and
multivariate cause-effect pairs over both single and mixed-type data.
| Alexander Marx and Jilles Vreeken | null | 1702.06385 | null | null |
A GPU-Outperforming FPGA Accelerator Architecture for Binary
Convolutional Neural Networks | cs.DC cs.AR cs.CV cs.LG | FPGA-based hardware accelerators for convolutional neural networks (CNNs)
have obtained great attentions due to their higher energy efficiency than GPUs.
However, it is challenging for FPGA-based solutions to achieve a higher
throughput than GPU counterparts. In this paper, we demonstrate that FPGA
acceleration can be a superior solution in terms of both throughput and energy
efficiency when a CNN is trained with binary constraints on weights and
activations. Specifically, we propose an optimized FPGA accelerator
architecture tailored for bitwise convolution and normalization that features
massive spatial parallelism with deep pipelines stages. A key advantage of the
FPGA accelerator is that its performance is insensitive to data batch size,
while the performance of GPU acceleration varies largely depending on the batch
size of the data. Experiment results show that the proposed accelerator
architecture for binary CNNs running on a Virtex-7 FPGA is 8.3x faster and 75x
more energy-efficient than a Titan X GPU for processing online individual
requests in small batch sizes. For processing static data in large batch sizes,
the proposed solution is on a par with a Titan X GPU in terms of throughput
while delivering 9.5x higher energy efficiency.
| Yixing Li, Zichuan Liu, Kai Xu, Hao Yu and Fengbo Ren | null | 1702.06392 | null | null |
A Unified Optimization View on Generalized Matching Pursuit and
Frank-Wolfe | cs.LG stat.ML | Two of the most fundamental prototypes of greedy optimization are the
matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
view on both classes of methods, leading to the first explicit convergence
rates of matching pursuit methods in an optimization sense, for general sets of
atoms. We derive sublinear ($1/t$) convergence for both classes on general
smooth objectives, and linear convergence on strongly convex objectives, as
well as a clear correspondence of algorithm variants. Our presented algorithms
and rates are affine invariant, and do not need any incoherence or sparsity
assumptions.
| Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi | null | 1702.06457 | null | null |
Predicting non-linear dynamics by stable local learning in a recurrent
spiking neural network | q-bio.NC cs.LG cs.NE cs.SY | Brains need to predict how the body reacts to motor commands. It is an open
question how networks of spiking neurons can learn to reproduce the non-linear
body dynamics caused by motor commands, using local, online and stable learning
rules. Here, we present a supervised learning scheme for the feedforward and
recurrent connections in a network of heterogeneous spiking neurons. The error
in the output is fed back through fixed random connections with a negative
gain, causing the network to follow the desired dynamics, while an online and
local rule changes the weights. The rule for Feedback-based Online Local
Learning Of Weights (FOLLOW) is local in the sense that weight changes depend
on the presynaptic activity and the error signal projected onto the
postsynaptic neuron. We provide examples of learning linear, non-linear and
chaotic dynamics, as well as the dynamics of a two-link arm. Using the Lyapunov
method, and under reasonable assumptions and approximations, we show that
FOLLOW learning is stable uniformly, with the error going to zero
asymptotically.
| Aditya Gilra, Wulfram Gerstner | 10.7554/eLife.28295 | 1702.06463 | null | null |
PixelNet: Representation of the pixels, by the pixels, and for the
pixels | cs.CV cs.LG cs.RO | We explore design principles for general pixel-level prediction problems,
from low-level edge detection to mid-level surface normal estimation to
high-level semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that stratified sampling of pixels allows one to (1) add diversity
during batch updates, speeding up learning; (2) explore complex nonlinear
predictors, improving accuracy; and (3) efficiently train state-of-the-art
models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our
single architecture produces state-of-the-art results for semantic segmentation
on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset,
and edge detection on BSDS.
| Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan | null | 1702.06506 | null | null |
Stochastic Canonical Correlation Analysis | cs.LG stat.ML | We study the sample complexity of canonical correlation analysis (CCA), \ie,
the number of samples needed to estimate the population canonical correlation
and directions up to arbitrarily small error. With mild assumptions on the data
distribution, we show that in order to achieve $\epsilon$-suboptimality in a
properly defined measure of alignment between the estimated canonical
directions and the population solution, we can solve the empirical objective
exactly with $N(\epsilon, \Delta, \gamma)$ samples, where $\Delta$ is the
singular value gap of the whitened cross-covariance matrix and $1/\gamma$ is an
upper bound of the condition number of auto-covariance matrices. Moreover, we
can achieve the same learning accuracy by drawing the same level of samples and
solving the empirical objective approximately with a stochastic optimization
algorithm; this algorithm is based on the shift-and-invert power iterations and
only needs to process the dataset for $\mathcal{O}\left(\log \frac{1}{\epsilon}
\right)$ passes. Finally, we show that, given an estimate of the canonical
correlation, the streaming version of the shift-and-invert power iterations
achieves the same learning accuracy with the same level of sample complexity,
by processing the data only once.
| Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang | null | 1702.06533 | null | null |
Active One-shot Learning | cs.LG | Recent advances in one-shot learning have produced models that can learn from
a handful of labeled examples, for passive classification and regression tasks.
This paper combines reinforcement learning with one-shot learning, allowing the
model to decide, during classification, which examples are worth labeling. We
introduce a classification task in which a stream of images are presented and,
on each time step, a decision must be made to either predict a label or pay to
receive the correct label. We present a recurrent neural network based
action-value function, and demonstrate its ability to learn how and when to
request labels. Through the choice of reward function, the model can achieve a
higher prediction accuracy than a similar model on a purely supervised task, or
trade prediction accuracy for fewer label requests.
| Mark Woodward and Chelsea Finn | null | 1702.06559 | null | null |
Exemplar-Centered Supervised Shallow Parametric Data Embedding | cs.LG stat.ML | Metric learning methods for dimensionality reduction in combination with
k-Nearest Neighbors (kNN) have been extensively deployed in many
classification, data embedding, and information retrieval applications.
However, most of these approaches involve pairwise training data comparisons,
and thus have quadratic computational complexity with respect to the size of
training set, preventing them from scaling to fairly big datasets. Moreover,
during testing, comparing test data against all the training data points is
also expensive in terms of both computational cost and resources required.
Furthermore, previous metrics are either too constrained or too expressive to
be well learned. To effectively solve these issues, we present an
exemplar-centered supervised shallow parametric data embedding model, using a
Maximally Collapsing Metric Learning (MCML) objective. Our strategy learns a
shallow high-order parametric embedding function and compares training/test
data only with learned or precomputed exemplars, resulting in a cost function
with linear computational complexity for both training and testing. We also
empirically demonstrate, using several benchmark datasets, that for
classification in two-dimensional embedding space, our approach not only gains
speedup of kNN by hundreds of times, but also outperforms state-of-the-art
supervised embedding approaches.
| Martin Renqiang Min, Hongyu Guo, Dongjin Song | null | 1702.06602 | null | null |
Counterfactual Control for Free from Generative Models | cs.LG stat.ML | We introduce a method by which a generative model learning the joint
distribution between actions and future states can be used to automatically
infer a control scheme for any desired reward function, which may be altered on
the fly without retraining the model. In this method, the problem of action
selection is reduced to one of gradient descent on the latent space of the
generative model, with the model itself providing the means of evaluating
outcomes and finding the gradient, much like how the reward network in Deep
Q-Networks (DQN) provides gradient information for the action generator. Unlike
DQN or Actor-Critic, which are conditional models for a specific reward, using
a generative model of the full joint distribution permits the reward to be
changed on the fly. In addition, the generated futures can be inspected to gain
insight in to what the network 'thinks' will happen, and to what went wrong
when the outcomes deviate from prediction.
| Nicholas Guttenberg, Yen Yu, Ryota Kanai | null | 1702.06676 | null | null |
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy
while Reducing Computational Costs | cs.LG | Shapelets are discriminative time series subsequences that allow generation
of interpretable classification models, which provide faster and generally
better classification than the nearest neighbor approach. However, the shapelet
discovery process requires the evaluation of all possible subsequences of all
time series in the training set, making it extremely computation intensive.
Consequently, shapelet discovery for large time series datasets quickly becomes
intractable. A number of improvements have been proposed to reduce the training
time. These techniques use approximation or discretization and often lead to
reduced classification accuracy compared to the exact method.
We are proposing the use of ensembles of shapelet-based classifiers obtained
using random sampling of the shapelet candidates. Using random sampling reduces
the number of evaluated candidates and consequently the required computational
cost, while the classification accuracy of the resulting models is also not
significantly different than that of the exact algorithm. The combination of
randomized classifiers rectifies the inaccuracies of individual models because
of the diversity of the solutions. Based on the experiments performed, it is
shown that the proposed approach of using an ensemble of inexpensive
classifiers provides better classification accuracy compared to the exact
method at a significantly lesser computational cost.
| Atif Raza and Stefan Kramer | null | 1702.06712 | null | null |
Memory Matching Networks for Genomic Sequence Classification | cs.LG q-bio.GN stat.ML | When analyzing the genome, researchers have discovered that proteins bind to
DNA based on certain patterns of the DNA sequence known as "motifs". However,
it is difficult to manually construct motifs due to their complexity. Recently,
externally learned memory models have proven to be effective methods for
reasoning over inputs and supporting sets. In this work, we present memory
matching networks (MMN) for classifying DNA sequences as protein binding sites.
Our model learns a memory bank of encoded motifs, which are dynamic memory
modules, and then matches a new test sequence to each of the motifs to classify
the sequence as a binding or nonbinding site.
| Jack Lanchantin, Ritambhara Singh, Yanjun Qi | null | 1702.0676 | null | null |
Style Transfer Generative Adversarial Networks: Learning to Play Chess
Differently | cs.LG | The idea of style transfer has largely only been explored in image-based
tasks, which we attribute in part to the specific nature of loss functions used
for style transfer. We propose a general formulation of style transfer as an
extension of generative adversarial networks, by using a discriminator to
regularize a generator with an otherwise separate loss function. We apply our
approach to the task of learning to play chess in the style of a specific
player, and present empirical evidence for the viability of our approach.
| Muthuraman Chidambaram, Yanjun Qi | null | 1702.06762 | null | null |
DeepCloak: Masking Deep Neural Network Models for Robustness Against
Adversarial Samples | cs.LG cs.AI cs.CR | Recent studies have shown that deep neural networks (DNN) are vulnerable to
adversarial samples: maliciously-perturbed samples crafted to yield incorrect
model outputs. Such attacks can severely undermine DNN systems, particularly in
security-sensitive settings. It was observed that an adversary could easily
generate adversarial samples by making a small perturbation on irrelevant
feature dimensions that are unnecessary for the current classification task. To
overcome this problem, we introduce a defensive mechanism called DeepCloak. By
identifying and removing unnecessary features in a DNN model, DeepCloak limits
the capacity an attacker can use generating adversarial samples and therefore
increase the robustness against such inputs. Comparing with other defensive
approaches, DeepCloak is easy to implement and computationally efficient.
Experimental results show that DeepCloak can increase the performance of
state-of-the-art DNN models against adversarial samples.
| Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu, Yanjun Qi | null | 1702.06763 | null | null |
Causal Inference by Stochastic Complexity | cs.LG cs.AI | The algorithmic Markov condition states that the most likely causal direction
between two random variables X and Y can be identified as that direction with
the lowest Kolmogorov complexity. Due to the halting problem, however, this
notion is not computable.
We hence propose to do causal inference by stochastic complexity. That is, we
propose to approximate Kolmogorov complexity via the Minimum Description Length
(MDL) principle, using a score that is mini-max optimal with regard to the
model class under consideration. This means that even in an adversarial
setting, such as when the true distribution is not in this class, we still
obtain the optimal encoding for the data relative to the class.
We instantiate this framework, which we call CISC, for pairs of univariate
discrete variables, using the class of multinomial distributions. Experiments
show that CISC is highly accurate on synthetic, benchmark, as well as
real-world data, outperforming the state of the art by a margin, and scales
extremely well with regard to sample and domain sizes.
| Kailash Budhathoki and Jilles Vreeken | null | 1702.06776 | null | null |
Stochastic Approximation for Canonical Correlation Analysis | cs.LG stat.ML | We propose novel first-order stochastic approximation algorithms for
canonical correlation analysis (CCA). Algorithms presented are instances of
inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated
gradient (MEG), and achieve $\epsilon$-suboptimality in the population
objective in $\operatorname{poly}(\frac{1}{\epsilon})$ iterations. We also
consider practical variants of the proposed algorithms and compare them with
other methods for CCA both theoretically and empirically.
| Raman Arora and Teodor V. Marinov and Poorya Mianjy and Nathan Srebro | null | 1702.06818 | null | null |
Distributed Representations of Signed Networks | stat.ML cs.LG cs.SI | Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.
| Mohammad Raihanul Islam and B. Aditya Prakash and Naren Ramakrishnan | 10.1007/978-3-319-93037-4_13 | 1702.06819 | null | null |
Adversarial examples for generative models | stat.ML cs.LG | We explore methods of producing adversarial examples on deep generative
models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
architectures are known to be vulnerable to adversarial examples, but previous
work has focused on the application of adversarial examples to classification
tasks. Deep generative models have recently become popular due to their ability
to model input data distributions and generate realistic examples from those
distributions. We present three classes of attacks on the VAE and VAE-GAN
architectures and demonstrate them against networks trained on MNIST, SVHN and
CelebA. Our first attack leverages classification-based adversaries by
attaching a classifier to the trained encoder of the target generative model,
which can then be used to indirectly manipulate the latent representation. Our
second attack directly uses the VAE loss function to generate a target
reconstruction image from the adversarial example. Our third attack moves
beyond relying on classification or the standard loss for the gradient and
directly optimizes against differences in source and target latent
representations. We also motivate why an attacker might be interested in
deploying such techniques against a target generative network.
| Jernej Kos, Ian Fischer, Dawn Song | null | 1702.06832 | null | null |
Scene Recognition by Combining Local and Global Image Descriptors | cs.CV cs.LG | Object recognition is an important problem in computer vision, having diverse
applications. In this work, we construct an end-to-end scene recognition
pipeline consisting of feature extraction, encoding, pooling and
classification. Our approach simultaneously utilize global feature descriptors
as well as local feature descriptors from images, to form a hybrid feature
descriptor corresponding to each image. We utilize DAISY features associated
with key points within images as our local feature descriptor and histogram of
oriented gradients (HOG) corresponding to an entire image as a global
descriptor. We make use of a bag-of-visual-words encoding and apply Mini- Batch
K-Means algorithm to reduce the complexity of our feature encoding scheme. A
2-level pooling procedure is used to combine DAISY and HOG features
corresponding to each image. Finally, we experiment with a multi-class SVM
classifier with several kernels, in a cross-validation setting, and tabulate
our results on the fifteen scene categories dataset. The average accuracy of
our model was 76.4% in the case of a 40%-60% random split of images into
training and testing datasets respectively. The primary objective of this work
is to clearly outline the practical implementation of a basic
screne-recognition pipeline having a reasonable accuracy, in python, using
open-source libraries. A full implementation of the proposed model is available
in our github repository.
| Jobin Wilson and Muhammad Arif | null | 1702.0685 | null | null |
Robustness to Adversarial Examples through an Ensemble of Specialists | cs.NE cs.LG | We are proposing to use an ensemble of diverse specialists, where speciality
is defined according to the confusion matrix. Indeed, we observed that for
adversarial instances originating from a given class, labeling tend to be done
into a small subset of (incorrect) classes. Therefore, we argue that an
ensemble of specialists should be better able to identify and reject fooling
instances, with a high entropy (i.e., disagreement) over the decisions in the
presence of adversaries. Experimental results obtained confirm that
interpretation, opening a way to make the system more robust to adversarial
examples through a rejection mechanism, rather than trying to classify them
properly at any cost.
| Mahdieh Abbasi and Christian Gagn\'e | null | 1702.06856 | null | null |
On the Power of Truncated SVD for General High-rank Matrix Estimation
Problems | stat.ML cs.LG math.NA | We show that given an estimate $\widehat{A}$ that is close to a general
high-rank positive semi-definite (PSD) matrix $A$ in spectral norm (i.e.,
$\|\widehat{A}-A\|_2 \leq \delta$), the simple truncated SVD of $\widehat{A}$
produces a multiplicative approximation of $A$ in Frobenius norm. This
observation leads to many interesting results on general high-rank matrix
estimation problems, which we briefly summarize below ($A$ is an $n\times n$
high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$):
(1) High-rank matrix completion: By observing
$\Omega(\frac{n\max\{\epsilon^{-4},k^2\}\mu_0^2\|A\|_F^2\log
n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is
the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence,
the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq
(1+O(\epsilon))\|A-A_k\|_F$ with high probability.
(2)High-rank matrix de-noising: Let $\widehat{A}=A+E$ where $E$ is a Gaussian
random noise matrix with zero mean and $\nu^2/n$ variance on each entry. Then
the truncated SVD of $\widehat{A}$ satisfies $\|\widehat{A}_k-A\|_F \leq
(1+O(\sqrt{\nu/\sigma_{k+1}(A)}))\|A-A_k\|_F + O(\sqrt{k}\nu)$.
(3) Low-rank Estimation of high-dimensional covariance: Given $N$
i.i.d.~samples $X_1,\cdots,X_N\sim\mathcal N_n(0,A)$, can we estimate $A$ with
a relative-error Frobenius norm bound? We show that if $N =
\Omega\left(n\max\{\epsilon^{-4},k^2\}\gamma_k(A)^2\log N\right)$ for
$\gamma_k(A)=\sigma_1(A)/\sigma_{k+1}(A)$, then $\|\widehat{A}_k-A\|_F \leq
(1+O(\epsilon))\|A-A_k\|_F$ with high probability, where
$\widehat{A}=\frac{1}{N}\sum_{i=1}^N{X_iX_i^\top}$ is the sample covariance.
| Simon S. Du and Yining Wang and Aarti Singh | null | 1702.06861 | null | null |
Knowledge Graph Completion via Complex Tensor Factorization | cs.AI cs.LG math.SP stat.ML | In statistical relational learning, knowledge graph completion deals with
automatically understanding the structure of large knowledge graphs---labeled
directed graphs---and predicting missing relationships---labeled edges.
State-of-the-art embedding models propose different trade-offs between modeling
expressiveness, and time and space complexity. We reconcile both expressiveness
and complexity through the use of complex-valued embeddings and explore the
link between such complex-valued embeddings and unitary diagonalization. We
corroborate our approach theoretically and show that all real square
matrices---thus all possible relation/adjacency matrices---are the real part of
some unitarily diagonalizable matrix. This results opens the door to a lot of
other applications of square matrices factorization. Our approach based on
complex embeddings is arguably simple, as it only involves a Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors, whereas other methods resort to more and more complicated composition
functions to increase their expressiveness. The proposed complex embeddings are
scalable to large data sets as it remains linear in both space and time, while
consistently outperforming alternative approaches on standard link prediction
benchmarks.
| Th\'eo Trouillon, Christopher R. Dance, Johannes Welbl, Sebastian
Riedel, \'Eric Gaussier, Guillaume Bouchard | null | 1702.06879 | null | null |
Learning Deep Features via Congenerous Cosine Loss for Person
Recognition | cs.CV cs.LG stat.ML | Person recognition aims at recognizing the same identity across time and
space with complicated scenes and similar appearance. In this paper, we propose
a novel method to address this task by training a network to obtain robust and
representative features. The intuition is that we directly compare and optimize
the cosine distance between two features - enlarging inter-class distinction as
well as alleviating inner-class variance. We propose a congenerous cosine loss
by minimizing the cosine distance between samples and their cluster centroid in
a cooperative way. Such a design reduces the complexity and could be
implemented via softmax with normalized inputs. Our method also differs from
previous work in person recognition that we do not conduct a second training on
the test subset. The identity of a person is determined by measuring the
similarity from several body regions in the reference set. Experimental results
show that the proposed approach achieves better classification accuracy against
previous state-of-the-arts.
| Yu Liu and Hongyang Li and Xiaogang Wang | null | 1702.0689 | null | null |
liquidSVM: A Fast and Versatile SVM package | stat.ML cs.LG | liquidSVM is a package written in C++ that provides SVM-type solvers for
various classification and regression tasks. Because of a fully integrated
hyper-parameter selection, very carefully implemented solvers, multi-threading
and GPU support, and several built-in data decomposition strategies it provides
unprecedented speed for small training sizes as well as for data sets of tens
of millions of samples. Besides the C++ API and a command line interface,
bindings to R, MATLAB, Java, Python, and Spark are available. We present a
brief description of the package and report experimental comparisons to other
SVM packages.
| Ingo Steinwart and Philipp Thomann | null | 1702.06899 | null | null |
Training a Subsampling Mechanism in Expectation | cs.LG | We describe a mechanism for subsampling sequences and show how to compute its
expected output so that it can be trained with standard backpropagation. We
test this approach on a simple toy problem and discuss its shortcomings.
| Colin Raffel and Dieterich Lawson | null | 1702.06914 | null | null |
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe | cs.LG math.OC stat.ML | We consider the problem of bandit optimization, inspired by stochastic
optimization and online learning problems with bandit feedback. In this
problem, the objective is to minimize a global loss function of all the
actions, not necessarily a cumulative loss. This framework allows us to study a
very general class of problems, with applications in statistics, machine
learning, and other fields. To solve this problem, we analyze the
Upper-Confidence Frank-Wolfe algorithm, inspired by techniques for bandits and
convex optimization. We give theoretical guarantees for the performance of this
algorithm over various classes of functions, and discuss the optimality of
these results.
| Quentin Berthet, Vianney Perchet | null | 1702.06917 | null | null |
Distributed Representation of Subgraphs | cs.SI cs.LG stat.ML | Network embeddings have become very popular in learning effective feature
representations of networks. Motivated by the recent successes of embeddings in
natural language processing, researchers have tried to find network embeddings
in order to exploit machine learning algorithms for mining tasks like node
classification and edge prediction. However, most of the work focuses on
finding distributed representations of nodes, which are inherently ill-suited
to tasks such as community detection which are intuitively dependent on
subgraphs.
Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature
representations of arbitrary subgraphs. We provide means to characterize
similarties between subgraphs and provide theoretical analysis of sub2vec and
demonstrate that it preserves the so-called local proximity. We also highlight
the usability of sub2vec by leveraging it for network mining tasks, like
community detection. We show that sub2vec gets significant gains over
state-of-the-art methods and node-embedding methods. In particular, sub2vec
offers an approach to generate a richer vocabulary of features of subgraphs to
support representation and reasoning.
| Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and B. Aditya Prakash | null | 1702.06921 | null | null |
Regularizing Face Verification Nets For Pain Intensity Regression | cs.CV cs.AI cs.LG cs.MM | Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain, such as face verification, can alleviate
this problem. In this paper, we propose a network that fine-tunes a
state-of-the-art face verification network using a regularized regression loss
and additional data with expression labels. In this way, the expression
intensity regression task can benefit from the rich feature representations
trained on a huge amount of data for face verification. The proposed
regularized deep regressor is applied to estimate the pain expression intensity
and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving
the state-of-the-art performance. A weighted evaluation metric is also proposed
to address the imbalance issue of different pain intensities.
| Feng Wang, Xiang Xiang, Chang Liu, Trac D. Tran, Austin Reiter,
Gregory D. Hager, Harry Quon, Jian Cheng, Alan L. Yuille | 10.13140/RG.2.2.20841.49765 | 1702.06925 | null | null |
An Algebraic Formalization of Forward and Forward-backward Algorithms | cs.LG | In this paper, we propose an algebraic formalization of the two important
classes of dynamic programming algorithms called forward and forward-backward
algorithms. They are generalized extensively in this study so that a wide range
of other existing algorithms is subsumed. Forward algorithms generalized in
this study subsume the ordinary forward algorithm on trellises for sequence
labeling, the inside algorithm on derivation forests for CYK parsing, a
unidirectional message passing on acyclic factor graphs, the forward mode of
automatic differentiation on computation graphs with addition and
multiplication, and so on. In addition, we reveal algebraic structures
underlying complicated computation with forward algorithms. By the aid of the
revealed algebraic structures, we also propose a systematic framework to design
complicated variants of forward algorithms. Forward-backward algorithms
generalized in this study subsume the ordinary forward-backward algorithm on
trellises for sequence labeling, the inside-outside algorithm on derivation
forests for CYK parsing, the sum-product algorithm on acyclic factor graphs,
the reverse mode of automatic differentiation (a.k.a. back propagation) on
computation graphs with addition and multiplication, and so on. We also propose
an algebraic characterization of what can be computed by forward-backward
algorithms and elucidate the relationship between forward and forward-backward
algorithms.
| Ai Azuma, Masashi Shimbo, Yuji Matsumoto | null | 1702.06941 | null | null |
Tuple-oriented Compression for Large-scale Mini-batch Stochastic
Gradient Descent | cs.LG cs.DB stat.ML | Data compression is a popular technique for improving the efficiency of data
processing workloads such as SQL queries and more recently, machine learning
(ML) with classical batch gradient methods. But the efficacy of such ideas for
mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm
of modern ML, is an open question. MGD's unique data access pattern renders
prior art, including those designed for batch gradient methods, less effective.
We fill this crucial research gap by proposing a new lossless compression
scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely
source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to
MGD in a way that preserves tuple boundaries within mini-batches. We then
present a suite of novel compressed matrix operation execution techniques
tailored to the TOC compression scheme that operate directly over the
compressed data representation and avoid decompression overheads. An extensive
empirical evaluation with real-world datasets shows that TOC consistently
achieves substantial compression ratios by up to 51x and reduces runtimes for
MGD workloads by up to 10.2x in popular ML systems.
| Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey F.
Naughton, Jignesh M. Patel, Xi Wu | 10.1145/3299869.3300070 | 1702.06943 | null | null |
Approximations of the Restless Bandit Problem | math.ST cs.LG math.PR stat.ML stat.TH | The multi-armed restless bandit problem is studied in the case where the
pay-off distributions are stationary $\varphi$-mixing. This version of the
problem provides a more realistic model for most real-world applications, but
cannot be optimally solved in practice, since it is known to be PSPACE-hard.
The objective of this paper is to characterize a sub-class of the problem where
{\em good} approximate solutions can be found using tractable approaches.
Specifically, it is shown that under some conditions on the $\varphi$-mixing
coefficients, a modified version of UCB can prove effective. The main challenge
is that, unlike in the i.i.d. setting, the distributions of the sampled
pay-offs may not have the same characteristics as those of the original bandit
arms. In particular, the $\varphi$-mixing property does not necessarily carry
over. This is overcome by carefully controlling the effect of a sampling policy
on the pay-off distributions. Some of the proof techniques developed in this
paper can be more generally used in the context of online sampling under
dependence. Proposed algorithms are accompanied with corresponding regret
analysis.
| Steffen Grunewalder and Azadeh Khaleghi | null | 1702.06972 | null | null |
Heavy-Tailed Analogues of the Covariance Matrix for ICA | cs.LG stat.ML | Independent Component Analysis (ICA) is the problem of learning a square
matrix $A$, given samples of $X=AS$, where $S$ is a random vector with
independent coordinates. Most existing algorithms are provably efficient only
when each $S_i$ has finite and moderately valued fourth moment. However, there
are practical applications where this assumption need not be true, such as
speech and finance. Algorithms have been proposed for heavy-tailed ICA, but
they are not practical, using random walks and the full power of the ellipsoid
algorithm multiple times. The main contributions of this paper are:
(1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide
theoretical guarantees and show that it outperforms other algorithms in some
heavy-tailed regimes, both on real and synthetic data. Like the current
state-of-the-art, the new algorithm is based on the centroid body (a first
moment analogue of the covariance matrix). Unlike the state-of-the-art, our
algorithm is practically efficient. To achieve this, we use explicit analytic
representations of the centroid body, which bypasses the use of the ellipsoid
method and random walks.
(2) We study how heavy tails affect different ICA algorithms, including
HTICA. Somewhat surprisingly, we show that some algorithms that use the
covariance matrix or higher moments can successfully solve a range of ICA
instances with infinite second moment. We study this theoretically and
experimentally, with both synthetic and real-world heavy-tailed data.
| Joseph Anderson and Navin Goyal and Anupama Nandi and Luis Rademacher | null | 1702.06976 | null | null |
On Polynomial Time Methods for Exact Low Rank Tensor Completion | stat.ML cs.IT cs.LG math.IT | In this paper, we investigate the sample size requirement for exact recovery
of a high order tensor of low rank from a subset of its entries. We show that a
gradient descent algorithm with initial value obtained from a spectral method
can, in particular, reconstruct a ${d\times d\times d}$ tensor of multilinear
ranks $(r,r,r)$ with high probability from as few as
$O(r^{7/2}d^{3/2}\log^{7/2}d+r^7d\log^6d)$ entries. In the case when the ranks
$r=O(1)$, our sample size requirement matches those for nuclear norm
minimization (Yuan and Zhang, 2016a), or alternating least squares assuming
orthogonal decomposability (Jain and Oh, 2014). Unlike these earlier
approaches, however, our method is efficient to compute, easy to implement, and
does not impose extra structures on the tensor. Numerical results are presented
to further demonstrate the merits of the proposed approach.
| Dong Xia and Ming Yuan | null | 1702.0698 | null | null |
Large-Scale Stochastic Learning using GPUs | cs.LG cs.DC | In this work we propose an accelerated stochastic learning system for very
large-scale applications. Acceleration is achieved by mapping the training
algorithm onto massively parallel processors: we demonstrate a parallel,
asynchronous GPU implementation of the widely used stochastic coordinate
descent/ascent algorithm that can provide up to 35x speed-up over a sequential
CPU implementation. In order to train on very large datasets that do not fit
inside the memory of a single GPU, we then consider techniques for distributed
stochastic learning. We propose a novel method for optimally aggregating model
updates from worker nodes when the training data is distributed either by
example or by feature. Using this technique, we demonstrate that one can scale
out stochastic learning across up to 8 worker nodes without any significant
loss of training time. Finally, we combine GPU acceleration with the optimized
distributed method to train on a dataset consisting of 200 million training
examples and 75 million features. We show by scaling out across 4 GPUs, one can
attain a high degree of training accuracy in around 4 seconds: a 20x speed-up
in training time compared to a multi-threaded, distributed implementation
across 4 CPUs.
| Thomas Parnell, Celestine D\"unner, Kubilay Atasu, Manolis Sifalakis
and Haris Pozidis | null | 1702.07005 | null | null |
Learning Hawkes Processes from Short Doubly-Censored Event Sequences | cs.LG math.PR stat.ML | Many real-world applications require robust algorithms to learn point
processes based on a type of incomplete data --- the so-called short
doubly-censored (SDC) event sequences. We study this critical problem of
quantitative asynchronous event sequence analysis under the framework of Hawkes
processes by leveraging the idea of data synthesis. Given SDC event sequences
observed in a variety of time intervals, we propose a sampling-stitching data
synthesis method --- sampling predecessors and successors for each SDC event
sequence from potential candidates and stitching them together to synthesize
long training sequences. The rationality and the feasibility of our method are
discussed in terms of arguments based on likelihood. Experiments on both
synthetic and real-world data demonstrate that the proposed data synthesis
method improves learning results indeed for both time-invariant and
time-varying Hawkes processes.
| Hongteng Xu, Dixin Luo, Hongyuan Zha | null | 1702.07013 | null | null |
One Size Fits Many: Column Bundle for Multi-X Learning | stat.ML cs.LG | Much recent machine learning research has been directed towards leveraging
shared statistics among labels, instances and data views, commonly referred to
as multi-label, multi-instance and multi-view learning. The underlying premises
are that there exist correlations among input parts and among output targets,
and the predictive performance would increase when the correlations are
incorporated. In this paper, we propose Column Bundle (CLB), a novel deep
neural network for capturing the shared statistics in data. CLB is generic that
the same architecture can be applied for various types of shared statistics by
changing only input and output handling. CLB is capable of scaling to thousands
of input parts and output labels by avoiding explicit modeling of pairwise
relations. We evaluate CLB on different types of data: (a) multi-label, (b)
multi-view, (c) multi-view/multi-label and (d) multi-instance. CLB demonstrates
a comparable and competitive performance in all datasets against
state-of-the-art methods designed specifically for each type.
| Trang Pham, Truyen Tran, Svetha Venkatesh | null | 1702.07021 | null | null |
On the ability of neural nets to express distributions | cs.LG | Deep neural nets have caused a revolution in many classification tasks. A
related ongoing revolution -- also theoretically not understood -- concerns
their ability to serve as generative models for complicated types of data such
as images and texts. These models are trained using ideas like variational
autoencoders and Generative Adversarial Networks.
We take a first cut at explaining the expressivity of multilayer nets by
giving a sufficient criterion for a function to be approximable by a neural
network with $n$ hidden layers. A key ingredient is Barron's Theorem
\cite{Barron1993}, which gives a Fourier criterion for approximability of a
function by a neural network with 1 hidden layer. We show that a composition of
$n$ functions which satisfy certain Fourier conditions ("Barron functions") can
be approximated by a $n+1$-layer neural network.
For probability distributions, this translates into a criterion for a
probability distribution to be approximable in Wasserstein distance -- a
natural metric on probability distributions -- by a neural network applied to a
fixed base distribution (e.g., multivariate gaussian).
Building up recent lower bound work, we also give an example function that
shows that composition of Barron functions is more expressive than Barron
functions alone.
| Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora | null | 1702.07028 | null | null |
Learning Model Predictive Control for Iterative Tasks: A Computationally
Efficient Approach for Linear System | math.OC cs.LG | A Learning Model Predictive Controller (LMPC) for linear system in presented.
The proposed controller is an extension of the LMPC [1] and it aims to decrease
the computational burden. The control scheme is reference-free and is able to
improve its performance by learning from previous iterations. A convex safe set
and a terminal cost function are used in order to guarantee recursive
feasibility and non-increasing performance at each iteration. The paper
presents the control design approach, and shows how to recursively construct
the convex terminal set and the terminal cost from state and input trajectories
of previous iterations. Simulation results show the effectiveness of the
proposed control logic.
| Ugo Rosolia and Francesco Borrelli | null | 1702.07064 | null | null |
Scalable Inference for Nested Chinese Restaurant Process Topic Models | stat.ML cs.DC cs.IR cs.LG | Nested Chinese Restaurant Process (nCRP) topic models are powerful
nonparametric Bayesian methods to extract a topic hierarchy from a given text
corpus, where the hierarchical structure is automatically determined by the
data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of
nCRP topic models. However, hLDA has only been evaluated at small scale,
because the existing collapsed Gibbs sampling and instantiated weight
variational inference algorithms either are not scalable or sacrifice inference
quality with mean-field assumptions. Moreover, an efficient distributed
implementation of the data structures, such as dynamically growing count
matrices and trees, is challenging.
In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS)
algorithm, which combines the advantages of collapsed and instantiated weight
algorithms to achieve good scalability as well as high model quality. An
initialization strategy is presented to further improve the model quality.
Finally, we propose an efficient distributed implementation of PCGS through
vectorization, pre-processing, and a careful design of the concurrent data
structures and communication strategy.
Empirical studies show that our algorithm is 111 times more efficient than
the previous open-source implementation for hLDA, with comparable or even
better model quality. Our distributed implementation can extract 1,722 topics
from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders
of magnitude larger than the previous largest corpus, with 50 machines in 7
hours.
| Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu | null | 1702.07083 | null | null |
Adaptive Bidirectional Backpropagation: Towards Biologically Plausible
Error Signal Transmission in Neural Networks | cs.NE cs.LG | The back-propagation (BP) algorithm has been considered the de-facto method
for training deep neural networks. It back-propagates errors from the output
layer to the hidden layers in an exact manner using the transpose of the
feedforward weights. However, it has been argued that this is not biologically
plausible because back-propagating error signals with the exact incoming
weights are not considered possible in biological neural systems. In this work,
we propose a biologically plausible paradigm of neural architecture based on
related literature in neuroscience and asymmetric BP-like methods.
Specifically, we propose two bidirectional learning algorithms with trainable
feedforward and feedback weights. The feedforward weights are used to relay
activations from the inputs to target outputs. The feedback weights pass the
error signals from the output layer to the hidden layers. Different from other
asymmetric BP-like methods, the feedback weights are also plastic in our
framework and are trained to approximate the forward activations. Preliminary
results show that our models outperform other asymmetric BP-like methods on the
MNIST and the CIFAR-10 datasets.
| Hongyin Luo, Jie Fu, James Glass | null | 1702.07097 | null | null |
Discriminating Traces with Time | cs.PL cs.CR cs.FL cs.LG cs.SE | What properties about the internals of a program explain the possible
differences in its overall running time for different inputs? In this paper, we
propose a formal framework for considering this question we dub trace-set
discrimination. We show that even though the algorithmic problem of computing
maximum likelihood discriminants is NP-hard, approaches based on integer linear
programming (ILP) and decision tree learning can be useful in zeroing-in on the
program internals. On a set of Java benchmarks, we find that
compactly-represented decision trees scalably discriminate with high
accuracy---more scalably than maximum likelihood discriminants and with
comparable accuracy. We demonstrate on three larger case studies how
decision-tree discriminants produced by our tool are useful for debugging
timing side-channel vulnerabilities (i.e., where a malicious observer infers
secrets simply from passively watching execution times) and availability
vulnerabilities.
| Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Sriram
Sankaranarayanan, Ashutosh Trivedi | null | 1702.07103 | null | null |
Consistent On-Line Off-Policy Evaluation | stat.ML cs.LG | The problem of on-line off-policy evaluation (OPE) has been actively studied
in the last decade due to its importance both as a stand-alone problem and as a
module in a policy improvement scheme. However, most Temporal Difference (TD)
based solutions ignore the discrepancy between the stationary distribution of
the behavior and target policies and its effect on the convergence limit when
function approximation is applied. In this paper we propose the Consistent
Off-Policy Temporal Difference (COP-TD($\lambda$, $\beta$)) algorithm that
addresses this issue and reduces this bias at some computational expense. We
show that COP-TD($\lambda$, $\beta$) can be designed to converge to the same
value that would have been obtained by using on-policy TD($\lambda$) with the
target policy. Subsequently, the proposed scheme leads to a related and
promising heuristic we call log-COP-TD($\lambda$, $\beta$). Both algorithms
have favorable empirical results to the current state of the art on-line OPE
algorithms. Finally, our formulation sheds some new light on the recently
proposed Emphatic TD learning.
| Assaf Hallak and Shie Mannor | null | 1702.07121 | null | null |
Automatic Representation for Lifetime Value Recommender Systems | stat.ML cs.LG | Many modern commercial sites employ recommender systems to propose relevant
content to users. While most systems are focused on maximizing the immediate
gain (clicks, purchases or ratings), a better notion of success would be the
lifetime value (LTV) of the user-system interaction. The LTV approach considers
the future implications of the item recommendation, and seeks to maximize the
cumulative gain over time. The Reinforcement Learning (RL) framework is the
standard formulation for optimizing cumulative successes over time. However, RL
is rarely used in practice due to its associated representation, optimization
and validation techniques which can be complex. In this paper we propose a new
architecture for combining RL with recommendation systems which obviates the
need for hand-tuned features, thus automating the state-space representation
construction process. We analyze the practical difficulties in this formulation
and test our solutions on batch off-line real-world recommendation data.
| Assaf Hallak, Yishay Mansour and Elad Yom-Tov | null | 1702.07125 | null | null |
Stability of Topic Modeling via Matrix Factorization | cs.IR cs.CL cs.LG stat.ML | Topic models can provide us with an insight into the underlying latent
structure of a large corpus of documents. A range of methods have been proposed
in the literature, including probabilistic topic models and techniques based on
matrix factorization. However, in both cases, standard implementations rely on
stochastic elements in their initialization phase, which can potentially lead
to different results being generated on the same corpus when using the same
parameter values. This corresponds to the concept of "instability" which has
previously been studied in the context of $k$-means clustering. In many
applications of topic modeling, this problem of instability is not considered
and topic models are treated as being definitive, even though the results may
change considerably if the initialization process is altered. In this paper we
demonstrate the inherent instability of popular topic modeling approaches,
using a number of new measures to assess stability. To address this issue in
the context of matrix factorization for topic modeling, we propose the use of
ensemble learning strategies. Based on experiments performed on annotated text
corpora, we show that a K-Fold ensemble strategy, combining both ensembles and
structured initialization, can significantly reduce instability, while
simultaneously yielding more accurate topic models.
| Mark Belford and Brian Mac Namee and Derek Greene | null | 1702.07186 | null | null |
A minimax and asymptotically optimal algorithm for stochastic bandits | stat.ML cs.LG math.ST stat.TH | We propose the kl-UCB ++ algorithm for regret minimization in stochastic
bandit models with exponential families of distributions. We prove that it is
simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
bound) and minimax optimal. This is the first algorithm proved to enjoy these
two properties at the same time. This work thus merges two different lines of
research with simple and clear proofs.
| Pierre M\'enard (1), Aur\'elien Garivier (1) ((1) IMT) | null | 1702.07211 | null | null |
Rotting Bandits | stat.ML cs.LG | The Multi-Armed Bandits (MAB) framework highlights the tension between
acquiring new knowledge (Exploration) and leveraging available knowledge
(Exploitation). In the classical MAB problem, a decision maker must choose an
arm at each time step, upon which she receives a reward. The decision maker's
objective is to maximize her cumulative expected reward over the time horizon.
The MAB problem has been studied extensively, specifically under the assumption
of the arms' rewards distributions being stationary, or quasi-stationary, over
time. We consider a variant of the MAB framework, which we termed Rotting
Bandits, where each arm's expected reward decays as a function of the number of
times it has been pulled. We are motivated by many real-world scenarios such as
online advertising, content recommendation, crowdsourcing, and more. We present
algorithms, accompanied by simulations, and derive theoretical guarantees.
| Nir Levine, Koby Crammer, Shie Mannor | null | 1702.07274 | null | null |
Online Multiclass Boosting | stat.ML cs.LG | Recent work has extended the theoretical analysis of boosting algorithms to
multiclass problems and to online settings. However, the multiclass extension
is in the batch setting and the online extensions only consider binary
classification. We fill this gap in the literature by defining, and justifying,
a weak learning condition for online multiclass boosting. This condition leads
to an optimal boosting algorithm that requires the minimal number of weak
learners to achieve a certain accuracy. Additionally, we propose an adaptive
algorithm which is near optimal and enjoys an excellent performance on real
data due to its adaptive property.
| Young Hun Jung, Jack Goetz, Ambuj Tewari | null | 1702.07305 | null | null |
Causal Discovery Using Proxy Variables | stat.ML cs.LG | Discovering causal relations is fundamental to reasoning and intelligence. In
particular, observational causal discovery algorithms estimate the cause-effect
relation between two random entities $X$ and $Y$, given $n$ samples from
$P(X,Y)$.
In this paper, we develop a framework to estimate the cause-effect relation
between two static entities $x$ and $y$: for instance, an art masterpiece $x$
and its fraudulent copy $y$. To this end, we introduce the notion of proxy
variables, which allow the construction of a pair of random entities $(A,B)$
from the pair of static entities $(x,y)$. Then, estimating the cause-effect
relation between $A$ and $B$ using an observational causal discovery algorithm
leads to an estimation of the cause-effect relation between $x$ and $y$. For
example, our framework detects the causal relation between unprocessed
photographs and their modifications, and orders in time a set of shuffled
frames from a video.
As our main case study, we introduce a human-elicited dataset of 10,000 pairs
of casually-linked pairs of words from natural language. Our methods discover
75% of these causal relations. Finally, we discuss the role of proxy variables
in machine learning, as a general tool to incorporate static knowledge into
prediction tasks.
| Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz | null | 1702.07306 | null | null |
A Goal-Based Movement Model for Continuous Multi-Agent Tasks | q-bio.NC cs.LG stat.ML | Despite increasing attention paid to the need for fast, scalable methods to
analyze next-generation neuroscience data, comparatively little attention has
been paid to the development of similar methods for behavioral analysis. Just
as the volume and complexity of brain data have grown, behavioral paradigms in
systems neuroscience have likewise become more naturalistic and less
constrained, necessitating an increase in the flexibility and scalability of
the models used to study them. In particular, key assumptions made in the
analysis of typical decision paradigms --- optimality; analytic tractability;
discrete, low-dimensional action spaces --- may be untenable in richer tasks.
Here, using the case of a two-player, real-time, continuous strategic game as
an example, we show how the use of modern machine learning methods allows us to
relax each of these assumptions. Following an inverse reinforcement learning
approach, we are able to succinctly characterize the joint distribution over
players' actions via a generative model that allows us to simulate realistic
game play. We compare simulated play from a number of generative time series
models and show that ours successfully resists mode collapse while generating
trajectories with the rich variability of real behavior. Together, these
methods offer a rich class of models for the analysis of continuous action
tasks at the single-trial level.
| Shariq Iqbal and John Pearson | null | 1702.07319 | null | null |
A Converse to Banach's Fixed Point Theorem and its CLS Completeness | cs.CC cs.LG math.GN stat.ML | Banach's fixed point theorem for contraction maps has been widely used to
analyze the convergence of iterative methods in non-convex problems. It is a
common experience, however, that iterative maps fail to be globally contracting
under the natural metric in their domain, making the applicability of Banach's
theorem limited. We explore how generally we can apply Banach's fixed point
theorem to establish the convergence of iterative methods when pairing it with
carefully designed metrics.
Our first result is a strong converse of Banach's theorem, showing that it is
a universal analysis tool for establishing global convergence of iterative
methods to unique fixed points, and for bounding their convergence rate. In
other words, we show that, whenever an iterative map globally converges to a
unique fixed point, there exists a metric under which the iterative map is
contracting and which can be used to bound the number of iterations until
convergence. We illustrate our approach in the widely used power method,
providing a new way of bounding its convergence rate through contraction
arguments.
We next consider the computational complexity of Banach's fixed point
theorem. Making the proof of our converse theorem constructive, we show that
computing a fixed point whose existence is guaranteed by Banach's fixed point
theorem is CLS-complete. We thus provide the first natural complete problem for
the class CLS, which was defined in [Daskalakis, Papadimitriou 2011] to capture
the complexity of problems such as P-matrix LCP, computing KKT-points, and
finding mixed Nash equilibria in congestion and network coordination games.
| Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis | null | 1702.07339 | null | null |
Neural Decision Trees | stat.ML cs.LG | In this paper we propose a synergistic melting of neural networks and
decision trees (DT) we call neural decision trees (NDT). NDT is an architecture
a la decision tree where each splitting node is an independent multilayer
perceptron allowing oblique decision functions or arbritrary nonlinear decision
function if more than one layer is used. This way, each MLP can be seen as a
node of the tree. We then show that with the weight sharing asumption among
those units, we end up with a Hashing Neural Network (HNN) which is a
multilayer perceptron with sigmoid activation function for the last layer as
opposed to the standard softmax. The output units then jointly represent the
probability to be in a particular region. The proposed framework allows for
global optimization as opposed to greedy in DT and differentiability w.r.t. all
parameters and the input, allowing easy integration in any learnable pipeline,
for example after CNNs for computer vision tasks. We also demonstrate the
modeling power of HNN allowing to learn union of disjoint regions for final
clustering or classification making it more general and powerful than standard
softmax MLP requiring linear separability thus reducing the need on the inner
layer to perform complex data transformations. We finally show experiments for
supervised, semi-suppervised and unsupervised tasks and compare results with
standard DTs and MLPs.
| Randall Balestriero | null | 1702.0736 | null | null |
Bandits with Movement Costs and Adaptive Pricing | cs.LG cs.GT | We extend the model of Multi-armed Bandit with unit switching cost to
incorporate a metric between the actions. We consider the case where the metric
over the actions can be modeled by a complete binary tree, and the distance
between two leaves is the size of the subtree of their least common ancestor,
which abstracts the case that the actions are points on the continuous interval
$[0,1]$ and the switching cost is their distance. In this setting, we give a
new algorithm that establishes a regret of $\widetilde{O}(\sqrt{kT} + T/k)$,
where $k$ is the number of actions and $T$ is the time horizon. When the set of
actions corresponds to whole $[0,1]$ interval we can exploit our method for the
task of bandit learning with Lipschitz loss functions, where our algorithm
achieves an optimal regret rate of $\widetilde{\Theta}(T^{2/3})$, which is the
same rate one obtains when there is no penalty for movements. As our main
application, we use our new algorithm to solve an adaptive pricing problem.
Specifically, we consider the case of a single seller faced with a stream of
patient buyers. Each buyer has a private value and a window of time in which
they are interested in buying, and they buy at the lowest price in the window,
if it is below their value. We show that with an appropriate discretization of
the prices, the seller can achieve a regret of $\widetilde{O}(T^{2/3})$
compared to the best fixed price in hindsight, which outperform the previous
regret bound of $\widetilde{O}(T^{3/4})$ for the problem.
| Tomer Koren, Roi Livni, Yishay Mansour | null | 1702.07444 | null | null |
Strongly-Typed Agents are Guaranteed to Interact Safely | cs.LG cs.AI cs.GT | As artificial agents proliferate, it is becoming increasingly important to
ensure that their interactions with one another are well-behaved. In this
paper, we formalize a common-sense notion of when algorithms are well-behaved:
an algorithm is safe if it does no harm. Motivated by recent progress in deep
learning, we focus on the specific case where agents update their actions
according to gradient descent. The paper shows that that gradient descent
converges to a Nash equilibrium in safe games. The main contribution is to
define strongly-typed agents and show they are guaranteed to interact safely,
thereby providing sufficient conditions to guarantee safe interactions. A
series of examples show that strong-typing generalizes certain key features of
convexity, is closely related to blind source separation, and introduces a new
perspective on classical multilinear games based on tensor decomposition.
| David Balduzzi | null | 1702.0745 | null | null |
Sequence Modeling via Segmentations | stat.ML cs.LG | Segmental structure is a common pattern in many types of sequences such as
phrases in human languages. In this paper, we present a probabilistic model for
sequences via their segmentations. The probability of a segmented sequence is
calculated as the product of the probabilities of all its segments, where each
segment is modeled using existing tools such as recurrent neural networks.
Since the segmentation of a sequence is usually unknown in advance, we sum over
all valid segmentations to obtain the final probability for the sequence. An
efficient dynamic programming algorithm is developed for forward and backward
computations without resorting to any approximation. We demonstrate our
approach on text segmentation and speech recognition tasks. In addition to
quantitative results, we also show that our approach can discover meaningful
segments in their respective application contexts.
| Chong Wang and Yining Wang and Po-Sen Huang and Abdelrahman Mohamed
and Dengyong Zhou and Li Deng | null | 1702.07463 | null | null |
Deep Models Under the GAN: Information Leakage from Collaborative Deep
Learning | cs.CR cs.LG stat.ML | Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner with all the data being
processed by the same training algorithm. If the data is a collection of users'
private data, including habits, personal pictures, geographical positions,
interests, and more, the centralized server will have access to sensitive
information that could potentially be mishandled. To tackle this problem,
collaborative deep learning models have recently been proposed where parties
locally train their deep learning structures and only share a subset of the
parameters in the attempt to keep their respective training sets private.
Parameters can also be obfuscated via differential privacy (DP) to make
information extraction even more challenging, as proposed by Shokri and
Shmatikov at CCS'15.
Unfortunately, we show that any privacy-preserving collaborative deep
learning is susceptible to a powerful attack that we devise in this paper. In
particular, we show that a distributed, federated, or decentralized deep
learning approach is fundamentally broken and does not protect the training
sets of honest participants. The attack we developed exploits the real-time
nature of the learning process that allows the adversary to train a Generative
Adversarial Network (GAN) that generates prototypical samples of the targeted
training set that was meant to be private (the samples generated by the GAN are
intended to come from the same distribution as the training data).
Interestingly, we show that record-level DP applied to the shared parameters of
the model, as suggested in previous work, is ineffective (i.e., record-level DP
is not designed to address our attack).
| Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz | null | 1702.07464 | null | null |
Online Meta-learning by Parallel Algorithm Competition | cs.LG | The efficiency of reinforcement learning algorithms depends critically on a
few meta-parameters that modulates the learning updates and the trade-off
between exploration and exploitation. The adaptation of the meta-parameters is
an open question in reinforcement learning, which arguably has become more of
an issue recently with the success of deep reinforcement learning in
high-dimensional state spaces. The long learning times in domains such as Atari
2600 video games makes it not feasible to perform comprehensive searches of
appropriate meta-parameter values. We propose the Online Meta-learning by
Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several
instances of a reinforcement learning algorithm are run in parallel with small
differences in the initial values of the meta-parameters. After a fixed number
of episodes, the instances are selected based on their performance in the task
at hand. Before continuing the learning, Gaussian noise is added to the
meta-parameters with a predefined probability. We validate the OMPAC method by
improving the state-of-the-art results in stochastic SZ-Tetris and in standard
Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and
by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600
games by 62% or more. The experiments also show the ability of the OMPAC method
to adapt the meta-parameters according to the learning progress in different
tasks.
| Stefan Elfwing, Eiji Uchibe, Kenji Doya | null | 1702.0749 | null | null |
Tight Bounds for Bandit Combinatorial Optimization | cs.LG | We revisit the study of optimal regret rates in bandit combinatorial
optimization---a fundamental framework for sequential decision making under
uncertainty that abstracts numerous combinatorial prediction problems. We prove
that the attainable regret in this setting grows as
$\widetilde{\Theta}(k^{3/2}\sqrt{dT})$ where $d$ is the dimension of the
problem and $k$ is a bound over the maximal instantaneous loss, disproving a
conjecture of Audibert, Bubeck, and Lugosi (2013) who argued that the optimal
rate should be of the form $\widetilde{\Theta}(k\sqrt{dT})$. Our bounds apply
to several important instances of the framework, and in particular, imply a
tight bound for the well-studied bandit shortest path problem. By that, we also
resolve an open problem posed by Cesa-Bianchi and Lugosi (2012).
| Alon Cohen, Tamir Hazan, Tomer Koren | null | 1702.07539 | null | null |
Learning Rates for Kernel-Based Expectile Regression | stat.ML cs.LG | Conditional expectiles are becoming an increasingly important tool in finance
as well as in other areas of applications. We analyse a support vector machine
type approach for estimating conditional expectiles and establish learning
rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF
kernels are used and the desired expectile is smooth in a Besov sense. As a
special case, our learning rates improve the best known rates for kernel-based
least squares regression in this scenario. Key ingredients of our statistical
analysis are a general calibration inequality for the asymmetric least squares
loss, a corresponding variance bound as well as an improved entropy number
bound for Gaussian RBF kernels.
| Muhammad Farooq and Ingo Steinwart | null | 1702.07552 | null | null |
RNN Decoding of Linear Block Codes | cs.IT cs.LG cs.NE math.IT | Designing a practical, low complexity, close to optimal, channel decoder for
powerful algebraic codes with short to moderate block length is an open
research problem. Recently it has been shown that a feed-forward neural network
architecture can improve on standard belief propagation decoding, despite the
large example space. In this paper we introduce a recurrent neural network
architecture for decoding linear block codes. Our method shows comparable bit
error rate results compared to the feed-forward neural network with
significantly less parameters. We also demonstrate improved performance over
belief propagation on sparser Tanner graph representations of the codes.
Furthermore, we demonstrate that the RNN decoder can be used to improve the
performance or alternatively reduce the computational complexity of the mRRD
algorithm for low complexity, close to optimal, decoding of short BCH codes.
| Eliya Nachmani, Elad Marciano, David Burshtein and Yair Be'ery | null | 1702.0756 | null | null |
Control of Gene Regulatory Networks with Noisy Measurements and
Uncertain Inputs | q-bio.MN cs.LG stat.ML | This paper is concerned with the problem of stochastic control of gene
regulatory networks (GRNs) observed indirectly through noisy measurements and
with uncertainty in the intervention inputs. The partial observability of the
gene states and uncertainty in the intervention process are accounted for by
modeling GRNs using the partially-observed Boolean dynamical system (POBDS)
signal model with noisy gene expression measurements. Obtaining the optimal
infinite-horizon control strategy for this problem is not attainable in
general, and we apply reinforcement learning and Gaussian process techniques to
find a near-optimal solution. The POBDS is first transformed to a
directly-observed Markov Decision Process in a continuous belief space, and the
Gaussian process is used for modeling the cost function over the belief and
intervention spaces. Reinforcement learning then is used to learn the cost
function from the available gene expression data. In addition, we employ
sparsification, which enables the control of large partially-observed GRNs. The
performance of the resulting algorithm is studied through a comprehensive set
of numerical experiments using synthetic gene expression data generated from a
melanoma gene regulatory network.
| Mahdi Imani and Ulisses Braga-Neto | null | 1702.07652 | null | null |
How ConvNets model Non-linear Transformations | cs.CV cs.LG | In this paper, we theoretically address three fundamental problems involving
deep convolutional networks regarding invariance, depth and hierarchy. We
introduce the paradigm of Transformation Networks (TN) which are a direct
generalization of Convolutional Networks (ConvNets). Theoretically, we show
that TNs (and thereby ConvNets) are can be invariant to non-linear
transformations of the input despite pooling over mere local translations. Our
analysis provides clear insights into the increase in invariance with depth in
these networks. Deeper networks are able to model much richer classes of
transformations. We also find that a hierarchical architecture allows the
network to generate invariance much more efficiently than a non-hierarchical
network. Our results provide useful insight into these three fundamental
problems in deep learning using ConvNets.
| Dipan K. Pal, Marios Savvides | null | 1702.07664 | null | null |
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference
Learning | stat.ML cs.IT cs.LG math.IT | We analyze the problem of learning a single user's preferences in an active
learning setting, sequentially and adaptively querying the user over a finite
time horizon. Learning is conducted via choice-based queries, where the user
selects her preferred option among a small subset of offered alternatives.
These queries have been shown to be a robust and efficient way to learn an
individual's preferences. We take a parametric approach and model the user's
preferences through a linear classifier, using a Bayesian prior to encode our
current knowledge of this classifier. The rate at which we learn depends on the
alternatives offered at every time epoch. Under certain noise assumptions, we
show that the Bayes-optimal policy for maximally reducing entropy of the
posterior distribution of this linear classifier is a greedy policy, and that
this policy achieves a linear lower bound when alternatives can be constructed
from the continuum. Further, we analyze a different metric called
misclassification error, proving that the performance of the optimal policy
that minimizes misclassification error is bounded below by a linear function of
differential entropy. Lastly, we numerically compare the greedy entropy
reduction policy with a knowledge gradient policy under a number of scenarios,
examining their performance under both differential entropy and
misclassification error.
| Stephen N. Pallone, Peter I. Frazier, and Shane G. Henderson | null | 1702.07694 | null | null |
Computationally Efficient Robust Estimation of Sparse Functionals | stat.ML cs.DS cs.LG | Many conventional statistical procedures are extremely sensitive to seemingly
minor deviations from modeling assumptions. This problem is exacerbated in
modern high-dimensional settings, where the problem dimension can grow with and
possibly exceed the sample size. We consider the problem of robust estimation
of sparse functionals, and provide a computationally and statistically
efficient algorithm in the high-dimensional setting. Our theory identifies a
unified set of deterministic conditions under which our algorithm guarantees
accurate recovery. By further establishing that these deterministic conditions
hold with high-probability for a wide range of statistical models, our theory
applies to many problems of considerable interest including sparse mean and
covariance estimation; sparse linear regression; and sparse generalized linear
models.
| Simon S. Du, Sivaraman Balakrishnan, Aarti Singh | null | 1702.07709 | null | null |
A supervised approach to time scale detection in dynamic networks | cs.SI cs.LG | For any stream of time-stamped edges that form a dynamic network, an
important choice is the aggregation granularity that an analyst uses to bin the
data. Picking such a windowing of the data is often done by hand, or left up to
the technology that is collecting the data. However, the choice can make a big
difference in the properties of the dynamic network. This is the time scale
detection problem. In previous work, this problem is often solved with a
heuristic as an unsupervised task. As an unsupervised problem, it is difficult
to measure how well a given algorithm performs. In addition, we show that the
quality of the windowing is dependent on which task an analyst wants to perform
on the network after windowing. Therefore the time scale detection problem
should not be handled independently from the rest of the analysis of the
network.
We introduce a framework that tackles both of these issues: By measuring the
performance of the time scale detection algorithm based on how well a given
task is accomplished on the resulting network, we are for the first time able
to directly compare different time scale detection algorithms to each other.
Using this framework, we introduce time scale detection algorithms that take a
supervised approach: they leverage ground truth on training data to find a good
windowing of the test data. We compare the supervised approach to previous
approaches and several baselines on real data.
| Benjamin Fish, Rajmonda S. Caceres | null | 1702.07752 | null | null |
Changing Model Behavior at Test-Time Using Reinforcement Learning | stat.ML cs.LG | Machine learning models are often used at test-time subject to constraints
and trade-offs not present at training-time. For example, a computer vision
model operating on an embedded device may need to perform real-time inference,
or a translation model operating on a cell phone may wish to bound its average
compute time in order to be power-efficient. In this work we describe a
mixture-of-experts model and show how to change its test-time resource-usage on
a per-input basis using reinforcement learning. We test our method on a small
MNIST-based example.
| Augustus Odena, Dieterich Lawson, Christopher Olah | null | 1702.0778 | null | null |
Convolutional Gated Recurrent Neural Network Incorporating Spatial
Features for Audio Tagging | cs.SD cs.LG cs.NE | Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network (CNN) to extract robust features from mel-filter banks (MFBs),
spectrograms or even raw waveforms for audio tagging. Gated recurrent unit
(GRU) based recurrent neural networks (RNNs) are then cascaded to model the
long-term temporal structure of the audio signal. To complement the input
information, an auxiliary CNN is designed to learn on the spatial features of
stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging)
of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE
2016) challenge. Compared with our recent DNN-based method, the proposed
structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the
development set. The spatial features can further reduce the EER to 0.10. The
performance of the end-to-end learning on raw waveforms is also comparable.
Finally, on the evaluation set, we get the state-of-the-art performance with
0.12 EER while the performance of the best existing system is 0.15 EER.
| Yong Xu and Qiuqiang Kong and Qiang Huang and Wenwu Wang and Mark D.
Plumbley | null | 1702.07787 | null | null |
Activation Ensembles for Deep Neural Networks | stat.ML cs.LG | Many activation functions have been proposed in the past, but selecting an
adequate one requires trial and error. We propose a new methodology of
designing activation functions within a neural network at each layer. We call
this technique an "activation ensemble" because it allows the use of multiple
activation functions at each layer. This is done by introducing additional
variables, $\alpha$, at each activation layer of a network to allow for
multiple activation functions to be active at each neuron. By design,
activations with larger $\alpha$ values at a neuron is equivalent to having the
largest magnitude. Hence, those higher magnitude activations are "chosen" by
the network. We implement the activation ensembles on a variety of datasets
using an array of Feed Forward and Convolutional Neural Networks. By using the
activation ensemble, we achieve superior results compared to traditional
techniques. In addition, because of the flexibility of this methodology, we
more deeply explore activation functions and the features that they capture.
| Mark Harmon, Diego Klabjan | null | 1702.0779 | null | null |
Rank-to-engage: New Listwise Approaches to Maximize Engagement | stat.ML cs.LG | For many internet businesses, presenting a given list of items in an order
that maximizes a certain metric of interest (e.g., click-through-rate, average
engagement time etc.) is crucial. We approach the aforementioned task from a
learning-to-rank perspective which reveals a new problem setup. In traditional
learning-to-rank literature, it is implicitly assumed that during the training
data generation one has access to the \emph{best or desired} order for the
given list of items. In this work, we consider a problem setup where we do not
observe the desired ranking. We present two novel solutions: the first solution
is an extension of already existing listwise learning-to-rank
technique--Listwise maximum likelihood estimation (ListMLE)--while the second
one is a generic machine learning based framework that tackles the problem in
its entire generality. We discuss several challenges associated with this
generic framework, and propose a simple \emph{item-payoff} and
\emph{positional-gain} model that addresses these challenges. We provide
training algorithms, inference procedures, and demonstrate the effectiveness of
the two approaches over traditional ListMLE on synthetic as well as on
real-life setting of ranking news articles for increased dwell time.
| Swayambhoo Jain, Akshay Soni, Nikolay Laptev, and Yashar Mehdad | null | 1702.07798 | null | null |
On the Origin of Deep Learning | cs.LG cs.NE stat.ML | This paper is a review of the evolutionary history of deep learning models.
It covers from the genesis of neural networks when associationism modeling of
the brain is studied, to the models that dominate the last decade of research
in deep learning like convolutional neural networks, deep belief networks, and
recurrent neural networks. In addition to a review of these models, this paper
primarily focuses on the precedents of the models above, examining how the
initial ideas are assembled to construct the early models and how these
preliminary models are developed into their current forms. Many of these
evolutionary paths last more than half a century and have a diversity of
directions. For example, CNN is built on prior knowledge of biological vision
system; DBN is evolved from a trade-off of modeling power and computation
complexity of graphical models and many nowadays models are neural counterparts
of ancient linear models. This paper reviews these evolutionary paths and
offers a concise thought flow of how these models are developed, and aims to
provide a thorough background for deep learning. More importantly, along with
the path, this paper summarizes the gist behind these milestones and proposes
many directions to guide the future research of deep learning.
| Haohan Wang and Bhiksha Raj | null | 1702.078 | null | null |
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