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Application of backpropagation neural networks to both stages of
fingerprinting based WIPS | stat.ML cs.LG | We propose a scheme to employ backpropagation neural networks (BPNNs) for
both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal
strengths (FWIPS): radio map construction during the offline stage, and
localization during the online stage. Given a training radio map (TRM), i.e., a
set of coordinate vectors and associated WLAN/WiFi signal strengths of the
available access points, a BPNN can be trained to output the expected signal
strengths for any input position within the region of interest (BPNN-RM). This
can be used to provide a continuous representation of the radio map and to
filter, densify or decimate a discrete radio map. Correspondingly, the TRM can
also be used to train another BPNN to output the expected position within the
region of interest for any input vector of recorded signal strengths and thus
carry out localization (BPNN-LA).Key aspects of the design of such artificial
neural networks for a specific application are the selection of design
parameters like the number of hidden layers and nodes within the network, and
the training procedure. Summarizing extensive numerical simulations, based on
real measurements in a testbed, we analyze the impact of these design choices
on the performance of the BPNN and compare the results in particular to those
obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest
neighbors approaches to FWIPS.
| Caifa Zhou and Andreas Wieser | null | 1703.06912 | null | null |
Applying Deep Machine Learning for psycho-demographic profiling of
Internet users using O.C.E.A.N. model of personality | cs.LG cs.CY | In the modern era, each Internet user leaves enormous amounts of auxiliary
digital residuals (footprints) by using a variety of on-line services. All this
data is already collected and stored for many years. In recent works, it was
demonstrated that it's possible to apply simple machine learning methods to
analyze collected digital footprints and to create psycho-demographic profiles
of individuals. However, while these works clearly demonstrated the
applicability of machine learning methods for such an analysis, created simple
prediction models still lacks accuracy necessary to be successfully applied for
practical needs. We have assumed that using advanced deep machine learning
methods may considerably increase the accuracy of predictions. We started with
simple machine learning methods to estimate basic prediction performance and
moved further by applying advanced methods based on shallow and deep neural
networks. Then we compared prediction power of studied models and made
conclusions about its performance. Finally, we made hypotheses how prediction
accuracy can be further improved. As result of this work, we provide full
source code used in the experiments for all interested researchers and
practitioners in corresponding GitHub repository. We believe that applying deep
machine learning for psycho-demographic profiling may have an enormous impact
on the society (for good or worse) and provides means for Artificial
Intelligence (AI) systems to better understand humans by creating their
psychological profiles. Thus AI agents may achieve the human-like ability to
participate in conversation (communication) flow by anticipating human
opponents' reactions, expectations, and behavior.
| Iaroslav Omelianenko | null | 1703.06914 | null | null |
Black-Box Optimization in Machine Learning with Trust Region Based
Derivative Free Algorithm | cs.LG | In this work, we utilize a Trust Region based Derivative Free Optimization
(DFO-TR) method to directly maximize the Area Under Receiver Operating
Characteristic Curve (AUC), which is a nonsmooth, noisy function. We show that
AUC is a smooth function, in expectation, if the distributions of the positive
and negative data points obey a jointly normal distribution. The practical
performance of this algorithm is compared to three prominent Bayesian
optimization methods and random search. The presented numerical results show
that DFO-TR surpasses Bayesian optimization and random search on various
black-box optimization problem, such as maximizing AUC and hyperparameter
tuning.
| Hiva Ghanbari, Katya Scheinberg | null | 1703.06925 | null | null |
Ensemble representation learning: an analysis of fitness and survival
for wrapper-based genetic programming methods | cs.NE cs.LG stat.ML | Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demonstrate that two fitness metrics, one introduced as an
adaptation of the silhouette score, outperform the more commonly used Fisher
criterion. We analyze survival methods and demonstrate that $\epsilon$-lexicase
survival works best across our test problems, followed by random survival which
outperforms both tournament and deterministic crowding. We conduct a benchmark
comparison to several classification methods using a large set of problems and
show that FEW can improve the best classifier performance in several cases. We
show that FEW generates consistent, meaningful features for a biomedical
problem with different ML pairings.
| William La Cava and Jason H. Moore | 10.1145/3071178/3071215 | 1703.06934 | null | null |
CSI: A Hybrid Deep Model for Fake News Detection | cs.LG cs.SI | The topic of fake news has drawn attention both from the public and the
academic communities. Such misinformation has the potential of affecting public
opinion, providing an opportunity for malicious parties to manipulate the
outcomes of public events such as elections. Because such high stakes are at
play, automatically detecting fake news is an important, yet challenging
problem that is not yet well understood. Nevertheless, there are three
generally agreed upon characteristics of fake news: the text of an article, the
user response it receives, and the source users promoting it. Existing work has
largely focused on tailoring solutions to one particular characteristic which
has limited their success and generality. In this work, we propose a model that
combines all three characteristics for a more accurate and automated
prediction. Specifically, we incorporate the behavior of both parties, users
and articles, and the group behavior of users who propagate fake news.
Motivated by the three characteristics, we propose a model called CSI which is
composed of three modules: Capture, Score, and Integrate. The first module is
based on the response and text; it uses a Recurrent Neural Network to capture
the temporal pattern of user activity on a given article. The second module
learns the source characteristic based on the behavior of users, and the two
are integrated with the third module to classify an article as fake or not.
Experimental analysis on real-world data demonstrates that CSI achieves higher
accuracy than existing models, and extracts meaningful latent representations
of both users and articles.
| Natali Ruchansky, Sungyong Seo, Yan Liu | 10.1145/3132847.3132877 | 1703.06959 | null | null |
Active Decision Boundary Annotation with Deep Generative Models | cs.CV cs.LG | This paper is on active learning where the goal is to reduce the data
annotation burden by interacting with a (human) oracle during training.
Standard active learning methods ask the oracle to annotate data samples.
Instead, we take a profoundly different approach: we ask for annotations of the
decision boundary. We achieve this using a deep generative model to create
novel instances along a 1d line. A point on the decision boundary is revealed
where the instances change class. Experimentally we show on three data sets
that our method can be plugged-in to other active learning schemes, that human
oracles can effectively annotate points on the decision boundary, that our
method is robust to annotation noise, and that decision boundary annotations
improve over annotating data samples.
| Miriam W. Huijser and Jan C. van Gemert | null | 1703.06971 | null | null |
Learning to Generate Samples from Noise through Infusion Training | stat.ML cs.LG | In this work, we investigate a novel training procedure to learn a generative
model as the transition operator of a Markov chain, such that, when applied
repeatedly on an unstructured random noise sample, it will denoise it into a
sample that matches the target distribution from the training set. The novel
training procedure to learn this progressive denoising operation involves
sampling from a slightly different chain than the model chain used for
generation in the absence of a denoising target. In the training chain we
infuse information from the training target example that we would like the
chains to reach with a high probability. The thus learned transition operator
is able to produce quality and varied samples in a small number of steps.
Experiments show competitive results compared to the samples generated with a
basic Generative Adversarial Net
| Florian Bordes, Sina Honari, Pascal Vincent | null | 1703.06975 | null | null |
Metalearning for Feature Selection | cs.LG stat.ML | A general formulation of optimization problems in which various candidate
solutions may use different feature-sets is presented, encompassing supervised
classification, automated program learning and other cases. A novel
characterization of the concept of a "good quality feature" for such an
optimization problem is provided; and a proposal regarding the integration of
quality based feature selection into metalearning is suggested, wherein the
quality of a feature for a problem is estimated using knowledge about related
features in the context of related problems. Results are presented regarding
extensive testing of this "feature metalearning" approach on supervised text
classification problems; it is demonstrated that, in this context, feature
metalearning can provide significant and sometimes dramatic speedup over
standard feature selection heuristics.
| Ben Goertzel and Nil Geisweiller and Chris Poulin | null | 1703.0699 | null | null |
The Use of Autoencoders for Discovering Patient Phenotypes | cs.LG | We use autoencoders to create low-dimensional embeddings of underlying
patient phenotypes that we hypothesize are a governing factor in determining
how different patients will react to different interventions. We compare the
performance of autoencoders that take fixed length sequences of concatenated
timesteps as input with a recurrent sequence-to-sequence autoencoder. We
evaluate our methods on around 35,500 patients from the latest MIMIC III
dataset from Beth Israel Deaconess Hospital.
| Harini Suresh, Peter Szolovits, Marzyeh Ghassemi | null | 1703.07004 | null | null |
Modeling Long- and Short-Term Temporal Patterns with Deep Neural
Networks | cs.LG | Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, for which traditional approaches such as Autoregressive
models and Gaussian Process may fail. In this paper, we proposed a novel deep
learning framework, namely Long- and Short-term Time-series network (LSTNet),
to address this open challenge. LSTNet uses the Convolution Neural Network
(CNN) and the Recurrent Neural Network (RNN) to extract short-term local
dependency patterns among variables and to discover long-term patterns for time
series trends. Furthermore, we leverage traditional autoregressive model to
tackle the scale insensitive problem of the neural network model. In our
evaluation on real-world data with complex mixtures of repetitive patterns,
LSTNet achieved significant performance improvements over that of several
state-of-the-art baseline methods. All the data and experiment codes are
available online.
| Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu | null | 1703.07015 | null | null |
Recurrent Topic-Transition GAN for Visual Paragraph Generation | cs.CV cs.AI cs.LG | A natural image usually conveys rich semantic content and can be viewed from
different angles. Existing image description methods are largely restricted by
small sets of biased visual paragraph annotations, and fail to cover rich
underlying semantics. In this paper, we investigate a semi-supervised paragraph
generative framework that is able to synthesize diverse and semantically
coherent paragraph descriptions by reasoning over local semantic regions and
exploiting linguistic knowledge. The proposed Recurrent Topic-Transition
Generative Adversarial Network (RTT-GAN) builds an adversarial framework
between a structured paragraph generator and multi-level paragraph
discriminators. The paragraph generator generates sentences recurrently by
incorporating region-based visual and language attention mechanisms at each
step. The quality of generated paragraph sentences is assessed by multi-level
adversarial discriminators from two aspects, namely, plausibility at sentence
level and topic-transition coherence at paragraph level. The joint adversarial
training of RTT-GAN drives the model to generate realistic paragraphs with
smooth logical transition between sentence topics. Extensive quantitative
experiments on image and video paragraph datasets demonstrate the effectiveness
of our RTT-GAN in both supervised and semi-supervised settings. Qualitative
results on telling diverse stories for an image also verify the
interpretability of RTT-GAN.
| Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing | null | 1703.07022 | null | null |
Cross-modal Deep Metric Learning with Multi-task Regularization | cs.LG cs.CV stat.ML | DNN-based cross-modal retrieval has become a research hotspot, by which users
can search results across various modalities like image and text. However,
existing methods mainly focus on the pairwise correlation and reconstruction
error of labeled data. They ignore the semantically similar and dissimilar
constraints between different modalities, and cannot take advantage of
unlabeled data. This paper proposes Cross-modal Deep Metric Learning with
Multi-task Regularization (CDMLMR), which integrates quadruplet ranking loss
and semi-supervised contrastive loss for modeling cross-modal semantic
similarity in a unified multi-task learning architecture. The quadruplet
ranking loss can model the semantically similar and dissimilar constraints to
preserve cross-modal relative similarity ranking information. The
semi-supervised contrastive loss is able to maximize the semantic similarity on
both labeled and unlabeled data. Compared to the existing methods, CDMLMR
exploits not only the similarity ranking information but also unlabeled
cross-modal data, and thus boosts cross-modal retrieval accuracy.
| Xin Huang and Yuxin Peng | null | 1703.07026 | null | null |
Nonparametric Variational Auto-encoders for Hierarchical Representation
Learning | cs.LG stat.ML | The recently developed variational autoencoders (VAEs) have proved to be an
effective confluence of the rich representational power of neural networks with
Bayesian methods. However, most work on VAEs use a rather simple prior over the
latent variables such as standard normal distribution, thereby restricting its
applications to relatively simple phenomena. In this work, we propose
hierarchical nonparametric variational autoencoders, which combines
tree-structured Bayesian nonparametric priors with VAEs, to enable infinite
flexibility of the latent representation space. Both the neural parameters and
Bayesian priors are learned jointly using tailored variational inference. The
resulting model induces a hierarchical structure of latent semantic concepts
underlying the data corpus, and infers accurate representations of data
instances. We apply our model in video representation learning. Our method is
able to discover highly interpretable activity hierarchies, and obtain improved
clustering accuracy and generalization capacity based on the learned rich
representations.
| Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing | null | 1703.07027 | null | null |
High-Resolution Breast Cancer Screening with Multi-View Deep
Convolutional Neural Networks | cs.CV cs.LG stat.ML | Advances in deep learning for natural images have prompted a surge of
interest in applying similar techniques to medical images. The majority of the
initial attempts focused on replacing the input of a deep convolutional neural
network with a medical image, which does not take into consideration the
fundamental differences between these two types of images. Specifically, fine
details are necessary for detection in medical images, unlike in natural images
where coarse structures matter most. This difference makes it inadequate to use
the existing network architectures developed for natural images, because they
work on heavily downscaled images to reduce the memory requirements. This hides
details necessary to make accurate predictions. Additionally, a single exam in
medical imaging often comes with a set of views which must be fused in order to
reach a correct conclusion. In our work, we propose to use a multi-view deep
convolutional neural network that handles a set of high-resolution medical
images. We evaluate it on large-scale mammography-based breast cancer screening
(BI-RADS prediction) using 886,000 images. We focus on investigating the impact
of the training set size and image size on the prediction accuracy. Our results
highlight that performance increases with the size of training set, and that
the best performance can only be achieved using the original resolution. In the
reader study, performed on a random subset of the test set, we confirmed the
efficacy of our model, which achieved performance comparable to a committee of
radiologists when presented with the same data.
| Krzysztof J. Geras and Stacey Wolfson and Yiqiu Shen and Nan Wu and S.
Gene Kim and Eric Kim and Laura Heacock and Ujas Parikh and Linda Moy and
Kyunghyun Cho | null | 1703.07047 | null | null |
Investigation of Language Understanding Impact for Reinforcement
Learning Based Dialogue Systems | cs.CL cs.AI cs.LG | Language understanding is a key component in a spoken dialogue system. In
this paper, we investigate how the language understanding module influences the
dialogue system performance by conducting a series of systematic experiments on
a task-oriented neural dialogue system in a reinforcement learning based
setting. The empirical study shows that among different types of language
understanding errors, slot-level errors can have more impact on the overall
performance of a dialogue system compared to intent-level errors. In addition,
our experiments demonstrate that the reinforcement learning based dialogue
system is able to learn when and what to confirm in order to achieve better
performance and greater robustness.
| Xiujun Li and Yun-Nung Chen and Lihong Li and Jianfeng Gao and Asli
Celikyilmaz | null | 1703.07055 | null | null |
Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based
on Optimality Violations | stat.ML cs.LG math.OC | We study primal-dual type stochastic optimization algorithms with non-uniform
sampling. Our main theoretical contribution in this paper is to present a
convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with
arbitrary sampling. Based on this theoretical framework, we propose Optimality
Violation-based Sampling SPDC (ovsSPDC), a non-uniform sampling method based on
Optimality Violation. We also propose two efficient heuristic variants of
ovsSPDC called ovsSDPC+ and ovsSDPC++. Through intensive numerical experiments,
we demonstrate that the proposed method and its variants are faster than other
state-of-the-art primal-dual type stochastic optimization methods.
| Atsushi Shibagaki, Ichiro Takeuchi | null | 1703.07056 | null | null |
SMILES Enumeration as Data Augmentation for Neural Network Modeling of
Molecules | cs.LG | Simplified Molecular Input Line Entry System (SMILES) is a single line text
representation of a unique molecule. One molecule can however have multiple
SMILES strings, which is a reason that canonical SMILES have been defined,
which ensures a one to one correspondence between SMILES string and molecule.
Here the fact that multiple SMILES represent the same molecule is explored as a
technique for data augmentation of a molecular QSAR dataset modeled by a long
short term memory (LSTM) cell based neural network. The augmented dataset was
130 times bigger than the original. The network trained with the augmented
dataset shows better performance on a test set when compared to a model built
with only one canonical SMILES string per molecule. The correlation coefficient
R2 on the test set was improved from 0.56 to 0.66 when using SMILES
enumeration, and the root mean square error (RMS) likewise fell from 0.62 to
0.55. The technique also works in the prediction phase. By taking the average
per molecule of the predictions for the enumerated SMILES a further improvement
to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
| Esben Jannik Bjerrum | null | 1703.07076 | null | null |
Layer-wise training of deep networks using kernel similarity | cs.LG | Deep learning has shown promising results in many machine learning
applications. The hierarchical feature representation built by deep networks
enable compact and precise encoding of the data. A kernel analysis of the
trained deep networks demonstrated that with deeper layers, more simple and
more accurate data representations are obtained. In this paper, we propose an
approach for layer-wise training of a deep network for the supervised
classification task. A transformation matrix of each layer is obtained by
solving an optimization aimed at a better representation where a subsequent
layer builds its representation on the top of the features produced by a
previous layer. We compared the performance of our approach with a DNN trained
using back-propagation which has same architecture as ours. Experimental
results on the real image datasets demonstrate efficacy of our approach. We
also performed kernel analysis of layer representations to validate the claim
of better feature encoding.
| Mandar Kulkarni, Shirish Karande | null | 1703.07115 | null | null |
Knowledge distillation using unlabeled mismatched images | cs.CV cs.LG stat.ML | Current approaches for Knowledge Distillation (KD) either directly use
training data or sample from the training data distribution. In this paper, we
demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for
image classification networks. For illustration, we consider scenarios where
this is a complete absence of training data, or mismatched stimulus has to be
used for augmenting a small amount of training data. We demonstrate that
stimulus complexity is a key factor for distillation's good performance. Our
examples include use of various datasets for stimulating MNIST and CIFAR
teachers.
| Mandar Kulkarni, Kalpesh Patil, Shirish Karande | null | 1703.07131 | null | null |
ZM-Net: Real-time Zero-shot Image Manipulation Network | cs.CV cs.AI cs.GR cs.LG stat.ML | Many problems in image processing and computer vision (e.g. colorization,
style transfer) can be posed as 'manipulating' an input image into a
corresponding output image given a user-specified guiding signal. A holy-grail
solution towards generic image manipulation should be able to efficiently alter
an input image with any personalized signals (even signals unseen during
training), such as diverse paintings and arbitrary descriptive attributes.
However, existing methods are either inefficient to simultaneously process
multiple signals (let alone generalize to unseen signals), or unable to handle
signals from other modalities. In this paper, we make the first attempt to
address the zero-shot image manipulation task. We cast this problem as
manipulating an input image according to a parametric model whose key
parameters can be conditionally generated from any guiding signal (even unseen
ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a
fully-differentiable architecture that jointly optimizes an
image-transformation network (TNet) and a parameter network (PNet). The PNet
learns to generate key transformation parameters for the TNet given any guiding
signal while the TNet performs fast zero-shot image manipulation according to
both signal-dependent parameters from the PNet and signal-invariant parameters
from the TNet itself. Extensive experiments show that our ZM-Net can perform
high-quality image manipulation conditioned on different forms of guiding
signals (e.g. style images and attributes) in real-time (tens of milliseconds
per image) even for unseen signals. Moreover, a large-scale style dataset with
over 20,000 style images is also constructed to promote further research.
| Hao Wang, Xiaodan Liang, Hao Zhang, Dit-Yan Yeung, Eric P. Xing | null | 1703.07255 | null | null |
Black-Box Data-efficient Policy Search for Robotics | cs.RO cs.LG | The most data-efficient algorithms for reinforcement learning (RL) in
robotics are based on uncertain dynamical models: after each episode, they
first learn a dynamical model of the robot, then they use an optimization
algorithm to find a policy that maximizes the expected return given the model
and its uncertainties. It is often believed that this optimization can be
tractable only if analytical, gradient-based algorithms are used; however,
these algorithms require using specific families of reward functions and
policies, which greatly limits the flexibility of the overall approach. In this
paper, we introduce a novel model-based RL algorithm, called Black-DROPS
(Black-box Data-efficient RObot Policy Search) that: (1) does not impose any
constraint on the reward function or the policy (they are treated as
black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for
data-efficient RL in robotics, and (3) is as fast (or faster) than analytical
approaches when several cores are available. The key idea is to replace the
gradient-based optimization algorithm with a parallel, black-box algorithm that
takes into account the model uncertainties. We demonstrate the performance of
our new algorithm on two standard control benchmark problems (in simulation)
and a low-cost robotic manipulator (with a real robot).
| Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian
Goepp, Vassilis Vassiliades and Jean-Baptiste Mouret | null | 1703.07261 | null | null |
From safe screening rules to working sets for faster Lasso-type solvers | stat.ML cs.LG math.OC stat.CO | Convex sparsity-promoting regularizations are ubiquitous in modern
statistical learning. By construction, they yield solutions with few non-zero
coefficients, which correspond to saturated constraints in the dual
optimization formulation. Working set (WS) strategies are generic optimization
techniques that consist in solving simpler problems that only consider a subset
of constraints, whose indices form the WS. Working set methods therefore
involve two nested iterations: the outer loop corresponds to the definition of
the WS and the inner loop calls a solver for the subproblems. For the Lasso
estimator a WS is a set of features, while for a Group Lasso it refers to a set
of groups. In practice, WS are generally small in this context so the
associated feature Gram matrix can fit in memory. Here we show that the
Gauss-Southwell rule (a greedy strategy for block coordinate descent
techniques) leads to fast solvers in this case. Combined with a working set
strategy based on an aggressive use of so-called Gap Safe screening rules, we
propose a solver achieving state-of-the-art performance on sparse learning
problems. Results are presented on Lasso and multi-task Lasso estimators.
| Mathurin Massias and Alexandre Gramfort and Joseph Salmon | null | 1703.07285 | null | null |
One-Shot Imitation Learning | cs.AI cs.LG cs.NE cs.RO | Imitation learning has been commonly applied to solve different tasks in
isolation. This usually requires either careful feature engineering, or a
significant number of samples. This is far from what we desire: ideally, robots
should be able to learn from very few demonstrations of any given task, and
instantly generalize to new situations of the same task, without requiring
task-specific engineering. In this paper, we propose a meta-learning framework
for achieving such capability, which we call one-shot imitation learning.
Specifically, we consider the setting where there is a very large set of
tasks, and each task has many instantiations. For example, a task could be to
stack all blocks on a table into a single tower, another task could be to place
all blocks on a table into two-block towers, etc. In each case, different
instances of the task would consist of different sets of blocks with different
initial states. At training time, our algorithm is presented with pairs of
demonstrations for a subset of all tasks. A neural net is trained that takes as
input one demonstration and the current state (which initially is the initial
state of the other demonstration of the pair), and outputs an action with the
goal that the resulting sequence of states and actions matches as closely as
possible with the second demonstration. At test time, a demonstration of a
single instance of a new task is presented, and the neural net is expected to
perform well on new instances of this new task. The use of soft attention
allows the model to generalize to conditions and tasks unseen in the training
data. We anticipate that by training this model on a much greater variety of
tasks and settings, we will obtain a general system that can turn any
demonstrations into robust policies that can accomplish an overwhelming variety
of tasks.
Videos available at https://bit.ly/nips2017-oneshot .
| Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas
Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba | null | 1703.07326 | null | null |
How far are we from solving the 2D & 3D Face Alignment problem? (and a
dataset of 230,000 3D facial landmarks) | cs.CV cs.LG | This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for landmark localization with a state-of-the-art residual block, train it on a
very large yet synthetically expanded 2D facial landmark dataset and finally
evaluate it on all other 2D facial landmark datasets. (b) We create a guided by
2D landmarks network which converts 2D landmark annotations to 3D and unifies
all existing datasets, leading to the creation of LS3D-W, the largest and most
challenging 3D facial landmark dataset to date ~230,000 images. (c) Following
that, we train a neural network for 3D face alignment and evaluate it on the
newly introduced LS3D-W. (d) We further look into the effect of all
"traditional" factors affecting face alignment performance like large pose,
initialization and resolution, and introduce a "new" one, namely the size of
the network. (e) We show that both 2D and 3D face alignment networks achieve
performance of remarkable accuracy which is probably close to saturating the
datasets used. Training and testing code as well as the dataset can be
downloaded from https://www.adrianbulat.com/face-alignment/
| Adrian Bulat and Georgios Tzimiropoulos | 10.1109/ICCV.2017.116 | 1703.07332 | null | null |
On The Projection Operator to A Three-view Cardinality Constrained Set | cs.LG stat.ML | The cardinality constraint is an intrinsic way to restrict the solution
structure in many domains, for example, sparse learning, feature selection, and
compressed sensing. To solve a cardinality constrained problem, the key
challenge is to solve the projection onto the cardinality constraint set, which
is NP-hard in general when there exist multiple overlapped cardinality
constraints. In this paper, we consider the scenario where the overlapped
cardinality constraints satisfy a Three-view Cardinality Structure (TVCS),
which reflects the natural restriction in many applications, such as
identification of gene regulatory networks and task-worker assignment problem.
We cast the projection into a linear programming, and show that for TVCS, the
vertex solution of this linear programming is the solution for the original
projection problem. We further prove that such solution can be found with the
complexity proportional to the number of variables and constraints. We finally
use synthetic experiments and two interesting applications in bioinformatics
and crowdsourcing to validate the proposed TVCS model and method.
| Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei
Fujimaki, and Ji Liu | null | 1703.07345 | null | null |
CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for
Forward and Backward Propagation of Convolutional Neural Networks | cs.LG cs.AR | Large-scale deep convolutional neural networks (CNNs) are widely used in
machine learning applications. While CNNs involve huge complexity, VLSI (ASIC
and FPGA) chips that deliver high-density integration of computational
resources are regarded as a promising platform for CNN's implementation. At
massive parallelism of computational units, however, the external memory
bandwidth, which is constrained by the pin count of the VLSI chip, becomes the
system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of
the flexibility to be reconfigured for the various parameters of CNNs. This
paper presents CNN-MERP to address these issues. CNN-MERP incorporates an
efficient memory hierarchy that significantly reduces the bandwidth
requirements from multiple optimizations including on/off-chip data allocation,
data flow optimization and data reuse. The proposed 2-level reconfigurability
is utilized to enable fast and efficient reconfiguration, which is based on the
control logic and the multiboot feature of FPGA. As a result, an external
memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower
than prior arts. Under limited DRAM bandwidth, a system throughput of
1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times
higher than the state-of-the-art FPGA implementations.
| Xushen Han, Dajiang Zhou, Shihao Wang, and Shinji Kimura | null | 1703.07348 | null | null |
REBAR: Low-variance, unbiased gradient estimates for discrete latent
variable models | cs.LG stat.ML | Learning in models with discrete latent variables is challenging due to high
variance gradient estimators. Generally, approaches have relied on control
variates to reduce the variance of the REINFORCE estimator. Recent work (Jang
et al. 2016, Maddison et al. 2016) has taken a different approach, introducing
a continuous relaxation of discrete variables to produce low-variance, but
biased, gradient estimates. In this work, we combine the two approaches through
a novel control variate that produces low-variance, \emph{unbiased} gradient
estimates. Then, we introduce a modification to the continuous relaxation and
show that the tightness of the relaxation can be adapted online, removing it as
a hyperparameter. We show state-of-the-art variance reduction on several
benchmark generative modeling tasks, generally leading to faster convergence to
a better final log-likelihood.
| George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson,
Jascha Sohl-Dickstein | null | 1703.0737 | null | null |
Deep Learning for Explicitly Modeling Optimization Landscapes | cs.NE cs.AI cs.LG | In all but the most trivial optimization problems, the structure of the
solutions exhibit complex interdependencies between the input parameters.
Decades of research with stochastic search techniques has shown the benefit of
explicitly modeling the interactions between sets of parameters and the overall
quality of the solutions discovered. We demonstrate a novel method, based on
learning deep networks, to model the global landscapes of optimization
problems. To represent the search space concisely and accurately, the deep
networks must encode information about the underlying parameter interactions
and their contributions to the quality of the solution. Once the networks are
trained, the networks are probed to reveal parameter combinations with high
expected performance with respect to the optimization task. These estimates are
used to initialize fast, randomized, local search algorithms, which in turn
expose more information about the search space that is subsequently used to
refine the models. We demonstrate the technique on multiple optimization
problems that have arisen in a variety of real-world domains, including:
packing, graphics, job scheduling, layout and compression. The problems include
combinatoric search spaces, discontinuous and highly non-linear spaces, and
span binary, higher-cardinality discrete, as well as continuous parameters.
Strengths, limitations, and extensions of the approach are extensively
discussed and demonstrated.
| Shumeet Baluja | null | 1703.07394 | null | null |
Efficient PAC Learning from the Crowd | cs.LG cs.DS | In recent years crowdsourcing has become the method of choice for gathering
labeled training data for learning algorithms. Standard approaches to
crowdsourcing view the process of acquiring labeled data separately from the
process of learning a classifier from the gathered data. This can give rise to
computational and statistical challenges. For example, in most cases there are
no known computationally efficient learning algorithms that are robust to the
high level of noise that exists in crowdsourced data, and efforts to eliminate
noise through voting often require a large number of queries per example.
In this paper, we show how by interleaving the process of labeling and
learning, we can attain computational efficiency with much less overhead in the
labeling cost. In particular, we consider the realizable setting where there
exists a true target function in $\mathcal{F}$ and consider a pool of labelers.
When a noticeable fraction of the labelers are perfect, and the rest behave
arbitrarily, we show that any $\mathcal{F}$ that can be efficiently learned in
the traditional realizable PAC model can be learned in a computationally
efficient manner by querying the crowd, despite high amounts of noise in the
responses. Moreover, we show that this can be done while each labeler only
labels a constant number of examples and the number of labels requested per
example, on average, is a constant. When no perfect labelers exist, a related
task is to find a set of the labelers which are good but not perfect. We show
that we can identify all good labelers, when at least the majority of labelers
are good.
| Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour | null | 1703.07432 | null | null |
Episode-Based Active Learning with Bayesian Neural Networks | cs.CV cs.LG stat.ML | We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final training on the CIFAR-10 dataset shows that incremental network
updates with final training on the accumulated acquisition set are essential
for best performance, while limiting the amount of required human labeling
labor.
| Feras Dayoub, Niko S\"underhauf, Peter Corke | null | 1703.07473 | null | null |
LogitBoost autoregressive networks | stat.ML cs.LG | Multivariate binary distributions can be decomposed into products of
univariate conditional distributions. Recently popular approaches have modeled
these conditionals through neural networks with sophisticated weight-sharing
structures. It is shown that state-of-the-art performance on several standard
benchmark datasets can actually be achieved by training separate probability
estimators for each dimension. In that case, model training can be trivially
parallelized over data dimensions. On the other hand, complexity control has to
be performed for each learned conditional distribution. Three possible methods
are considered and experimentally compared. The estimator that is employed for
each conditional is LogitBoost. Similarities and differences between the
proposed approach and autoregressive models based on neural networks are
discussed in detail.
| Marc Goessling | 10.1016/j.csda.2017.03.010 | 1703.07506 | null | null |
Gate Activation Signal Analysis for Gated Recurrent Neural Networks and
Its Correlation with Phoneme Boundaries | cs.SD cs.CL cs.LG | In this paper we analyze the gate activation signals inside the gated
recurrent neural networks, and find the temporal structure of such signals is
highly correlated with the phoneme boundaries. This correlation is further
verified by a set of experiments for phoneme segmentation, in which better
results compared to standard approaches were obtained.
| Yu-Hsuan Wang, Cheng-Tao Chung, Hung-yi Lee | null | 1703.07588 | null | null |
Deep Exploration via Randomized Value Functions | stat.ML cs.AI cs.LG | We study the use of randomized value functions to guide deep exploration in
reinforcement learning. This offers an elegant means for synthesizing
statistically and computationally efficient exploration with common practical
approaches to value function learning. We present several reinforcement
learning algorithms that leverage randomized value functions and demonstrate
their efficacy through computational studies. We also prove a regret bound that
establishes statistical efficiency with a tabular representation.
| Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen | null | 1703.07608 | null | null |
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted
K-means Algorithm | cs.LG cs.DS stat.ML | This paper describes a method for clustering data that are spread out over
large regions and which dimensions are on different scales of measurement. Such
an algorithm was developed to implement a robotics application consisting in
sorting and storing objects in an unsupervised way. The toy dataset used to
validate such application consists of Lego bricks of different shapes and
colors. The uncontrolled lighting conditions together with the use of RGB color
features, respectively involve data with a large spread and different levels of
measurement between data dimensions. To overcome the combination of these two
characteristics in the data, we have developed a new weighted K-means
algorithm, called gap-ratio K-means, which consists in weighting each dimension
of the feature space before running the K-means algorithm. The weight
associated with a feature is proportional to the ratio of the biggest gap
between two consecutive data points, and the average of all the other gaps.
This method is compared with two other variants of K-means on the Lego bricks
clustering problem as well as two other common classification datasets.
| Joris Gu\'erin, Olivier Gibaru, St\'ephane Thiery and Eric Nyiri | null | 1703.07625 | null | null |
Machine Learning Based Source Code Classification Using Syntax Oriented
Features | cs.LG cs.PL | As of today the programming language of the vast majority of the published
source code is manually specified or programmatically assigned based on the
sole file extension. In this paper we show that the source code programming
language identification task can be fully automated using machine learning
techniques. We first define the criteria that a production-level automatic
programming language identification solution should meet. Our criteria include
accuracy, programming language coverage, extensibility and performance. We then
describe our approach: How training files are preprocessed for extracting
features that mimic grammar productions, and then how these extracted grammar
productions are used for the training and testing of our classifier. We achieve
a 99 percent accuracy rate while classifying 29 of the most popular programming
languages with a Maximum Entropy classifier.
| Shaul Zevin, Catherine Holzem | null | 1703.07638 | null | null |
Predicting Deeper into the Future of Semantic Segmentation | cs.CV cs.LG | The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future video frames has been studied in previous work, here we introduce the
novel task of predicting semantic segmentations of future frames. Given a
sequence of video frames, our goal is to predict segmentation maps of not yet
observed video frames that lie up to a second or further in the future. We
develop an autoregressive convolutional neural network that learns to
iteratively generate multiple frames. Our results on the Cityscapes dataset
show that directly predicting future segmentations is substantially better than
predicting and then segmenting future RGB frames. Prediction results up to half
a second in the future are visually convincing and are much more accurate than
those of a baseline based on warping semantic segmentations using optical flow.
| Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann
LeCun | null | 1703.07684 | null | null |
Characterization of Deterministic and Probabilistic Sampling Patterns
for Finite Completability of Low Tensor-Train Rank Tensor | cs.LG cs.IT math.AG math.IT stat.ML | In this paper, we analyze the fundamental conditions for low-rank tensor
completion given the separation or tensor-train (TT) rank, i.e., ranks of
unfoldings. We exploit the algebraic structure of the TT decomposition to
obtain the deterministic necessary and sufficient conditions on the locations
of the samples to ensure finite completability. Specifically, we propose an
algebraic geometric analysis on the TT manifold that can incorporate the whole
rank vector simultaneously in contrast to the existing approach based on the
Grassmannian manifold that can only incorporate one rank component. Our
proposed technique characterizes the algebraic independence of a set of
polynomials defined based on the sampling pattern and the TT decomposition,
which is instrumental to obtaining the deterministic condition on the sampling
pattern for finite completability. In addition, based on the proposed analysis,
assuming that the entries of the tensor are sampled independently with
probability $p$, we derive a lower bound on the sampling probability $p$, or
equivalently, the number of sampled entries that ensures finite completability
with high probability. Moreover, we also provide the deterministic and
probabilistic conditions for unique completability.
| Morteza Ashraphijuo, Xiaodong Wang | null | 1703.07698 | null | null |
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement
Learning | cs.LG cs.AI stat.ML | Statistical performance bounds for reinforcement learning (RL) algorithms can
be critical for high-stakes applications like healthcare. This paper introduces
a new framework for theoretically measuring the performance of such algorithms
called Uniform-PAC, which is a strengthening of the classical Probably
Approximately Correct (PAC) framework. In contrast to the PAC framework, the
uniform version may be used to derive high probability regret guarantees and so
forms a bridge between the two setups that has been missing in the literature.
We demonstrate the benefits of the new framework for finite-state episodic MDPs
with a new algorithm that is Uniform-PAC and simultaneously achieves optimal
regret and PAC guarantees except for a factor of the horizon.
| Christoph Dann, Tor Lattimore, Emma Brunskill | null | 1703.0771 | null | null |
Independently Controllable Features | cs.LG | Finding features that disentangle the different causes of variation in real
data is a difficult task, that has nonetheless received considerable attention
in static domains like natural images. Interactive environments, in which an
agent can deliberately take actions, offer an opportunity to tackle this task
better, because the agent can experiment with different actions and observe
their effects. We introduce the idea that in interactive environments, latent
factors that control the variation in observed data can be identified by
figuring out what the agent can control. We propose a naive method to find
factors that explain or measure the effect of the actions of a learner, and
test it in illustrative experiments.
| Emmanuel Bengio, Valentin Thomas, Joelle Pineau, Doina Precup, Yoshua
Bengio | null | 1703.07718 | null | null |
Sample and Computationally Efficient Learning Algorithms under S-Concave
Distributions | stat.ML cs.AI cs.LG | We provide new results for noise-tolerant and sample-efficient learning
algorithms under $s$-concave distributions. The new class of $s$-concave
distributions is a broad and natural generalization of log-concavity, and
includes many important additional distributions, e.g., the Pareto distribution
and $t$-distribution. This class has been studied in the context of efficient
sampling, integration, and optimization, but much remains unknown about the
geometry of this class of distributions and their applications in the context
of learning. The challenge is that unlike the commonly used distributions in
learning (uniform or more generally log-concave distributions), this broader
class is not closed under the marginalization operator and many such
distributions are fat-tailed. In this work, we introduce new convex geometry
tools to study the properties of $s$-concave distributions and use these
properties to provide bounds on quantities of interest to learning including
the probability of disagreement between two halfspaces, disagreement outside a
band, and the disagreement coefficient. We use these results to significantly
generalize prior results for margin-based active learning, disagreement-based
active learning, and passive learning of intersections of halfspaces. Our
analysis of geometric properties of $s$-concave distributions might be of
independent interest to optimization more broadly.
| Maria-Florina Balcan and Hongyang Zhang | null | 1703.07758 | null | null |
Multitask learning and benchmarking with clinical time series data | stat.ML cs.LG | Health care is one of the most exciting frontiers in data mining and machine
learning. Successful adoption of electronic health records (EHRs) created an
explosion in digital clinical data available for analysis, but progress in
machine learning for healthcare research has been difficult to measure because
of the absence of publicly available benchmark data sets. To address this
problem, we propose four clinical prediction benchmarks using data derived from
the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
database. These tasks cover a range of clinical problems including modeling
risk of mortality, forecasting length of stay, detecting physiologic decline,
and phenotype classification. We propose strong linear and neural baselines for
all four tasks and evaluate the effect of deep supervision, multitask training
and data-specific architectural modifications on the performance of neural
models.
| Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg,
and Aram Galstyan | 10.1038/s41597-019-0103-9 | 1703.07771 | null | null |
Learning to Partition using Score Based Compatibilities | cs.LG | We study the problem of learning to partition users into groups, where one
must learn the compatibilities between the users to achieve optimal groupings.
We define four natural objectives that optimize for average and worst case
compatibilities and propose new algorithms for adaptively learning optimal
groupings. When we do not impose any structure on the compatibilities, we show
that the group formation objectives considered are $NP$ hard to solve and we
either give approximation guarantees or prove inapproximability results. We
then introduce an elegant structure, namely that of \textit{intrinsic scores},
that makes many of these problems polynomial time solvable. We explicitly
characterize the optimal groupings under this structure and show that the
optimal solutions are related to \emph{homophilous} and \emph{heterophilous}
partitions, well-studied in the psychology literature. For one of the four
objectives, we show $NP$ hardness under the score structure and give a
$\frac{1}{2}$ approximation algorithm for which no constant approximation was
known thus far. Finally, under the score structure, we propose an online low
sample complexity PAC algorithm for learning the optimal partition. We
demonstrate the efficacy of the proposed algorithm on synthetic and real world
datasets.
| Arun Rajkumar and Koyel Mukherjee and Theja Tulabandhula | null | 1703.07807 | null | null |
Information-theoretic Model Identification and Policy Search using
Physics Engines with Application to Robotic Manipulation | cs.RO cs.AI cs.LG | We consider the problem of a robot learning the mechanical properties of
objects through physical interaction with the object, and introduce a
practical, data-efficient approach for identifying the motion models of these
objects. The proposed method utilizes a physics engine, where the robot seeks
to identify the inertial and friction parameters of the object by simulating
its motion under different values of the parameters and identifying those that
result in a simulation which matches the observed real motions. The problem is
solved in a Bayesian optimization framework. The same framework is used for
both identifying the model of an object online and searching for a policy that
would minimize a given cost function according to the identified model.
Experimental results both in simulation and using a real robot indicate that
the proposed method outperforms state-of-the-art model-free reinforcement
learning approaches.
| Shaojun Zhu, Andrew Kimmel, Abdeslam Boularias | null | 1703.07822 | null | null |
Fake News Mitigation via Point Process Based Intervention | cs.LG cs.SI | We propose the first multistage intervention framework that tackles fake news
in social networks by combining reinforcement learning with a point process
network activity model. The spread of fake news and mitigation events within
the network is modeled by a multivariate Hawkes process with additional
exogenous control terms. By choosing a feature representation of states,
defining mitigation actions and constructing reward functions to measure the
effectiveness of mitigation activities, we map the problem of fake news
mitigation into the reinforcement learning framework. We develop a policy
iteration method unique to the multivariate networked point process, with the
goal of optimizing the actions for maximal total reward under budget
constraints. Our method shows promising performance in real-time intervention
experiments on a Twitter network to mitigate a surrogate fake news campaign,
and outperforms alternatives on synthetic datasets.
| Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit
Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha | null | 1703.07823 | null | null |
Randomized Kernel Methods for Least-Squares Support Vector Machines | cs.LG physics.data-an stat.ML | The least-squares support vector machine is a frequently used kernel method
for non-linear regression and classification tasks. Here we discuss several
approximation algorithms for the least-squares support vector machine
classifier. The proposed methods are based on randomized block kernel matrices,
and we show that they provide good accuracy and reliable scaling for
multi-class classification problems with relatively large data sets. Also, we
present several numerical experiments that illustrate the practical
applicability of the proposed methods.
| M. Andrecut | 10.1142/S0129183117500152 | 1703.0783 | null | null |
Classification-based RNN machine translation using GRUs | cs.NE cs.LG | We report the results of our classification-based machine translation model,
built upon the framework of a recurrent neural network using gated recurrent
units. Unlike other RNN models that attempt to maximize the overall conditional
log probability of sentences against sentences, our model focuses a
classification approach of estimating the conditional probability of the next
word given the input sequence. This simpler approach using GRUs was hoped to be
comparable with more complicated RNN models, but achievements in this
implementation were modest and there remains a lot of room for improving this
classification approach.
| Ri Wang, Maysum Panju, Mahmood Gohari | null | 1703.07841 | null | null |
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning | q-bio.GN cs.LG q-bio.QM | We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning),
an open-source tool that implements a novel framework to learn a
sample-to-sample similarity measure from expression data observed for
heterogenous samples. SIMLR can be effectively used to perform tasks such as
dimension reduction, clustering, and visualization of heterogeneous populations
of samples. SIMLR was benchmarked against state-of-the-art methods for these
three tasks on several public datasets, showing it to be scalable and capable
of greatly improving clustering performance, as well as providing valuable
insights by making the data more interpretable via better a visualization.
Availability and Implementation
SIMLR is available on GitHub in both R and MATLAB implementations.
Furthermore, it is also available as an R package on http://bioconductor.org.
| Bo Wang and Daniele Ramazzotti and Luca De Sano and Junjie Zhu and
Emma Pierson and Serafim Batzoglou | 10.1002/pmic.201700232 | 1703.07844 | null | null |
Faster Reinforcement Learning Using Active Simulators | cs.LG | In this work, we propose several online methods to build a \emph{learning
curriculum} from a given set of target-task-specific training tasks in order to
speed up reinforcement learning (RL). These methods can decrease the total
training time needed by an RL agent compared to training on the target task
from scratch. Unlike traditional transfer learning, we consider creating a
sequence from several training tasks in order to provide the most benefit in
terms of reducing the total time to train.
Our methods utilize the learning trajectory of the agent on the curriculum
tasks seen so far to decide which tasks to train on next. An attractive feature
of our methods is that they are weakly coupled to the choice of the RL
algorithm as well as the transfer learning method. Further, when there is
domain information available, our methods can incorporate such knowledge to
further speed up the learning. We experimentally show that these methods can be
used to obtain suitable learning curricula that speed up the overall training
time on two different domains.
| Vikas Jain and Theja Tulabandhula | null | 1703.07853 | null | null |
Random Features for Compositional Kernels | cs.LG | We describe and analyze a simple random feature scheme (RFS) from prescribed
compositional kernels. The compositional kernels we use are inspired by the
structure of convolutional neural networks and kernels. The resulting scheme
yields sparse and efficiently computable features. Each random feature can be
represented as an algebraic expression over a small number of (random) paths in
a composition tree. Thus, compositional random features can be stored
compactly. The discrete nature of the generation process enables de-duplication
of repeated features, further compacting the representation and increasing the
diversity of the embeddings. Our approach complements and can be combined with
previous random feature schemes.
| Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer | null | 1703.07872 | null | null |
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial
Domains | stat.ML cs.CR cs.LG | While modern day web applications aim to create impact at the civilization
level, they have become vulnerable to adversarial activity, where the next
cyber-attack can take any shape and can originate from anywhere. The increasing
scale and sophistication of attacks, has prompted the need for a data driven
solution, with machine learning forming the core of many cybersecurity systems.
Machine learning was not designed with security in mind, and the essential
assumption of stationarity, requiring that the training and testing data follow
similar distributions, is violated in an adversarial domain. In this paper, an
adversary's view point of a classification based system, is presented. Based on
a formal adversarial model, the Seed-Explore-Exploit framework is presented,
for simulating the generation of data driven and reverse engineering attacks on
classifiers. Experimental evaluation, on 10 real world datasets and using the
Google Cloud Prediction Platform, demonstrates the innate vulnerability of
classifiers and the ease with which evasion can be carried out, without any
explicit information about the classifier type, the training data or the
application domain. The proposed framework, algorithms and empirical
evaluation, serve as a white hat analysis of the vulnerabilities, and aim to
foster the development of secure machine learning frameworks.
| Tegjyot Singh Sethi, Mehmed Kantardzic | 10.1016/j.neucom.2018.02.007 | 1703.07909 | null | null |
Perspective: Energy Landscapes for Machine Learning | stat.ML cond-mat.dis-nn cs.CV cs.LG hep-th | Machine learning techniques are being increasingly used as flexible
non-linear fitting and prediction tools in the physical sciences. Fitting
functions that exhibit multiple solutions as local minima can be analysed in
terms of the corresponding machine learning landscape. Methods to explore and
visualise molecular potential energy landscapes can be applied to these machine
learning landscapes to gain new insight into the solution space involved in
training and the nature of the corresponding predictions. In particular, we can
define quantities analogous to molecular structure, thermodynamics, and
kinetics, and relate these emergent properties to the structure of the
underlying landscape. This Perspective aims to describe these analogies with
examples from recent applications, and suggest avenues for new
interdisciplinary research.
| Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta,
Levent Sagun, Jacob D. Stevenson, David J. Wales | 10.1039/C7CP01108C | 1703.07915 | null | null |
Role of zero synapses in unsupervised feature learning | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech cs.LG cs.NE | Synapses in real neural circuits can take discrete values, including zero
(silent or potential) synapses. The computational role of zero synapses in
unsupervised feature learning of unlabeled noisy data is still unclear, thus it
is important to understand how the sparseness of synaptic activity is shaped
during learning and its relationship with receptive field formation. Here, we
formulate this kind of sparse feature learning by a statistical mechanics
approach. We find that learning decreases the fraction of zero synapses, and
when the fraction decreases rapidly around a critical data size, an
intrinsically structured receptive field starts to develop. Further increasing
the data size refines the receptive field, while a very small fraction of zero
synapses remain to act as contour detectors. This phenomenon is discovered not
only in learning a handwritten digits dataset, but also in learning retinal
neural activity measured in a natural-movie-stimuli experiment.
| Haiping Huang | 10.1088/1751-8121/aaa631 | 1703.07943 | null | null |
Fast Stochastic Variance Reduced Gradient Method with Momentum
Acceleration for Machine Learning | cs.LG cs.AI math.OC stat.ML | Recently, research on accelerated stochastic gradient descent methods (e.g.,
SVRG) has made exciting progress (e.g., linear convergence for strongly convex
problems). However, the best-known methods (e.g., Katyusha) requires at least
two auxiliary variables and two momentum parameters. In this paper, we propose
a fast stochastic variance reduction gradient (FSVRG) method, in which we
design a novel update rule with the Nesterov's momentum and incorporate the
technique of growing epoch size. FSVRG has only one auxiliary variable and one
momentum weight, and thus it is much simpler and has much lower per-iteration
complexity. We prove that FSVRG achieves linear convergence for strongly convex
problems and the optimal $\mathcal{O}(1/T^2)$ convergence rate for non-strongly
convex problems, where $T$ is the number of outer-iterations. We also extend
FSVRG to directly solve the problems with non-smooth component functions, such
as SVM. Finally, we empirically study the performance of FSVRG for solving
various machine learning problems such as logistic regression, ridge
regression, Lasso and SVM. Our results show that FSVRG outperforms the
state-of-the-art stochastic methods, including Katyusha.
| Fanhua Shang, Yuanyuan Liu, James Cheng, and Jiacheng Zhuo | null | 1703.07948 | null | null |
Failures of Gradient-Based Deep Learning | cs.LG cs.NE stat.ML | In recent years, Deep Learning has become the go-to solution for a broad
range of applications, often outperforming state-of-the-art. However, it is
important, for both theoreticians and practitioners, to gain a deeper
understanding of the difficulties and limitations associated with common
approaches and algorithms. We describe four types of simple problems, for which
the gradient-based algorithms commonly used in deep learning either fail or
suffer from significant difficulties. We illustrate the failures through
practical experiments, and provide theoretical insights explaining their
source, and how they might be remedied.
| Shai Shalev-Shwartz and Ohad Shamir and Shaked Shammah | null | 1703.0795 | null | null |
Discriminatively Boosted Image Clustering with Fully Convolutional
Auto-Encoders | cs.CV cs.LG | Traditional image clustering methods take a two-step approach, feature
learning and clustering, sequentially. However, recent research results
demonstrated that combining the separated phases in a unified framework and
training them jointly can achieve a better performance. In this paper, we first
introduce fully convolutional auto-encoders for image feature learning and then
propose a unified clustering framework to learn image representations and
cluster centers jointly based on a fully convolutional auto-encoder and soft
$k$-means scores. At initial stages of the learning procedure, the
representations extracted from the auto-encoder may not be very discriminative
for latter clustering. We address this issue by adopting a boosted
discriminative distribution, where high score assignments are highlighted and
low score ones are de-emphasized. With the gradually boosted discrimination,
clustering assignment scores are discriminated and cluster purities are
enlarged. Experiments on several vision benchmark datasets show that our
methods can achieve a state-of-the-art performance.
| Fengfu Li, Hong Qiao, Bo Zhang, Xuanyang Xi | null | 1703.0798 | null | null |
A network of deep neural networks for distant speech recognition | cs.CL cs.LG | Despite the remarkable progress recently made in distant speech recognition,
state-of-the-art technology still suffers from a lack of robustness, especially
when adverse acoustic conditions characterized by non-stationary noises and
reverberation are met. A prominent limitation of current systems lies in the
lack of matching and communication between the various technologies involved in
the distant speech recognition process. The speech enhancement and speech
recognition modules are, for instance, often trained independently. Moreover,
the speech enhancement normally helps the speech recognizer, but the output of
the latter is not commonly used, in turn, to improve the speech enhancement. To
address both concerns, we propose a novel architecture based on a network of
deep neural networks, where all the components are jointly trained and better
cooperate with each other thanks to a full communication scheme between them.
Experiments, conducted using different datasets, tasks and acoustic conditions,
revealed that the proposed framework can overtake other competitive solutions,
including recent joint training approaches.
| Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, Yoshua Bengio | null | 1703.08002 | null | null |
Content-based similar document image retrieval using fusion of CNN
features | cs.CV cs.IR cs.LG | Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.
| Mao Tan, Si-Ping Yuan, Yong-Xin Su | null | 1703.08013 | null | null |
Online Distributed Learning Over Networks in RKH Spaces Using Random
Fourier Features | cs.LG | We present a novel diffusion scheme for online kernel-based learning over
networks. So far, a major drawback of any online learning algorithm, operating
in a reproducing kernel Hilbert space (RKHS), is the need for updating a
growing number of parameters as time iterations evolve. Besides complexity,
this leads to an increased need of communication resources, in a distributed
setting. In contrast, the proposed method approximates the solution as a
fixed-size vector (of larger dimension than the input space) using Random
Fourier Features. This paves the way to use standard linear combine-then-adapt
techniques. To the best of our knowledge, this is the first time that a
complete protocol for distributed online learning in RKHS is presented.
Conditions for asymptotic convergence and boundness of the networkwise regret
are also provided. The simulated tests illustrate the performance of the
proposed scheme.
| Pantelis Bouboulis, Symeon Chouvardas, Sergios Theodoridis | 10.1109/TSP.2017.2781640 | 1703.08131 | null | null |
An embedded segmental K-means model for unsupervised segmentation and
clustering of speech | cs.CL cs.LG | Unsupervised segmentation and clustering of unlabelled speech are core
problems in zero-resource speech processing. Most approaches lie at
methodological extremes: some use probabilistic Bayesian models with
convergence guarantees, while others opt for more efficient heuristic
techniques. Despite competitive performance in previous work, the full Bayesian
approach is difficult to scale to large speech corpora. We introduce an
approximation to a recent Bayesian model that still has a clear objective
function but improves efficiency by using hard clustering and segmentation
rather than full Bayesian inference. Like its Bayesian counterpart, this
embedded segmental K-means model (ES-KMeans) represents arbitrary-length word
segments as fixed-dimensional acoustic word embeddings. We first compare
ES-KMeans to previous approaches on common English and Xitsonga data sets (5
and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in
word segmentation, giving similar scores to the Bayesian model while being 5
times faster with fewer hyperparameters. However, its clusters are less pure
than those of the other models. We then show that ES-KMeans scales to larger
corpora by applying it to the 5 languages of the Zero Resource Speech Challenge
2017 (up to 45 hours), where it performs competitively compared to the
challenge baseline.
| Herman Kamper, Karen Livescu, Sharon Goldwater | null | 1703.08135 | null | null |
On the Robustness of Convolutional Neural Networks to Internal
Architecture and Weight Perturbations | cs.LG cs.CV | Deep convolutional neural networks are generally regarded as robust function
approximators. So far, this intuition is based on perturbations to external
stimuli such as the images to be classified. Here we explore the robustness of
convolutional neural networks to perturbations to the internal weights and
architecture of the network itself. We show that convolutional networks are
surprisingly robust to a number of internal perturbations in the higher
convolutional layers but the bottom convolutional layers are much more fragile.
For instance, Alexnet shows less than a 30% decrease in classification
performance when randomly removing over 70% of weight connections in the top
convolutional or dense layers but performance is almost at chance with the same
perturbation in the first convolutional layer. Finally, we suggest further
investigations which could continue to inform the robustness of convolutional
networks to internal perturbations.
| Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman | null | 1703.08245 | null | null |
Experimental Identification of Hard Data Sets for Classification and
Feature Selection Methods with Insights on Method Selection | cs.LG | The paper reports an experimentally identified list of benchmark data sets
that are hard for representative classification and feature selection methods.
This was done after systematically evaluating a total of 48 combinations of
methods, involving eight state-of-the-art classification algorithms and six
commonly used feature selection methods, on 129 data sets from the UCI
repository (some data sets with known high classification accuracy were
excluded). In this paper, a data set for classification is called hard if none
of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve
an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of
the 129 data sets were found to be hard in that sense. This paper also compares
the performance of different methods, and it produces rankings of
classification methods, separately on the hard data sets and on the easy data
sets. This paper is the first to rank methods separately for hard data sets and
for easy data sets. It turns out that the classifier rankings resulting from
our experiments are somehow different from those in the literature and hence
they offer new insights on method selection. It should be noted that the Random
Forest method remains to be the best in all groups of experiments.
| Cuiju Luan and Guozhu Dong | null | 1703.08283 | null | null |
Multi-Level Discovery of Deep Options | cs.LG | Augmenting an agent's control with useful higher-level behaviors called
options can greatly reduce the sample complexity of reinforcement learning, but
manually designing options is infeasible in high-dimensional and abstract state
spaces. While recent work has proposed several techniques for automated option
discovery, they do not scale to multi-level hierarchies and to expressive
representations such as deep networks. We present Discovery of Deep Options
(DDO), a policy-gradient algorithm that discovers parametrized options from a
set of demonstration trajectories, and can be used recursively to discover
additional levels of the hierarchy. The scalability of our approach to
multi-level hierarchies stems from the decoupling of low-level option discovery
from high-level meta-control policy learning, facilitated by
under-parametrization of the high level. We demonstrate that using the
discovered options to augment the action space of Deep Q-Network agents can
accelerate learning by guiding exploration in tasks where random actions are
unlikely to reach valuable states. We show that DDO is effective in adding
options that accelerate learning in 4 out of 5 Atari RAM environments chosen in
our experiments. We also show that DDO can discover structure in robot-assisted
surgical videos and kinematics that match expert annotation with 72% accuracy.
| Roy Fox, Sanjay Krishnan, Ion Stoica, Ken Goldberg | null | 1703.08294 | null | null |
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient
Descent for Robot | cs.CV cs.LG cs.RO | Robot vision is a fundamental device for human-robot interaction and robot
complex tasks. In this paper, we use Kinect and propose a feature graph fusion
(FGF) for robot recognition. Our feature fusion utilizes RGB and depth
information to construct fused feature from Kinect. FGF involves multi-Jaccard
similarity to compute a robust graph and utilize word embedding method to
enhance the recognition results. We also collect DUT RGB-D face dataset and a
benchmark datset to evaluate the effectiveness and efficiency of our method.
The experimental results illustrate FGF is robust and effective to face and
object datasets in robot applications.
| Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang | null | 1703.08378 | null | null |
Smart Augmentation - Learning an Optimal Data Augmentation Strategy | cs.AI cs.LG stat.ML | A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional method which we call Smart Augmentation and we show how to use it to
increase the accuracy and reduce overfitting on a target network. Smart
Augmentation works by creating a network that learns how to generate augmented
data during the training process of a target network in a way that reduces that
networks loss. This allows us to learn augmentations that minimize the error of
that network.
Smart Augmentation has shown the potential to increase accuracy by
demonstrably significant measures on all datasets tested. In addition, it has
shown potential to achieve similar or improved performance levels with
significantly smaller network sizes in a number of tested cases.
| Joseph Lemley, Shabab Bazrafkan, Peter Corcoran | 10.1109/ACCESS.2017.2696121 | 1703.08383 | null | null |
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor
Classification in Dynamic Time Warping Spaces | cs.LG stat.ML | The nearest neighbor method together with the dynamic time warping (DTW)
distance is one of the most popular approaches in time series classification.
This method suffers from high storage and computation requirements for large
training sets. As a solution to both drawbacks, this article extends learning
vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ
scheme uses asymmetric weighted averaging as update rule. Empirical results
exhibited superior performance of asymmetric generalized LVQ (GLVQ) over other
state-of-the-art prototype generation methods for nearest neighbor
classification.
| Brijnesh Jain and David Schultz | null | 1703.08403 | null | null |
Linear classifier design under heteroscedasticity in Linear Discriminant
Analysis | cs.LG | Under normality and homoscedasticity assumptions, Linear Discriminant
Analysis (LDA) is known to be optimal in terms of minimising the Bayes error
for binary classification. In the heteroscedastic case, LDA is not guaranteed
to minimise this error. Assuming heteroscedasticity, we derive a linear
classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the
Bayes error for binary classification. In addition, we also propose a local
neighbourhood search (LNS) algorithm to obtain a more robust classifier if the
data is known to have a non-normal distribution. We evaluate the proposed
classifiers on two artificial and ten real-world datasets that cut across a
wide range of application areas including handwriting recognition, medical
diagnosis and remote sensing, and then compare our algorithm against existing
LDA approaches and other linear classifiers. The GLD is shown to outperform the
original LDA procedure in terms of the classification accuracy under
heteroscedasticity. While it compares favourably with other existing
heteroscedastic LDA approaches, the GLD requires as much as 60 times lower
training time on some datasets. Our comparison with the support vector machine
(SVM) also shows that, the GLD, together with the LNS, requires as much as 150
times lower training time to achieve an equivalent classification accuracy on
some of the datasets. Thus, our algorithms can provide a cheap and reliable
option for classification in a lot of expert systems.
| Kojo Sarfo Gyamfi, James Brusey, Andrew Hunt and Elena Gaura | 10.1016/j.eswa.2017.02.039 | 1703.08434 | null | null |
K-Means Clustering using Tabu Search with Quantized Means | cs.LG | The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering
as an alternative to Lloyd's algorithm, which for all its ease of
implementation and fast runtime, has the major drawback of being trapped at
local optima. While the TS approach can yield superior performance, it involves
a high computational complexity. Moreover, the difficulty in parameter
selection in the existing TS approach does not make it any more attractive.
This paper presents an alternative, low-complexity formulation of the TS
optimization procedure for K-Means clustering. This approach does not require
many parameter settings. We initially constrain the centers to points in the
dataset. We then aim at evolving these centers using a unique neighborhood
structure that makes use of gradient information of the objective function.
This results in an efficient exploration of the search space, after which the
means are refined. The proposed scheme is implemented in MATLAB and tested on
four real-world datasets, and it achieves a significant improvement over the
existing TS approach in terms of the intra cluster sum of squares and
computational time.
| Kojo Sarfo Gyamfi, James Brusey and Andrew Hunt | null | 1703.0844 | null | null |
Batch-normalized joint training for DNN-based distant speech recognition | cs.CL cs.LG | Improving distant speech recognition is a crucial step towards flexible
human-machine interfaces. Current technology, however, still exhibits a lack of
robustness, especially when adverse acoustic conditions are met. Despite the
significant progress made in the last years on both speech enhancement and
speech recognition, one potential limitation of state-of-the-art technology
lies in composing modules that are not well matched because they are not
trained jointly. To address this concern, a promising approach consists in
concatenating a speech enhancement and a speech recognition deep neural network
and to jointly update their parameters as if they were within a single bigger
network. Unfortunately, joint training can be difficult because the output
distribution of the speech enhancement system may change substantially during
the optimization procedure. The speech recognition module would have to deal
with an input distribution that is non-stationary and unnormalized. To mitigate
this issue, we propose a joint training approach based on a fully
batch-normalized architecture. Experiments, conducted using different datasets,
tasks and acoustic conditions, revealed that the proposed framework
significantly overtakes other competitive solutions, especially in challenging
environments.
| Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, Yoshua Bengio | null | 1703.08471 | null | null |
Overcoming Catastrophic Forgetting by Incremental Moment Matching | cs.LG cs.AI | Catastrophic forgetting is a problem of neural networks that loses the
information of the first task after training the second task. Here, we propose
a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM
incrementally matches the moment of the posterior distribution of the neural
network which is trained on the first and the second task, respectively. To
make the search space of posterior parameter smooth, the IMM procedure is
complemented by various transfer learning techniques including weight transfer,
L2-norm of the old and the new parameter, and a variant of dropout with the old
parameter. We analyze our approach on a variety of datasets including the
MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental
results show that IMM achieves state-of-the-art performance by balancing the
information between an old and a new network.
| Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang | null | 1703.08475 | null | null |
Joint Modeling of Event Sequence and Time Series with Attentional Twin
Recurrent Neural Networks | cs.LG | A variety of real-world processes (over networks) produce sequences of data
whose complex temporal dynamics need to be studied. More especially, the event
timestamps can carry important information about the underlying network
dynamics, which otherwise are not available from the time-series evenly sampled
from continuous signals. Moreover, in most complex processes, event sequences
and evenly-sampled times series data can interact with each other, which
renders joint modeling of those two sources of data necessary. To tackle the
above problems, in this paper, we utilize the rich framework of (temporal)
point processes to model event data and timely update its intensity function by
the synergic twin Recurrent Neural Networks (RNNs). In the proposed
architecture, the intensity function is synergistically modulated by one RNN
with asynchronous events as input and another RNN with time series as input.
Furthermore, to enhance the interpretability of the model, the attention
mechanism for the neural point process is introduced. The whole model with
event type and timestamp prediction output layers can be trained end-to-end and
allows a black-box treatment for modeling the intensity. We substantiate the
superiority of our model in synthetic data and three real-world benchmark
datasets.
| Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang,
Hongyuan Zha | null | 1703.08524 | null | null |
Sequence-to-Sequence Models Can Directly Translate Foreign Speech | cs.CL cs.LG stat.ML | We present a recurrent encoder-decoder deep neural network architecture that
directly translates speech in one language into text in another. The model does
not explicitly transcribe the speech into text in the source language, nor does
it require supervision from the ground truth source language transcription
during training. We apply a slightly modified sequence-to-sequence with
attention architecture that has previously been used for speech recognition and
show that it can be repurposed for this more complex task, illustrating the
power of attention-based models. A single model trained end-to-end obtains
state-of-the-art performance on the Fisher Callhome Spanish-English speech
translation task, outperforming a cascade of independently trained
sequence-to-sequence speech recognition and machine translation models by 1.8
BLEU points on the Fisher test set. In addition, we find that making use of the
training data in both languages by multi-task training sequence-to-sequence
speech translation and recognition models with a shared encoder network can
improve performance by a further 1.4 BLEU points.
| Ron J. Weiss, Jan Chorowski, Navdeep Jaitly, Yonghui Wu, Zhifeng Chen | null | 1703.08581 | null | null |
Low Precision Neural Networks using Subband Decomposition | cs.LG | Large-scale deep neural networks (DNN) have been successfully used in a
number of tasks from image recognition to natural language processing. They are
trained using large training sets on large models, making them computationally
and memory intensive. As such, there is much interest in research development
for faster training and test time. In this paper, we present a unique approach
using lower precision weights for more efficient and faster training phase. We
separate imagery into different frequency bands (e.g. with different
information content) such that the neural net can better learn using less bits.
We present this approach as a complement existing methods such as pruning
network connections and encoding learning weights. We show results where this
approach supports more stable learning with 2-4X reduction in precision with
17X reduction in DNN parameters.
| Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields | null | 1703.08595 | null | null |
Jointly Optimizing Placement and Inference for Beacon-based Localization | cs.RO cs.LG | The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.
| Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter | null | 1703.08612 | null | null |
Exploration--Exploitation in MDPs with Options | cs.LG stat.ML | While a large body of empirical results show that temporally-extended actions
and options may significantly affect the learning performance of an agent, the
theoretical understanding of how and when options can be beneficial in online
reinforcement learning is relatively limited. In this paper, we derive an upper
and lower bound on the regret of a variant of UCRL using options. While we
first analyze the algorithm in the general case of semi-Markov decision
processes (SMDPs), we show how these results can be translated to the specific
case of MDPs with options and we illustrate simple scenarios in which the
regret of learning with options can be \textit{provably} much smaller than the
regret suffered when learning with primitive actions.
| Ronan Fruit, Alessandro Lazaric | null | 1703.08667 | null | null |
Count-ception: Counting by Fully Convolutional Redundant Counting | cs.CV cs.LG stat.ML | Counting objects in digital images is a process that should be replaced by
machines. This tedious task is time consuming and prone to errors due to
fatigue of human annotators. The goal is to have a system that takes as input
an image and returns a count of the objects inside and justification for the
prediction in the form of object localization. We repose a problem, originally
posed by Lempitsky and Zisserman, to instead predict a count map which contains
redundant counts based on the receptive field of a smaller regression network.
The regression network predicts a count of the objects that exist inside this
frame. By processing the image in a fully convolutional way each pixel is going
to be accounted for some number of times, the number of windows which include
it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true
count we take the average over the redundant predictions. Our contribution is
redundant counting instead of predicting a density map in order to average over
errors. We also propose a novel deep neural network architecture adapted from
the Inception family of networks called the Count-ception network. Together our
approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state
of the art method by Xie, Noble, and Zisserman in 2016.
| Joseph Paul Cohen and Genevieve Boucher and Craig A. Glastonbury and
Henry Z. Lo and Yoshua Bengio | null | 1703.0871 | null | null |
Who Said What: Modeling Individual Labelers Improves Classification | cs.LG cs.CV | Data are often labeled by many different experts with each expert only
labeling a small fraction of the data and each data point being labeled by
several experts. This reduces the workload on individual experts and also gives
a better estimate of the unobserved ground truth. When experts disagree, the
standard approaches are to treat the majority opinion as the correct label or
to model the correct label as a distribution. These approaches, however, do not
make any use of potentially valuable information about which expert produced
which label. To make use of this extra information, we propose modeling the
experts individually and then learning averaging weights for combining them,
possibly in sample-specific ways. This allows us to give more weight to more
reliable experts and take advantage of the unique strengths of individual
experts at classifying certain types of data. Here we show that our approach
leads to improvements in computer-aided diagnosis of diabetic retinopathy. We
also show that our method performs better than competing algorithms by Welinder
and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative
approach for dealing with the myriad real-world settings that use expert
opinions to define labels for training.
| Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton | null | 1703.08774 | null | null |
Uncertainty quantification in graph-based classification of high
dimensional data | cs.LG stat.ML | Classification of high dimensional data finds wide-ranging applications. In
many of these applications equipping the resulting classification with a
measure of uncertainty may be as important as the classification itself. In
this paper we introduce, develop algorithms for, and investigate the properties
of, a variety of Bayesian models for the task of binary classification; via the
posterior distribution on the classification labels, these methods
automatically give measures of uncertainty. The methods are all based around
the graph formulation of semi-supervised learning.
We provide a unified framework which brings together a variety of methods
which have been introduced in different communities within the mathematical
sciences. We study probit classification in the graph-based setting, generalize
the level-set method for Bayesian inverse problems to the classification
setting, and generalize the Ginzburg-Landau optimization-based classifier to a
Bayesian setting; we also show that the probit and level set approaches are
natural relaxations of the harmonic function approach introduced in [Zhu et al
2003].
We introduce efficient numerical methods, suited to large data-sets, for both
MCMC-based sampling as well as gradient-based MAP estimation. Through numerical
experiments we study classification accuracy and uncertainty quantification for
our models; these experiments showcase a suite of datasets commonly used to
evaluate graph-based semi-supervised learning algorithms.
| Andrea L. Bertozzi and Xiyang Luo and Andrew M. Stuart and
Konstantinos C. Zygalakis | null | 1703.08816 | null | null |
Learned Multi-Patch Similarity | cs.CV cs.LG | Estimating a depth map from multiple views of a scene is a fundamental task
in computer vision. As soon as more than two viewpoints are available, one
faces the very basic question how to measure similarity across >2 image
patches. Surprisingly, no direct solution exists, instead it is common to fall
back to more or less robust averaging of two-view similarities. Encouraged by
the success of machine learning, and in particular convolutional neural
networks, we propose to learn a matching function which directly maps multiple
image patches to a scalar similarity score. Experiments on several multi-view
datasets demonstrate that this approach has advantages over methods based on
pairwise patch similarity.
| Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool,
Konrad Schindler | null | 1703.08836 | null | null |
Distributed Voting/Ranking with Optimal Number of States per Node | cs.DC cs.LG | Considering a network with $n$ nodes, where each node initially votes for one
(or more) choices out of $K$ possible choices, we present a Distributed
Multi-choice Voting/Ranking (DMVR) algorithm to determine either the choice
with maximum vote (the voting problem) or to rank all the choices in terms of
their acquired votes (the ranking problem). The algorithm consolidates node
votes across the network by updating the states of interacting nodes using two
key operations, the union and the intersection. The proposed algorithm is
simple, independent from network size, and easily scalable in terms of the
number of choices $K$, using only $K\times 2^{K-1}$ nodal states for voting,
and $K\times K!$ nodal states for ranking. We prove the number of states to be
optimal in the ranking case, this optimality is conjectured to also apply to
the voting case. The time complexity of the algorithm is analyzed in complete
graphs. We show that the time complexity for both ranking and voting is
$O(\log(n))$ for given vote percentages, and is inversely proportional to the
minimum of the vote percentage differences among various choices.
| Saber Salehkaleybar, Arsalan Sharif-Nassab, S. Jamaloddin Golestani | null | 1703.08838 | null | null |
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations | cs.LG cs.AI cs.CV | The goal of imitation learning is to mimic expert behavior without access to
an explicit reward signal. Expert demonstrations provided by humans, however,
often show significant variability due to latent factors that are typically not
explicitly modeled. In this paper, we propose a new algorithm that can infer
the latent structure of expert demonstrations in an unsupervised way. Our
method, built on top of Generative Adversarial Imitation Learning, can not only
imitate complex behaviors, but also learn interpretable and meaningful
representations of complex behavioral data, including visual demonstrations. In
the driving domain, we show that a model learned from human demonstrations is
able to both accurately reproduce a variety of behaviors and accurately
anticipate human actions using raw visual inputs. Compared with various
baselines, our method can better capture the latent structure underlying expert
demonstrations, often recovering semantically meaningful factors of variation
in the data.
| Yunzhu Li, Jiaming Song, Stefano Ermon | null | 1703.0884 | null | null |
Multiple Instance Learning with the Optimal Sub-Pattern Assignment
Metric | cs.LG | Multiple instance data are sets or multi-sets of unordered elements. Using
metrics or distances for sets, we propose an approach to several multiple
instance learning tasks, such as clustering (unsupervised learning),
classification (supervised learning), and novelty detection (semi-supervised
learning). In particular, we introduce the Optimal Sub-Pattern Assignment
metric to multiple instance learning so as to provide versatile design choices.
Numerical experiments on both simulated and real data are presented to
illustrate the versatility of the proposed solution.
| Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo, and Thuong Nguyen | null | 1703.08933 | null | null |
Scaling the Scattering Transform: Deep Hybrid Networks | cs.CV cs.LG | We use the scattering network as a generic and fixed ini-tialization of the
first layers of a supervised hybrid deep network. We show that early layers do
not necessarily need to be learned, providing the best results to-date with
pre-defined representations while being competitive with Deep CNNs. Using a
shallow cascade of 1 x 1 convolutions, which encodes scattering coefficients
that correspond to spatial windows of very small sizes, permits to obtain
AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local
encoding explicitly learns invariance w.r.t. rotations. Combining scattering
networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on
imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing
only 10 layers. We also find that hybrid architectures can yield excellent
performance in the small sample regime, exceeding their end-to-end
counterparts, through their ability to incorporate geometrical priors. We
demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.
| Edouard Oyallon (DI-ENS), Eugene Belilovsky (CVN, GALEN), Sergey
Zagoruyko (ENPC) | null | 1703.08961 | null | null |
Multimodal deep learning approach for joint EEG-EMG data compression and
classification | cs.LG | In this paper, we present a joint compression and classification approach of
EEG and EMG signals using a deep learning approach. Specifically, we build our
system based on the deep autoencoder architecture which is designed not only to
extract discriminant features in the multimodal data representation but also to
reconstruct the data from the latent representation using encoder-decoder
layers. Since autoencoder can be seen as a compression approach, we extend it
to handle multimodal data at the encoder layer, reconstructed and retrieved at
the decoder layer. We show through experimental results, that exploiting both
multimodal data intercorellation and intracorellation 1) Significantly reduces
signal distortion particularly for high compression levels 2) Achieves better
accuracy in classifying EEG and EMG signals recorded and labeled according to
the sentiments of the volunteer.
| Ahmed Ben Said and Amr Mohamed and Tarek Elfouly and Khaled Harras and
Z. Jane Wang | null | 1703.0897 | null | null |
Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels | cs.LG | Hawkes Processes capture self-excitation and mutual-excitation between events
when the arrival of an event makes future events more likely to happen.
Identification of such temporal covariance can reveal the underlying structure
to better predict future events. In this paper, we present a new framework to
decompose discrete events with a composition of multiple self-triggering
kernels. The composition scheme allows us to decompose empirical covariance
densities into the sum or the product of base kernels which are easily
interpretable. Here, we present the first multiplicative kernel composition
methods for Hawkes Processes. We demonstrate that the new automatic kernel
decomposition procedure outperforms the existing methods on the prediction of
discrete events in real-world data.
| Rafael Lima and Jaesik Choi | null | 1703.09068 | null | null |
GPU Activity Prediction using Representation Learning | cs.LG | GPU activity prediction is an important and complex problem. This is due to
the high level of contention among thousands of parallel threads. This problem
was mostly addressed using heuristics. We propose a representation learning
approach to address this problem. We model any performance metric as a temporal
function of the executed instructions with the intuition that the flow of
instructions can be identified as distinct activities of the code. Our
experiments show high accuracy and non-trivial predictive power of
representation learning on a benchmark.
| Aswin Raghavan, Mohamed Amer, Timothy Shields, David Zhang, Sek Chai | null | 1703.09146 | null | null |
Private Learning on Networks: Part II | cs.DC cs.LG math.OC | This paper considers a distributed multi-agent optimization problem, with the
global objective consisting of the sum of local objective functions of the
agents. The agents solve the optimization problem using local computation and
communication between adjacent agents in the network. We present two randomized
iterative algorithms for distributed optimization. To improve privacy, our
algorithms add "structured" randomization to the information exchanged between
the agents. We prove deterministic correctness (in every execution) of the
proposed algorithms despite the information being perturbed by noise with
non-zero mean. We prove that a special case of a proposed algorithm (called
function sharing) preserves privacy of individual polynomial objective
functions under a suitable connectivity condition on the network topology.
| Shripad Gade and Nitin H. Vaidya | null | 1703.09185 | null | null |
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
Variational Inference | stat.ML cs.LG | We propose a simple and general variant of the standard reparameterized
gradient estimator for the variational evidence lower bound. Specifically, we
remove a part of the total derivative with respect to the variational
parameters that corresponds to the score function. Removing this term produces
an unbiased gradient estimator whose variance approaches zero as the
approximate posterior approaches the exact posterior. We analyze the behavior
of this gradient estimator theoretically and empirically, and generalize it to
more complex variational distributions such as mixtures and importance-weighted
posteriors.
| Geoffrey Roeder, Yuhuai Wu, David Duvenaud | null | 1703.09194 | null | null |
Deep Architectures for Modulation Recognition | cs.LG | We survey the latest advances in machine learning with deep neural networks
by applying them to the task of radio modulation recognition. Results show that
radio modulation recognition is not limited by network depth and further work
should focus on improving learned synchronization and equalization. Advances in
these areas will likely come from novel architectures designed for these tasks
or through novel training methods.
| Nathan E West and Timothy J. O'Shea | null | 1703.09197 | null | null |
Biologically inspired protection of deep networks from adversarial
attacks | stat.ML cs.LG q-bio.NC | Inspired by biophysical principles underlying nonlinear dendritic computation
in neural circuits, we develop a scheme to train deep neural networks to make
them robust to adversarial attacks. Our scheme generates highly nonlinear,
saturated neural networks that achieve state of the art performance on gradient
based adversarial examples on MNIST, despite never being exposed to
adversarially chosen examples during training. Moreover, these networks exhibit
unprecedented robustness to targeted, iterative schemes for generating
adversarial examples, including second-order methods. We further identify
principles governing how these networks achieve their robustness, drawing on
methods from information geometry. We find these networks progressively create
highly flat and compressed internal representations that are sensitive to very
few input dimensions, while still solving the task. Moreover, they employ
highly kurtotic weight distributions, also found in the brain, and we
demonstrate how such kurtosis can protect even linear classifiers from
adversarial attack.
| Aran Nayebi, Surya Ganguli | null | 1703.09202 | null | null |
Goal-Driven Dynamics Learning via Bayesian Optimization | cs.SY cs.LG | Real-world robots are becoming increasingly complex and commonly act in
poorly understood environments where it is extremely challenging to model or
learn their true dynamics. Therefore, it might be desirable to take a
task-specific approach, wherein the focus is on explicitly learning the
dynamics model which achieves the best control performance for the task at
hand, rather than learning the true dynamics. In this work, we use Bayesian
optimization in an active learning framework where a locally linear dynamics
model is learned with the intent of maximizing the control performance, and
used in conjunction with optimal control schemes to efficiently design a
controller for a given task. This model is updated directly based on the
performance observed in experiments on the physical system in an iterative
manner until a desired performance is achieved. We demonstrate the efficacy of
the proposed approach through simulations and real experiments on a quadrotor
testbed.
| Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, Claire J.
Tomlin | null | 1703.0926 | null | null |
Adaptive Simulation-based Training of AI Decision-makers using Bayesian
Optimization | cs.LG cs.AI cs.RO stat.ML | This work studies how an AI-controlled dog-fighting agent with tunable
decision-making parameters can learn to optimize performance against an
intelligent adversary, as measured by a stochastic objective function evaluated
on simulated combat engagements. Gaussian process Bayesian optimization (GPBO)
techniques are developed to automatically learn global Gaussian Process (GP)
surrogate models, which provide statistical performance predictions in both
explored and unexplored areas of the parameter space. This allows a learning
engine to sample full-combat simulations at parameter values that are most
likely to optimize performance and also provide highly informative data points
for improving future predictions. However, standard GPBO methods do not provide
a reliable surrogate model for the highly volatile objective functions found in
aerial combat, and thus do not reliably identify global maxima. These issues
are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point
Sampling (HRMS) techniques. Simulation studies show that HRMS improves the
accuracy of GP surrogate models, allowing AI decision-makers to more accurately
predict performance and efficiently tune parameters.
| Brett W. Israelsen, Nisar Ahmed, Kenneth Center, Roderick Green,
Winston Bennett Jr | null | 1703.0931 | null | null |
DART: Noise Injection for Robust Imitation Learning | cs.LG | One approach to Imitation Learning is Behavior Cloning, in which a robot
observes a supervisor and infers a control policy. A known problem with this
"off-policy" approach is that the robot's errors compound when drifting away
from the supervisor's demonstrations. On-policy, techniques alleviate this by
iteratively collecting corrective actions for the current robot policy.
However, these techniques can be tedious for human supervisors, add significant
computation burden, and may visit dangerous states during training. We propose
an off-policy approach that injects noise into the supervisor's policy while
demonstrating. This forces the supervisor to demonstrate how to recover from
errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot
Trajectories), that collects demonstrations with injected noise, and optimizes
the noise level to approximate the error of the robot's trained policy during
data collection. We compare DART with DAgger and Behavior Cloning in two
domains: in simulation with an algorithmic supervisor on the MuJoCo tasks
(Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human
supervisors training a Toyota HSR robot to perform grasping in clutter. For
high dimensional tasks like Humanoid, DART can be up to $3x$ faster in
computation time and only decreases the supervisor's cumulative reward by $5\%$
during training, whereas DAgger executes policies that have $80\%$ less
cumulative reward than the supervisor. On the grasping in clutter task, DART
obtains on average a $62\%$ performance increase over Behavior Cloning.
| Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg | null | 1703.09327 | null | null |
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables | cs.LG cs.AI cs.CV | Recently, deep learning (DL) methods have been introduced very successfully
into human activity recognition (HAR) scenarios in ubiquitous and wearable
computing. Especially the prospect of overcoming the need for manual feature
design combined with superior classification capabilities render deep neural
networks very attractive for real-life HAR application. Even though DL-based
approaches now outperform the state-of-the-art in a number of recognitions
tasks of the field, yet substantial challenges remain. Most prominently, issues
with real-life datasets, typically including imbalanced datasets and
problematic data quality, still limit the effectiveness of activity recognition
using wearables. In this paper we tackle such challenges through Ensembles of
deep Long Short Term Memory (LSTM) networks. We have developed modified
training procedures for LSTM networks and combine sets of diverse LSTM learners
into classifier collectives. We demonstrate, both formally and empirically,
that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Through an extensive experimental evaluation on three standard benchmarks
(Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition
capabilities of our approach and its potential for real-life applications of
human activity recognition.
| Yu Guan and Thomas Ploetz | 10.1145/3090076 | 1703.0937 | null | null |
Factoring Exogenous State for Model-Free Monte Carlo | cs.LG stat.ML | Policy analysts wish to visualize a range of policies for large
simulator-defined Markov Decision Processes (MDPs). One visualization approach
is to invoke the simulator to generate on-policy trajectories and then
visualize those trajectories. When the simulator is expensive, this is not
practical, and some method is required for generating trajectories for new
policies without invoking the simulator. The method of Model-Free Monte Carlo
(MFMC) can do this by stitching together state transitions for a new policy
based on previously-sampled trajectories from other policies. This "off-policy
Monte Carlo simulation" method works well when the state space has low
dimension but fails as the dimension grows. This paper describes a method for
factoring out some of the state and action variables so that MFMC can work in
high-dimensional MDPs. The new method, MFMCi, is evaluated on a very
challenging wildfire management MDP.
| Sean McGregor, Rachel Houtman, Claire Montgomery, Ronald Metoyer,
Thomas G. Dietterich | null | 1703.0939 | null | null |
Fast Optimization of Wildfire Suppression Policies with SMAC | cs.LG stat.ML | Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into multi-stakeholder negotiations is to provide a high-fidelity
simulation environment in which stakeholders can explore the space of
alternative policies and understand the tradeoffs therein. Such an environment
needs to support fast optimization of MDP policies so that users can adjust
reward functions and analyze the resulting optimal policies. This paper
assesses the suitability of SMAC---a black-box empirical function optimization
algorithm---for rapid optimization of MDP policies. The paper describes five
reward function components and four stakeholder constituencies. It then
introduces a parameterized class of policies that can be easily understood by
the stakeholders. SMAC is applied to find the optimal policy in this class for
the reward functions of each of the stakeholder constituencies. The results
confirm that SMAC is able to rapidly find good policies that make sense from
the domain perspective. Because the full-fidelity forest fire simulator is far
too expensive to support interactive optimization, SMAC is applied to a
surrogate model constructed from a modest number of runs of the full-fidelity
simulator. To check the quality of the SMAC-optimized policies, the policies
are evaluated on the full-fidelity simulator. The results confirm that the
surrogate values estimates are valid. This is the first successful optimization
of wildfire management policies using a full-fidelity simulation. The same
methodology should be applicable to other contentious natural resource
management problems where high-fidelity simulation is extremely expensive.
| Sean McGregor, Rachel Houtman, Claire Montgomery, Ronald Metoyer,
Thomas G. Dietterich | null | 1703.09391 | null | null |
Solving Non-parametric Inverse Problem in Continuous Markov Random Field
using Loopy Belief Propagation | stat.ML cond-mat.dis-nn cs.LG | In this paper, we address the inverse problem, or the statistical machine
learning problem, in Markov random fields with a non-parametric pair-wise
energy function with continuous variables. The inverse problem is formulated by
maximum likelihood estimation. The exact treatment of maximum likelihood
estimation is intractable because of two problems: (1) it includes the
evaluation of the partition function and (2) it is formulated in the form of
functional optimization. We avoid Problem (1) by using Bethe approximation.
Bethe approximation is an approximation technique equivalent to the loopy
belief propagation. Problem (2) can be solved by using orthonormal function
expansion. Orthonormal function expansion can reduce a functional optimization
problem to a function optimization problem. Our method can provide an analytic
form of the solution of the inverse problem within the framework of Bethe
approximation.
| Muneki Yasuda and Shun Kataoka | 10.7566/JPSJ.86.084806 | 1703.09397 | null | null |
SEGAN: Speech Enhancement Generative Adversarial Network | cs.LG cs.NE cs.SD | Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn complex functions from large example sets. In this work, we propose the
use of generative adversarial networks for speech enhancement. In contrast to
current techniques, we operate at the waveform level, training the model
end-to-end, and incorporate 28 speakers and 40 different noise conditions into
the same model, such that model parameters are shared across them. We evaluate
the proposed model using an independent, unseen test set with two speakers and
20 alternative noise conditions. The enhanced samples confirm the viability of
the proposed model, and both objective and subjective evaluations confirm the
effectiveness of it. With that, we open the exploration of generative
architectures for speech enhancement, which may progressively incorporate
further speech-centric design choices to improve their performance.
| Santiago Pascual, Antonio Bonafonte, Joan Serr\`a | null | 1703.09452 | null | null |
Learned Spectral Super-Resolution | cs.CV cs.LG | We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hyper-spectral) wave-length bands. Just like the spatial statistics of
natural images has rich structure, which one can exploit as prior to predict
high-frequency content from a low resolution image, the same is also true in
the spectral domain: the materials and lighting conditions of the observed
world induce structure in the spectrum of wavelengths observed at a given
pixel. Surprisingly, very little work exists that attempts to use this
diagnosis and achieve blind spectral super-resolution from single images. We
start from the conjecture that, just like in the spatial domain, we can learn
the statistics of natural image spectra, and with its help generate finely
resolved hyper-spectral images from RGB input. Technically, we follow the
current best practice and implement a convolutional neural network (CNN), which
is trained to carry out the end-to-end mapping from an entire RGB image to the
corresponding hyperspectral image of equal size. We demonstrate spectral
super-resolution both for conventional RGB images and for multi-spectral
satellite data, outperforming the state-of-the-art.
| Silvano Galliani, Charis Lanaras, Dimitrios Marmanis, Emmanuel
Baltsavias, Konrad Schindler | null | 1703.0947 | null | null |
Simulated Data Experiments for Time Series Classification Part 1:
Accuracy Comparison with Default Settings | cs.LG stat.ML | There are now a broad range of time series classification (TSC) algorithms
designed to exploit different representations of the data. These have been
evaluated on a range of problems hosted at the UCR-UEA TSC Archive
(www.timeseriesclassification.com), and there have been extensive comparative
studies. However, our understanding of why one algorithm outperforms another is
still anecdotal at best. This series of experiments is meant to help provide
insights into what sort of discriminatory features in the data lead one set of
algorithms that exploit a particular representation to be better than other
algorithms. We categorise five different feature spaces exploited by TSC
algorithms then design data simulators to generate randomised data from each
representation. We describe what results we expected from each class of
algorithm and data representation, then observe whether these prior beliefs are
supported by the experimental evidence. We provide an open source
implementation of all the simulators to allow for the controlled testing of
hypotheses relating to classifier performance on different data
representations. We identify many surprising results that confounded our
expectations, and use these results to highlight how an over simplified view of
classifier structure can often lead to erroneous prior beliefs. We believe
ensembling can often overcome prior bias, and our results support the belief by
showing that the ensemble approach adopted by the Hierarchical Collective of
Transform based Ensembles (HIVE-COTE) is significantly better than the
alternatives when the data representation is unknown, and is significantly
better than, or not significantly significantly better than, or not
significantly worse than, the best other approach on three out of five of the
individual simulators.
| Anthony Bagnall, Aaron Bostrom, James Large and Jason Lines | null | 1703.0948 | null | null |
Early Stopping without a Validation Set | cs.LG stat.ML | Early stopping is a widely used technique to prevent poor generalization
performance when training an over-expressive model by means of gradient-based
optimization. To find a good point to halt the optimizer, a common practice is
to split the dataset into a training and a smaller validation set to obtain an
ongoing estimate of the generalization performance. We propose a novel early
stopping criterion based on fast-to-compute local statistics of the computed
gradients and entirely removes the need for a held-out validation set. Our
experiments show that this is a viable approach in the setting of least-squares
and logistic regression, as well as neural networks.
| Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig | null | 1703.0958 | null | null |
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