categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
cs.PL cs.LG cs.SE
| null |
1610.09543
| null | null |
http://arxiv.org/pdf/1610.09543v1
|
2016-10-29T17:10:15Z
|
2016-10-29T17:10:15Z
|
FEAST: An Automated Feature Selection Framework for Compilation Tasks
|
The success of the application of machine-learning techniques to compilation
tasks can be largely attributed to the recent development and advancement of
program characterization, a process that numerically or structurally quantifies
a target program. While great achievements have been made in identifying key
features to characterize programs, choosing a correct set of features for a
specific compiler task remains an ad hoc procedure. In order to guarantee a
comprehensive coverage of features, compiler engineers usually need to select
excessive number of features. This, unfortunately, would potentially lead to a
selection of multiple similar features, which in turn could create a new
problem of bias that emphasizes certain aspects of a program's characteristics,
hence reducing the accuracy and performance of the target compiler task. In
this paper, we propose FEAture Selection for compilation Tasks (FEAST), an
efficient and automated framework for determining the most relevant and
representative features from a feature pool. Specifically, FEAST utilizes
widely used statistics and machine-learning tools, including LASSO, sequential
forward and backward selection, for automatic feature selection, and can in
general be applied to any numerical feature set. This paper further proposes an
automated approach to compiler parameter assignment for assessing the
performance of FEAST. Intensive experimental results demonstrate that, under
the compiler parameter assignment task, FEAST can achieve comparable results
with about 18% of features that are automatically selected from the entire
feature pool. We also inspect these selected features and discuss their roles
in program execution.
|
[
"Pai-Shun Ting, Chun-Chen Tu, Pin-Yu Chen, Ya-Yun Lo, Shin-Ming Cheng",
"['Pai-Shun Ting' 'Chun-Chen Tu' 'Pin-Yu Chen' 'Ya-Yun Lo'\n 'Shin-Ming Cheng']"
] |
cs.LG
| null |
1610.09555
| null | null |
http://arxiv.org/pdf/1610.09555v2
|
2018-05-09T13:54:12Z
|
2016-10-29T18:32:27Z
|
TensorLy: Tensor Learning in Python
|
Tensors are higher-order extensions of matrices. While matrix methods form
the cornerstone of machine learning and data analysis, tensor methods have been
gaining increasing traction. However, software support for tensor operations is
not on the same footing. In order to bridge this gap, we have developed
\emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural
networks in Python. TensorLy aims to follow the same standards adopted by the
main projects of the Python scientific community, and seamlessly integrates
with them. Its BSD license makes it suitable for both academic and commercial
applications. TensorLy's backend system allows users to perform computations
with NumPy, MXNet, PyTorch, TensorFlow and CuPy. They can be scaled on multiple
CPU or GPU machines. In addition, using the deep-learning frameworks as backend
allows users to easily design and train deep tensorized neural networks.
TensorLy is available at https://github.com/tensorly/tensorly
|
[
"['Jean Kossaifi' 'Yannis Panagakis' 'Anima Anandkumar' 'Maja Pantic']",
"Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic"
] |
cs.LG
| null |
1610.09559
| null | null |
http://arxiv.org/pdf/1610.09559v4
|
2017-06-29T15:46:55Z
|
2016-10-29T18:46:11Z
|
Fair Algorithms for Infinite and Contextual Bandits
|
We study fairness in linear bandit problems. Starting from the notion of
meritocratic fairness introduced in Joseph et al. [2016], we carry out a more
refined analysis of a more general problem, achieving better performance
guarantees with fewer modelling assumptions on the number and structure of
available choices as well as the number selected. We also analyze the
previously-unstudied question of fairness in infinite linear bandit problems,
obtaining instance-dependent regret upper bounds as well as lower bounds
demonstrating that this instance-dependence is necessary. The result is a
framework for meritocratic fairness in an online linear setting that is
substantially more powerful, general, and realistic than the current state of
the art.
|
[
"['Matthew Joseph' 'Michael Kearns' 'Jamie Morgenstern' 'Seth Neel'\n 'Aaron Roth']",
"Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and\n Aaron Roth"
] |
cs.LG cs.NE
| null |
1610.09608
| null | null |
http://arxiv.org/pdf/1610.09608v1
|
2016-10-30T06:34:19Z
|
2016-10-30T06:34:19Z
|
A Theoretical Study of The Relationship Between Whole An ELM Network and
Its Subnetworks
|
A biological neural network is constituted by numerous subnetworks and
modules with different functionalities. For an artificial neural network, the
relationship between a network and its subnetworks is also important and useful
for both theoretical and algorithmic research, i.e. it can be exploited to
develop incremental network training algorithm or parallel network training
algorithm. In this paper we explore the relationship between an ELM neural
network and its subnetworks. To the best of our knowledge, we are the first to
prove a theorem that shows an ELM neural network can be scattered into
subnetworks and its optimal solution can be constructed recursively by the
optimal solutions of these subnetworks. Based on the theorem we also present
two algorithms to train a large ELM neural network efficiently: one is a
parallel network training algorithm and the other is an incremental network
training algorithm. The experimental results demonstrate the usefulness of the
theorem and the validity of the developed algorithms.
|
[
"['Enmei Tu' 'Guanghao Zhang' 'Lily Rachmawati' 'Eshan Rajabally'\n 'Guang-Bin Huang']",
"Enmei Tu, Guanghao Zhang, Lily Rachmawati, Eshan Rajabally and\n Guang-Bin Huang"
] |
q-bio.NC cs.CV cs.LG
|
10.1016/j.cognition.2018.11.001
|
1610.09625
| null | null |
http://arxiv.org/abs/1610.09625v1
|
2016-10-30T10:26:22Z
|
2016-10-30T10:26:22Z
|
Discovering containment: from infants to machines
|
Current artificial learning systems can recognize thousands of visual
categories, or play Go at a champion"s level, but cannot explain infants
learning, in particular the ability to learn complex concepts without guidance,
in a specific order. A notable example is the category of 'containers' and the
notion of containment, one of the earliest spatial relations to be learned,
starting already at 2.5 months, and preceding other common relations (e.g.,
support). Such spontaneous unsupervised learning stands in contrast with
current highly successful computational models, which learn in a supervised
manner, that is, by using large data sets of labeled examples. How can
meaningful concepts be learned without guidance, and what determines the
trajectory of infant learning, making some notions appear consistently earlier
than others?
|
[
"Shimon Ullman, Nimrod Dorfman, Daniel Harari",
"['Shimon Ullman' 'Nimrod Dorfman' 'Daniel Harari']"
] |
cs.LG cs.NE
| null |
1610.09639
| null | null |
http://arxiv.org/pdf/1610.09639v1
|
2016-10-30T11:57:20Z
|
2016-10-30T11:57:20Z
|
Compact Deep Convolutional Neural Networks With Coarse Pruning
|
The learning capability of a neural network improves with increasing depth at
higher computational costs. Wider layers with dense kernel connectivity
patterns furhter increase this cost and may hinder real-time inference. We
propose feature map and kernel level pruning for reducing the computational
complexity of a deep convolutional neural network. Pruning feature maps reduces
the width of a layer and hence does not need any sparse representation.
Further, kernel pruning converts the dense connectivity pattern into a sparse
one. Due to coarse nature, these pruning granularities can be exploited by GPUs
and VLSI based implementations. We propose a simple and generic strategy to
choose the least adversarial pruning masks for both granularities. The pruned
networks are retrained which compensates the loss in accuracy. We obtain the
best pruning ratios when we prune a network with both granularities.
Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be
induced in the convolution layers with less than 1% increase in the
missclassification rate of the baseline network.
|
[
"Sajid Anwar, Wonyong Sung",
"['Sajid Anwar' 'Wonyong Sung']"
] |
cs.LG
| null |
1610.0965
| null | null | null | null | null |
Deep Model Compression: Distilling Knowledge from Noisy Teachers
|
The remarkable successes of deep learning models across various applications
have resulted in the design of deeper networks that can solve complex problems.
However, the increasing depth of such models also results in a higher storage
and runtime complexity, which restricts the deployability of such very deep
models on mobile and portable devices, which have limited storage and battery
capacity. While many methods have been proposed for deep model compression in
recent years, almost all of them have focused on reducing storage complexity.
In this work, we extend the teacher-student framework for deep model
compression, since it has the potential to address runtime and train time
complexity too. We propose a simple methodology to include a noise-based
regularizer while training the student from the teacher, which provides a
healthy improvement in the performance of the student network. Our experiments
on the CIFAR-10, SVHN and MNIST datasets show promising improvement, with the
best performance on the CIFAR-10 dataset. We also conduct a comprehensive
empirical evaluation of the proposed method under related settings on the
CIFAR-10 dataset to show the promise of the proposed approach.
|
[
"Bharat Bhusan Sau and Vineeth N. Balasubramanian"
] |
null | null |
1610.09650
| null | null |
http://arxiv.org/pdf/1610.09650v2
|
2016-11-02T16:32:23Z
|
2016-10-30T13:54:39Z
|
Deep Model Compression: Distilling Knowledge from Noisy Teachers
|
The remarkable successes of deep learning models across various applications have resulted in the design of deeper networks that can solve complex problems. However, the increasing depth of such models also results in a higher storage and runtime complexity, which restricts the deployability of such very deep models on mobile and portable devices, which have limited storage and battery capacity. While many methods have been proposed for deep model compression in recent years, almost all of them have focused on reducing storage complexity. In this work, we extend the teacher-student framework for deep model compression, since it has the potential to address runtime and train time complexity too. We propose a simple methodology to include a noise-based regularizer while training the student from the teacher, which provides a healthy improvement in the performance of the student network. Our experiments on the CIFAR-10, SVHN and MNIST datasets show promising improvement, with the best performance on the CIFAR-10 dataset. We also conduct a comprehensive empirical evaluation of the proposed method under related settings on the CIFAR-10 dataset to show the promise of the proposed approach.
|
[
"['Bharat Bhusan Sau' 'Vineeth N. Balasubramanian']"
] |
cs.LG
| null |
1610.09716
| null | null |
http://arxiv.org/pdf/1610.09716v1
|
2016-10-30T22:07:16Z
|
2016-10-30T22:07:16Z
|
Doubly Convolutional Neural Networks
|
Building large models with parameter sharing accounts for most of the success
of deep convolutional neural networks (CNNs). In this paper, we propose doubly
convolutional neural networks (DCNNs), which significantly improve the
performance of CNNs by further exploring this idea. In stead of allocating a
set of convolutional filters that are independently learned, a DCNN maintains
groups of filters where filters within each group are translated versions of
each other. Practically, a DCNN can be easily implemented by a two-step
convolution procedure, which is supported by most modern deep learning
libraries. We perform extensive experiments on three image classification
benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently
outperform other competing architectures. We have also verified that replacing
a convolutional layer with a doubly convolutional layer at any depth of a CNN
can improve its performance. Moreover, various design choices of DCNNs are
demonstrated, which shows that DCNN can serve the dual purpose of building more
accurate models and/or reducing the memory footprint without sacrificing the
accuracy.
|
[
"['Shuangfei Zhai' 'Yu Cheng' 'Weining Lu' 'Zhongfei Zhang']",
"Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang"
] |
cs.LG
| null |
1610.09726
| null | null |
http://arxiv.org/pdf/1610.09726v1
|
2016-10-30T23:07:49Z
|
2016-10-30T23:07:49Z
|
The Multi-fidelity Multi-armed Bandit
|
We study a variant of the classical stochastic $K$-armed bandit where
observing the outcome of each arm is expensive, but cheap approximations to
this outcome are available. For example, in online advertising the performance
of an ad can be approximated by displaying it for shorter time periods or to
narrower audiences. We formalise this task as a multi-fidelity bandit, where,
at each time step, the forecaster may choose to play an arm at any one of $M$
fidelities. The highest fidelity (desired outcome) expends cost
$\lambda^{(m)}$. The $m^{\text{th}}$ fidelity (an approximation) expends
$\lambda^{(m)} < \lambda^{(M)}$ and returns a biased estimate of the highest
fidelity. We develop MF-UCB, a novel upper confidence bound procedure for this
setting and prove that it naturally adapts to the sequence of available
approximations and costs thus attaining better regret than naive strategies
which ignore the approximations. For instance, in the above online advertising
example, MF-UCB would use the lower fidelities to quickly eliminate suboptimal
ads and reserve the larger expensive experiments on a small set of promising
candidates. We complement this result with a lower bound and show that MF-UCB
is nearly optimal under certain conditions.
|
[
"['Kirthevasan Kandasamy' 'Gautam Dasarathy' 'Jeff Schneider'\n 'Barnabás Póczos']",
"Kirthevasan Kandasamy and Gautam Dasarathy and Jeff Schneider and\n Barnab\\'as P\\'oczos"
] |
cs.LG stat.ML
| null |
1610.0973
| null | null | null | null | null |
Active Learning from Imperfect Labelers
|
We study active learning where the labeler can not only return incorrect
labels but also abstain from labeling. We consider different noise and
abstention conditions of the labeler. We propose an algorithm which utilizes
abstention responses, and analyze its statistical consistency and query
complexity under fairly natural assumptions on the noise and abstention rate of
the labeler. This algorithm is adaptive in a sense that it can automatically
request less queries with a more informed or less noisy labeler. We couple our
algorithm with lower bounds to show that under some technical conditions, it
achieves nearly optimal query complexity.
|
[
"Songbai Yan, Kamalika Chaudhuri and Tara Javidi"
] |
null | null |
1610.09730
| null | null |
http://arxiv.org/pdf/1610.09730v1
|
2016-10-30T23:39:18Z
|
2016-10-30T23:39:18Z
|
Active Learning from Imperfect Labelers
|
We study active learning where the labeler can not only return incorrect labels but also abstain from labeling. We consider different noise and abstention conditions of the labeler. We propose an algorithm which utilizes abstention responses, and analyze its statistical consistency and query complexity under fairly natural assumptions on the noise and abstention rate of the labeler. This algorithm is adaptive in a sense that it can automatically request less queries with a more informed or less noisy labeler. We couple our algorithm with lower bounds to show that under some technical conditions, it achieves nearly optimal query complexity.
|
[
"['Songbai Yan' 'Kamalika Chaudhuri' 'Tara Javidi']"
] |
cs.CL cs.LG
| null |
1610.09756
| null | null |
http://arxiv.org/pdf/1610.09756v2
|
2016-11-16T17:15:14Z
|
2016-10-31T01:31:52Z
|
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled
Data Scarcity
|
In this paper we describe an end to end Neural Model for Named Entity
Recognition NER) which is based on Bi-Directional RNN-LSTM. Almost all NER
systems for Hindi use Language Specific features and handcrafted rules with
gazetteers. Our model is language independent and uses no domain specific
features or any handcrafted rules. Our models rely on semantic information in
the form of word vectors which are learnt by an unsupervised learning algorithm
on an unannotated corpus. Our model attained state of the art performance in
both English and Hindi without the use of any morphological analysis or without
using gazetteers of any sort.
|
[
"['Vinayak Athavale' 'Shreenivas Bharadwaj' 'Monik Pamecha' 'Ameya Prabhu'\n 'Manish Shrivastava']",
"Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu\n and Manish Shrivastava"
] |
cs.SI cs.LG
| null |
1610.09769
| null | null |
http://arxiv.org/pdf/1610.09769v1
|
2016-10-31T03:15:02Z
|
2016-10-31T03:15:02Z
|
Meta-Path Guided Embedding for Similarity Search in Large-Scale
Heterogeneous Information Networks
|
Most real-world data can be modeled as heterogeneous information networks
(HINs) consisting of vertices of multiple types and their relationships. Search
for similar vertices of the same type in large HINs, such as bibliographic
networks and business-review networks, is a fundamental problem with broad
applications. Although similarity search in HINs has been studied previously,
most existing approaches neither explore rich semantic information embedded in
the network structures nor take user's preference as a guidance.
In this paper, we re-examine similarity search in HINs and propose a novel
embedding-based framework. It models vertices as low-dimensional vectors to
explore network structure-embedded similarity. To accommodate user preferences
at defining similarity semantics, our proposed framework, ESim, accepts
user-defined meta-paths as guidance to learn vertex vectors in a user-preferred
embedding space. Moreover, an efficient and parallel sampling-based
optimization algorithm has been developed to learn embeddings in large-scale
HINs. Extensive experiments on real-world large-scale HINs demonstrate a
significant improvement on the effectiveness of ESim over several
state-of-the-art algorithms as well as its scalability.
|
[
"['Jingbo Shang' 'Meng Qu' 'Jialu Liu' 'Lance M. Kaplan' 'Jiawei Han'\n 'Jian Peng']",
"Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian\n Peng"
] |
cs.LG cs.AI
| null |
1610.09778
| null | null |
http://arxiv.org/pdf/1610.09778v1
|
2016-10-31T03:43:04Z
|
2016-10-31T03:43:04Z
|
DPPred: An Effective Prediction Framework with Concise Discriminative
Patterns
|
In the literature, two series of models have been proposed to address
prediction problems including classification and regression. Simple models,
such as generalized linear models, have ordinary performance but strong
interpretability on a set of simple features. The other series, including
tree-based models, organize numerical, categorical and high dimensional
features into a comprehensive structure with rich interpretable information in
the data.
In this paper, we propose a novel Discriminative Pattern-based Prediction
framework (DPPred) to accomplish the prediction tasks by taking their
advantages of both effectiveness and interpretability. Specifically, DPPred
adopts the concise discriminative patterns that are on the prefix paths from
the root to leaf nodes in the tree-based models. DPPred selects a limited
number of the useful discriminative patterns by searching for the most
effective pattern combination to fit generalized linear models. Extensive
experiments show that in many scenarios, DPPred provides competitive accuracy
with the state-of-the-art as well as the valuable interpretability for
developers and experts. In particular, taking a clinical application dataset as
a case study, our DPPred outperforms the baselines by using only 40 concise
discriminative patterns out of a potentially exponentially large set of
patterns.
|
[
"Jingbo Shang, Meng Jiang, Wenzhu Tong, Jinfeng Xiao, Jian Peng, Jiawei\n Han",
"['Jingbo Shang' 'Meng Jiang' 'Wenzhu Tong' 'Jinfeng Xiao' 'Jian Peng'\n 'Jiawei Han']"
] |
cs.LG cs.NE stat.ML
| null |
1610.09887
| null | null |
http://arxiv.org/pdf/1610.09887v3
|
2020-05-13T12:08:04Z
|
2016-10-31T12:08:46Z
|
Depth-Width Tradeoffs in Approximating Natural Functions with Neural
Networks
|
We provide several new depth-based separation results for feed-forward neural
networks, proving that various types of simple and natural functions can be
better approximated using deeper networks than shallower ones, even if the
shallower networks are much larger. This includes indicators of balls and
ellipses; non-linear functions which are radial with respect to the $L_1$ norm;
and smooth non-linear functions. We also show that these gaps can be observed
experimentally: Increasing the depth indeed allows better learning than
increasing width, when training neural networks to learn an indicator of a unit
ball.
|
[
"['Itay Safran' 'Ohad Shamir']",
"Itay Safran, Ohad Shamir"
] |
cs.CL cs.LG
| null |
1610.09893
| null | null |
http://arxiv.org/pdf/1610.09893v1
|
2016-10-31T12:24:13Z
|
2016-10-31T12:24:13Z
|
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
|
Recurrent neural networks (RNNs) have achieved state-of-the-art performances
in many natural language processing tasks, such as language modeling and
machine translation. However, when the vocabulary is large, the RNN model will
become very big (e.g., possibly beyond the memory capacity of a GPU device) and
its training will become very inefficient. In this work, we propose a novel
technique to tackle this challenge. The key idea is to use 2-Component (2C)
shared embedding for word representations. We allocate every word in the
vocabulary into a table, each row of which is associated with a vector, and
each column associated with another vector. Depending on its position in the
table, a word is jointly represented by two components: a row vector and a
column vector. Since the words in the same row share the row vector and the
words in the same column share the column vector, we only need $2 \sqrt{|V|}$
vectors to represent a vocabulary of $|V|$ unique words, which are far less
than the $|V|$ vectors required by existing approaches. Based on the
2-Component shared embedding, we design a new RNN algorithm and evaluate it
using the language modeling task on several benchmark datasets. The results
show that our algorithm significantly reduces the model size and speeds up the
training process, without sacrifice of accuracy (it achieves similar, if not
better, perplexity as compared to state-of-the-art language models).
Remarkably, on the One-Billion-Word benchmark Dataset, our algorithm achieves
comparable perplexity to previous language models, whilst reducing the model
size by a factor of 40-100, and speeding up the training process by a factor of
2. We name our proposed algorithm \emph{LightRNN} to reflect its very small
model size and very high training speed.
|
[
"['Xiang Li' 'Tao Qin' 'Jian Yang' 'Tie-Yan Liu']",
"Xiang Li and Tao Qin and Jian Yang and Tie-Yan Liu"
] |
cs.AI cs.LG stat.ML
| null |
1610.099
| null | null | null | null | null |
Inference Compilation and Universal Probabilistic Programming
|
We introduce a method for using deep neural networks to amortize the cost of
inference in models from the family induced by universal probabilistic
programming languages, establishing a framework that combines the strengths of
probabilistic programming and deep learning methods. We call what we do
"compilation of inference" because our method transforms a denotational
specification of an inference problem in the form of a probabilistic program
written in a universal programming language into a trained neural network
denoted in a neural network specification language. When at test time this
neural network is fed observational data and executed, it performs approximate
inference in the original model specified by the probabilistic program. Our
training objective and learning procedure are designed to allow the trained
neural network to be used as a proposal distribution in a sequential importance
sampling inference engine. We illustrate our method on mixture models and
Captcha solving and show significant speedups in the efficiency of inference.
|
[
"Tuan Anh Le, Atilim Gunes Baydin, Frank Wood"
] |
null | null |
1610.09900
| null | null |
http://arxiv.org/pdf/1610.09900v2
|
2017-03-02T17:11:01Z
|
2016-10-31T12:53:20Z
|
Inference Compilation and Universal Probabilistic Programming
|
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of inference" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.
|
[
"['Tuan Anh Le' 'Atilim Gunes Baydin' 'Frank Wood']"
] |
cs.LG
| null |
1610.09903
| null | null |
http://arxiv.org/pdf/1610.09903v1
|
2016-10-31T12:57:25Z
|
2016-10-31T12:57:25Z
|
Learning Runtime Parameters in Computer Systems with Delayed Experience
Injection
|
Learning effective configurations in computer systems without hand-crafting
models for every parameter is a long-standing problem. This paper investigates
the use of deep reinforcement learning for runtime parameters of cloud
databases under latency constraints. Cloud services serve up to thousands of
concurrent requests per second and can adjust critical parameters by leveraging
performance metrics. In this work, we use continuous deep reinforcement
learning to learn optimal cache expirations for HTTP caching in content
delivery networks. To this end, we introduce a technique for asynchronous
experience management called delayed experience injection, which facilitates
delayed reward and next-state computation in concurrent environments where
measurements are not immediately available. Evaluation results show that our
approach based on normalized advantage functions and asynchronous CPU-only
training outperforms a statistical estimator.
|
[
"['Michael Schaarschmidt' 'Felix Gessert' 'Valentin Dalibard' 'Eiko Yoneki']",
"Michael Schaarschmidt, Felix Gessert, Valentin Dalibard, Eiko Yoneki"
] |
stat.ML cs.LG
|
10.1109/TSP.2017.2726991
|
1610.09915
| null | null |
http://arxiv.org/abs/1610.09915v1
|
2016-10-31T13:36:53Z
|
2016-10-31T13:36:53Z
|
Complex-Valued Kernel Methods for Regression
|
Usually, complex-valued RKHS are presented as an straightforward application
of the real-valued case. In this paper we prove that this procedure yields a
limited solution for regression. We show that another kernel, here denoted as
pseudo kernel, is needed to learn any function in complex-valued fields.
Accordingly, we derive a novel RKHS to include it, the widely RKHS (WRKHS).
When the pseudo-kernel cancels, WRKHS reduces to complex-valued RKHS of
previous approaches. We address the kernel and pseudo-kernel design, paying
attention to the kernel and the pseudo-kernel being complex-valued. In the
experiments included we report remarkable improvements in simple scenarios
where real a imaginary parts have different similitude relations for given
inputs or cases where real and imaginary parts are correlated. In the context
of these novel results we revisit the problem of non-linear channel
equalization, to show that the WRKHS helps to design more efficient solutions.
|
[
"Rafael Boloix-Tortosa, Juan Jos\\'e Murillo-Fuentes, Irene Santos\n Vel\\'azquez, and Fernando P\\'erez-Cruz",
"['Rafael Boloix-Tortosa' 'Juan José Murillo-Fuentes'\n 'Irene Santos Velázquez' 'Fernando Pérez-Cruz']"
] |
physics.data-an cs.LG hep-ex
|
10.1088/1742-6596/762/1/012052
|
1610.09932
| null | null |
http://arxiv.org/abs/1610.09932v1
|
2016-10-19T15:13:03Z
|
2016-10-19T15:13:03Z
|
Support Vector Machines and Generalisation in HEP
|
We review the concept of support vector machines (SVMs) and discuss examples
of their use. One of the benefits of SVM algorithms, compared with neural
networks and decision trees is that they can be less susceptible to over
fitting than those other algorithms are to over training. This issue is related
to the generalisation of a multivariate algorithm (MVA); a problem that has
often been overlooked in particle physics. We discuss cross validation and how
this can be used to improve the generalisation of a MVA in the context of High
Energy Physics analyses. The examples presented use the Toolkit for
Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the
SVM functionality and new tools introduced for cross validation within this
framework.
|
[
"A. Bethani, A. J. Bevan, J. Hays and T. J. Stevenson",
"['A. Bethani' 'A. J. Bevan' 'J. Hays' 'T. J. Stevenson']"
] |
cs.CL cs.LG cs.NE
| null |
1610.09975
| null | null |
http://arxiv.org/pdf/1610.09975v1
|
2016-10-31T15:36:42Z
|
2016-10-31T15:36:42Z
|
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large
Vocabulary Speech Recognition
|
We present results that show it is possible to build a competitive, greatly
simplified, large vocabulary continuous speech recognition system with whole
words as acoustic units. We model the output vocabulary of about 100,000 words
directly using deep bi-directional LSTM RNNs with CTC loss. The model is
trained on 125,000 hours of semi-supervised acoustic training data, which
enables us to alleviate the data sparsity problem for word models. We show that
the CTC word models work very well as an end-to-end all-neural speech
recognition model without the use of traditional context-dependent sub-word
phone units that require a pronunciation lexicon, and without any language
model removing the need to decode. We demonstrate that the CTC word models
perform better than a strong, more complex, state-of-the-art baseline with
sub-word units.
|
[
"['Hagen Soltau' 'Hank Liao' 'Hasim Sak']",
"Hagen Soltau, Hank Liao, Hasim Sak"
] |
stat.ML cs.LG
| null |
1610.1006
| null | null | null | null | null |
Optimization for Large-Scale Machine Learning with Distributed Features
and Observations
|
As the size of modern data sets exceeds the disk and memory capacities of a
single computer, machine learning practitioners have resorted to parallel and
distributed computing. Given that optimization is one of the pillars of machine
learning and predictive modeling, distributed optimization methods have
recently garnered ample attention in the literature. Although previous research
has mostly focused on settings where either the observations, or features of
the problem at hand are stored in distributed fashion, the situation where both
are partitioned across the nodes of a computer cluster (doubly distributed) has
barely been studied. In this work we propose two doubly distributed
optimization algorithms. The first one falls under the umbrella of distributed
dual coordinate ascent methods, while the second one belongs to the class of
stochastic gradient/coordinate descent hybrid methods. We conduct numerical
experiments in Spark using real-world and simulated data sets and study the
scaling properties of our methods. Our empirical evaluation of the proposed
algorithms demonstrates the out-performance of a block distributed ADMM method,
which, to the best of our knowledge is the only other existing doubly
distributed optimization algorithm.
|
[
"Alexandros Nathan, Diego Klabjan"
] |
null | null |
1610.10060
| null | null |
http://arxiv.org/pdf/1610.10060v2
|
2017-04-15T01:10:43Z
|
2016-10-31T18:43:21Z
|
Optimization for Large-Scale Machine Learning with Distributed Features
and Observations
|
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention in the literature. Although previous research has mostly focused on settings where either the observations, or features of the problem at hand are stored in distributed fashion, the situation where both are partitioned across the nodes of a computer cluster (doubly distributed) has barely been studied. In this work we propose two doubly distributed optimization algorithms. The first one falls under the umbrella of distributed dual coordinate ascent methods, while the second one belongs to the class of stochastic gradient/coordinate descent hybrid methods. We conduct numerical experiments in Spark using real-world and simulated data sets and study the scaling properties of our methods. Our empirical evaluation of the proposed algorithms demonstrates the out-performance of a block distributed ADMM method, which, to the best of our knowledge is the only other existing doubly distributed optimization algorithm.
|
[
"['Alexandros Nathan' 'Diego Klabjan']"
] |
cs.NE cs.LG stat.ML
| null |
1610.10087
| null | null |
http://arxiv.org/pdf/1610.10087v1
|
2016-10-31T19:44:50Z
|
2016-10-31T19:44:50Z
|
Tensor Switching Networks
|
We present a novel neural network algorithm, the Tensor Switching (TS)
network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to
tensor-valued hidden units. The TS network copies its entire input vector to
different locations in an expanded representation, with the location determined
by its hidden unit activity. In this way, even a simple linear readout from the
TS representation can implement a highly expressive deep-network-like function.
The TS network hence avoids the vanishing gradient problem by construction, at
the cost of larger representation size. We develop several methods to train the
TS network, including equivalent kernels for infinitely wide and deep TS
networks, a one-pass linear learning algorithm, and two
backpropagation-inspired representation learning algorithms. Our experimental
results demonstrate that the TS network is indeed more expressive and
consistently learns faster than standard ReLU networks.
|
[
"['Chuan-Yung Tsai' 'Andrew Saxe' 'David Cox']",
"Chuan-Yung Tsai, Andrew Saxe, David Cox"
] |
cs.CL cs.LG
| null |
1610.10099
| null | null |
http://arxiv.org/pdf/1610.10099v2
|
2017-03-15T18:09:51Z
|
2016-10-31T19:56:39Z
|
Neural Machine Translation in Linear Time
|
We present a novel neural network for processing sequences. The ByteNet is a
one-dimensional convolutional neural network that is composed of two parts, one
to encode the source sequence and the other to decode the target sequence. The
two network parts are connected by stacking the decoder on top of the encoder
and preserving the temporal resolution of the sequences. To address the
differing lengths of the source and the target, we introduce an efficient
mechanism by which the decoder is dynamically unfolded over the representation
of the encoder. The ByteNet uses dilation in the convolutional layers to
increase its receptive field. The resulting network has two core properties: it
runs in time that is linear in the length of the sequences and it sidesteps the
need for excessive memorization. The ByteNet decoder attains state-of-the-art
performance on character-level language modelling and outperforms the previous
best results obtained with recurrent networks. The ByteNet also achieves
state-of-the-art performance on character-to-character machine translation on
the English-to-German WMT translation task, surpassing comparable neural
translation models that are based on recurrent networks with attentional
pooling and run in quadratic time. We find that the latent alignment structure
contained in the representations reflects the expected alignment between the
tokens.
|
[
"['Nal Kalchbrenner' 'Lasse Espeholt' 'Karen Simonyan' 'Aaron van den Oord'\n 'Alex Graves' 'Koray Kavukcuoglu']",
"Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord,\n Alex Graves, Koray Kavukcuoglu"
] |
cs.CL cs.AI cs.LG
| null |
1611.0002
| null | null | null | null | null |
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with
Weak Supervision
|
Harnessing the statistical power of neural networks to perform language
understanding and symbolic reasoning is difficult, when it requires executing
efficient discrete operations against a large knowledge-base. In this work, we
introduce a Neural Symbolic Machine, which contains (a) a neural "programmer",
i.e., a sequence-to-sequence model that maps language utterances to programs
and utilizes a key-variable memory to handle compositionality (b) a symbolic
"computer", i.e., a Lisp interpreter that performs program execution, and helps
find good programs by pruning the search space. We apply REINFORCE to directly
optimize the task reward of this structured prediction problem. To train with
weak supervision and improve the stability of REINFORCE, we augment it with an
iterative maximum-likelihood training process. NSM outperforms the
state-of-the-art on the WebQuestionsSP dataset when trained from
question-answer pairs only, without requiring any feature engineering or
domain-specific knowledge.
|
[
"Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao"
] |
null | null |
1611.00020
| null | null |
http://arxiv.org/pdf/1611.00020v4
|
2017-04-23T07:16:13Z
|
2016-10-31T20:07:23Z
|
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with
Weak Supervision
|
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.
|
[
"['Chen Liang' 'Jonathan Berant' 'Quoc Le' 'Kenneth D. Forbus' 'Ni Lao']"
] |
stat.ML cs.LG cs.NE
| null |
1611.00035
| null | null |
http://arxiv.org/pdf/1611.00035v1
|
2016-10-31T20:43:21Z
|
2016-10-31T20:43:21Z
|
Full-Capacity Unitary Recurrent Neural Networks
|
Recurrent neural networks are powerful models for processing sequential data,
but they are generally plagued by vanishing and exploding gradient problems.
Unitary recurrent neural networks (uRNNs), which use unitary recurrence
matrices, have recently been proposed as a means to avoid these issues.
However, in previous experiments, the recurrence matrices were restricted to be
a product of parameterized unitary matrices, and an open question remains: when
does such a parameterization fail to represent all unitary matrices, and how
does this restricted representational capacity limit what can be learned? To
address this question, we propose full-capacity uRNNs that optimize their
recurrence matrix over all unitary matrices, leading to significantly improved
performance over uRNNs that use a restricted-capacity recurrence matrix. Our
contribution consists of two main components. First, we provide a theoretical
argument to determine if a unitary parameterization has restricted capacity.
Using this argument, we show that a recently proposed unitary parameterization
has restricted capacity for hidden state dimension greater than 7. Second, we
show how a complete, full-capacity unitary recurrence matrix can be optimized
over the differentiable manifold of unitary matrices. The resulting
multiplicative gradient step is very simple and does not require gradient
clipping or learning rate adaptation. We confirm the utility of our claims by
empirically evaluating our new full-capacity uRNNs on both synthetic and
natural data, achieving superior performance compared to both LSTMs and the
original restricted-capacity uRNNs.
|
[
"Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, and\n Les Atlas",
"['Scott Wisdom' 'Thomas Powers' 'John R. Hershey' 'Jonathan Le Roux'\n 'Les Atlas']"
] |
cs.LG cs.CV
| null |
1611.0005
| null | null | null | null | null |
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All
Networks
|
We propose a convolutional recurrent neural network, with Winner-Take-All
dropout for high dimensional unsupervised feature learning in multi-dimensional
time series. We apply the proposedmethod for object recognition with temporal
context in videos and obtain better results than comparable methods in the
literature, including the Deep Predictive Coding Networks previously proposed
by Chalasani and Principe.Our contributions can be summarized as a scalable
reinterpretation of the Deep Predictive Coding Networks trained end-to-end with
backpropagation through time, an extension of the previously proposed
Winner-Take-All Autoencoders to sequences in time, and a new technique for
initializing and regularizing convolutional-recurrent neural networks.
|
[
"Eder Santana, Matthew Emigh, Pablo Zegers, Jose C Principe"
] |
null | null |
1611.00050
| null | null |
http://arxiv.org/pdf/1611.00050v2
|
2017-03-15T16:01:43Z
|
2016-10-31T21:16:46Z
|
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All
Networks
|
We propose a convolutional recurrent neural network, with Winner-Take-All dropout for high dimensional unsupervised feature learning in multi-dimensional time series. We apply the proposedmethod for object recognition with temporal context in videos and obtain better results than comparable methods in the literature, including the Deep Predictive Coding Networks previously proposed by Chalasani and Principe.Our contributions can be summarized as a scalable reinterpretation of the Deep Predictive Coding Networks trained end-to-end with backpropagation through time, an extension of the previously proposed Winner-Take-All Autoencoders to sequences in time, and a new technique for initializing and regularizing convolutional-recurrent neural networks.
|
[
"['Eder Santana' 'Matthew Emigh' 'Pablo Zegers' 'Jose C Principe']"
] |
cs.LG stat.ML
|
10.1109/BigData.2017.8258344
|
1611.00058
| null | null |
http://arxiv.org/abs/1611.00058v3
|
2017-05-19T19:13:20Z
|
2016-10-31T22:04:54Z
|
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large
Data
|
Support Vector Data Description (SVDD) provides a useful approach to
construct a description of multivariate data for single-class classification
and outlier detection with various practical applications. Gaussian kernel used
in SVDD formulation allows flexible data description defined by observations
designated as support vectors. The data boundary of such description is
non-spherical and conforms to the geometric features of the data. By varying
the Gaussian kernel bandwidth parameter, the SVDD-generated boundary can be
made either smoother (more spherical) or tighter/jagged. The former case may
lead to under-fitting, whereas the latter may result in overfitting. Peak
criterion has been proposed to select an optimal value of the kernel bandwidth
to strike the balance between the data boundary smoothness and its ability to
capture the general geometric shape of the data. Peak criterion involves
training SVDD at various values of the kernel bandwidth parameter. When
training datasets are large, the time required to obtain the optimal value of
the Gaussian kernel bandwidth parameter according to Peak method can become
prohibitively large. This paper proposes an extension of Peak method for the
case of large data. The proposed method gives good results when applied to
several datasets. Two existing alternative methods of computing the Gaussian
kernel bandwidth parameter (Coefficient of Variation and Distance to the
Farthest Neighbor) were modified to allow comparison with the proposed method
on convergence. Empirical comparison demonstrates the advantage of the proposed
method.
|
[
"Sergiy Peredriy, Deovrat Kakde, Arin Chaudhuri",
"['Sergiy Peredriy' 'Deovrat Kakde' 'Arin Chaudhuri']"
] |
cs.LG math.PR stat.ML
| null |
1611.00065
| null | null |
http://arxiv.org/pdf/1611.00065v3
|
2017-03-20T20:07:55Z
|
2016-10-31T22:24:49Z
|
Bayesian Adaptive Data Analysis Guarantees from Subgaussianity
|
The new field of adaptive data analysis seeks to provide algorithms and
provable guarantees for models of machine learning that allow researchers to
reuse their data, which normally falls outside of the usual statistical
paradigm of static data analysis. In 2014, Dwork, Feldman, Hardt, Pitassi,
Reingold and Roth introduced one potential model and proposed several solutions
based on differential privacy. In previous work in 2016, we described a problem
with this model and instead proposed a Bayesian variant, but also found that
the analogous Bayesian methods cannot achieve the same statistical guarantees
as in the static case.
In this paper, we prove the first positive results for the Bayesian model,
showing that with a Dirichlet prior, the posterior mean algorithm indeed
matches the statistical guarantees of the static case. The main ingredient is a
new theorem showing that the $\mathrm{Beta}(\alpha,\beta)$ distribution is
subgaussian with variance proxy $O(1/(\alpha+\beta+1))$, a concentration result
also of independent interest. We provide two proofs of this result: a
probabilistic proof utilizing a simple condition for the raw moments of a
positive random variable and a learning-theoretic proof based on considering
the beta distribution as a posterior, both of which have implications to other
related problems.
|
[
"['Sam Elder']",
"Sam Elder"
] |
cs.CV cs.LG
| null |
1611.00137
| null | null |
http://arxiv.org/pdf/1611.00137v1
|
2016-11-01T06:03:48Z
|
2016-11-01T06:03:48Z
|
Embedding Deep Metric for Person Re-identication A Study Against Large
Variations
|
Person re-identification is challenging due to the large variations of pose,
illumination, occlusion and camera view. Owing to these variations, the
pedestrian data is distributed as highly-curved manifolds in the feature space,
despite the current convolutional neural networks (CNN)'s capability of feature
extraction. However, the distribution is unknown, so it is difficult to use the
geodesic distance when comparing two samples. In practice, the current deep
embedding methods use the Euclidean distance for the training and test. On the
other hand, the manifold learning methods suggest to use the Euclidean distance
in the local range, combining with the graphical relationship between samples,
for approximating the geodesic distance. From this point of view, selecting
suitable positive i.e. intra-class) training samples within a local range is
critical for training the CNN embedding, especially when the data has large
intra-class variations. In this paper, we propose a novel moderate positive
sample mining method to train robust CNN for person re-identification, dealing
with the problem of large variation. In addition, we improve the learning by a
metric weight constraint, so that the learned metric has a better
generalization ability. Experiments show that these two strategies are
effective in learning robust deep metrics for person re-identification, and
accordingly our deep model significantly outperforms the state-of-the-art
methods on several benchmarks of person re-identification. Therefore, the study
presented in this paper may be useful in inspiring new designs of deep models
for person re-identification.
|
[
"['Hailin Shi' 'Yang Yang' 'Xiangyu Zhu' 'Shengcai Liao' 'Zhen Lei'\n 'Weishi Zheng' 'Stan Z. Li']",
"Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Weishi\n Zheng, Stan Z. Li"
] |
cs.LG cs.CL cs.IR
| null |
1611.00138
| null | null |
http://arxiv.org/pdf/1611.00138v1
|
2016-11-01T06:05:49Z
|
2016-11-01T06:05:49Z
|
MusicMood: Predicting the mood of music from song lyrics using machine
learning
|
Sentiment prediction of contemporary music can have a wide-range of
applications in modern society, for instance, selecting music for public
institutions such as hospitals or restaurants to potentially improve the
emotional well-being of personnel, patients, and customers, respectively. In
this project, music recommendation system built upon on a naive Bayes
classifier, trained to predict the sentiment of songs based on song lyrics
alone. The experimental results show that music corresponding to a happy mood
can be detected with high precision based on text features obtained from song
lyrics.
|
[
"['Sebastian Raschka']",
"Sebastian Raschka"
] |
cs.LG cs.IR
| null |
1611.00144
| null | null |
http://arxiv.org/pdf/1611.00144v1
|
2016-11-01T07:10:22Z
|
2016-11-01T07:10:22Z
|
Product-based Neural Networks for User Response Prediction
|
Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.
|
[
"Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, Jun Wang",
"['Yanru Qu' 'Han Cai' 'Kan Ren' 'Weinan Zhang' 'Yong Yu' 'Ying Wen'\n 'Jun Wang']"
] |
cs.LG cs.AI
| null |
1611.00175
| null | null |
http://arxiv.org/pdf/1611.00175v1
|
2016-11-01T10:06:57Z
|
2016-11-01T10:06:57Z
|
Robust Spectral Inference for Joint Stochastic Matrix Factorization
|
Spectral inference provides fast algorithms and provable optimality for
latent topic analysis. But for real data these algorithms require additional
ad-hoc heuristics, and even then often produce unusable results. We explain
this poor performance by casting the problem of topic inference in the
framework of Joint Stochastic Matrix Factorization (JSMF) and showing that
previous methods violate the theoretical conditions necessary for a good
solution to exist. We then propose a novel rectification method that learns
high quality topics and their interactions even on small, noisy data. This
method achieves results comparable to probabilistic techniques in several
domains while maintaining scalability and provable optimality.
|
[
"Moontae Lee, David Bindel, David Mimno",
"['Moontae Lee' 'David Bindel' 'David Mimno']"
] |
cs.OH cs.LG
| null |
1611.00228
| null | null |
http://arxiv.org/pdf/1611.00228v1
|
2016-10-31T10:58:12Z
|
2016-10-31T10:58:12Z
|
Application Specific Instrumentation (ASIN): A Bio-inspired Paradigm to
Instrumentation using recognition before detection
|
In this paper we present a new scheme for instrumentation, which has been
inspired by the way small mammals sense their environment. We call this scheme
Application Specific Instrumentation (ASIN). A conventional instrumentation
system focuses on gathering as much information about the scene as possible.
This, usually, is a generic system whose data can be used by another system to
take a specific action. ASIN fuses these two steps into one. The major merit of
the proposed scheme is that it uses low resolution sensors and much less
computational overhead to give good performance for a highly specialised
application
|
[
"Amit Kumar Mishra",
"['Amit Kumar Mishra']"
] |
cs.LG
| null |
1611.00252
| null | null |
http://arxiv.org/pdf/1611.00252v2
|
2017-03-01T23:40:46Z
|
2016-10-30T21:09:27Z
|
Improving a Credit Scoring Model by Incorporating Bank Statement Derived
Features
|
In this paper, we investigate the extent to which features derived from bank
statements provided by loan applicants, and which are not declared on an
application form, can enhance a credit scoring model for a New Zealand lending
company. Exploring the potential of such information to improve credit scoring
models in this manner has not been studied previously. We construct a baseline
model based solely on the existing scoring features obtained from the loan
application form, and a second baseline model based solely on the new bank
statement-derived features. A combined feature model is then created by
augmenting the application form features with the new bank statement derived
features. Our experimental results using ROC analysis show that a combined
feature model performs better than both of the two baseline models, and show
that a number of the bank statement-derived features have value in improving
the credit scoring model. The target data set used for modelling was highly
imbalanced, and Naive Bayes was found to be the best performing model, and
outperformed a number of other classifiers commonly used in credit scoring,
suggesting its potential for future use on highly imbalanced data sets.
|
[
"Rory P. Bunker, Wenjun Zhang, M. Asif Naeem",
"['Rory P. Bunker' 'Wenjun Zhang' 'M. Asif Naeem']"
] |
cs.LG cs.DS stat.ML
| null |
1611.00255
| null | null |
http://arxiv.org/pdf/1611.00255v3
|
2019-07-08T12:34:27Z
|
2016-11-01T14:56:33Z
|
Stationary time-vertex signal processing
|
This paper considers regression tasks involving high-dimensional multivariate
processes whose structure is dependent on some {known} graph topology. We put
forth a new definition of time-vertex wide-sense stationarity, or joint
stationarity for short, that goes beyond product graphs. Joint stationarity
helps by reducing the estimation variance and recovery complexity. In
particular, for any jointly stationary process (a) one reliably learns the
covariance structure from as little as a single realization of the process, and
(b) solves MMSE recovery problems, such as interpolation and denoising, in
computational time nearly linear on the number of edges and timesteps.
Experiments with three datasets suggest that joint stationarity can yield
accuracy improvements in the recovery of high-dimensional processes evolving
over a graph, even when the latter is only approximately known, or the process
is not strictly stationary.
|
[
"['Andreas Loukas' 'Nathanaël Perraudin']",
"Andreas Loukas and Nathana\\\"el Perraudin"
] |
cs.LG
| null |
1611.00301
| null | null |
http://arxiv.org/pdf/1611.00301v1
|
2016-11-01T17:17:26Z
|
2016-11-01T17:17:26Z
|
Recurrent Neural Radio Anomaly Detection
|
We introduce a powerful recurrent neural network based method for novelty
detection to the application of detecting radio anomalies. This approach holds
promise in significantly increasing the ability of naive anomaly detection to
detect small anomalies in highly complex complexity multi-user radio bands. We
demonstrate the efficacy of this approach on a number of common real over the
air radio communications bands of interest and quantify detection performance
in terms of probability of detection an false alarm rates across a range of
interference to band power ratios and compare to baseline methods.
|
[
"[\"Timothy J O'Shea\" 'T. Charles Clancy' 'Robert W. McGwier']",
"Timothy J O'Shea, T. Charles Clancy, Robert W. McGwier"
] |
cs.LG cs.IT math.IT stat.ML
| null |
1611.00303
| null | null |
http://arxiv.org/pdf/1611.00303v2
|
2017-01-17T18:23:49Z
|
2016-11-01T17:21:50Z
|
Semi-Supervised Radio Signal Identification
|
Radio emitter recognition in dense multi-user environments is an important
tool for optimizing spectrum utilization, identifying and minimizing
interference, and enforcing spectrum policy. Radio data is readily available
and easy to obtain from an antenna, but labeled and curated data is often
scarce making supervised learning strategies difficult and time consuming in
practice. We demonstrate that semi-supervised learning techniques can be used
to scale learning beyond supervised datasets, allowing for discerning and
recalling new radio signals by using sparse signal representations based on
both unsupervised and supervised methods for nonlinear feature learning and
clustering methods.
|
[
"Timothy J. O'Shea, Nathan West, Matthew Vondal, T. Charles Clancy",
"[\"Timothy J. O'Shea\" 'Nathan West' 'Matthew Vondal' 'T. Charles Clancy']"
] |
cs.SD cs.LG stat.ML
| null |
1611.00326
| null | null |
http://arxiv.org/pdf/1611.00326v3
|
2017-04-20T18:43:29Z
|
2016-11-01T18:38:12Z
|
Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech
Detection
|
In this letter, we propose enhanced factored three way restricted Boltzmann
machines (EFTW-RBMs) for speech detection. The proposed model incorporates
conditional feature learning by multiplying the dynamical state of the third
unit, which allows a modulation over the visible-hidden node pairs. Instead of
stacking previous frames of speech as the third unit in a recursive manner, the
correlation related weighting coefficients are assigned to the contextual
neighboring frames. Specifically, a threshold function is designed to capture
the long-term features and blend the globally stored speech structure. A
factored low rank approximation is introduced to reduce the parameters of the
three-dimensional interaction tensor, on which non-negative constraint is
imposed to address the sparsity characteristic. The validations through the
area-under-ROC-curve (AUC) and signal distortion ratio (SDR) show that our
approach outperforms several existing 1D and 2D (i.e., time and time-frequency
domain) speech detection algorithms in various noisy environments.
|
[
"['Pengfei Sun' 'Jun Qin']",
"Pengfei Sun and Jun Qin"
] |
stat.ML cs.LG stat.CO stat.ME
| null |
1611.00328
| null | null |
http://arxiv.org/pdf/1611.00328v4
|
2017-11-12T19:00:57Z
|
2016-11-01T18:40:23Z
|
Variational Inference via $\chi$-Upper Bound Minimization
|
Variational inference (VI) is widely used as an efficient alternative to
Markov chain Monte Carlo. It posits a family of approximating distributions $q$
and finds the closest member to the exact posterior $p$. Closeness is usually
measured via a divergence $D(q || p)$ from $q$ to $p$. While successful, this
approach also has problems. Notably, it typically leads to underestimation of
the posterior variance. In this paper we propose CHIVI, a black-box variational
inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence
from $p$ to $q$. CHIVI minimizes an upper bound of the model evidence, which we
term the $\chi$ upper bound (CUBO). Minimizing the CUBO leads to improved
posterior uncertainty, and it can also be used with the classical VI lower
bound (ELBO) to provide a sandwich estimate of the model evidence. We study
CHIVI on three models: probit regression, Gaussian process classification, and
a Cox process model of basketball plays. When compared to expectation
propagation and classical VI, CHIVI produces better error rates and more
accurate estimates of posterior variance.
|
[
"Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M.\n Blei",
"['Adji B. Dieng' 'Dustin Tran' 'Rajesh Ranganath' 'John Paisley'\n 'David M. Blei']"
] |
stat.ML cs.LG stat.ME
| null |
1611.00336
| null | null |
http://arxiv.org/pdf/1611.00336v2
|
2016-11-02T18:06:16Z
|
2016-11-01T19:04:47Z
|
Stochastic Variational Deep Kernel Learning
|
Deep kernel learning combines the non-parametric flexibility of kernel
methods with the inductive biases of deep learning architectures. We propose a
novel deep kernel learning model and stochastic variational inference procedure
which generalizes deep kernel learning approaches to enable classification,
multi-task learning, additive covariance structures, and stochastic gradient
training. Specifically, we apply additive base kernels to subsets of output
features from deep neural architectures, and jointly learn the parameters of
the base kernels and deep network through a Gaussian process marginal
likelihood objective. Within this framework, we derive an efficient form of
stochastic variational inference which leverages local kernel interpolation,
inducing points, and structure exploiting algebra. We show improved performance
over stand alone deep networks, SVMs, and state of the art scalable Gaussian
processes on several classification benchmarks, including an airline delay
dataset containing 6 million training points, CIFAR, and ImageNet.
|
[
"Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing",
"['Andrew Gordon Wilson' 'Zhiting Hu' 'Ruslan Salakhutdinov' 'Eric P. Xing']"
] |
math.OC cs.LG
| null |
1611.00347
| null | null |
http://arxiv.org/pdf/1611.00347v2
|
2018-02-07T21:25:45Z
|
2016-11-01T19:40:33Z
|
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with
Linear Convergence Rate
|
Recently, there has been growing interest in developing optimization methods
for solving large-scale machine learning problems. Most of these problems boil
down to the problem of minimizing an average of a finite set of smooth and
strongly convex functions where the number of functions $n$ is large. Gradient
descent method (GD) is successful in minimizing convex problems at a fast
linear rate; however, it is not applicable to the considered large-scale
optimization setting because of the high computational complexity. Incremental
methods resolve this drawback of gradient methods by replacing the required
gradient for the descent direction with an incremental gradient approximation.
They operate by evaluating one gradient per iteration and executing the average
of the $n$ available gradients as a gradient approximate. Although, incremental
methods reduce the computational cost of GD, their convergence rates do not
justify their advantage relative to GD in terms of the total number of gradient
evaluations until convergence. In this paper, we introduce a Double Incremental
Aggregated Gradient method (DIAG) that computes the gradient of only one
function at each iteration, which is chosen based on a cyclic scheme, and uses
the aggregated average gradient of all the functions to approximate the full
gradient. The iterates of the proposed DIAG method uses averages of both
iterates and gradients in oppose to classic incremental methods that utilize
gradient averages but do not utilize iterate averages. We prove that not only
the proposed DIAG method converges linearly to the optimal solution, but also
its linear convergence factor justifies the advantage of incremental methods on
GD. In particular, we prove that the worst case performance of DIAG is better
than the worst case performance of GD.
|
[
"Aryan Mokhtari and Mert G\\\"urb\\\"uzbalaban and Alejandro Ribeiro",
"['Aryan Mokhtari' 'Mert Gürbüzbalaban' 'Alejandro Ribeiro']"
] |
cs.SI cs.LG stat.ML
| null |
1611.0035
| null | null | null | null | null |
Adversarial Influence Maximization
|
We consider the problem of influence maximization in fixed networks for
contagion models in an adversarial setting. The goal is to select an optimal
set of nodes to seed the influence process, such that the number of influenced
nodes at the conclusion of the campaign is as large as possible. We formulate
the problem as a repeated game between a player and adversary, where the
adversary specifies the edges along which the contagion may spread, and the
player chooses sets of nodes to influence in an online fashion. We establish
upper and lower bounds on the minimax pseudo-regret in both undirected and
directed networks.
|
[
"Justin Khim, Varun Jog, Po-Ling Loh"
] |
null | null |
1611.00350
| null | null |
http://arxiv.org/pdf/1611.00350v2
|
2019-01-19T16:55:50Z
|
2016-11-01T19:46:01Z
|
Adversarial Influence Maximization
|
We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible. We formulate the problem as a repeated game between a player and adversary, where the adversary specifies the edges along which the contagion may spread, and the player chooses sets of nodes to influence in an online fashion. We establish upper and lower bounds on the minimax pseudo-regret in both undirected and directed networks.
|
[
"['Justin Khim' 'Varun Jog' 'Po-Ling Loh']"
] |
cs.CY cs.CL cs.LG
| null |
1611.00356
| null | null |
http://arxiv.org/pdf/1611.00356v1
|
2016-11-01T19:59:48Z
|
2016-11-01T19:59:48Z
|
Using Artificial Intelligence to Identify State Secrets
|
Whether officials can be trusted to protect national security information has
become a matter of great public controversy, reigniting a long-standing debate
about the scope and nature of official secrecy. The declassification of
millions of electronic records has made it possible to analyze these issues
with greater rigor and precision. Using machine-learning methods, we examined
nearly a million State Department cables from the 1970s to identify features of
records that are more likely to be classified, such as international
negotiations, military operations, and high-level communications. Even with
incomplete data, algorithms can use such features to identify 90% of classified
cables with <11% false positives. But our results also show that there are
longstanding problems in the identification of sensitive information. Error
analysis reveals many examples of both overclassification and
underclassification. This indicates both the need for research on inter-coder
reliability among officials as to what constitutes classified material and the
opportunity to develop recommender systems to better manage both classification
and declassification.
|
[
"['Renato Rocha Souza' 'Flavio Codeco Coelho' 'Rohan Shah'\n 'Matthew Connelly']",
"Renato Rocha Souza, Flavio Codeco Coelho, Rohan Shah, Matthew Connelly"
] |
cs.HC cs.LG
| null |
1611.00379
| null | null |
http://arxiv.org/pdf/1611.00379v1
|
2016-11-01T20:35:46Z
|
2016-11-01T20:35:46Z
|
The Machine Learning Algorithm as Creative Musical Tool
|
Machine learning is the capacity of a computational system to learn
structures from datasets in order to make predictions on newly seen data. Such
an approach offers a significant advantage in music scenarios in which
musicians can teach the system to learn an idiosyncratic style, or can break
the rules to explore the system's capacity in unexpected ways. In this chapter
we draw on music, machine learning, and human-computer interaction to elucidate
an understanding of machine learning algorithms as creative tools for music and
the sonic arts. We motivate a new understanding of learning algorithms as
human-computer interfaces. We show that, like other interfaces, learning
algorithms can be characterised by the ways their affordances intersect with
goals of human users. We also argue that the nature of interaction between
users and algorithms impacts the usability and usefulness of those algorithms
in profound ways. This human-centred view of machine learning motivates our
concluding discussion of what it means to employ machine learning as a creative
tool.
|
[
"Rebecca Fiebrink, Baptiste Caramiaux",
"['Rebecca Fiebrink' 'Baptiste Caramiaux']"
] |
cs.IR cs.CL cs.LG
| null |
1611.00384
| null | null |
http://arxiv.org/pdf/1611.00384v2
|
2019-09-21T13:59:21Z
|
2016-11-01T20:48:34Z
|
CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for
Completely Cold Item Recommendations
|
In Recommender Systems research, algorithms are often characterized as either
Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained
using a dataset of user preferences while CB algorithms are typically based on
item profiles. These approaches harness different data sources and therefore
the resulting recommended items are generally very different. This paper
presents the CB2CF, a deep neural multiview model that serves as a bridge from
items content into their CF representations. CB2CF is a real-world algorithm
designed for Microsoft Store services that handle around a billion users
worldwide. CB2CF is demonstrated on movies and apps recommendations, where it
is shown to outperform an alternative CB model on completely cold items.
|
[
"['Oren Barkan' 'Noam Koenigstein' 'Eylon Yogev' 'Ori Katz']",
"Oren Barkan, Noam Koenigstein, Eylon Yogev and Ori Katz"
] |
cs.LG
| null |
1611.00429
| null | null |
http://arxiv.org/pdf/1611.00429v3
|
2017-09-25T15:10:54Z
|
2016-11-02T00:16:18Z
|
Distributed Mean Estimation with Limited Communication
|
Motivated by the need for distributed learning and optimization algorithms
with low communication cost, we study communication efficient algorithms for
distributed mean estimation. Unlike previous works, we make no probabilistic
assumptions on the data. We first show that for $d$ dimensional data with $n$
clients, a naive stochastic binary rounding approach yields a mean squared
error (MSE) of $\Theta(d/n)$ and uses a constant number of bits per dimension
per client. We then extend this naive algorithm in two ways: we show that
applying a structured random rotation before quantization reduces the error to
$\mathcal{O}((\log d)/n)$ and a better coding strategy further reduces the
error to $\mathcal{O}(1/n)$ and uses a constant number of bits per dimension
per client. We also show that the latter coding strategy is optimal up to a
constant in the minimax sense i.e., it achieves the best MSE for a given
communication cost. We finally demonstrate the practicality of our algorithms
by applying them to distributed Lloyd's algorithm for k-means and power
iteration for PCA.
|
[
"['Ananda Theertha Suresh' 'Felix X. Yu' 'Sanjiv Kumar'\n 'H. Brendan McMahan']",
"Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan"
] |
cs.LG cs.AI cs.CL cs.CV stat.ML
| null |
1611.00448
| null | null |
http://arxiv.org/pdf/1611.00448v1
|
2016-11-02T02:32:05Z
|
2016-11-02T02:32:05Z
|
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
|
Neural networks (NN) have achieved state-of-the-art performance in various
applications. Unfortunately in applications where training data is
insufficient, they are often prone to overfitting. One effective way to
alleviate this problem is to exploit the Bayesian approach by using Bayesian
neural networks (BNN). Another shortcoming of NN is the lack of flexibility to
customize different distributions for the weights and neurons according to the
data, as is often done in probabilistic graphical models. To address these
problems, we propose a class of probabilistic neural networks, dubbed
natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment
of NN. NPN allows the usage of arbitrary exponential-family distributions to
model the weights and neurons. Different from traditional NN and BNN, NPN takes
distributions as input and goes through layers of transformation before
producing distributions to match the target output distributions. As a Bayesian
treatment, efficient backpropagation (BP) is performed to learn the natural
parameters for the distributions over both the weights and neurons. The output
distributions of each layer, as byproducts, may be used as second-order
representations for the associated tasks such as link prediction. Experiments
on real-world datasets show that NPN can achieve state-of-the-art performance.
|
[
"Hao Wang, Xingjian Shi, Dit-Yan Yeung",
"['Hao Wang' 'Xingjian Shi' 'Dit-Yan Yeung']"
] |
cs.LG cs.AI cs.CL cs.CV stat.ML
| null |
1611.00454
| null | null |
http://arxiv.org/pdf/1611.00454v1
|
2016-11-02T02:49:44Z
|
2016-11-02T02:49:44Z
|
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in
the Blanks
|
Hybrid methods that utilize both content and rating information are commonly
used in many recommender systems. However, most of them use either handcrafted
features or the bag-of-words representation as a surrogate for the content
information but they are neither effective nor natural enough. To address this
problem, we develop a collaborative recurrent autoencoder (CRAE) which is a
denoising recurrent autoencoder (DRAE) that models the generation of content
sequences in the collaborative filtering (CF) setting. The model generalizes
recent advances in recurrent deep learning from i.i.d. input to non-i.i.d.
(CF-based) input and provides a new denoising scheme along with a novel
learnable pooling scheme for the recurrent autoencoder. To do this, we first
develop a hierarchical Bayesian model for the DRAE and then generalize it to
the CF setting. The synergy between denoising and CF enables CRAE to make
accurate recommendations while learning to fill in the blanks in sequences.
Experiments on real-world datasets from different domains (CiteULike and
Netflix) show that, by jointly modeling the order-aware generation of sequences
for the content information and performing CF for the ratings, CRAE is able to
significantly outperform the state of the art on both the recommendation task
based on ratings and the sequence generation task based on content information.
|
[
"Hao Wang, Xingjian Shi, Dit-Yan Yeung",
"['Hao Wang' 'Xingjian Shi' 'Dit-Yan Yeung']"
] |
cs.LG
| null |
1611.00481
| null | null |
http://arxiv.org/pdf/1611.00481v2
|
2016-11-06T18:05:35Z
|
2016-11-02T06:29:46Z
|
Online Multi-view Clustering with Incomplete Views
|
In the era of big data, it is common to have data with multiple modalities or
coming from multiple sources, known as "multi-view data". Multi-view clustering
provides a natural way to generate clusters from such data. Since different
views share some consistency and complementary information, previous works on
multi-view clustering mainly focus on how to combine various numbers of views
to improve clustering performance. However, in reality, each view may be
incomplete, i.e., instances missing in the view. Furthermore, the size of data
could be extremely huge. It is unrealistic to apply multi-view clustering in
large real-world applications without considering the incompleteness of views
and the memory requirement. None of previous works have addressed all these
challenges simultaneously. In this paper, we propose an online multi-view
clustering algorithm, OMVC, which deals with large-scale incomplete views. We
model the multi-view clustering problem as a joint weighted nonnegative matrix
factorization problem and process the multi-view data chunk by chunk to reduce
the memory requirement. OMVC learns the latent feature matrices for all the
views and pushes them towards a consensus. We further increase the robustness
of the learned latent feature matrices in OMVC via lasso regularization. To
minimize the influence of incompleteness, dynamic weight setting is introduced
to give lower weights to the incoming missing instances in different views.
More importantly, to reduce the computational time, we incorporate a faster
projected gradient descent by utilizing the Hessian matrices in OMVC. Extensive
experiments conducted on four real data demonstrate the effectiveness of the
proposed OMVC method.
|
[
"['Weixiang Shao' 'Lifang He' 'Chun-Ta Lu' 'Philip S. Yu']",
"Weixiang Shao, Lifang He, Chun-Ta Lu, Philip S. Yu"
] |
cs.CV cs.LG cs.NE
| null |
1611.00591
| null | null |
http://arxiv.org/pdf/1611.00591v1
|
2016-09-04T16:20:13Z
|
2016-09-04T16:20:13Z
|
Deep Neural Networks for HDR imaging
|
We propose novel methods of solving two tasks using Convolutional Neural
Networks, firstly the task of generating HDR map of a static scene using
differently exposed LDR images of the scene captured using conventional cameras
and secondly the task of finding an optimal tone mapping operator that would
give a better score on the TMQI metric compared to the existing methods. We
quantitatively show the performance of our networks and illustrate the cases
where our networks performs good as well as bad.
|
[
"Kshiteej Sheth",
"['Kshiteej Sheth']"
] |
cs.LG cs.AI
| null |
1611.00625
| null | null |
http://arxiv.org/pdf/1611.00625v2
|
2016-11-03T21:54:28Z
|
2016-11-01T05:01:24Z
|
TorchCraft: a Library for Machine Learning Research on Real-Time
Strategy Games
|
We present TorchCraft, a library that enables deep learning research on
Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it
easier to control these games from a machine learning framework, here Torch.
This white paper argues for using RTS games as a benchmark for AI research, and
describes the design and components of TorchCraft.
|
[
"['Gabriel Synnaeve' 'Nantas Nardelli' 'Alex Auvolat' 'Soumith Chintala'\n 'Timothée Lacroix' 'Zeming Lin' 'Florian Richoux' 'Nicolas Usunier']",
"Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala,\n Timoth\\'ee Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier"
] |
cs.NE cs.LG
| null |
1611.0071
| null | null | null | null | null |
Deep counter networks for asynchronous event-based processing
|
Despite their advantages in terms of computational resources, latency, and
power consumption, event-based implementations of neural networks have not been
able to achieve the same performance figures as their equivalent
state-of-the-art deep network models. We propose counter neurons as minimal
spiking neuron models which only require addition and comparison operations,
thus avoiding costly multiplications. We show how inference carried out in deep
counter networks converges to the same accuracy levels as are achieved with
state-of-the-art conventional networks. As their event-based style of
computation leads to reduced latency and sparse updates, counter networks are
ideally suited for efficient compact and low-power hardware implementation. We
present theory and training methods for counter networks, and demonstrate on
the MNIST benchmark that counter networks converge quickly, both in terms of
time and number of operations required, to state-of-the-art classification
accuracy.
|
[
"Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer"
] |
null | null |
1611.00710
| null | null |
http://arxiv.org/pdf/1611.00710v1
|
2016-11-02T18:22:33Z
|
2016-11-02T18:22:33Z
|
Deep counter networks for asynchronous event-based processing
|
Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models. We propose counter neurons as minimal spiking neuron models which only require addition and comparison operations, thus avoiding costly multiplications. We show how inference carried out in deep counter networks converges to the same accuracy levels as are achieved with state-of-the-art conventional networks. As their event-based style of computation leads to reduced latency and sparse updates, counter networks are ideally suited for efficient compact and low-power hardware implementation. We present theory and training methods for counter networks, and demonstrate on the MNIST benchmark that counter networks converge quickly, both in terms of time and number of operations required, to state-of-the-art classification accuracy.
|
[
"['Jonathan Binas' 'Giacomo Indiveri' 'Michael Pfeiffer']"
] |
cs.LG stat.ML
| null |
1611.00712
| null | null |
http://arxiv.org/pdf/1611.00712v3
|
2017-03-05T16:59:44Z
|
2016-11-02T18:25:40Z
|
The Concrete Distribution: A Continuous Relaxation of Discrete Random
Variables
|
The reparameterization trick enables optimizing large scale stochastic
computation graphs via gradient descent. The essence of the trick is to
refactor each stochastic node into a differentiable function of its parameters
and a random variable with fixed distribution. After refactoring, the gradients
of the loss propagated by the chain rule through the graph are low variance
unbiased estimators of the gradients of the expected loss. While many
continuous random variables have such reparameterizations, discrete random
variables lack useful reparameterizations due to the discontinuous nature of
discrete states. In this work we introduce Concrete random
variables---continuous relaxations of discrete random variables. The Concrete
distribution is a new family of distributions with closed form densities and a
simple reparameterization. Whenever a discrete stochastic node of a computation
graph can be refactored into a one-hot bit representation that is treated
continuously, Concrete stochastic nodes can be used with automatic
differentiation to produce low-variance biased gradients of objectives
(including objectives that depend on the log-probability of latent stochastic
nodes) on the corresponding discrete graph. We demonstrate the effectiveness of
Concrete relaxations on density estimation and structured prediction tasks
using neural networks.
|
[
"Chris J. Maddison, Andriy Mnih, Yee Whye Teh",
"['Chris J. Maddison' 'Andriy Mnih' 'Yee Whye Teh']"
] |
cs.LG cs.DC
| null |
1611.00714
| null | null |
http://arxiv.org/pdf/1611.00714v1
|
2016-11-02T18:27:53Z
|
2016-11-02T18:27:53Z
|
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex
Optimization
|
We propose a scalable method for semi-supervised (transductive) learning from
massive network-structured datasets. Our approach to semi-supervised learning
is based on representing the underlying hypothesis as a graph signal with small
total variation. Requiring a small total variation of the graph signal
representing the underlying hypothesis corresponds to the central smoothness
assumption that forms the basis for semi-supervised learning, i.e., input
points forming clusters have similar output values or labels. We formulate the
learning problem as a nonsmooth convex optimization problem which we solve by
appealing to Nesterovs optimal first-order method for nonsmooth optimization.
We also provide a message passing formulation of the learning method which
allows for a highly scalable implementation in big data frameworks.
|
[
"['Alexander Jung' 'Alfred O. Hero III' 'Alexandru Mara' 'Sabeur Aridhi']",
"Alexander Jung and Alfred O. Hero III and Alexandru Mara and Sabeur\n Aridhi"
] |
cs.LG
| null |
1611.0074
| null | null | null | null | null |
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of
Dimensionality: a Review
|
The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage.
|
[
"Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda,\n Qianli Liao"
] |
null | null |
1611.00740
| null | null |
http://arxiv.org/pdf/1611.00740v5
|
2017-02-04T09:10:41Z
|
2016-11-02T19:35:52Z
|
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of
Dimensionality: a Review
|
The paper characterizes classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
|
[
"['Tomaso Poggio' 'Hrushikesh Mhaskar' 'Lorenzo Rosasco' 'Brando Miranda'\n 'Qianli Liao']"
] |
quant-ph cs.LG
| null |
1611.0076
| null | null | null | null | null |
Quantum Laplacian Eigenmap
|
Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality
reduction in classical machine learning. We propose an efficient quantum
Laplacian eigenmap algorithm to exponentially speed up the original
counterparts. In our work, we demonstrate that the Hermitian chain product
proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be
applied to implement quantum Laplacian eigenmap algorithm. While classical
Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector
problem, our algorithm is able to exponentially speed up nonlinear
dimensionality reduction.
|
[
"Yiming Huang, Xiaoyu Li"
] |
null | null |
1611.00760
| null | null |
http://arxiv.org/pdf/1611.00760v1
|
2016-11-02T03:48:01Z
|
2016-11-02T03:48:01Z
|
Quantum Laplacian Eigenmap
|
Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning. We propose an efficient quantum Laplacian eigenmap algorithm to exponentially speed up the original counterparts. In our work, we demonstrate that the Hermitian chain product proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be applied to implement quantum Laplacian eigenmap algorithm. While classical Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector problem, our algorithm is able to exponentially speed up nonlinear dimensionality reduction.
|
[
"['Yiming Huang' 'Xiaoyu Li']"
] |
cs.LG cs.CV stat.ML
| null |
1611.008
| null | null | null | null | null |
Temporal Matrix Completion with Locally Linear Latent Factors for
Medical Applications
|
Regular medical records are useful for medical practitioners to analyze and
monitor patient health status especially for those with chronic disease, but
such records are usually incomplete due to unpunctuality and absence of
patients. In order to resolve the missing data problem over time, tensor-based
model is suggested for missing data imputation in recent papers because this
approach makes use of low rank tensor assumption for highly correlated data.
However, when the time intervals between records are long, the data correlation
is not high along temporal direction and such assumption is not valid. To
address this problem, we propose to decompose a matrix with missing data into
its latent factors. Then, the locally linear constraint is imposed on these
factors for matrix completion in this paper. By using a publicly available
dataset and two medical datasets collected from hospital, experimental results
show that the proposed algorithm achieves the best performance by comparing
with the existing methods.
|
[
"Frodo Kin Sun Chan, Andy J Ma, Pong C Yuen, Terry Cheuk-Fung Yip,\n Yee-Kit Tse, Vincent Wai-Sun Wong and Grace Lai-Hung Wong"
] |
null | null |
1611.00800
| null | null |
http://arxiv.org/pdf/1611.00800v1
|
2016-10-31T12:02:53Z
|
2016-10-31T12:02:53Z
|
Temporal Matrix Completion with Locally Linear Latent Factors for
Medical Applications
|
Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggested for missing data imputation in recent papers because this approach makes use of low rank tensor assumption for highly correlated data. However, when the time intervals between records are long, the data correlation is not high along temporal direction and such assumption is not valid. To address this problem, we propose to decompose a matrix with missing data into its latent factors. Then, the locally linear constraint is imposed on these factors for matrix completion in this paper. By using a publicly available dataset and two medical datasets collected from hospital, experimental results show that the proposed algorithm achieves the best performance by comparing with the existing methods.
|
[
"['Frodo Kin Sun Chan' 'Andy J Ma' 'Pong C Yuen' 'Terry Cheuk-Fung Yip'\n 'Yee-Kit Tse' 'Vincent Wai-Sun Wong' 'Grace Lai-Hung Wong']"
] |
cs.DS cs.LG
| null |
1611.00829
| null | null |
http://arxiv.org/pdf/1611.00829v2
|
2017-04-26T02:29:51Z
|
2016-11-02T22:38:32Z
|
Multidimensional Binary Search for Contextual Decision-Making
|
We consider a multidimensional search problem that is motivated by questions
in contextual decision-making, such as dynamic pricing and personalized
medicine. Nature selects a state from a $d$-dimensional unit ball and then
generates a sequence of $d$-dimensional directions. We are given access to the
directions, but not access to the state. After receiving a direction, we have
to guess the value of the dot product between the state and the direction. Our
goal is to minimize the number of times when our guess is more than $\epsilon$
away from the true answer. We construct a polynomial time algorithm that we
call Projected Volume achieving regret $O(d\log(d/\epsilon))$, which is optimal
up to a $\log d$ factor. The algorithm combines a volume cutting strategy with
a new geometric technique that we call cylindrification.
|
[
"['Ilan Lobel' 'Renato Paes Leme' 'Adrian Vladu']",
"Ilan Lobel, Renato Paes Leme, Adrian Vladu"
] |
stat.ML cs.CV cs.LG
| null |
1611.00838
| null | null |
http://arxiv.org/pdf/1611.00838v5
|
2019-07-18T05:34:20Z
|
2016-11-02T23:12:05Z
|
Initialization and Coordinate Optimization for Multi-way Matching
|
We consider the problem of consistently matching multiple sets of elements to
each other, which is a common task in fields such as computer vision. To solve
the underlying NP-hard objective, existing methods often relax or approximate
it, but end up with unsatisfying empirical performance due to a misaligned
objective. We propose a coordinate update algorithm that directly optimizes the
target objective. By using pairwise alignment information to build an
undirected graph and initializing the permutation matrices along the edges of
its Maximum Spanning Tree, our algorithm successfully avoids bad local optima.
Theoretically, with high probability our algorithm guarantees an optimal
solution under reasonable noise assumptions. Empirically, our algorithm
consistently and significantly outperforms existing methods on several
benchmark tasks on real datasets.
|
[
"['Da Tang' 'Tony Jebara']",
"Da Tang and Tony Jebara"
] |
cs.LG cs.CV cs.NE
| null |
1611.00847
| null | null |
http://arxiv.org/pdf/1611.00847v3
|
2016-11-14T14:10:41Z
|
2016-11-02T23:48:04Z
|
Deep Convolutional Neural Network Design Patterns
|
Recent research in the deep learning field has produced a plethora of new
architectures. At the same time, a growing number of groups are applying deep
learning to new applications. Some of these groups are likely to be composed of
inexperienced deep learning practitioners who are baffled by the dizzying array
of architecture choices and therefore opt to use an older architecture (i.e.,
Alexnet). Here we attempt to bridge this gap by mining the collective knowledge
contained in recent deep learning research to discover underlying principles
for designing neural network architectures. In addition, we describe several
architectural innovations, including Fractal of FractalNet network, Stagewise
Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt
files is available at https://github.com/iPhysicist/CNNDesignPatterns). We hope
others are inspired to build on our preliminary work.
|
[
"Leslie N. Smith and Nicholay Topin",
"['Leslie N. Smith' 'Nicholay Topin']"
] |
cs.LG cs.AI
| null |
1611.00862
| null | null |
http://arxiv.org/pdf/1611.00862v1
|
2016-11-03T02:28:53Z
|
2016-11-03T02:28:53Z
|
Quantile Reinforcement Learning
|
In reinforcement learning, the standard criterion to evaluate policies in a
state is the expectation of (discounted) sum of rewards. However, this
criterion may not always be suitable, we consider an alternative criterion
based on the notion of quantiles. In the case of episodic reinforcement
learning problems, we propose an algorithm based on stochastic approximation
with two timescales. We evaluate our proposition on a simple model of the TV
show, Who wants to be a millionaire.
|
[
"['Hugo Gilbert' 'Paul Weng']",
"Hugo Gilbert and Paul Weng"
] |
cs.AI cs.LG
| null |
1611.00873
| null | null |
http://arxiv.org/pdf/1611.00873v1
|
2016-11-03T03:53:41Z
|
2016-11-03T03:53:41Z
|
Extracting Actionability from Machine Learning Models by Sub-optimal
Deterministic Planning
|
A main focus of machine learning research has been improving the
generalization accuracy and efficiency of prediction models. Many models such
as SVM, random forest, and deep neural nets have been proposed and achieved
great success. However, what emerges as missing in many applications is
actionability, i.e., the ability to turn prediction results into actions. For
example, in applications such as customer relationship management, clinical
prediction, and advertisement, the users need not only accurate prediction, but
also actionable instructions which can transfer an input to a desirable goal
(e.g., higher profit repays, lower morbidity rates, higher ads hit rates).
Existing effort in deriving such actionable knowledge is few and limited to
simple action models which restricted to only change one attribute for each
action. The dilemma is that in many real applications those action models are
often more complex and harder to extract an optimal solution.
In this paper, we propose a novel approach that achieves actionability by
combining learning with planning, two core areas of AI. In particular, we
propose a framework to extract actionable knowledge from random forest, one of
the most widely used and best off-the-shelf classifiers. We formulate the
actionability problem to a sub-optimal action planning (SOAP) problem, which is
to find a plan to alter certain features of a given input so that the random
forest would yield a desirable output, while minimizing the total costs of
actions. Technically, the SOAP problem is formulated in the SAS+ planning
formalism, and solved using a Max-SAT based approach. Our experimental results
demonstrate the effectiveness and efficiency of the proposed approach on a
personal credit dataset and other benchmarks. Our work represents a new
application of automated planning on an emerging and challenging machine
learning paradigm.
|
[
"Qiang Lyu, Yixin Chen, Zhaorong Li, Zhicheng Cui, Ling Chen, Xing\n Zhang, Haihua Shen",
"['Qiang Lyu' 'Yixin Chen' 'Zhaorong Li' 'Zhicheng Cui' 'Ling Chen'\n 'Xing Zhang' 'Haihua Shen']"
] |
cs.DS cs.CC cs.LG
| null |
1611.00898
| null | null |
http://arxiv.org/pdf/1611.00898v2
|
2020-04-16T13:57:43Z
|
2016-11-03T07:13:20Z
|
Low Rank Approximation with Entrywise $\ell_1$-Norm Error
|
We study the $\ell_1$-low rank approximation problem, where for a given $n
\times d$ matrix $A$ and approximation factor $\alpha \geq 1$, the goal is to
output a rank-$k$ matrix $\widehat{A}$ for which
$$\|A-\widehat{A}\|_1 \leq \alpha \cdot \min_{\textrm{rank-}k\textrm{
matrices}~A'}\|A-A'\|_1,$$ where for an $n \times d$ matrix $C$, we let
$\|C\|_1 = \sum_{i=1}^n \sum_{j=1}^d |C_{i,j}|$. This error measure is known to
be more robust than the Frobenius norm in the presence of outliers and is
indicated in models where Gaussian assumptions on the noise may not apply. The
problem was shown to be NP-hard by Gillis and Vavasis and a number of
heuristics have been proposed. It was asked in multiple places if there are any
approximation algorithms.
We give the first provable approximation algorithms for $\ell_1$-low rank
approximation, showing that it is possible to achieve approximation factor
$\alpha = (\log d) \cdot \mathrm{poly}(k)$ in $\mathrm{nnz}(A) + (n+d)
\mathrm{poly}(k)$ time, where $\mathrm{nnz}(A)$ denotes the number of non-zero
entries of $A$. If $k$ is constant, we further improve the approximation ratio
to $O(1)$ with a $\mathrm{poly}(nd)$-time algorithm. Under the Exponential Time
Hypothesis, we show there is no $\mathrm{poly}(nd)$-time algorithm achieving a
$(1+\frac{1}{\log^{1+\gamma}(nd)})$-approximation, for $\gamma > 0$ an
arbitrarily small constant, even when $k = 1$.
We give a number of additional results for $\ell_1$-low rank approximation:
nearly tight upper and lower bounds for column subset selection, CUR
decompositions, extensions to low rank approximation with respect to
$\ell_p$-norms for $1 \leq p < 2$ and earthmover distance, low-communication
distributed protocols and low-memory streaming algorithms, algorithms with
limited randomness, and bicriteria algorithms. We also give a preliminary
empirical evaluation.
|
[
"Zhao Song, David P. Woodruff, Peilin Zhong",
"['Zhao Song' 'David P. Woodruff' 'Peilin Zhong']"
] |
cs.DS cs.LG stat.ML
| null |
1611.00938
| null | null |
http://arxiv.org/pdf/1611.00938v2
|
2016-11-04T09:25:41Z
|
2016-11-03T10:08:22Z
|
Fast Eigenspace Approximation using Random Signals
|
We focus in this work on the estimation of the first $k$ eigenvectors of any
graph Laplacian using filtering of Gaussian random signals. We prove that we
only need $k$ such signals to be able to exactly recover as many of the
smallest eigenvectors, regardless of the number of nodes in the graph. In
addition, we address key issues in implementing the theoretical concepts in
practice using accurate approximated methods. We also propose fast algorithms
both for eigenspace approximation and for the determination of the $k$th
smallest eigenvalue $\lambda_k$. The latter proves to be extremely efficient
under the assumption of locally uniform distribution of the eigenvalue over the
spectrum. Finally, we present experiments which show the validity of our method
in practice and compare it to state-of-the-art methods for clustering and
visualization both on synthetic small-scale datasets and larger real-world
problems of millions of nodes. We show that our method allows a better scaling
with the number of nodes than all previous methods while achieving an almost
perfect reconstruction of the eigenspace formed by the first $k$ eigenvectors.
|
[
"['Johan Paratte' 'Lionel Martin']",
"Johan Paratte and Lionel Martin"
] |
stat.ML cs.LG q-bio.QM
|
10.1109/TCBB.2017.2684127
|
1611.00962
| null | null |
http://arxiv.org/abs/1611.00962v1
|
2016-11-03T11:40:59Z
|
2016-11-03T11:40:59Z
|
Multitask Protein Function Prediction Through Task Dissimilarity
|
Automated protein function prediction is a challenging problem with
distinctive features, such as the hierarchical organization of protein
functions and the scarcity of annotated proteins for most biological functions.
We propose a multitask learning algorithm addressing both issues. Unlike
standard multitask algorithms, which use task (protein functions) similarity
information as a bias to speed up learning, we show that dissimilarity
information enforces separation of rare class labels from frequent class
labels, and for this reason is better suited for solving unbalanced protein
function prediction problems. We support our claim by showing that a multitask
extension of the label propagation algorithm empirically works best when the
task relatedness information is represented using a dissimilarity matrix as
opposed to a similarity matrix. Moreover, the experimental comparison carried
out on three model organism shows that our method has a more stable performance
in both "protein-centric" and "function-centric" evaluation settings.
|
[
"Marco Frasca and Nicol\\`o Cesa Bianchi",
"['Marco Frasca' 'Nicolò Cesa Bianchi']"
] |
stat.ML cs.LG cs.NE physics.data-an stat.ME
| null |
1611.01046
| null | null |
http://arxiv.org/pdf/1611.01046v3
|
2017-06-01T19:04:01Z
|
2016-11-03T14:41:40Z
|
Learning to Pivot with Adversarial Networks
|
Several techniques for domain adaptation have been proposed to account for
differences in the distribution of the data used for training and testing. The
majority of this work focuses on a binary domain label. Similar problems occur
in a scientific context where there may be a continuous family of plausible
data generation processes associated to the presence of systematic
uncertainties. Robust inference is possible if it is based on a pivot -- a
quantity whose distribution does not depend on the unknown values of the
nuisance parameters that parametrize this family of data generation processes.
In this work, we introduce and derive theoretical results for a training
procedure based on adversarial networks for enforcing the pivotal property (or,
equivalently, fairness with respect to continuous attributes) on a predictive
model. The method includes a hyperparameter to control the trade-off between
accuracy and robustness. We demonstrate the effectiveness of this approach with
a toy example and examples from particle physics.
|
[
"Gilles Louppe, Michael Kagan, Kyle Cranmer",
"['Gilles Louppe' 'Michael Kagan' 'Kyle Cranmer']"
] |
cs.LG cs.GR cs.RO
|
10.1145/3099564.3099567
|
1611.01055
| null | null |
http://arxiv.org/abs/1611.01055v1
|
2016-11-03T15:15:00Z
|
2016-11-03T15:15:00Z
|
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space
Matter?
|
The use of deep reinforcement learning allows for high-dimensional state
descriptors, but little is known about how the choice of action representation
impacts the learning difficulty and the resulting performance. We compare the
impact of four different action parameterizations (torques, muscle-activations,
target joint angles, and target joint-angle velocities) in terms of learning
time, policy robustness, motion quality, and policy query rates. Our results
are evaluated on a gait-cycle imitation task for multiple planar articulated
figures and multiple gaits. We demonstrate that the local feedback provided by
higher-level action parameterizations can significantly impact the learning,
robustness, and quality of the resulting policies.
|
[
"Xue Bin Peng, Michiel van de Panne",
"['Xue Bin Peng' 'Michiel van de Panne']"
] |
cs.LG stat.ML
|
10.1016/j.ins.2016.07.076
|
1611.0106
| null | null | null | null | null |
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski
metric and a fast k -means initialisation
|
In this paper we make two novel contributions to hierarchical clustering.
First, we introduce an anomalous pattern initialisation method for hierarchical
clustering algorithms, called A-Ward, capable of substantially reducing the
time they take to converge. This method generates an initial partition with a
sufficiently large number of clusters. This allows the cluster merging process
to start from this partition rather than from a trivial partition composed
solely of singletons. Our second contribution is an extension of the Ward and
Ward p algorithms to the situation where the feature weight exponent can differ
from the exponent of the Minkowski distance. This new method, called A-Ward
p\b{eta} , is able to generate a much wider variety of clustering solutions. We
also demonstrate that its parameters can be estimated reasonably well by using
a cluster validity index. We perform numerous experiments using data sets with
two types of noise, insertion of noise features and blurring within-cluster
values of some features. These experiments allow us to conclude: (i) our
anomalous pattern initialisation method does indeed reduce the time a
hierarchical clustering algorithm takes to complete, without negatively
impacting its cluster recovery ability; (ii) A-Ward p\b{eta} provides better
cluster recovery than both Ward and Ward p.
|
[
"Renato Cordeiro de Amorim, Vladimir Makarenkov, Boris Mirkin"
] |
null | null |
1611.01060
| null | null |
http://arxiv.org/abs/1611.01060v1
|
2016-11-03T15:23:53Z
|
2016-11-03T15:23:53Z
|
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski
metric and a fast k -means initialisation
|
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start from this partition rather than from a trivial partition composed solely of singletons. Our second contribution is an extension of the Ward and Ward p algorithms to the situation where the feature weight exponent can differ from the exponent of the Minkowski distance. This new method, called A-Ward pb{eta} , is able to generate a much wider variety of clustering solutions. We also demonstrate that its parameters can be estimated reasonably well by using a cluster validity index. We perform numerous experiments using data sets with two types of noise, insertion of noise features and blurring within-cluster values of some features. These experiments allow us to conclude: (i) our anomalous pattern initialisation method does indeed reduce the time a hierarchical clustering algorithm takes to complete, without negatively impacting its cluster recovery ability; (ii) A-Ward pb{eta} provides better cluster recovery than both Ward and Ward p.
|
[
"['Renato Cordeiro de Amorim' 'Vladimir Makarenkov' 'Boris Mirkin']"
] |
stat.ME cs.LG math.ST stat.ML stat.TH
| null |
1611.01129
| null | null |
http://arxiv.org/pdf/1611.01129v2
|
2018-11-27T18:08:38Z
|
2016-11-03T19:02:02Z
|
Cross: Efficient Low-rank Tensor Completion
|
The completion of tensors, or high-order arrays, attracts significant
attention in recent research. Current literature on tensor completion primarily
focuses on recovery from a set of uniformly randomly measured entries, and the
required number of measurements to achieve recovery is not guaranteed to be
optimal. In addition, the implementation of some previous methods is NP-hard.
In this article, we propose a framework for low-rank tensor completion via a
novel tensor measurement scheme we name Cross. The proposed procedure is
efficient and easy to implement. In particular, we show that a third order
tensor of Tucker rank-$(r_1, r_2, r_3)$ in $p_1$-by-$p_2$-by-$p_3$ dimensional
space can be recovered from as few as $r_1r_2r_3 + r_1(p_1-r_1) + r_2(p_2-r_2)
+ r_3(p_3-r_3)$ noiseless measurements, which matches the sample complexity
lower-bound. In the case of noisy measurements, we also develop a theoretical
upper bound and the matching minimax lower bound for recovery error over
certain classes of low-rank tensors for the proposed procedure. The results can
be further extended to fourth or higher-order tensors. Simulation studies show
that the method performs well under a variety of settings. Finally, the
procedure is illustrated through a real dataset in neuroimaging.
|
[
"['Anru Zhang']",
"Anru Zhang"
] |
cs.LG cs.SY
| null |
1611.01142
| null | null |
http://arxiv.org/pdf/1611.01142v1
|
2016-11-03T19:46:19Z
|
2016-11-03T19:46:19Z
|
Using a Deep Reinforcement Learning Agent for Traffic Signal Control
|
Ensuring transportation systems are efficient is a priority for modern
society. Technological advances have made it possible for transportation
systems to collect large volumes of varied data on an unprecedented scale. We
propose a traffic signal control system which takes advantage of this new, high
quality data, with minimal abstraction compared to other proposed systems. We
apply modern deep reinforcement learning methods to build a truly adaptive
traffic signal control agent in the traffic microsimulator SUMO. We propose a
new state space, the discrete traffic state encoding, which is information
dense. The discrete traffic state encoding is used as input to a deep
convolutional neural network, trained using Q-learning with experience replay.
Our agent was compared against a one hidden layer neural network traffic signal
control agent and reduces average cumulative delay by 82%, average queue length
by 66% and average travel time by 20%.
|
[
"['Wade Genders' 'Saiedeh Razavi']",
"Wade Genders, Saiedeh Razavi"
] |
stat.ML cs.LG
| null |
1611.01144
| null | null |
http://arxiv.org/pdf/1611.01144v5
|
2017-08-05T22:45:19Z
|
2016-11-03T19:48:08Z
|
Categorical Reparameterization with Gumbel-Softmax
|
Categorical variables are a natural choice for representing discrete
structure in the world. However, stochastic neural networks rarely use
categorical latent variables due to the inability to backpropagate through
samples. In this work, we present an efficient gradient estimator that replaces
the non-differentiable sample from a categorical distribution with a
differentiable sample from a novel Gumbel-Softmax distribution. This
distribution has the essential property that it can be smoothly annealed into a
categorical distribution. We show that our Gumbel-Softmax estimator outperforms
state-of-the-art gradient estimators on structured output prediction and
unsupervised generative modeling tasks with categorical latent variables, and
enables large speedups on semi-supervised classification.
|
[
"['Eric Jang' 'Shixiang Gu' 'Ben Poole']",
"Eric Jang, Shixiang Gu, Ben Poole"
] |
cs.LG cs.CR stat.ML
| null |
1611.0117
| null | null | null | null | null |
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring
Numerical Optimizers
|
Safeguarding privacy in machine learning is highly desirable, especially in
collaborative studies across many organizations. Privacy-preserving distributed
machine learning (based on cryptography) is popular to solve the problem.
However, existing cryptographic protocols still incur excess computational
overhead. Here, we make a novel observation that this is partially due to naive
adoption of mainstream numerical optimization (e.g., Newton method) and failing
to tailor for secure computing. This work presents a contrasting perspective:
customizing numerical optimization specifically for secure settings. We propose
a seemingly less-favorable optimization method that can in fact significantly
accelerate privacy-preserving logistic regression. Leveraging this new method,
we propose two new secure protocols for conducting logistic regression in a
privacy-preserving and distributed manner. Extensive theoretical and empirical
evaluations prove the competitive performance of our two secure proposals while
without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x,
respectively, over state-of-the-art; and even faster as data scales up. Such
drastic speedup is on top of and in addition to performance improvements from
existing (and future) state-of-the-art cryptography. Our work provides a new
way towards efficient and practical privacy-preserving logistic regression for
large-scale studies which are common for modern science.
|
[
"Wei Xie, Yang Wang, Steven M. Boker, Donald E. Brown"
] |
null | null |
1611.01170
| null | null |
http://arxiv.org/pdf/1611.01170v1
|
2016-11-03T20:04:29Z
|
2016-11-03T20:04:29Z
|
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring
Numerical Optimizers
|
Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing. This work presents a contrasting perspective: customizing numerical optimization specifically for secure settings. We propose a seemingly less-favorable optimization method that can in fact significantly accelerate privacy-preserving logistic regression. Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner. Extensive theoretical and empirical evaluations prove the competitive performance of our two secure proposals while without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x, respectively, over state-of-the-art; and even faster as data scales up. Such drastic speedup is on top of and in addition to performance improvements from existing (and future) state-of-the-art cryptography. Our work provides a new way towards efficient and practical privacy-preserving logistic regression for large-scale studies which are common for modern science.
|
[
"['Wei Xie' 'Yang Wang' 'Steven M. Boker' 'Donald E. Brown']"
] |
cs.NE cs.LG stat.ML
| null |
1611.01186
| null | null |
http://arxiv.org/pdf/1611.01186v2
|
2017-05-20T10:18:06Z
|
2016-11-03T20:55:49Z
|
Demystifying ResNet
|
The Residual Network (ResNet), proposed in He et al. (2015), utilized
shortcut connections to significantly reduce the difficulty of training, which
resulted in great performance boosts in terms of both training and
generalization error.
It was empirically observed in He et al. (2015) that stacking more layers of
residual blocks with shortcut 2 results in smaller training error, while it is
not true for shortcut of length 1 or 3. We provide a theoretical explanation
for the uniqueness of shortcut 2.
We show that with or without nonlinearities, by adding shortcuts that have
depth two, the condition number of the Hessian of the loss function at the zero
initial point is depth-invariant, which makes training very deep models no more
difficult than shallow ones. Shortcuts of higher depth result in an extremely
flat (high-order) stationary point initially, from which the optimization
algorithm is hard to escape. The shortcut 1, however, is essentially equivalent
to no shortcuts, which has a condition number exploding to infinity as the
number of layers grows. We further argue that as the number of layers tends to
infinity, it suffices to only look at the loss function at the zero initial
point.
Extensive experiments are provided accompanying our theoretical results. We
show that initializing the network to small weights with shortcut 2 achieves
significantly better results than random Gaussian (Xavier) initialization,
orthogonal initialization, and shortcuts of deeper depth, from various
perspectives ranging from final loss, learning dynamics and stability, to the
behavior of the Hessian along the learning process.
|
[
"Sihan Li, Jiantao Jiao, Yanjun Han, Tsachy Weissman",
"['Sihan Li' 'Jiantao Jiao' 'Yanjun Han' 'Tsachy Weissman']"
] |
cs.CC cs.CR cs.DS cs.LG
| null |
1611.0119
| null | null | null | null | null |
Conspiracies between Learning Algorithms, Circuit Lower Bounds and
Pseudorandomness
|
We prove several results giving new and stronger connections between
learning, circuit lower bounds and pseudorandomness. Among other results, we
show a generic learning speedup lemma, equivalences between various learning
models in the exponential time and subexponential time regimes, a dichotomy
between learning and pseudorandomness, consequences of non-trivial learning for
circuit lower bounds, Karp-Lipton theorems for probabilistic exponential time,
and NC$^1$-hardness for the Minimum Circuit Size Problem.
|
[
"Igor C. Oliveira, Rahul Santhanam"
] |
null | null |
1611.01190
| null | null |
http://arxiv.org/pdf/1611.01190v1
|
2016-11-03T21:08:38Z
|
2016-11-03T21:08:38Z
|
Conspiracies between Learning Algorithms, Circuit Lower Bounds and
Pseudorandomness
|
We prove several results giving new and stronger connections between learning, circuit lower bounds and pseudorandomness. Among other results, we show a generic learning speedup lemma, equivalences between various learning models in the exponential time and subexponential time regimes, a dichotomy between learning and pseudorandomness, consequences of non-trivial learning for circuit lower bounds, Karp-Lipton theorems for probabilistic exponential time, and NC$^1$-hardness for the Minimum Circuit Size Problem.
|
[
"['Igor C. Oliveira' 'Rahul Santhanam']"
] |
cs.LG cs.NE stat.ML
| null |
1611.01211
| null | null |
http://arxiv.org/pdf/1611.01211v8
|
2018-03-13T21:24:47Z
|
2016-11-03T22:30:10Z
|
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
|
Many practical environments contain catastrophic states that an optimal agent
would visit infrequently or never. Even on toy problems, Deep Reinforcement
Learning (DRL) agents tend to periodically revisit these states upon forgetting
their existence under a new policy. We introduce intrinsic fear (IF), a learned
reward shaping that guards DRL agents against periodic catastrophes. IF agents
possess a fear model trained to predict the probability of imminent
catastrophe. This score is then used to penalize the Q-learning objective. Our
theoretical analysis bounds the reduction in average return due to learning on
the perturbed objective. We also prove robustness to classification errors. As
a bonus, IF models tend to learn faster, owing to reward shaping. Experiments
demonstrate that intrinsic-fear DQNs solve otherwise pathological environments
and improve on several Atari games.
|
[
"['Zachary C. Lipton' 'Kamyar Azizzadenesheli' 'Abhishek Kumar' 'Lihong Li'\n 'Jianfeng Gao' 'Li Deng']",
"Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li,\n Jianfeng Gao, Li Deng"
] |
cs.LG
| null |
1611.01224
| null | null |
http://arxiv.org/pdf/1611.01224v2
|
2017-07-10T14:38:10Z
|
2016-11-03T23:21:32Z
|
Sample Efficient Actor-Critic with Experience Replay
|
This paper presents an actor-critic deep reinforcement learning agent with
experience replay that is stable, sample efficient, and performs remarkably
well on challenging environments, including the discrete 57-game Atari domain
and several continuous control problems. To achieve this, the paper introduces
several innovations, including truncated importance sampling with bias
correction, stochastic dueling network architectures, and a new trust region
policy optimization method.
|
[
"Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos,\n Koray Kavukcuoglu, Nando de Freitas",
"['Ziyu Wang' 'Victor Bapst' 'Nicolas Heess' 'Volodymyr Mnih' 'Remi Munos'\n 'Koray Kavukcuoglu' 'Nando de Freitas']"
] |
stat.ML cs.LG
| null |
1611.01232
| null | null |
http://arxiv.org/pdf/1611.01232v2
|
2017-04-04T19:36:14Z
|
2016-11-04T00:44:32Z
|
Deep Information Propagation
|
We study the behavior of untrained neural networks whose weights and biases
are randomly distributed using mean field theory. We show the existence of
depth scales that naturally limit the maximum depth of signal propagation
through these random networks. Our main practical result is to show that random
networks may be trained precisely when information can travel through them.
Thus, the depth scales that we identify provide bounds on how deep a network
may be trained for a specific choice of hyperparameters. As a corollary to
this, we argue that in networks at the edge of chaos, one of these depth scales
diverges. Thus arbitrarily deep networks may be trained only sufficiently close
to criticality. We show that the presence of dropout destroys the
order-to-chaos critical point and therefore strongly limits the maximum
trainable depth for random networks. Finally, we develop a mean field theory
for backpropagation and we show that the ordered and chaotic phases correspond
to regions of vanishing and exploding gradient respectively.
|
[
"Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli and Jascha\n Sohl-Dickstein",
"['Samuel S. Schoenholz' 'Justin Gilmer' 'Surya Ganguli'\n 'Jascha Sohl-Dickstein']"
] |
cs.CV cs.CR cs.LG stat.ML
| null |
1611.01236
| null | null |
http://arxiv.org/pdf/1611.01236v2
|
2017-02-11T00:15:46Z
|
2016-11-04T01:11:02Z
|
Adversarial Machine Learning at Scale
|
Adversarial examples are malicious inputs designed to fool machine learning
models. They often transfer from one model to another, allowing attackers to
mount black box attacks without knowledge of the target model's parameters.
Adversarial training is the process of explicitly training a model on
adversarial examples, in order to make it more robust to attack or to reduce
its test error on clean inputs. So far, adversarial training has primarily been
applied to small problems. In this research, we apply adversarial training to
ImageNet. Our contributions include: (1) recommendations for how to succesfully
scale adversarial training to large models and datasets, (2) the observation
that adversarial training confers robustness to single-step attack methods, (3)
the finding that multi-step attack methods are somewhat less transferable than
single-step attack methods, so single-step attacks are the best for mounting
black-box attacks, and (4) resolution of a "label leaking" effect that causes
adversarially trained models to perform better on adversarial examples than on
clean examples, because the adversarial example construction process uses the
true label and the model can learn to exploit regularities in the construction
process.
|
[
"['Alexey Kurakin' 'Ian Goodfellow' 'Samy Bengio']",
"Alexey Kurakin, Ian Goodfellow, Samy Bengio"
] |
stat.ML cs.LG
| null |
1611.01239
| null | null |
http://arxiv.org/pdf/1611.01239v1
|
2016-11-04T01:46:47Z
|
2016-11-04T01:46:47Z
|
Reparameterization trick for discrete variables
|
Low-variance gradient estimation is crucial for learning directed graphical
models parameterized by neural networks, where the reparameterization trick is
widely used for those with continuous variables. While this technique gives
low-variance gradient estimates, it has not been directly applicable to
discrete variables, the sampling of which inherently requires discontinuous
operations. We argue that the discontinuity can be bypassed by marginalizing
out the variable of interest, which results in a new reparameterization trick
for discrete variables. This reparameterization greatly reduces the variance,
which is understood by regarding the method as an application of common random
numbers to the estimation. The resulting estimator is theoretically guaranteed
to have a variance not larger than that of the likelihood-ratio method with the
optimal input-dependent baseline. We give empirical results for variational
learning of sigmoid belief networks.
|
[
"['Seiya Tokui' 'Issei sato']",
"Seiya Tokui and Issei sato"
] |
cs.LG cs.CL cs.DS cs.IR
| null |
1611.01259
| null | null |
http://arxiv.org/pdf/1611.01259v1
|
2016-11-04T03:45:03Z
|
2016-11-04T03:45:03Z
|
Generalized Topic Modeling
|
Recently there has been significant activity in developing algorithms with
provable guarantees for topic modeling. In standard topic models, a topic (such
as sports, business, or politics) is viewed as a probability distribution $\vec
a_i$ over words, and a document is generated by first selecting a mixture $\vec
w$ over topics, and then generating words i.i.d. from the associated mixture
$A{\vec w}$. Given a large collection of such documents, the goal is to recover
the topic vectors and then to correctly classify new documents according to
their topic mixture.
In this work we consider a broad generalization of this framework in which
words are no longer assumed to be drawn i.i.d. and instead a topic is a complex
distribution over sequences of paragraphs. Since one could not hope to even
represent such a distribution in general (even if paragraphs are given using
some natural feature representation), we aim instead to directly learn a
document classifier. That is, we aim to learn a predictor that given a new
document, accurately predicts its topic mixture, without learning the
distributions explicitly. We present several natural conditions under which one
can do this efficiently and discuss issues such as noise tolerance and sample
complexity in this model. More generally, our model can be viewed as a
generalization of the multi-view or co-training setting in machine learning.
|
[
"Avrim Blum, Nika Haghtalab",
"['Avrim Blum' 'Nika Haghtalab']"
] |
cs.CV cs.LG
| null |
1611.0126
| null | null | null | null | null |
Learning Identity Mappings with Residual Gates
|
We propose a new layer design by adding a linear gating mechanism to shortcut
connections. By using a scalar parameter to control each gate, we provide a way
to learn identity mappings by optimizing only one parameter. We build upon the
motivation behind Residual Networks, where a layer is reformulated in order to
make learning identity mappings less problematic to the optimizer. The
augmentation introduces only one extra parameter per layer, and provides easier
optimization by making degeneration into identity mappings simpler. We propose
a new model, the Gated Residual Network, which is the result when augmenting
Residual Networks. Experimental results show that augmenting layers provides
better optimization, increased performance, and more layer independence. We
evaluate our method on MNIST using fully-connected networks, showing empirical
indications that our augmentation facilitates the optimization of deep models,
and that it provides high tolerance to full layer removal: the model retains
over 90% of its performance even after half of its layers have been randomly
removed. We also evaluate our model on CIFAR-10 and CIFAR-100 using Wide Gated
ResNets, achieving 3.65% and 18.27% error, respectively.
|
[
"Pedro H. P. Savarese and Leonardo O. Mazza and Daniel R. Figueiredo"
] |
null | null |
1611.01260
| null | null |
http://arxiv.org/pdf/1611.01260v2
|
2016-12-29T01:36:47Z
|
2016-11-04T04:34:38Z
|
Learning Identity Mappings with Residual Gates
|
We propose a new layer design by adding a linear gating mechanism to shortcut connections. By using a scalar parameter to control each gate, we provide a way to learn identity mappings by optimizing only one parameter. We build upon the motivation behind Residual Networks, where a layer is reformulated in order to make learning identity mappings less problematic to the optimizer. The augmentation introduces only one extra parameter per layer, and provides easier optimization by making degeneration into identity mappings simpler. We propose a new model, the Gated Residual Network, which is the result when augmenting Residual Networks. Experimental results show that augmenting layers provides better optimization, increased performance, and more layer independence. We evaluate our method on MNIST using fully-connected networks, showing empirical indications that our augmentation facilitates the optimization of deep models, and that it provides high tolerance to full layer removal: the model retains over 90% of its performance even after half of its layers have been randomly removed. We also evaluate our model on CIFAR-10 and CIFAR-100 using Wide Gated ResNets, achieving 3.65% and 18.27% error, respectively.
|
[
"['Pedro H. P. Savarese' 'Leonardo O. Mazza' 'Daniel R. Figueiredo']"
] |
cs.LG cs.NE
| null |
1611.01268
| null | null |
http://arxiv.org/pdf/1611.01268v1
|
2016-11-04T05:52:17Z
|
2016-11-04T05:52:17Z
|
Semantic Noise Modeling for Better Representation Learning
|
Latent representation learned from multi-layered neural networks via
hierarchical feature abstraction enables recent success of deep learning. Under
the deep learning framework, generalization performance highly depends on the
learned latent representation which is obtained from an appropriate training
scenario with a task-specific objective on a designed network model. In this
work, we propose a novel latent space modeling method to learn better latent
representation. We designed a neural network model based on the assumption that
good base representation can be attained by maximizing the total correlation
between the input, latent, and output variables. From the base model, we
introduce a semantic noise modeling method which enables class-conditional
perturbation on latent space to enhance the representational power of learned
latent feature. During training, latent vector representation can be
stochastically perturbed by a modeled class-conditional additive noise while
maintaining its original semantic feature. It implicitly brings the effect of
semantic augmentation on the latent space. The proposed model can be easily
learned by back-propagation with common gradient-based optimization algorithms.
Experimental results show that the proposed method helps to achieve performance
benefits against various previous approaches. We also provide the empirical
analyses for the proposed class-conditional perturbation process including
t-SNE visualization.
|
[
"Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho",
"['Hyo-Eun Kim' 'Sangheum Hwang' 'Kyunghyun Cho']"
] |
cs.LG
| null |
1611.01276
| null | null |
http://arxiv.org/pdf/1611.01276v1
|
2016-11-04T07:09:03Z
|
2016-11-04T07:09:03Z
|
A Communication-Efficient Parallel Algorithm for Decision Tree
|
Decision tree (and its extensions such as Gradient Boosting Decision Trees
and Random Forest) is a widely used machine learning algorithm, due to its
practical effectiveness and model interpretability. With the emergence of big
data, there is an increasing need to parallelize the training process of
decision tree. However, most existing attempts along this line suffer from high
communication costs. In this paper, we propose a new algorithm, called
\emph{Parallel Voting Decision Tree (PV-Tree)}, to tackle this challenge. After
partitioning the training data onto a number of (e.g., $M$) machines, this
algorithm performs both local voting and global voting in each iteration. For
local voting, the top-$k$ attributes are selected from each machine according
to its local data. Then, globally top-$2k$ attributes are determined by a
majority voting among these local candidates. Finally, the full-grained
histograms of the globally top-$2k$ attributes are collected from local
machines in order to identify the best (most informative) attribute and its
split point. PV-Tree can achieve a very low communication cost (independent of
the total number of attributes) and thus can scale out very well. Furthermore,
theoretical analysis shows that this algorithm can learn a near optimal
decision tree, since it can find the best attribute with a large probability.
Our experiments on real-world datasets show that PV-Tree significantly
outperforms the existing parallel decision tree algorithms in the trade-off
between accuracy and efficiency.
|
[
"Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma and\n Tie-Yan Liu",
"['Qi Meng' 'Guolin Ke' 'Taifeng Wang' 'Wei Chen' 'Qiwei Ye' 'Zhi-Ming Ma'\n 'Tie-Yan Liu']"
] |
stat.ML cs.LG stat.CO
| null |
1611.01353
| null | null |
http://arxiv.org/pdf/1611.01353v3
|
2017-02-12T09:26:25Z
|
2016-11-04T12:46:37Z
|
Information Dropout: Learning Optimal Representations Through Noisy
Computation
|
The cross-entropy loss commonly used in deep learning is closely related to
the defining properties of optimal representations, but does not enforce some
of the key properties. We show that this can be solved by adding a
regularization term, which is in turn related to injecting multiplicative noise
in the activations of a Deep Neural Network, a special case of which is the
common practice of dropout. We show that our regularized loss function can be
efficiently minimized using Information Dropout, a generalization of dropout
rooted in information theoretic principles that automatically adapts to the
data and can better exploit architectures of limited capacity. When the task is
the reconstruction of the input, we show that our loss function yields a
Variational Autoencoder as a special case, thus providing a link between
representation learning, information theory and variational inference. Finally,
we prove that we can promote the creation of disentangled representations
simply by enforcing a factorized prior, a fact that has been observed
empirically in recent work. Our experiments validate the theoretical intuitions
behind our method, and we find that information dropout achieves a comparable
or better generalization performance than binary dropout, especially on smaller
models, since it can automatically adapt the noise to the structure of the
network, as well as to the test sample.
|
[
"Alessandro Achille, Stefano Soatto",
"['Alessandro Achille' 'Stefano Soatto']"
] |
cs.IR cs.CL cs.DL cs.LG cs.SI
| null |
1611.014
| null | null | null | null | null |
Learning to Rank Scientific Documents from the Crowd
|
Finding related published articles is an important task in any science, but
with the explosion of new work in the biomedical domain it has become
especially challenging. Most existing methodologies use text similarity metrics
to identify whether two articles are related or not. However biomedical
knowledge discovery is hypothesis-driven. The most related articles may not be
ones with the highest text similarities. In this study, we first develop an
innovative crowd-sourcing approach to build an expert-annotated
document-ranking corpus. Using this corpus as the gold standard, we then
evaluate the approaches of using text similarity to rank the relatedness of
articles. Finally, we develop and evaluate a new supervised model to
automatically rank related scientific articles. Our results show that authors'
ranking differ significantly from rankings by text-similarity-based models. By
training a learning-to-rank model on a subset of the annotated corpus, we found
the best supervised learning-to-rank model (SVM-Rank) significantly surpassed
state-of-the-art baseline systems.
|
[
"Jesse M Lingeman, Hong Yu"
] |
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