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Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep
Learning
|
cs.CR cs.LG stat.ML
|
In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the output; and (3) It could be applied in a variety of different
deep neural networks. To achieve this, we figure out a way to perturb affine
transformations of neurons, and loss functions used in deep neural networks. In
addition, our mechanism intentionally adds "more noise" into features which are
"less relevant" to the model output, and vice-versa. Our theoretical analysis
further derives the sensitivities and error bounds of our mechanism. Rigorous
experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
highly effective and outperforms existing solutions.
|
NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou
| null |
1709.0575
| null | null |
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model
for Short Text Multi-class Classification Problems
|
cs.CL cs.LG
|
The bag-of-words model is a standard representation of text for many linear
classifier learners. In many problem domains, linear classifiers are preferred
over more complex models due to their efficiency, robustness and
interpretability, and the bag-of-words text representation can capture
sufficient information for linear classifiers to make highly accurate
predictions. However in settings where there is a large vocabulary, large
variance in the frequency of terms in the training corpus, many classes and
very short text (e.g., single sentences or document titles) the bag-of-words
representation becomes extremely sparse, and this can reduce the accuracy of
classifiers. A particular issue in such settings is that short texts tend to
contain infrequently occurring or rare terms which lack class-conditional
evidence. In this work we introduce a method for enriching the bag-of-words
model by complementing such rare term information with related terms from both
general and domain-specific Word Vector models. By reducing sparseness in the
bag-of-words models, our enrichment approach achieves improved classification
over several baseline classifiers in a variety of text classification problems.
Our approach is also efficient because it requires no change to the linear
classifier before or during training, since bag-of-words enrichment applies
only to text being classified.
|
Bradford Heap, Michael Bain, Wayne Wobcke, Alfred Krzywicki and
Susanne Schmeidl
| null |
1709.05778
| null | null |
Minimal Effort Back Propagation for Convolutional Neural Networks
|
cs.LG cs.NE stat.ML
|
As traditional neural network consumes a significant amount of computing
resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet
effective technique to alleviate this problem. In this technique, only a small
subset of the full gradients are computed to update the model parameters. In
this paper we extend this technique into the Convolutional Neural Network(CNN)
to reduce calculation in back propagation, and the surprising results verify
its validity in CNN: only 5\% of the gradients are passed back but the model
still achieves the same effect as the traditional CNN, or even better. We also
show that the top-$k$ selection of gradients leads to a sparse calculation in
back propagation, which may bring significant computational benefits for high
computational complexity of convolution operation in CNN.
|
Bingzhen Wei, Xu Sun, Xuancheng Ren, Jingjing Xu
| null |
1709.05804
| null | null |
Autoencoder-Driven Weather Clustering for Source Estimation during
Nuclear Events
|
cs.LG
|
Emergency response applications for nuclear or radiological events can be
significantly improved via deep feature learning due to the hidden complexity
of the data and models involved. In this paper we present a novel methodology
for rapid source estimation during radiological releases based on deep feature
extraction and weather clustering. Atmospheric dispersions are then calculated
based on identified predominant weather patterns and are matched against
simulated incidents indicated by radiation readings on the ground. We evaluate
the accuracy of our methods over multiple years of weather reanalysis data in
the European region. We juxtapose these results with deep classification
convolution networks and discuss advantages and disadvantages.
|
I. A. Klampanos, A. Davvetas, S. Andronopoulos, C. Pappas, A.
Ikonomopoulos and V. Karkaletsis
|
10.1016/j.envsoft.2018.01.014
|
1709.0584
| null | null |
Neonatal Seizure Detection using Convolutional Neural Networks
|
stat.ML cs.LG
|
This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.
|
Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
| null |
1709.05849
| null | null |
Continuous Multimodal Emotion Recognition Approach for AVEC 2017
|
cs.CV cs.LG cs.MM
|
This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodings of SIFT (Scale-Invariant Feature Transform) features based on
Gaussian mixture model (GMM) and some pretrained Convolutional Neural Network
layers as features; all these extracted for each video clip. For audio features
we used the Bag-of-audio-words (BoAW) representation of the LLDs (low-level
descriptors) generated by openXBOW provided by the organisers of the event.
Then we trained fully connected neural network regression model on the dataset
for all these different modalities. We applied multimodal fusion on the output
models to get the Concordance correlation coefficient on Development set as
well as Test set.
|
Narotam Singh (1), Nittin Singh (1), Abhinav Dhall (1) ((1) Indian
Institute of Technology Ropar)
| null |
1709.05861
| null | null |
Depression Scale Recognition from Audio, Visual and Text Analysis
|
cs.CV cs.LG cs.MM
|
Depression is a major mental health disorder that is rapidly affecting lives
worldwide. Depression not only impacts emotional but also physical and
psychological state of the person. Its symptoms include lack of interest in
daily activities, feeling low, anxiety, frustration, loss of weight and even
feeling of self-hatred. This report describes work done by us for Audio Visual
Emotion Challenge (AVEC) 2017 during our second year BTech summer internship.
With the increase in demand to detect depression automatically with the help of
machine learning algorithms, we present our multimodal feature extraction and
decision level fusion approach for the same. Features are extracted by
processing on the provided Distress Analysis Interview Corpus-Wizard of Oz
(DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector
approach were applied on the visual data; statistical descriptors on gaze,
pose; low level audio features and head pose and text features were also
extracted. Classification is done on fused as well as independent features
using Support Vector Machine (SVM) and neural networks. The results obtained
were able to cross the provided baseline on validation data set by 17% on audio
features and 24.5% on video features.
|
Shubham Dham, Anirudh Sharma, Abhinav Dhall
| null |
1709.05865
| null | null |
ZhuSuan: A Library for Bayesian Deep Learning
|
stat.ML cs.AI cs.LG cs.NE stat.CO
|
In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.
|
Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong
Gu, Yuhao Zhou
| null |
1709.0587
| null | null |
Institutionally Distributed Deep Learning Networks
|
cs.CV cs.LG physics.med-ph
|
Deep learning has become a promising approach for automated medical
diagnoses. When medical data samples are limited, collaboration among multiple
institutions is necessary to achieve high algorithm performance. However,
sharing patient data often has limitations due to technical, legal, or ethical
concerns. In such cases, sharing a deep learning model is a more attractive
alternative. The best method of performing such a task is unclear, however. In
this study, we simulate the dissemination of learning deep learning network
models across four institutions using various heuristics and compare the
results with a deep learning model trained on centrally hosted patient data.
The heuristics investigated include ensembling single institution models,
single weight transfer, and cyclical weight transfer. We evaluated these
approaches for image classification in three independent image collections
(retinal fundus photos, mammography, and ImageNet). We find that cyclical
weight transfer resulted in a performance (testing accuracy = 77.3%) that was
closest to that of centrally hosted patient data (testing accuracy = 78.7%). We
also found that there is an improvement in the performance of cyclical weight
transfer heuristic with high frequency of weight transfer.
|
Ken Chang, Niranjan Balachandar, Carson K Lam, Darvin Yi, James M
Brown, Andrew Beers, Bruce R Rosen, Daniel L Rubin, Jayashree Kalpathy-Cramer
| null |
1709.05929
| null | null |
Machine learning approximation algorithms for high-dimensional fully
nonlinear partial differential equations and second-order backward stochastic
differential equations
|
math.NA cs.LG cs.NE math.PR stat.ML
|
High-dimensional partial differential equations (PDE) appear in a number of
models from the financial industry, such as in derivative pricing models,
credit valuation adjustment (CVA) models, or portfolio optimization models. The
PDEs in such applications are high-dimensional as the dimension corresponds to
the number of financial assets in a portfolio. Moreover, such PDEs are often
fully nonlinear due to the need to incorporate certain nonlinear phenomena in
the model such as default risks, transaction costs, volatility uncertainty
(Knightian uncertainty), or trading constraints in the model. Such
high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the
computational effort for standard approximation methods grows exponentially
with the dimension. In this work we propose a new method for solving
high-dimensional fully nonlinear second-order PDEs. Our method can in
particular be used to sample from high-dimensional nonlinear expectations. The
method is based on (i) a connection between fully nonlinear second-order PDEs
and second-order backward stochastic differential equations (2BSDEs), (ii) a
merged formulation of the PDE and the 2BSDE problem, (iii) a temporal forward
discretization of the 2BSDE and a spatial approximation via deep neural nets,
and (iv) a stochastic gradient descent-type optimization procedure. Numerical
results obtained using ${\rm T{\small ENSOR}F{\small LOW}}$ in ${\rm P{\small
YTHON}}$ illustrate the efficiency and the accuracy of the method in the cases
of a $100$-dimensional Black-Scholes-Barenblatt equation, a $100$-dimensional
Hamilton-Jacobi-Bellman equation, and a nonlinear expectation of a $ 100
$-dimensional $ G $-Brownian motion.
|
Christian Beck, Weinan E, and Arnulf Jentzen
|
10.1007/s00332-018-9525-3
|
1709.05963
| null | null |
Why Pay More When You Can Pay Less: A Joint Learning Framework for
Active Feature Acquisition and Classification
|
cs.LG stat.ML
|
We consider the problem of active feature acquisition, where we sequentially
select the subset of features in order to achieve the maximum prediction
performance in the most cost-effective way. In this work, we formulate this
active feature acquisition problem as a reinforcement learning problem, and
provide a novel framework for jointly learning both the RL agent and the
classifier (environment). We also introduce a more systematic way of encoding
subsets of features that can properly handle innate challenge with missing
entries in active feature acquisition problems, that uses the orderless
LSTM-based set encoding mechanism that readily fits in the joint learning
framework. We evaluate our model on a carefully designed synthetic dataset for
the active feature acquisition as well as several real datasets such as
electric health record (EHR) datasets, on which it outperforms all baselines in
terms of prediction performance as well feature acquisition cost.
|
Hajin Shim, Sung Ju Hwang, Eunho Yang
| null |
1709.05964
| null | null |
Leveraging Distributional Semantics for Multi-Label Learning
|
cs.LG cs.AI
|
We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely used to learn word embeddings for natural language processing
tasks. Learning such embeddings can be reduced to a certain matrix
factorization. Our approach is novel in that it highlights interesting
connections between label embedding methods used for multi-label learning and
paragraph/document embedding methods commonly used for learning representations
of text data. The framework can also be easily extended to incorporate
auxiliary information such as label-label correlations; this is crucial
especially when there are a lot of missing labels in the training data. We
demonstrate the effectiveness of our approach through an extensive set of
experiments on a variety of benchmark datasets, and show that the proposed
learning methods perform favorably compared to several baselines and
state-of-the-art methods for large-scale multi-label learning. To facilitate
end-to-end learning, we develop a joint learning algorithm that can learn the
embeddings as well as a regression model that predicts these embeddings given
input features, via efficient gradient-based methods.
|
Rahul Wadbude, Vivek Gupta, Piyush Rai, Nagarajan Natarajan, Harish
Karnick, Prateek Jain
| null |
1709.05976
| null | null |
Revisiting the Arcade Learning Environment: Evaluation Protocols and
Open Problems for General Agents
|
cs.LG
|
The Arcade Learning Environment (ALE) is an evaluation platform that poses
the challenge of building AI agents with general competency across dozens of
Atari 2600 games. It supports a variety of different problem settings and it
has been receiving increasing attention from the scientific community, leading
to some high-profile success stories such as the much publicized Deep
Q-Networks (DQN). In this article we take a big picture look at how the ALE is
being used by the research community. We show how diverse the evaluation
methodologies in the ALE have become with time, and highlight some key concerns
when evaluating agents in the ALE. We use this discussion to present some
methodological best practices and provide new benchmark results using these
best practices. To further the progress in the field, we introduce a new
version of the ALE that supports multiple game modes and provides a form of
stochasticity we call sticky actions. We conclude this big picture look by
revisiting challenges posed when the ALE was introduced, summarizing the
state-of-the-art in various problems and highlighting problems that remain
open.
|
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness,
Matthew Hausknecht, Michael Bowling
| null |
1709.06009
| null | null |
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
|
cs.DS cs.LG stat.ML
|
We give a polynomial-time algorithm for learning neural networks with one
layer of sigmoids feeding into any Lipschitz, monotone activation function
(e.g., sigmoid or ReLU). We make no assumptions on the structure of the
network, and the algorithm succeeds with respect to {\em any} distribution on
the unit ball in $n$ dimensions (hidden weight vectors also have unit norm).
This is the first assumption-free, provably efficient algorithm for learning
neural networks with two nonlinear layers.
Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that
combines isotonic regression with kernel methods. It outputs a hypothesis that
yields efficient oracle access to interpretable features. It also suggests a
new approach to Boolean learning problems via real-valued conditional-mean
functions, sidestepping traditional hardness results from computational
learning theory.
Along these lines, we subsume and improve many longstanding results for PAC
learning Boolean functions to the more general, real-valued setting of {\em
probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d.
noise-tolerance.
|
Surbhi Goel and Adam Klivans
| null |
1709.0601
| null | null |
Guided Deep Reinforcement Learning for Swarm Systems
|
cs.MA cs.AI cs.LG cs.SY stat.ML
|
In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a policy for a group of agents is difficult due to distributed partial
observability of the state. Here, we follow a guided approach where a critic
has central access to the global state during learning, which simplifies the
policy evaluation problem from a reinforcement learning point of view. For
example, we can get the positions of all robots of the swarm using a camera
image of a scene. This camera image is only available to the critic and not to
the control policies of the robots. We follow an actor-critic approach, where
the actors base their decisions only on locally sensed information. In
contrast, the critic is learned based on the true global state. Our algorithm
uses deep reinforcement learning to approximate both the Q-function and the
policy. The performance of the algorithm is evaluated on two tasks with simple
simulated 2D agents: 1) finding and maintaining a certain distance to each
others and 2) locating a target.
|
Maximilian H\"uttenrauch and Adrian \v{S}o\v{s}i\'c and Gerhard
Neumann
| null |
1709.06011
| null | null |
N2N Learning: Network to Network Compression via Policy Gradient
Reinforcement Learning
|
cs.LG stat.ML
|
While bigger and deeper neural network architectures continue to advance the
state-of-the-art for many computer vision tasks, real-world adoption of these
networks is impeded by hardware and speed constraints. Conventional model
compression methods attempt to address this problem by modifying the
architecture manually or using pre-defined heuristics. Since the space of all
reduced architectures is very large, modifying the architecture of a deep
neural network in this way is a difficult task. In this paper, we tackle this
issue by introducing a principled method for learning reduced network
architectures in a data-driven way using reinforcement learning. Our approach
takes a larger `teacher' network as input and outputs a compressed `student'
network derived from the `teacher' network. In the first stage of our method, a
recurrent policy network aggressively removes layers from the large `teacher'
model. In the second stage, another recurrent policy network carefully reduces
the size of each remaining layer. The resulting network is then evaluated to
obtain a reward -- a score based on the accuracy and compression of the
network. Our approach uses this reward signal with policy gradients to train
the policies to find a locally optimal student network. Our experiments show
that we can achieve compression rates of more than 10x for models such as
ResNet-34 while maintaining similar performance to the input `teacher' network.
We also present a valuable transfer learning result which shows that policies
which are pre-trained on smaller `teacher' networks can be used to rapidly
speed up training on larger `teacher' networks.
|
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani
| null |
1709.0603
| null | null |
Deep Graph Attention Model
|
cs.LG cs.AI
|
Graph classification is a problem with practical applications in many
different domains. Most of the existing methods take the entire graph into
account when calculating graph features. In a graphlet-based approach, for
instance, the entire graph is processed to get the total count of different
graphlets or sub-graphs. In the real-world, however, graphs can be both large
and noisy with discriminative patterns confined to certain regions in the graph
only. In this work, we study the problem of attentional processing for graph
classification. The use of attention allows us to focus on small but
informative parts of the graph, avoiding noise in the rest of the graph. We
present a novel RNN model, called the Graph Attention Model (GAM), that
processes only a portion of the graph by adaptively selecting a sequence of
"interesting" nodes. The model is equipped with an external memory component
which allows it to integrate information gathered from different parts of the
graph. We demonstrate the effectiveness of the model through various
experiments.
|
John Boaz Lee, Ryan Rossi, Xiangnan Kong
| null |
1709.06075
| null | null |
Modelling Energy Consumption based on Resource Utilization
|
cs.LG cs.DC
|
Power management is an expensive and important issue for large computational
infrastructures such as datacenters, large clusters, and computational grids.
However, measuring energy consumption of scalable systems may be impractical
due to both cost and complexity for deploying power metering devices on a large
number of machines. In this paper, we propose the use of information about
resource utilization (e.g. processor, memory, disk operations, and network
traffic) as proxies for estimating power consumption. We employ machine
learning techniques to estimate power consumption using such information which
are provided by common operating systems. Experiments with linear regression,
regression tree, and multilayer perceptron on data from different hardware
resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the
best case.
|
Lucas Venezian Povoa and Cesar Marcondes and Hermes Senger
| null |
1709.06076
| null | null |
Orthogonal Weight Normalization: Solution to Optimization over Multiple
Dependent Stiefel Manifolds in Deep Neural Networks
|
cs.LG
|
Orthogonal matrix has shown advantages in training Recurrent Neural Networks
(RNNs), but such matrix is limited to be square for the hidden-to-hidden
transformation in RNNs. In this paper, we generalize such square orthogonal
matrix to orthogonal rectangular matrix and formulating this problem in
feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent
Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can
stabilize the distribution of network activations and regularize FNNs. We also
propose a novel orthogonal weight normalization method to solve OMDSM.
Particularly, it constructs orthogonal transformation over proxy parameters to
ensure the weight matrix is orthogonal and back-propagates gradient information
through the transformation during training. To guarantee stability, we minimize
the distortions between proxy parameters and canonical weights over all
tractable orthogonal transformations. In addition, we design an orthogonal
linear module (OLM) to learn orthogonal filter banks in practice, which can be
used as an alternative to standard linear module. Extensive experiments
demonstrate that by simply substituting OLM for standard linear module without
revising any experimental protocols, our method largely improves the
performance of the state-of-the-art networks, including Inception and residual
networks on CIFAR and ImageNet datasets. In particular, we have reduced the
test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with
such simple substitution. Our code is available online for result reproduction.
|
Lei Huang, Xianglong Liu, Bo Lang, Adams Wei Yu, Yongliang Wang, Bo Li
| null |
1709.06079
| null | null |
Feedforward and Recurrent Neural Networks Backward Propagation and
Hessian in Matrix Form
|
cs.LG cs.AI math.NA
|
In this paper we focus on the linear algebra theory behind feedforward (FNN)
and recurrent (RNN) neural networks. We review backward propagation, including
backward propagation through time (BPTT). Also, we obtain a new exact
expression for Hessian, which represents second order effects. We show that for
$t$ time steps the weight gradient can be expressed as a rank-$t$ matrix, while
the weight Hessian is as a sum of $t^{2}$ Kronecker products of rank-$1$ and
$W^{T}AW$ matrices, for some matrix $A$ and weight matrix $W$. Also, we show
that for a mini-batch of size $r$, the weight update can be expressed as a
rank-$rt$ matrix. Finally, we briefly comment on the eigenvalues of the Hessian
matrix.
|
Maxim Naumov
| null |
1709.0608
| null | null |
A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models
|
stat.ML cs.LG
|
In this short note we consider a dynamic assortment planning problem under
the capacitated multinomial logit (MNL) bandit model. We prove a tight lower
bound on the accumulated regret that matches existing regret upper bounds for
all parameters (time horizon $T$, number of items $N$ and maximum assortment
capacity $K$) up to logarithmic factors. Our results close an $O(\sqrt{K})$ gap
between upper and lower regret bounds from existing works.
|
Xi Chen, Yining Wang
| null |
1709.06109
| null | null |
A Probabilistic Framework for Nonlinearities in Stochastic Neural
Networks
|
stat.ML cs.LG
|
We present a probabilistic framework for nonlinearities, based on doubly
truncated Gaussian distributions. By setting the truncation points
appropriately, we are able to generate various types of nonlinearities within a
unified framework, including sigmoid, tanh and ReLU, the most commonly used
nonlinearities in neural networks. The framework readily integrates into
existing stochastic neural networks (with hidden units characterized as random
variables), allowing one for the first time to learn the nonlinearities
alongside model weights in these networks. Extensive experiments demonstrate
the performance improvements brought about by the proposed framework when
integrated with the restricted Boltzmann machine (RBM), temporal RBM and the
truncated Gaussian graphical model (TGGM).
|
Qinliang Su, Xuejun Liao, Lawrence Carin
| null |
1709.06123
| null | null |
When is a Convolutional Filter Easy To Learn?
|
cs.LG cs.AI cs.CV math.OC stat.ML
|
We analyze the convergence of (stochastic) gradient descent algorithm for
learning a convolutional filter with Rectified Linear Unit (ReLU) activation
function. Our analysis does not rely on any specific form of the input
distribution and our proofs only use the definition of ReLU, in contrast with
previous works that are restricted to standard Gaussian input. We show that
(stochastic) gradient descent with random initialization can learn the
convolutional filter in polynomial time and the convergence rate depends on the
smoothness of the input distribution and the closeness of patches. To the best
of our knowledge, this is the first recovery guarantee of gradient-based
algorithms for convolutional filter on non-Gaussian input distributions. Our
theory also justifies the two-stage learning rate strategy in deep neural
networks. While our focus is theoretical, we also present experiments that
illustrate our theoretical findings.
|
Simon S. Du, Jason D. Lee, Yuandong Tian
| null |
1709.06129
| null | null |
Model-Powered Conditional Independence Test
|
stat.ML cs.AI cs.IT cs.LG math.IT
|
We consider the problem of non-parametric Conditional Independence testing
(CI testing) for continuous random variables. Given i.i.d samples from the
joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we
determine whether $X \perp Y | Z$. We approach this by converting the
conditional independence test into a classification problem. This allows us to
harness very powerful classifiers like gradient-boosted trees and deep neural
networks. These models can handle complex probability distributions and allow
us to perform significantly better compared to the prior state of the art, for
high-dimensional CI testing. The main technical challenge in the classification
problem is the need for samples from the conditional product distribution
$f^{CI}(x,y,z) = f(x|z)f(y|z)f(z)$ -- the joint distribution if and only if $X
\perp Y | Z.$ -- when given access only to i.i.d. samples from the true joint
distribution $f(x,y,z)$. To tackle this problem we propose a novel nearest
neighbor bootstrap procedure and theoretically show that our generated samples
are indeed close to $f^{CI}$ in terms of total variational distance. We then
develop theoretical results regarding the generalization bounds for
classification for our problem, which translate into error bounds for CI
testing. We provide a novel analysis of Rademacher type classification bounds
in the presence of non-i.i.d near-independent samples. We empirically validate
the performance of our algorithm on simulated and real datasets and show
performance gains over previous methods.
|
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros
G. Dimakis and Sanjay Shakkottai
| null |
1709.06138
| null | null |
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural
Network Training
|
cs.LG
|
Massive data exist among user local platforms that usually cannot support
deep neural network (DNN) training due to computation and storage resource
constraints. Cloud-based training schemes provide beneficial services but
suffer from potential privacy risks due to excessive user data collection. To
enable cloud-based DNN training while protecting the data privacy
simultaneously, we propose to leverage the intermediate representations of the
data, which is achieved by splitting the DNNs and deploying them separately
onto local platforms and the cloud. The local neural network (NN) is used to
generate the feature representations. To avoid local training and protect data
privacy, the local NN is derived from pre-trained NNs. The cloud NN is then
trained based on the extracted intermediate representations for the target
learning task. We validate the idea of DNN splitting by characterizing the
dependency of privacy loss and classification accuracy on the local NN topology
for a convolutional NN (CNN) based image classification task. Based on the
characterization, we further propose PrivyNet to determine the local NN
topology, which optimizes the accuracy of the target learning task under the
constraints on privacy loss, local computation, and storage. The efficiency and
effectiveness of PrivyNet are demonstrated with the CIFAR-10 dataset.
|
Meng Li, Liangzhen Lai, Naveen Suda, Vikas Chandra and David Z. Pan
| null |
1709.06161
| null | null |
A Survey of Machine Learning for Big Code and Naturalness
|
cs.SE cs.LG cs.PL
|
Research at the intersection of machine learning, programming languages, and
software engineering has recently taken important steps in proposing learnable
probabilistic models of source code that exploit code's abundance of patterns.
In this article, we survey this work. We contrast programming languages against
natural languages and discuss how these similarities and differences drive the
design of probabilistic models. We present a taxonomy based on the underlying
design principles of each model and use it to navigate the literature. Then, we
review how researchers have adapted these models to application areas and
discuss cross-cutting and application-specific challenges and opportunities.
|
Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
| null |
1709.06182
| null | null |
Bias Correction with Jackknife, Bootstrap, and Taylor Series
|
math.ST cs.IT cs.LG math.IT stat.TH
|
We analyze bias correction methods using jackknife, bootstrap, and Taylor
series. We focus on the binomial model, and consider the problem of bias
correction for estimating $f(p)$, where $f \in C[0,1]$ is arbitrary. We
characterize the supremum norm of the bias of general jackknife and bootstrap
estimators for any continuous functions, and demonstrate the in delete-$d$
jackknife, different values of $d$ may lead to drastically different behaviors
in jackknife. We show that in the binomial model, iterating the bootstrap bias
correction infinitely many times may lead to divergence of bias and variance,
and demonstrate that the bias properties of the bootstrap bias corrected
estimator after $r-1$ rounds are of the same order as that of the $r$-jackknife
estimator if a bounded coefficients condition is satisfied.
|
Jiantao Jiao and Yanjun Han
| null |
1709.06183
| null | null |
Human Understandable Explanation Extraction for Black-box Classification
Models Based on Matrix Factorization
|
cs.AI cs.LG stat.ML
|
In recent years, a number of artificial intelligent services have been
developed such as defect detection system or diagnosis system for customer
services. Unfortunately, the core in these services is a black-box in which
human cannot understand the underlying decision making logic, even though the
inspection of the logic is crucial before launching a commercial service. Our
goal in this paper is to propose an analytic method of a model explanation that
is applicable to general classification models. To this end, we introduce the
concept of a contribution matrix and an explanation embedding in a constraint
space by using a matrix factorization. We extract a rule-like model explanation
from the contribution matrix with the help of the nonnegative matrix
factorization. To validate our method, the experiment results provide with open
datasets as well as an industry dataset of a LTE network diagnosis and the
results show our method extracts reasonable explanations.
|
Jaedeok Kim, and Jingoo Seo
| null |
1709.06201
| null | null |
Estimating Mutual Information for Discrete-Continuous Mixtures
|
cs.IT cs.LG math.IT
|
Estimating mutual information from observed samples is a basic primitive,
useful in several machine learning tasks including correlation mining,
information bottleneck clustering, learning a Chow-Liu tree, and conditional
independence testing in (causal) graphical models. While mutual information is
a well-defined quantity in general probability spaces, existing estimators can
only handle two special cases of purely discrete or purely continuous pairs of
random variables. The main challenge is that these methods first estimate the
(differential) entropies of X, Y and the pair (X;Y) and add them up with
appropriate signs to get an estimate of the mutual information. These
3H-estimators cannot be applied in general mixture spaces, where entropy is not
well-defined. In this paper, we design a novel estimator for mutual information
of discrete-continuous mixtures. We prove that the proposed estimator is
consistent. We provide numerical experiments suggesting superiority of the
proposed estimator compared to other heuristics of adding small continuous
noise to all the samples and applying standard estimators tailored for purely
continuous variables, and quantizing the samples and applying standard
estimators tailored for purely discrete variables. This significantly widens
the applicability of mutual information estimation in real-world applications,
where some variables are discrete, some continuous, and others are a mixture
between continuous and discrete components.
|
Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
| null |
1709.06212
| null | null |
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy
Regularization for Reinforcement Learning
|
cs.LG cs.AI stat.ML
|
In this paper, a sparse Markov decision process (MDP) with novel causal
sparse Tsallis entropy regularization is proposed.The proposed policy
regularization induces a sparse and multi-modal optimal policy distribution of
a sparse MDP. The full mathematical analysis of the proposed sparse MDP is
provided.We first analyze the optimality condition of a sparse MDP. Then, we
propose a sparse value iteration method which solves a sparse MDP and then
prove the convergence and optimality of sparse value iteration using the Banach
fixed point theorem. The proposed sparse MDP is compared to soft MDPs which
utilize causal entropy regularization. We show that the performance error of a
sparse MDP has a constant bound, while the error of a soft MDP increases
logarithmically with respect to the number of actions, where this performance
error is caused by the introduced regularization term. In experiments, we apply
sparse MDPs to reinforcement learning problems. The proposed method outperforms
existing methods in terms of the convergence speed and performance.
|
Kyungjae Lee, Sungjoon Choi and Songhwai Oh
| null |
1709.06293
| null | null |
MuseGAN: Multi-track Sequential Generative Adversarial Networks for
Symbolic Music Generation and Accompaniment
|
eess.AS cs.AI cs.LG cs.SD stat.ML
|
Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical notes are often grouped into chords, arpeggios or melodies in
polyphonic music, and thereby introducing a chronological ordering of notes is
not naturally suitable. In this paper, we propose three models for symbolic
multi-track music generation under the framework of generative adversarial
networks (GANs). The three models, which differ in the underlying assumptions
and accordingly the network architectures, are referred to as the jamming
model, the composer model and the hybrid model. We trained the proposed models
on a dataset of over one hundred thousand bars of rock music and applied them
to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
A few intra-track and inter-track objective metrics are also proposed to
evaluate the generative results, in addition to a subjective user study. We
show that our models can generate coherent music of four bars right from
scratch (i.e. without human inputs). We also extend our models to human-AI
cooperative music generation: given a specific track composed by human, we can
generate four additional tracks to accompany it. All code, the dataset and the
rendered audio samples are available at https://salu133445.github.io/musegan/ .
|
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, Yi-Hsuan Yang
| null |
1709.06298
| null | null |
Scalable Estimation of Dirichlet Process Mixture Models on Distributed
Data
|
stat.ML cs.LG
|
We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in
distributed environments, where data are distributed across multiple computing
nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that
they allow new components to be introduced on the fly as needed. This, however,
posts an important challenge to distributed estimation -- how to handle new
components efficiently and consistently. To tackle this problem, we propose a
new estimation method, which allows new components to be created locally in
individual computing nodes. Components corresponding to the same cluster will
be identified and merged via a probabilistic consolidation scheme. In this way,
we can maintain the consistency of estimation with very low communication cost.
Experiments on large real-world data sets show that the proposed method can
achieve high scalability in distributed and asynchronous environments without
compromising the mixing performance.
|
Ruohui Wang, Dahua Lin
|
10.24963/ijcai.2017/646
|
1709.06304
| null | null |
Analogical-based Bayesian Optimization
|
cs.LG stat.ML
|
Some real-world problems revolve to solve the optimization problem
\max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box
function and X might be the set of non-vectorial objects (e.g., distributions)
where we can only define a symmetric and non-negative similarity score on it.
This setting requires a novel view for the standard framework of Bayesian
Optimization that generalizes the core insightful spirit of this framework.
With this spirit, in this paper, we propose Analogical-based Bayesian
Optimization that can maximize black-box function over a domain where only a
similarity score can be defined. Our pathway is as follows: we first base on
the geometric view of Gaussian Processes (GP) to define the concept of
influence level that allows us to analytically represent predictive means and
variances of GP posteriors and base on that view to enable replacing kernel
similarity by a more genetic similarity score. Furthermore, we also propose two
strategies to find a batch of query points that can efficiently handle high
dimensional data.
|
Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Phung
| null |
1709.0639
| null | null |
Interactive Music Generation with Positional Constraints using
Anticipation-RNNs
|
cs.AI cs.LG stat.ML
|
Recurrent Neural Networks (RNNS) are now widely used on sequence generation
tasks due to their ability to learn long-range dependencies and to generate
sequences of arbitrary length. However, their left-to-right generation
procedure only allows a limited control from a potential user which makes them
unsuitable for interactive and creative usages such as interactive music
generation. This paper introduces a novel architecture called Anticipation-RNN
which possesses the assets of the RNN-based generative models while allowing to
enforce user-defined positional constraints. We demonstrate its efficiency on
the task of generating melodies satisfying positional constraints in the style
of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using
the Anticipation-RNN is of the same order of complexity than sampling from the
traditional RNN model. This fast and interactive generation of musical
sequences opens ways to devise real-time systems that could be used for
creative purposes.
|
Ga\"etan Hadjeres and Frank Nielsen
| null |
1709.06404
| null | null |
Neural Networks for Text Correction and Completion in Keyboard Decoding
|
cs.CL cs.LG
|
Despite the ubiquity of mobile and wearable text messaging applications, the
problem of keyboard text decoding is not tackled sufficiently in the light of
the enormous success of the deep learning Recurrent Neural Network (RNN) and
Convolutional Neural Networks (CNN) for natural language understanding. In
particular, considering that the keyboard decoders should operate on devices
with memory and processor resource constraints, makes it challenging to deploy
industrial scale deep neural network (DNN) models. This paper proposes a
sequence-to-sequence neural attention network system for automatic text
correction and completion. Given an erroneous sequence, our model encodes
character level hidden representations and then decodes the revised sequence
thus enabling auto-correction and completion. We achieve this by a combination
of character level CNN and gated recurrent unit (GRU) encoder along with and a
word level gated recurrent unit (GRU) attention decoder. Unlike traditional
language models that learn from billions of words, our corpus size is only 12
million words; an order of magnitude smaller. The memory footprint of our
learnt model for inference and prediction is also an order of magnitude smaller
than the conventional language model based text decoders. We report baseline
performance for neural keyboard decoders in such limited domain. Our models
achieve a word level accuracy of $90\%$ and a character error rate CER of
$2.4\%$ over the Twitter typo dataset. We present a novel dataset of noisy to
corrected mappings by inducing the noise distribution from the Twitter data
over the OpenSubtitles 2009 dataset; on which our model predicts with a word
level accuracy of $98\%$ and sequence accuracy of $68.9\%$. In our user study,
our model achieved an average CER of $2.6\%$ with the state-of-the-art
non-neural touch-screen keyboard decoder at CER of $1.6\%$.
|
Shaona Ghosh, Per Ola Kristensson
| null |
1709.06429
| null | null |
Scalable Support Vector Clustering Using Budget
|
cs.LG
|
Owing to its application in solving the difficult and diverse clustering or
outlier detection problem, support-based clustering has recently drawn plenty
of attention. Support-based clustering method always undergoes two phases:
finding the domain of novelty and performing clustering assignment. To find the
domain of novelty, the training time given by the current solvers is typically
over-quadratic in the training size, and hence precluding the usage of
support-based clustering method for large-scale datasets. In this paper, we
propose applying Stochastic Gradient Descent (SGD) framework to the first phase
of support-based clustering for finding the domain of novelty and a new
strategy to perform the clustering assignment. However, the direct application
of SGD to the first phase of support-based clustering is vulnerable to the
curse of kernelization, that is, the model size linearly grows up with the data
size accumulated overtime. To address this issue, we invoke the budget approach
which allows us to restrict the model size to a small budget. Our new strategy
for clustering assignment enables a fast computation by means of reducing the
task of clustering assignment on the full training set to the same task on a
significantly smaller set. We also provide a rigorous theoretical analysis
about the convergence rate for the proposed method. Finally, we validate our
proposed method on the well-known datasets for clustering to show that the
proposed method offers a comparable clustering quality while simultaneously
achieving significant speedup in comparison with the baselines.
|
Tung Pham, Trung Le, Hang Dang
| null |
1709.06444
| null | null |
Accurate Genomic Prediction Of Human Height
|
q-bio.GN cs.LG q-bio.QM stat.ML
|
We construct genomic predictors for heritable and extremely complex human
quantitative traits (height, heel bone density, and educational attainment)
using modern methods in high dimensional statistics (i.e., machine learning).
Replication tests show that these predictors capture, respectively, $\sim$40,
20, and 9 percent of total variance for the three traits. For example,
predicted heights correlate $\sim$0.65 with actual height; actual heights of
most individuals in validation samples are within a few cm of the prediction.
The variance captured for height is comparable to the estimated SNP
heritability from GCTA (GREML) analysis, and seems to be close to its
asymptotic value (i.e., as sample size goes to infinity), suggesting that we
have captured most of the heritability for the SNPs used. Thus, our results
resolve the common SNP portion of the "missing heritability" problem -- i.e.,
the gap between prediction R-squared and SNP heritability. The $\sim$20k
activated SNPs in our height predictor reveal the genetic architecture of human
height, at least for common SNPs. Our primary dataset is the UK Biobank cohort,
comprised of almost 500k individual genotypes with multiple phenotypes. We also
use other datasets and SNPs found in earlier GWAS for out-of-sample validation
of our results.
|
Louis Lello, Steven G. Avery, Laurent Tellier, Ana Vazquez, Gustavo de
los Campos, Stephen D.H. Hsu
| null |
1709.06489
| null | null |
Learning to update Auto-associative Memory in Recurrent Neural Networks
for Improving Sequence Memorization
|
cs.AI cs.LG stat.ML
|
Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks.
|
Wei Zhang, Bowen Zhou
| null |
1709.06493
| null | null |
Summable Reparameterizations of Wasserstein Critics in the
One-Dimensional Setting
|
cs.LG cs.AI stat.ML
|
Generative adversarial networks (GANs) are an exciting alternative to
algorithms for solving density estimation problems---using data to assess how
likely samples are to be drawn from the same distribution. Instead of
explicitly computing these probabilities, GANs learn a generator that can match
the given probabilistic source. This paper looks particularly at this matching
capability in the context of problems with one-dimensional outputs. We identify
a class of function decompositions with properties that make them well suited
to the critic role in a leading approach to GANs known as Wasserstein GANs. We
show that Taylor and Fourier series decompositions belong to our class, provide
examples of these critics outperforming standard GAN approaches, and suggest
how they can be scaled to higher dimensional problems in the future.
|
Christopher Grimm, Yuhang Song and Michael L. Littman
| null |
1709.06533
| null | null |
Triangle Generative Adversarial Networks
|
cs.LG stat.ML
|
A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for
semi-supervised cross-domain joint distribution matching, where the training
data consists of samples from each domain, and supervision of domain
correspondence is provided by only a few paired samples. $\Delta$-GAN consists
of four neural networks, two generators and two discriminators. The generators
are designed to learn the two-way conditional distributions between the two
domains, while the discriminators implicitly define a ternary discriminative
function, which is trained to distinguish real data pairs and two kinds of fake
data pairs. The generators and discriminators are trained together using
adversarial learning. Under mild assumptions, in theory the joint distributions
characterized by the two generators concentrate to the data distribution. In
experiments, three different kinds of domain pairs are considered, image-label,
image-image and image-attribute pairs. Experiments on semi-supervised image
classification, image-to-image translation and attribute-based image generation
demonstrate the superiority of the proposed approach.
|
Zhe Gan, Liqun Chen, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Hao Liu,
Chunyuan Li, Lawrence Carin
| null |
1709.06548
| null | null |
Deep Reinforcement Learning that Matters
|
cs.LG stat.ML
|
In recent years, significant progress has been made in solving challenging
problems across various domains using deep reinforcement learning (RL).
Reproducing existing work and accurately judging the improvements offered by
novel methods is vital to sustaining this progress. Unfortunately, reproducing
results for state-of-the-art deep RL methods is seldom straightforward. In
particular, non-determinism in standard benchmark environments, combined with
variance intrinsic to the methods, can make reported results tough to
interpret. Without significance metrics and tighter standardization of
experimental reporting, it is difficult to determine whether improvements over
the prior state-of-the-art are meaningful. In this paper, we investigate
challenges posed by reproducibility, proper experimental techniques, and
reporting procedures. We illustrate the variability in reported metrics and
results when comparing against common baselines and suggest guidelines to make
future results in deep RL more reproducible. We aim to spur discussion about
how to ensure continued progress in the field by minimizing wasted effort
stemming from results that are non-reproducible and easily misinterpreted.
|
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina
Precup, David Meger
| null |
1709.0656
| null | null |
Unsupervised Machine Learning for Networking: Techniques, Applications
and Research Challenges
|
cs.NI cs.LG
|
While machine learning and artificial intelligence have long been applied in
networking research, the bulk of such works has focused on supervised learning.
Recently there has been a rising trend of employing unsupervised machine
learning using unstructured raw network data to improve network performance and
provide services such as traffic engineering, anomaly detection, Internet
traffic classification, and quality of service optimization. The interest in
applying unsupervised learning techniques in networking emerges from their
great success in other fields such as computer vision, natural language
processing, speech recognition, and optimal control (e.g., for developing
autonomous self-driving cars). Unsupervised learning is interesting since it
can unconstrain us from the need of labeled data and manual handcrafted feature
engineering thereby facilitating flexible, general, and automated methods of
machine learning. The focus of this survey paper is to provide an overview of
the applications of unsupervised learning in the domain of networking. We
provide a comprehensive survey highlighting the recent advancements in
unsupervised learning techniques and describe their applications for various
learning tasks in the context of networking. We also provide a discussion on
future directions and open research issues, while also identifying potential
pitfalls. While a few survey papers focusing on the applications of machine
learning in networking have previously been published, a survey of similar
scope and breadth is missing in literature. Through this paper, we advance the
state of knowledge by carefully synthesizing the insights from these survey
papers while also providing contemporary coverage of recent advances.
|
Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin
Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
| null |
1709.06599
| null | null |
An Analog Neural Network Computing Engine using CMOS-Compatible
Charge-Trap-Transistor (CTT)
|
cs.ET cs.AR cs.LG
|
An analog neural network computing engine based on CMOS-compatible
charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as
analog multipliers. Compared to digital multipliers, CTT-based analog
multiplier shows significant area and power reduction. The proposed computing
engine is composed of a scalable CTT multiplier array and energy efficient
analog-digital interfaces. Through implementing the sequential analog fabric
(SAF), the engine mixed-signal interfaces are simplified and hardware overhead
remains constant regardless of the size of the array. A proof-of-concept 784 by
784 CTT computing engine is implemented using TSMC 28nm CMOS technology and
occupied 0.68mm2. The simulated performance achieves 76.8 TOPS (8-bit) with 500
MHz clock frequency and consumes 14.8 mW. As an example, we utilize this
computing engine to address a classic pattern recognition problem --
classifying handwritten digits on MNIST database and obtained a performance
comparable to state-of-the-art fully connected neural networks using 8-bit
fixed-point resolution.
|
Yuan Du, Li Du, Xuefeng Gu, Jieqiong Du, X. Shawn Wang, Boyu Hu,
Mingzhe Jiang, Xiaoliang Chen, Junjie Su, Subramanian S. Iyer, Mau-Chung
Frank Chang
| null |
1709.06614
| null | null |
A PAC-Bayesian Analysis of Randomized Learning with Application to
Stochastic Gradient Descent
|
cs.LG
|
We study the generalization error of randomized learning algorithms --
focusing on stochastic gradient descent (SGD) -- using a novel combination of
PAC-Bayes and algorithmic stability. Importantly, our generalization bounds
hold for all posterior distributions on an algorithm's random hyperparameters,
including distributions that depend on the training data. This inspires an
adaptive sampling algorithm for SGD that optimizes the posterior at runtime. We
analyze this algorithm in the context of our generalization bounds and evaluate
it on a benchmark dataset. Our experiments demonstrate that adaptive sampling
can reduce empirical risk faster than uniform sampling while also improving
out-of-sample accuracy.
|
Ben London
| null |
1709.06617
| null | null |
Learning of Coordination Policies for Robotic Swarms
|
cs.RO cs.AI cs.LG cs.MA cs.NE
|
Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achieve a global objective can be challenging. Although it is
often possible to design an optimal centralized strategy for smaller numbers of
agents, those methods can fail as the number of agents increases. Motivated by
the growing success of machine learning, we develop a deep learning approach
that learns distributed coordination policies from centralized policies. In
contrast to traditional distributed control approaches, which are usually based
on human-designed policies for relatively simple tasks, this learning-based
approach can be adapted to more difficult tasks. We demonstrate the efficacy of
our proposed approach on two different tasks, the well-known rendezvous problem
and a more difficult particle assignment problem. For the latter, no known
distributed policy exists. From extensive simulations, it is shown that the
performance of the learned coordination policies is comparable to the
centralized policies, surpassing state-of-the-art distributed policies.
Thereby, our proposed approach provides a promising alternative for real-world
coordination problems that would be otherwise computationally expensive to
solve or intangible to explore.
|
Qiyang Li, Xintong Du, Yizhou Huang, Quinlan Sykora, and Angela P.
Schoellig
| null |
1709.0662
| null | null |
Distributed Training Large-Scale Deep Architectures
|
cs.DC cs.LG stat.ML
|
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training.
|
Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin
Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, and Edward Y. Chang
| null |
1709.06622
| null | null |
An Attention-based Collaboration Framework for Multi-View Network
Representation Learning
|
cs.SI cs.LG stat.ML
|
Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.
|
Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
| null |
1709.06636
| null | null |
Unique Information via Dependency Constraints
|
cond-mat.stat-mech cs.IT cs.LG math.IT math.ST stat.TH
|
The partial information decomposition (PID) is perhaps the leading proposal
for resolving information shared between a set of sources and a target into
redundant, synergistic, and unique constituents. Unfortunately, the PID
framework has been hindered by a lack of a generally agreed-upon, multivariate
method of quantifying the constituents. Here, we take a step toward rectifying
this by developing a decomposition based on a new method that quantifies unique
information. We first develop a broadly applicable method---the dependency
decomposition---that delineates how statistical dependencies influence the
structure of a joint distribution. The dependency decomposition then allows us
to define a measure of the information about a target that can be uniquely
attributed to a particular source as the least amount which the source-target
statistical dependency can influence the information shared between the sources
and the target. The result is the first measure that satisfies the core axioms
of the PID framework while not satisfying the Blackwell relation, which depends
on a particular interpretation of how the variables are related. This makes a
key step forward to a practical PID.
|
Ryan G. James, Jeffrey Emenheiser, and James P. Crutchfield
| null |
1709.06653
| null | null |
Verifying Properties of Binarized Deep Neural Networks
|
stat.ML cs.AI cs.CR cs.LG
|
Understanding properties of deep neural networks is an important challenge in
deep learning. In this paper, we take a step in this direction by proposing a
rigorous way of verifying properties of a popular class of neural networks,
Binarized Neural Networks, using the well-developed means of Boolean
satisfiability. Our main contribution is a construction that creates a
representation of a binarized neural network as a Boolean formula. Our encoding
is the first exact Boolean representation of a deep neural network. Using this
encoding, we leverage the power of modern SAT solvers along with a proposed
counterexample-guided search procedure to verify various properties of these
networks. A particular focus will be on the critical property of robustness to
adversarial perturbations. For this property, our experimental results
demonstrate that our approach scales to medium-size deep neural networks used
in image classification tasks. To the best of our knowledge, this is the first
work on verifying properties of deep neural networks using an exact Boolean
encoding of the network.
|
Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk, Mooly
Sagiv, Toby Walsh
| null |
1709.06662
| null | null |
A textual transform of multivariate time-series for prognostics
|
stat.ML cs.LG
|
Prognostics or early detection of incipient faults is an important industrial
challenge for condition-based and preventive maintenance. Physics-based
approaches to modeling fault progression are infeasible due to multiple
interacting components, uncontrolled environmental factors and observability
constraints. Moreover, such approaches to prognostics do not generalize to new
domains. Consequently, domain-agnostic data-driven machine learning approaches
to prognostics are desirable. Damage progression is a path-dependent process
and explicitly modeling the temporal patterns is critical for accurate
estimation of both the current damage state and its progression leading to
total failure. In this paper, we present a novel data-driven approach to
prognostics that employs a novel textual representation of multivariate
temporal sensor observations for predicting the future health state of the
monitored equipment early in its life. This representation enables us to
utilize well-understood concepts from text-mining for modeling, prediction and
understanding distress patterns in a domain agnostic way. The approach has been
deployed and successfully tested on large scale multivariate time-series data
from commercial aircraft engines. We report experiments on well-known publicly
available benchmark datasets and simulation datasets. The proposed approach is
shown to be superior in terms of prediction accuracy, lead time to prediction
and interpretability.
|
Abhay Harpale (1), Abhishek Srivastav (1) ((1) GE Global Research)
| null |
1709.06669
| null | null |
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
|
cs.CL cs.LG cs.NE
|
Distributed word embeddings have shown superior performances in numerous
Natural Language Processing (NLP) tasks. However, their performances vary
significantly across different tasks, implying that the word embeddings learnt
by those methods capture complementary aspects of lexical semantics. Therefore,
we believe that it is important to combine the existing word embeddings to
produce more accurate and complete \emph{meta-embeddings} of words. For this
purpose, we propose an unsupervised locally linear meta-embedding learning
method that takes pre-trained word embeddings as the input, and produces more
accurate meta embeddings. Unlike previously proposed meta-embedding learning
methods that learn a global projection over all words in a vocabulary, our
proposed method is sensitive to the differences in local neighbourhoods of the
individual source word embeddings. Moreover, we show that vector concatenation,
a previously proposed highly competitive baseline approach for integrating word
embeddings, can be derived as a special case of the proposed method.
Experimental results on semantic similarity, word analogy, relation
classification, and short-text classification tasks show that our
meta-embeddings to significantly outperform prior methods in several benchmark
datasets, establishing a new state of the art for meta-embeddings.
|
Danushka Bollegala, Kohei Hayashi and Ken-ichi Kawarabayashi
| null |
1709.06671
| null | null |
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational
Compositional Operators for Analogy Detection
|
cs.CL cs.AI cs.LG cs.NE
|
Representing the semantic relations that exist between two given words (or
entities) is an important first step in a wide-range of NLP applications such
as analogical reasoning, knowledge base completion and relational information
retrieval. A simple, yet surprisingly accurate method for representing a
relation between two words is to compute the vector offset (\PairDiff) between
their corresponding word embeddings. Despite the empirical success, it remains
unclear as to whether \PairDiff is the best operator for obtaining a relational
representation from word embeddings. We conduct a theoretical analysis of
generalised bilinear operators that can be used to measure the $\ell_{2}$
relational distance between two word-pairs. We show that, if the word
embeddings are standardised and uncorrelated, such an operator will be
independent of bilinear terms, and can be simplified to a linear form, where
\PairDiff is a special case. For numerous word embedding types, we empirically
verify the uncorrelation assumption, demonstrating the general applicability of
our theoretical result. Moreover, we experimentally discover \PairDiff from the
bilinear relation composition operator on several benchmark analogy datasets.
|
Huda Hakami and Danushka Bollegala and Hayashi Kohei
| null |
1709.06673
| null | null |
Deep Lattice Networks and Partial Monotonic Functions
|
stat.ML cs.LG
|
We propose learning deep models that are monotonic with respect to a
user-specified set of inputs by alternating layers of linear embeddings,
ensembles of lattices, and calibrators (piecewise linear functions), with
appropriate constraints for monotonicity, and jointly training the resulting
network. We implement the layers and projections with new computational graph
nodes in TensorFlow and use the ADAM optimizer and batched stochastic
gradients. Experiments on benchmark and real-world datasets show that six-layer
monotonic deep lattice networks achieve state-of-the art performance for
classification and regression with monotonicity guarantees.
|
Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
| null |
1709.0668
| null | null |
OptionGAN: Learning Joint Reward-Policy Options using Generative
Adversarial Inverse Reinforcement Learning
|
cs.LG
|
Reinforcement learning has shown promise in learning policies that can solve
complex problems. However, manually specifying a good reward function can be
difficult, especially for intricate tasks. Inverse reinforcement learning
offers a useful paradigm to learn the underlying reward function directly from
expert demonstrations. Yet in reality, the corpus of demonstrations may contain
trajectories arising from a diverse set of underlying reward functions rather
than a single one. Thus, in inverse reinforcement learning, it is useful to
consider such a decomposition. The options framework in reinforcement learning
is specifically designed to decompose policies in a similar light. We therefore
extend the options framework and propose a method to simultaneously recover
reward options in addition to policy options. We leverage adversarial methods
to learn joint reward-policy options using only observed expert states. We show
that this approach works well in both simple and complex continuous control
tasks and shows significant performance increases in one-shot transfer
learning.
|
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle
Pineau, Doina Precup
| null |
1709.06683
| null | null |
Online Learning of a Memory for Learning Rates
|
cs.LG
|
The promise of learning to learn for robotics rests on the hope that by
extracting some information about the learning process itself we can speed up
subsequent similar learning tasks. Here, we introduce a computationally
efficient online meta-learning algorithm that builds and optimizes a memory
model of the optimal learning rate landscape from previously observed gradient
behaviors. While performing task specific optimization, this memory of learning
rates predicts how to scale currently observed gradients. After applying the
gradient scaling our meta-learner updates its internal memory based on the
observed effect its prediction had. Our meta-learner can be combined with any
gradient-based optimizer, learns on the fly and can be transferred to new
optimization tasks. In our evaluations we show that our meta-learning algorithm
speeds up learning of MNIST classification and a variety of learning control
tasks, either in batch or online learning settings.
|
Franziska Meier, Daniel Kappler and Stefan Schaal
| null |
1709.06709
| null | null |
Contrastive Principal Component Analysis
|
stat.ML cs.LG
|
We present a new technique called contrastive principal component analysis
(cPCA) that is designed to discover low-dimensional structure that is unique to
a dataset, or enriched in one dataset relative to other data. The technique is
a generalization of standard PCA, for the setting where multiple datasets are
available -- e.g. a treatment and a control group, or a mixed versus a
homogeneous population -- and the goal is to explore patterns that are specific
to one of the datasets. We conduct a wide variety of experiments in which cPCA
identifies important dataset-specific patterns that are missed by PCA,
demonstrating that it is useful for many applications: subgroup discovery,
visualizing trends, feature selection, denoising, and data-dependent
standardization. We provide geometrical interpretations of cPCA and show that
it satisfies desirable theoretical guarantees. We also extend cPCA to nonlinear
settings in the form of kernel cPCA. We have released our code as a python
package and documentation is on Github.
|
Abubakar Abid, Martin J. Zhang, Vivek K. Bagaria, James Zou
| null |
1709.06716
| null | null |
Bandits with Delayed, Aggregated Anonymous Feedback
|
stat.ML cs.LG
|
We study a variant of the stochastic $K$-armed bandit problem, which we call
"bandits with delayed, aggregated anonymous feedback". In this problem, when
the player pulls an arm, a reward is generated, however it is not immediately
observed. Instead, at the end of each round the player observes only the sum of
a number of previously generated rewards which happen to arrive in the given
round. The rewards are stochastically delayed and due to the aggregated nature
of the observations, the information of which arm led to a particular reward is
lost. The question is what is the cost of the information loss due to this
delayed, aggregated anonymous feedback? Previous works have studied bandits
with stochastic, non-anonymous delays and found that the regret increases only
by an additive factor relating to the expected delay. In this paper, we show
that this additive regret increase can be maintained in the harder delayed,
aggregated anonymous feedback setting when the expected delay (or a bound on
it) is known. We provide an algorithm that matches the worst case regret of the
non-anonymous problem exactly when the delays are bounded, and up to
logarithmic factors or an additive variance term for unbounded delays.
|
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen
Grunewalder
| null |
1709.06853
| null | null |
Optimized Structured Sparse Sensing Matrices for Compressive Sensing
|
eess.SP cs.LG
|
We consider designing a robust structured sparse sensing matrix consisting of
a sparse matrix with a few non-zero entries per row and a dense base matrix for
capturing signals efficiently We design the robust structured sparse sensing
matrix through minimizing the distance between the Gram matrix of the
equivalent dictionary and the target Gram of matrix holding small mutual
coherence. Moreover, a regularization is added to enforce the robustness of the
optimized structured sparse sensing matrix to the sparse representation error
(SRE) of signals of interests. An alternating minimization algorithm with
global sequence convergence is proposed for solving the corresponding
optimization problem. Numerical experiments on synthetic data and natural
images show that the obtained structured sensing matrix results in a higher
signal reconstruction than a random dense sensing matrix.
|
Tao Hong, Xiao Li, Zhihui Zhu and Qiuwei Li
| null |
1709.06895
| null | null |
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy
Search for Robotics
|
cs.RO cs.AI cs.LG cs.NE stat.ML
|
The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. Among the few proposed
approaches, the recently introduced Black-DROPS algorithm exploits a black-box
optimization algorithm to achieve both high data-efficiency and good
computation times when several cores are used; nevertheless, like all
model-based policy search approaches, Black-DROPS does not scale to high
dimensional state/action spaces. In this paper, we introduce a new model
learning procedure in Black-DROPS that leverages parameterized black-box priors
to (1) scale up to high-dimensional systems, and (2) be robust to large
inaccuracies of the prior information. We demonstrate the effectiveness of our
approach with the "pendubot" swing-up task in simulation and with a physical
hexapod robot (48D state space, 18D action space) that has to walk forward as
fast as possible. The results show that our new algorithm is more
data-efficient than previous model-based policy search algorithms (with and
without priors) and that it can allow a physical 6-legged robot to learn new
gaits in only 16 to 30 seconds of interaction time.
|
Konstantinos Chatzilygeroudis and Jean-Baptiste Mouret
| null |
1709.06917
| null | null |
Bayesian Optimization with Automatic Prior Selection for Data-Efficient
Direct Policy Search
|
cs.RO cs.AI cs.LG cs.NE stat.ML
|
One of the most interesting features of Bayesian optimization for direct
policy search is that it can leverage priors (e.g., from simulation or from
previous tasks) to accelerate learning on a robot. In this paper, we are
interested in situations for which several priors exist but we do not know in
advance which one fits best the current situation. We tackle this problem by
introducing a novel acquisition function, called Most Likely Expected
Improvement (MLEI), that combines the likelihood of the priors and the expected
improvement. We evaluate this new acquisition function on a transfer learning
task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has
to learn to walk on flat ground and on stairs, with priors corresponding to
different stairs and different kinds of damages. Our results show that MLEI
effectively identifies and exploits the priors, even when there is no obvious
match between the current situations and the priors.
|
R\'emi Pautrat, Konstantinos Chatzilygeroudis and Jean-Baptiste Mouret
| null |
1709.06919
| null | null |
Stock-out Prediction in Multi-echelon Networks
|
cs.LG
|
In multi-echelon inventory systems the performance of a given node is
affected by events that occur at many other nodes and in many other time
periods. For example, a supply disruption upstream will have an effect on
downstream, customer-facing nodes several periods later as the disruption
"cascades" through the system. There is very little research on stock-out
prediction in single-echelon systems and (to the best of our knowledge) none on
multi-echelon systems. However, in real the world, it is clear that there is
significant interest in techniques for this sort of stock-out prediction.
Therefore, our research aims to fill this gap by using deep neural networks
(DNN) to predict stock-outs in multi-echelon supply chains.
|
Afshin Oroojlooyjadid, Lawrence Snyder, Martin Tak\'a\v{c}
| null |
1709.06922
| null | null |
Spatial features of synaptic adaptation affecting learning performance
|
q-bio.NC cond-mat.dis-nn cs.LG cs.NE
|
Recent studies have proposed that the diffusion of messenger molecules, such
as monoamines, can mediate the plastic adaptation of synapses in supervised
learning of neural networks. Based on these findings we developed a model for
neural learning, where the signal for plastic adaptation is assumed to
propagate through the extracellular space. We investigate the conditions
allowing learning of Boolean rules in a neural network. Even fully excitatory
networks show very good learning performances. Moreover, the investigation of
the plastic adaptation features optimizing the performance suggests that
learning is very sensitive to the extent of the plastic adaptation and the
spatial range of synaptic connections.
|
Damian L. Berger, Lucilla de Arcangelis, and Hans J. Herrmann
|
10.1038/s41598-017-11424-5
|
1709.0695
| null | null |
Structured Probabilistic Pruning for Convolutional Neural Network
Acceleration
|
cs.LG stat.ML
|
In this paper, we propose a novel progressive parameter pruning method for
Convolutional Neural Network acceleration, named Structured Probabilistic
Pruning (SPP), which effectively prunes weights of convolutional layers in a
probabilistic manner. Unlike existing deterministic pruning approaches, where
unimportant weights are permanently eliminated, SPP introduces a pruning
probability for each weight, and pruning is guided by sampling from the pruning
probabilities. A mechanism is designed to increase and decrease pruning
probabilities based on importance criteria in the training process. Experiments
show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of
top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet
classification. Moreover, SPP can be directly applied to accelerate
multi-branch CNN networks, such as ResNet, without specific adaptations. Our 2x
speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet. We
further show the effectiveness of SPP on transfer learning tasks.
|
Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu
| null |
1709.06994
| null | null |
Estimated Depth Map Helps Image Classification
|
cs.CV cs.LG
|
We consider image classification with estimated depth. This problem falls
into the domain of transfer learning, since we are using a model trained on a
set of depth images to generate depth maps (additional features) for use in
another classification problem using another disjoint set of images. It's
challenging as no direct depth information is provided. Though depth estimation
has been well studied, none have attempted to aid image classification with
estimated depth. Therefore, we present a way of transferring domain knowledge
on depth estimation to a separate image classification task over a disjoint set
of train, and test data. We build a RGBD dataset based on RGB dataset and do
image classification on it. Then evaluation the performance of neural networks
on the RGBD dataset compared to the RGB dataset. From our experiments, the
benefit is significant with shallow and deep networks. It improves ResNet-20 by
0.55% and ResNet-56 by 0.53%. Our code and dataset are available publicly.
|
Yihui He
| null |
1709.07077
| null | null |
On the Design of LQR Kernels for Efficient Controller Learning
|
cs.SY cs.LG stat.ML
|
Finding optimal feedback controllers for nonlinear dynamic systems from data
is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful
framework for direct controller tuning from experimental trials. For selecting
the next query point and finding the global optimum, BO relies on a
probabilistic description of the latent objective function, typically a
Gaussian process (GP). As is shown herein, GPs with a common kernel choice can,
however, lead to poor learning outcomes on standard quadratic control problems.
For a first-order system, we construct two kernels that specifically leverage
the structure of the well-known Linear Quadratic Regulator (LQR), yet retain
the flexibility of Bayesian nonparametric learning. Simulations of uncertain
linear and nonlinear systems demonstrate that the LQR kernels yield superior
learning performance.
|
Alonso Marco, Philipp Hennig, Stefan Schaal and Sebastian Trimpe
|
10.1109/CDC.2017.8264429
|
1709.07089
| null | null |
Near Optimal Sketching of Low-Rank Tensor Regression
|
cs.LG cs.DS stat.ML
|
We study the least squares regression problem \begin{align*} \min_{\Theta \in
\mathcal{S}_{\odot D,R}} \|A\Theta-b\|_2, \end{align*} where
$\mathcal{S}_{\odot D,R}$ is the set of $\Theta$ for which $\Theta =
\sum_{r=1}^{R} \theta_1^{(r)} \circ \cdots \circ \theta_D^{(r)}$ for vectors
$\theta_d^{(r)} \in \mathbb{R}^{p_d}$ for all $r \in [R]$ and $d \in [D]$, and
$\circ$ denotes the outer product of vectors. That is, $\Theta$ is a
low-dimensional, low-rank tensor. This is motivated by the fact that the number
of parameters in $\Theta$ is only $R \cdot \sum_{d=1}^D p_d$, which is
significantly smaller than the $\prod_{d=1}^{D} p_d$ number of parameters in
ordinary least squares regression. We consider the above CP decomposition model
of tensors $\Theta$, as well as the Tucker decomposition. For both models we
show how to apply data dimensionality reduction techniques based on {\it
sparse} random projections $\Phi \in \mathbb{R}^{m \times n}$, with $m \ll n$,
to reduce the problem to a much smaller problem $\min_{\Theta} \|\Phi A \Theta
- \Phi b\|_2$, for which if $\Theta'$ is a near-optimum to the smaller problem,
then it is also a near optimum to the original problem. We obtain significantly
smaller dimension and sparsity in $\Phi$ than is possible for ordinary least
squares regression, and we also provide a number of numerical simulations
supporting our theory.
|
Jarvis Haupt and Xingguo Li and David P. Woodruff
| null |
1709.07093
| null | null |
Deconvolutional Latent-Variable Model for Text Sequence Matching
|
cs.CL cs.LG stat.ML
|
A latent-variable model is introduced for text matching, inferring sentence
representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for
text, we employ deconvolutional networks as the sequence decoder (generator),
providing learned latent codes with more semantic information and better
generalization. Our model, trained in an unsupervised manner, yields stronger
empirical predictive performance than a decoder based on Long Short-Term Memory
(LSTM), with less parameters and considerably faster training. Further, we
apply it to text sequence-matching problems. The proposed model significantly
outperforms several strong sentence-encoding baselines, especially in the
semi-supervised setting.
|
Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin
| null |
1709.07109
| null | null |
Variational Memory Addressing in Generative Models
|
cs.LG
|
Aiming to augment generative models with external memory, we interpret the
output of a memory module with stochastic addressing as a conditional mixture
distribution, where a read operation corresponds to sampling a discrete memory
address and retrieving the corresponding content from memory. This perspective
allows us to apply variational inference to memory addressing, which enables
effective training of the memory module by using the target information to
guide memory lookups. Stochastic addressing is particularly well-suited for
generative models as it naturally encourages multimodality which is a prominent
aspect of most high-dimensional datasets. Treating the chosen address as a
latent variable also allows us to quantify the amount of information gained
with a memory lookup and measure the contribution of the memory module to the
generative process. To illustrate the advantages of this approach we
incorporate it into a variational autoencoder and apply the resulting model to
the task of generative few-shot learning. The intuition behind this
architecture is that the memory module can pick a relevant template from memory
and the continuous part of the model can concentrate on modeling remaining
variations. We demonstrate empirically that our model is able to identify and
access the relevant memory contents even with hundreds of unseen Omniglot
characters in memory
|
J\"org Bornschein and Andriy Mnih and Daniel Zoran and Danilo J.
Rezende
| null |
1709.07116
| null | null |
Deep Recurrent NMF for Speech Separation by Unfolding Iterative
Thresholding
|
cs.SD cs.LG stat.ML
|
In this paper, we propose a novel recurrent neural network architecture for
speech separation. This architecture is constructed by unfolding the iterations
of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
optimization problem for sparse nonnegative matrix factorization (NMF) of
spectrograms. We name this network architecture deep recurrent NMF (DR-NMF).
The proposed DR-NMF network has three distinct advantages. First, DR-NMF
provides better interpretability than other deep architectures, since the
weights correspond to NMF model parameters, even after training. This
interpretability also provides principled initializations that enable faster
training and convergence to better solutions compared to conventional random
initialization. Second, like many deep networks, DR-NMF is an order of
magnitude faster at test time than NMF, since computation of the network output
only requires evaluating a few layers at each time step. Third, when a limited
amount of training data is available, DR-NMF exhibits stronger generalization
and separation performance compared to sparse NMF and state-of-the-art
long-short term memory (LSTM) networks. When a large amount of training data is
available, DR-NMF achieves lower yet competitive separation performance
compared to LSTM networks.
|
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas
| null |
1709.07124
| null | null |
Learning RBM with a DC programming Approach
|
cs.LG stat.ML
|
By exploiting the property that the RBM log-likelihood function is the
difference of convex functions, we formulate a stochastic variant of the
difference of convex functions (DC) programming to minimize the negative
log-likelihood. Interestingly, the traditional contrastive divergence algorithm
is a special case of the above formulation and the hyperparameters of the two
algorithms can be chosen such that the amount of computation per mini-batch is
identical. We show that for a given computational budget the proposed algorithm
almost always reaches a higher log-likelihood more rapidly, compared to the
standard contrastive divergence algorithm. Further, we modify this algorithm to
use the centered gradients and show that it is more efficient and effective
compared to the standard centered gradient algorithm on benchmark datasets.
|
Vidyadhar Upadhya, P. S. Sastry
| null |
1709.07149
| null | null |
Feature Engineering for Predictive Modeling using Reinforcement Learning
|
cs.AI cs.LG stat.ML
|
Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using
mathematical functions, with the objective of reducing the modeling error for a
given target. However, there is no well-defined basis for performing effective
feature engineering. It involves domain knowledge, intuition, and most of all,
a lengthy process of trial and error. The human attention involved in
overseeing this process significantly influences the cost of model generation.
We present a new framework to automate feature engineering. It is based on
performance driven exploration of a transformation graph, which systematically
and compactly enumerates the space of given options. A highly efficient
exploration strategy is derived through reinforcement learning on past
examples.
|
Udayan Khurana and Horst Samulowitz and Deepak Turaga
| null |
1709.0715
| null | null |
SpectralLeader: Online Spectral Learning for Single Topic Models
|
cs.LG stat.ML
|
We study the problem of learning a latent variable model from a stream of
data. Latent variable models are popular in practice because they can explain
observed data in terms of unobserved concepts. These models have been
traditionally studied in the offline setting. In the online setting, on the
other hand, the online EM is arguably the most popular algorithm for learning
latent variable models. Although the online EM is computationally efficient, it
typically converges to a local optimum. In this work, we develop a new online
learning algorithm for latent variable models, which we call SpectralLeader.
SpectralLeader always converges to the global optimum, and we derive a
sublinear upper bound on its $n$-step regret in the bag-of-words model. In both
synthetic and real-world experiments, we show that SpectralLeader performs
similarly to or better than the online EM with tuned hyper-parameters.
|
Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel
| null |
1709.07172
| null | null |
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
|
cs.CV cs.LG cs.MM
|
This paper addresses the question of emotion classification. The task
consists in predicting emotion labels (taken among a set of possible labels)
best describing the emotions contained in short video clips. Building on a
standard framework -- lying in describing videos by audio and visual features
used by a supervised classifier to infer the labels -- this paper investigates
several novel directions. First of all, improved face descriptors based on 2D
and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores
several fusion methods, temporal and multimodal, including a novel hierarchical
method combining features and scores. In addition, we carefully reviewed the
different stages of the pipeline and designed a CNN architecture adapted to the
task; this is important as the size of the training set is small compared to
the difficulty of the problem, making generalization difficult. The so-obtained
model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of
58.8 %.
|
Valentin Vielzeuf, St\'ephane Pateux, Fr\'ed\'eric Jurie
| null |
1709.072
| null | null |
Convolutional neural networks that teach microscopes how to image
|
cs.CV cs.AI cs.LG physics.optics
|
Deep learning algorithms offer a powerful means to automatically analyze the
content of medical images. However, many biological samples of interest are
primarily transparent to visible light and contain features that are difficult
to resolve with a standard optical microscope. Here, we use a convolutional
neural network (CNN) not only to classify images, but also to optimize the
physical layout of the imaging device itself. We increase the classification
accuracy of a microscope's recorded images by merging an optical model of image
formation into the pipeline of a CNN. The resulting network simultaneously
determines an ideal illumination arrangement to highlight important sample
features during image acquisition, along with a set of convolutional weights to
classify the detected images post-capture. We demonstrate our joint
optimization technique with an experimental microscope configuration that
automatically identifies malaria-infected cells with 5-10% higher accuracy than
standard and alternative microscope lighting designs.
|
Roarke Horstmeyer, Richard Y. Chen, Barbara Kappes and Benjamin
Judkewitz
| null |
1709.07223
| null | null |
Local Communication Protocols for Learning Complex Swarm Behaviors with
Deep Reinforcement Learning
|
cs.MA cs.AI cs.LG cs.SY stat.ML
|
Swarm systems constitute a challenging problem for reinforcement learning
(RL) as the algorithm needs to learn decentralized control policies that can
cope with limited local sensing and communication abilities of the agents.
While it is often difficult to directly define the behavior of the agents,
simple communication protocols can be defined more easily using prior knowledge
about the given task. In this paper, we propose a number of simple
communication protocols that can be exploited by deep reinforcement learning to
find decentralized control policies in a multi-robot swarm environment. The
protocols are based on histograms that encode the local neighborhood relations
of the agents and can also transmit task-specific information, such as the
shortest distance and direction to a desired target. In our framework, we use
an adaptation of Trust Region Policy Optimization to learn complex
collaborative tasks, such as formation building and building a communication
link. We evaluate our findings in a simulated 2D-physics environment, and
compare the implications of different communication protocols.
|
Maximilian H\"uttenrauch and Adrian \v{S}o\v{s}i\'c and Gerhard
Neumann
| null |
1709.07224
| null | null |
Predicting Positive and Negative Links with Noisy Queries: Theory &
Practice
|
cs.DS cs.DM cs.LG cs.SI math.CO
|
Social networks involve both positive and negative relationships, which can
be captured in signed graphs. The {\em edge sign prediction problem} aims to
predict whether an interaction between a pair of nodes will be positive or
negative. We provide theoretical results for this problem that motivate natural
improvements to recent heuristics.
The edge sign prediction problem is related to correlation clustering; a
positive relationship means being in the same cluster. We consider the
following model for two clusters: we are allowed to query any pair of nodes
whether they belong to the same cluster or not, but the answer to the query is
corrupted with some probability $0<q<\frac{1}{2}$. Let $\delta=1-2q$ be the
bias. We provide an algorithm that recovers all signs correctly with high
probability in the presence of noise with $O(\frac{n\log
n}{\delta^2}+\frac{\log^2 n}{\delta^6})$ queries. This is the best known result
for this problem for all but tiny $\delta$, improving on the recent work of
Mazumdar and Saha \cite{mazumdar2017clustering}. We also provide an algorithm
that performs $O(\frac{n\log n}{\delta^4})$ queries, and uses breadth first
search as its main algorithmic primitive. While both the running time and the
number of queries for this algorithm are sub-optimal, our result relies on
novel theoretical techniques, and naturally suggests the use of edge-disjoint
paths as a feature for predicting signs in online social networks.
Correspondingly, we experiment with using edge disjoint $s-t$ paths of short
length as a feature for predicting the sign of edge $(s,t)$ in real-world
signed networks. Empirical findings suggest that the use of such paths improves
the classification accuracy, especially for pairs of nodes with no common
neighbors.
|
Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen,
Jaros{\l}aw B{\l}asiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos
| null |
1709.07308
| null | null |
Exact Learning of Lightweight Description Logic Ontologies
|
cs.LG cs.AI cs.LO
|
We study the problem of learning description logic (DL) ontologies in Angluin
et al.'s framework of exact learning via queries. We admit membership queries
("is a given subsumption entailed by the target ontology?") and equivalence
queries ("is a given ontology equivalent to the target ontology?"). We present
three main results: (1) ontologies formulated in (two relevant versions of) the
description logic DL-Lite can be learned with polynomially many queries of
polynomial size; (2) this is not the case for ontologies formulated in the
description logic EL, even when only acyclic ontologies are admitted; and (3)
ontologies formulated in a fragment of EL related to the web ontology language
OWL 2 RL can be learned in polynomial time. We also show that neither
membership nor equivalence queries alone are sufficient in cases (1) and (3).
|
Boris Konev, Carsten Lutz, Ana Ozaki and Frank Wolter
| null |
1709.07314
| null | null |
Class-Splitting Generative Adversarial Networks
|
stat.ML cs.CV cs.LG
|
Generative Adversarial Networks (GANs) produce systematically better quality
samples when class label information is provided., i.e. in the conditional GAN
setup. This is still observed for the recently proposed Wasserstein GAN
formulation which stabilized adversarial training and allows considering high
capacity network architectures such as ResNet. In this work we show how to
boost conditional GAN by augmenting available class labels. The new classes
come from clustering in the representation space learned by the same GAN model.
The proposed strategy is also feasible when no class information is available,
i.e. in the unsupervised setup. Our generated samples reach state-of-the-art
Inception scores for CIFAR-10 and STL-10 datasets in both supervised and
unsupervised setup.
|
Guillermo L. Grinblat, Lucas C. Uzal and Pablo M. Granitto
| null |
1709.07359
| null | null |
Geometric SMOTE: Effective oversampling for imbalanced learning through
a geometric extension of SMOTE
|
cs.LG
|
Classification of imbalanced datasets is a challenging task for standard
algorithms. Although many methods exist to address this problem in different
ways, generating artificial data for the minority class is a more general
approach compared to algorithmic modifications. SMOTE algorithm and its
variations generate synthetic samples along a line segment that joins minority
class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
generalization of the SMOTE data generation mechanism. G-SMOTE generates
synthetic samples in a geometric region of the input space, around each
selected minority instance. While in the basic configuration this region is a
hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
a line segment, emulating, in the last case, the SMOTE mechanism. The
performance of G-SMOTE is compared against multiple standard oversampling
algorithms. We present empirical results that show a significant improvement in
the quality of the generated data when G-SMOTE is used as an oversampling
algorithm.
|
Georgios Douzas and Fernando Bacao
| null |
1709.07377
| null | null |
Quantum autoencoders via quantum adders with genetic algorithms
|
quant-ph cs.LG cs.NE
|
The quantum autoencoder is a recent paradigm in the field of quantum machine
learning, which may enable an enhanced use of resources in quantum
technologies. To this end, quantum neural networks with less nodes in the inner
than in the outer layers were considered. Here, we propose a useful connection
between approximate quantum adders and quantum autoencoders. Specifically, this
link allows us to employ optimized approximate quantum adders, obtained with
genetic algorithms, for the implementation of quantum autoencoders for a
variety of initial states. Furthermore, we can also directly optimize the
quantum autoencoders via genetic algorithms. Our approach opens a different
path for the design of quantum autoencoders in controllable quantum platforms.
|
L. Lamata, U. Alvarez-Rodriguez, J. D. Mart\'in-Guerrero, M. Sanz, E.
Solano
|
10.1088/2058-9565/aae22b
|
1709.07409
| null | null |
Neural Optimizer Search with Reinforcement Learning
|
cs.AI cs.LG stat.ML
|
We present an approach to automate the process of discovering optimization
methods, with a focus on deep learning architectures. We train a Recurrent
Neural Network controller to generate a string in a domain specific language
that describes a mathematical update equation based on a list of primitive
functions, such as the gradient, running average of the gradient, etc. The
controller is trained with Reinforcement Learning to maximize the performance
of a model after a few epochs. On CIFAR-10, our method discovers several update
rules that are better than many commonly used optimizers, such as Adam,
RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two
new optimizers, named PowerSign and AddSign, which we show transfer well and
improve training on a variety of different tasks and architectures, including
ImageNet classification and Google's neural machine translation system.
|
Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le
| null |
1709.07417
| null | null |
Perturbative Black Box Variational Inference
|
stat.ML cs.LG
|
Black box variational inference (BBVI) with reparameterization gradients
triggered the exploration of divergence measures other than the
Kullback-Leibler (KL) divergence, such as alpha divergences. In this paper, we
view BBVI with generalized divergences as a form of estimating the marginal
likelihood via biased importance sampling. The choice of divergence determines
a bias-variance trade-off between the tightness of a bound on the marginal
likelihood (low bias) and the variance of its gradient estimators. Drawing on
variational perturbation theory of statistical physics, we use these insights
to construct a family of new variational bounds. Enumerated by an odd integer
order $K$, this family captures the standard KL bound for $K=1$, and converges
to the exact marginal likelihood as $K\to\infty$. Compared to
alpha-divergences, our reparameterization gradients have a lower variance. We
show in experiments on Gaussian Processes and Variational Autoencoders that the
new bounds are more mass covering, and that the resulting posterior covariances
are closer to the true posterior and lead to higher likelihoods on held-out
data.
|
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
| null |
1709.07433
| null | null |
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product
Embeddings
|
cs.AI cs.LG stat.ML
|
E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
billions of products. Machine learning (ML) algorithms involving products are
often used to improve the customer experience and increase revenue, e.g.,
product similarity, recommendation, and price estimation. The products are
required to be represented as features before training an ML algorithm. In this
paper, we propose an approach called MRNet-Product2Vec for creating generic
embeddings of products within an e-commerce ecosystem. We learn a dense and
low-dimensional embedding where a diverse set of signals related to a product
are explicitly injected into its representation. We train a Discriminative
Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a
product title fed through a Bidirectional RNN and at the output, product labels
corresponding to fifteen different tasks are predicted. The task set includes
several intrinsic characteristics about a product such as price, weight, size,
color, popularity, and material. We evaluate the proposed embedding
quantitatively and qualitatively. We demonstrate that they are almost as good
as sparse and extremely high-dimensional TF-IDF representation in spite of
having less than 3% of the TF-IDF dimension. We also use a multimodal
autoencoder for comparing products from different language-regions and show
preliminary yet promising qualitative results.
|
Arijit Biswas, Mukul Bhutani and Subhajit Sanyal
| null |
1709.07534
| null | null |
Attention-based Mixture Density Recurrent Networks for History-based
Recommendation
|
cs.LG cs.IR
|
The goal of personalized history-based recommendation is to automatically
output a distribution over all the items given a sequence of previous purchases
of a user. In this work, we present a novel approach that uses a recurrent
network for summarizing the history of purchases, continuous vectors
representing items for scalability, and a novel attention-based recurrent
mixture density network, which outputs each component in a mixture
sequentially, for modelling a multi-modal conditional distribution. We evaluate
the proposed approach on two publicly available datasets, MovieLens-20M and
RecSys15. The experiments show that the proposed approach, which explicitly
models the multi-modal nature of the predictive distribution, is able to
improve the performance over various baselines in terms of precision, recall
and nDCG.
|
Tian Wang, Kyunghyun Cho
| null |
1709.07545
| null | null |
Neural Networks for Predicting Algorithm Runtime Distributions
|
cs.AI cs.LG
|
Many state-of-the-art algorithms for solving hard combinatorial problems in
artificial intelligence (AI) include elements of stochasticity that lead to
high variations in runtime, even for a fixed problem instance. Knowledge about
the resulting runtime distributions (RTDs) of algorithms on given problem
instances can be exploited in various meta-algorithmic procedures, such as
algorithm selection, portfolios, and randomized restarts. Previous work has
shown that machine learning can be used to individually predict mean, median
and variance of RTDs. To establish a new state-of-the-art in predicting RTDs,
we demonstrate that the parameters of an RTD should be learned jointly and that
neural networks can do this well by directly optimizing the likelihood of an
RTD given runtime observations. In an empirical study involving five algorithms
for SAT solving and AI planning, we show that neural networks predict the true
RTDs of unseen instances better than previous methods, and can even do so when
only few runtime observations are available per training instance.
|
Katharina Eggensperger, Marius Lindauer and Frank Hutter
|
10.24963/ijcai.2018/200
|
1709.07615
| null | null |
Total stability of kernel methods
|
stat.ML cs.LG
|
Regularized empirical risk minimization using kernels and their corresponding
reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine
learning. However, the actually used kernel often depends on one or on a few
hyperparameters or the kernel is even data dependent in a much more complicated
manner. Examples are Gaussian RBF kernels, kernel learning, and hierarchical
Gaussian kernels which were recently proposed for deep learning. Therefore, the
actually used kernel is often computed by a grid search or in an iterative
manner and can often only be considered as an approximation to the "ideal" or
"optimal" kernel. The paper gives conditions under which classical kernel based
methods based on a convex Lipschitz loss function and on a bounded and smooth
kernel are stable, if the probability measure $P$, the regularization parameter
$\lambda$, and the kernel $k$ may slightly change in a simultaneous manner.
Similar results are also given for pairwise learning. Therefore, the topic of
this paper is somewhat more general than in classical robust statistics, where
usually only the influence of small perturbations of the probability measure
$P$ on the estimated function is considered.
|
Andreas Christmann and Daohong Xiang and Ding-Xuan Zhou
| null |
1709.07625
| null | null |
BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT
Devices using RNNs
|
cs.CR cs.LG cs.NE
|
Recurrent neural networks (RNNs) have shown promising results in audio and
speech processing applications due to their strong capabilities in modelling
sequential data. In many applications, RNNs tend to outperform conventional
models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices
makes a strong case for implementing RNN based inferences for applications such
as acoustics based authentication, voice commands, and edge analytics for smart
homes. Nonetheless, the feasibility and performance of RNN based inferences on
resources-constrained IoT devices remain largely unexplored. In this paper, we
investigate the feasibility of using RNNs for an end-to-end authentication
system based on breathing acoustics. We evaluate the performance of RNN models
on three types of devices; smartphone, smartwatch, and Raspberry Pi and show
that unlike CNN models, RNN models can be easily ported onto
resource-constrained devices without a significant loss in accuracy.
|
Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna
Seneviratne, Youngki Lee
| null |
1709.07626
| null | null |
Approximate Bayesian Inference in Linear State Space Models for
Intermittent Demand Forecasting at Scale
|
stat.ML cs.LG
|
We present a scalable and robust Bayesian inference method for linear state
space models. The method is applied to demand forecasting in the context of a
large e-commerce platform, paying special attention to intermittent and bursty
target statistics. Inference is approximated by the Newton-Raphson algorithm,
reduced to linear-time Kalman smoothing, which allows us to operate on several
orders of magnitude larger problems than previous related work. In a study on
large real-world sales datasets, our method outperforms competing approaches on
fast and medium moving items.
|
Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan
Gasthaus, Tim Januschowski, Valentin Flunkert
| null |
1709.07638
| null | null |
Probabilistic Synchronous Parallel
|
cs.DC cs.LG
|
Most machine learning and deep neural network algorithms rely on certain
iterative algorithms to optimise their utility/cost functions, e.g. Stochastic
Gradient Descent. In distributed learning, the networked nodes have to work
collaboratively to update the model parameters, and the way how they proceed is
referred to as synchronous parallel design (or barrier control). Synchronous
parallel protocol is the building block of any distributed learning framework,
and its design has direct impact on the performance and scalability of the
system.
In this paper, we propose a new barrier control technique - Probabilistic
Synchronous Parallel (PSP). Com- paring to the previous Bulk Synchronous
Parallel (BSP), Stale Synchronous Parallel (SSP), and (Asynchronous Parallel)
ASP, the proposed solution e ectively improves both the convergence speed and
the scalability of the SGD algorithm by introducing a sampling primitive into
the system. Moreover, we also show that the sampling primitive can be applied
atop of the existing barrier control mechanisms to derive fully distributed
PSP-based synchronous parallel.
We not only provide a thorough theoretical analysis1 on the convergence of
PSP-based SGD algorithm, but also implement a full-featured distributed
learning framework called Actor and perform intensive evaluation atop of it.
|
Liang Wang, Ben Catterall and Richard Mortier
| null |
1709.07772
| null | null |
Computation Error Analysis of Block Floating Point Arithmetic Oriented
Convolution Neural Network Accelerator Design
|
cs.LG
|
The heavy burdens of computation and off-chip traffic impede deploying the
large scale convolution neural network on embedded platforms. As CNN is
attributed to the strong endurance to computation errors, employing block
floating point (BFP) arithmetics in CNN accelerators could save the hardware
cost and data traffics efficiently, while maintaining the classification
accuracy. In this paper, we verify the effects of word width definitions in BFP
to the CNN performance without retraining. Several typical CNN models,
including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper.
Experiments revealed that 8-bit mantissa, including sign bit, in BFP
representation merely induced less than 0.3% accuracy loss. In addition, we
investigate the computational errors in theory and develop the noise-to-signal
ratio (NSR) upper bound, which provides the promising guidance for BFP based
CNN engine design.
|
Zhourui Song, Zhenyu Liu and Dongsheng Wang
| null |
1709.07776
| null | null |
On overfitting and asymptotic bias in batch reinforcement learning with
partial observability
|
stat.ML cs.AI cs.LG
|
This paper provides an analysis of the tradeoff between asymptotic bias
(suboptimality with unlimited data) and overfitting (additional suboptimality
due to limited data) in the context of reinforcement learning with partial
observability. Our theoretical analysis formally characterizes that while
potentially increasing the asymptotic bias, a smaller state representation
decreases the risk of overfitting. This analysis relies on expressing the
quality of a state representation by bounding L1 error terms of the associated
belief states. Theoretical results are empirically illustrated when the state
representation is a truncated history of observations, both on synthetic POMDPs
and on a large-scale POMDP in the context of smartgrids, with real-world data.
Finally, similarly to known results in the fully observable setting, we also
briefly discuss and empirically illustrate how using function approximators and
adapting the discount factor may enhance the tradeoff between asymptotic bias
and overfitting in the partially observable context.
|
Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien
Ernst, Raphael Fonteneau
| null |
1709.07796
| null | null |
Attention-based Wav2Text with Feature Transfer Learning
|
cs.CL cs.LG cs.SD
|
Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learning, interest has emerged in the possibility of
developing a purely end-to-end ASR system from the raw waveform to the
transcription without any predefined alignments and hand-engineered models.
However, the successful attempts in end-to-end architecture still used
spectral-based features, while the successful attempts in using raw waveform
were still based on the hybrid deep neural network - Hidden Markov model
(DNN-HMM) framework. In this paper, we construct the first end-to-end
attention-based encoder-decoder model to process directly from raw speech
waveform to the text transcription. We called the model as "Attention-based
Wav2Text". To assist the training process of the end-to-end model, we propose
to utilize a feature transfer learning. Experimental results also reveal that
the proposed Attention-based Wav2Text model directly with raw waveform could
achieve a better result in comparison with the attentional encoder-decoder
model trained on standard front-end filterbank features.
|
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
| null |
1709.07814
| null | null |
Using Simulation and Domain Adaptation to Improve Efficiency of Deep
Robotic Grasping
|
cs.LG cs.AI cs.CV cs.RO
|
Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.
|
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew
Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt
Konolige, Sergey Levine, Vincent Vanhoucke
| null |
1709.07857
| null | null |
Machine Learning Models that Remember Too Much
|
cs.CR cs.LG
|
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data.
|
Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov
| null |
1709.07886
| null | null |
On the Discrimination Power and Effective Utilization of Active Learning
Measures in Version Space Search
|
cs.LG
|
Active Learning (AL) methods have proven cost-saving against passive
supervised methods in many application domains. An active learner, aiming to
find some target hypothesis, formulates sequential queries to some oracle. The
set of hypotheses consistent with the already answered queries is called
version space. Several query selection measures (QSMs) for determining the best
query to ask next have been proposed. Assuming binaryoutcome queries, we
analyze various QSMs wrt. to the discrimination power of their selected queries
within the current version space. As a result, we derive superiority and
equivalence relations between these QSMs and introduce improved versions of
existing QSMs to overcome identified issues. The obtained picture gives a hint
about which QSMs should preferably be used in pool-based AL scenarios.
Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
these, we demonstrate how efficient heuristic search methods for optimal
queries in query synthesis AL scenarios can be devised.
|
Patrick Rodler
| null |
1709.07899
| null | null |
Unsupervised Learning of Disentangled and Interpretable Representations
from Sequential Data
|
cs.LG cs.CL cs.SD eess.AS stat.ML
|
We present a factorized hierarchical variational autoencoder, which learns
disentangled and interpretable representations from sequential data without
supervision. Specifically, we exploit the multi-scale nature of information in
sequential data by formulating it explicitly within a factorized hierarchical
graphical model that imposes sequence-dependent priors and sequence-independent
priors to different sets of latent variables. The model is evaluated on two
speech corpora to demonstrate, qualitatively, its ability to transform speakers
or linguistic content by manipulating different sets of latent variables; and
quantitatively, its ability to outperform an i-vector baseline for speaker
verification and reduce the word error rate by as much as 35% in mismatched
train/test scenarios for automatic speech recognition tasks.
|
Wei-Ning Hsu, Yu Zhang, and James Glass
| null |
1709.07902
| null | null |
Ensemble Multi-task Gaussian Process Regression with Multiple Latent
Processes
|
stat.ML cs.LG
|
Multi-task/Multi-output learning seeks to exploit correlation among tasks to
enhance performance over learning or solving each task independently. In this
paper, we investigate this problem in the context of Gaussian Processes (GPs)
and propose a new model which learns a mixture of latent processes by
decomposing the covariance matrix into a sum of structured hidden components
each of which is controlled by a latent GP over input features and a "weight"
over tasks. From this sum structure, we propose a parallelizable parameter
learning algorithm with a predetermined initialization for the "weights". We
also notice that an ensemble parameter learning approach using mini-batches of
training data not only reduces the computation complexity of learning but also
improves the regression performance. We evaluate our model on two datasets, the
smaller Swiss Jura dataset and another relatively larger ATMS dataset from
NOAA. Substantial improvements are observed compared with established
alternatives.
|
Weitong Ruan and Eric L. Miller
| null |
1709.07903
| null | null |
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through
Multi-Sensory Semi-Supervised Learning
|
cs.LG cs.RO
|
Imitation learning holds the promise to address challenging robotic tasks
such as autonomous navigation. It however requires a human supervisor to
oversee the training process and send correct control commands to robots
without feedback, which is always prone to error and expensive. To minimize
human involvement and avoid manual labeling of data in the robotic autonomous
navigation with imitation learning, this paper proposes a novel semi-supervised
imitation learning solution based on a multi-sensory design. This solution
includes a suboptimal sensor policy based on sensor fusion to automatically
label states encountered by a robot to avoid human supervision during training.
In addition, a recording policy is developed to throttle the adversarial affect
of learning too much from the suboptimal sensor policy. This solution allows
the robot to learn a navigation policy in a self-supervised manner. With
extensive experiments in indoor environments, this solution can achieve near
human performance in most of the tasks and even surpasses human performance in
case of unexpected events such as hardware failures or human operation errors.
To best of our knowledge, this is the first work that synthesizes sensor fusion
and imitation learning to enable robotic autonomous navigation in the real
world without human supervision.
|
Junhong Xu, Shangyue Zhu, Hanqing Guo and Shaoen Wu
| null |
1709.07911
| null | null |
Cascaded Region-based Densely Connected Network for Event Detection: A
Seismic Application
|
cs.LG cs.CV
|
Automatic event detection from time series signals has wide applications,
such as abnormal event detection in video surveillance and event detection in
geophysical data. Traditional detection methods detect events primarily by the
use of similarity and correlation in data. Those methods can be inefficient and
yield low accuracy. In recent years, because of the significantly increased
computational power, machine learning techniques have revolutionized many
science and engineering domains. In this study, we apply a deep-learning-based
method to the detection of events from time series seismic signals. However, a
direct adaptation of the similar ideas from 2D object detection to our problem
faces two challenges. The first challenge is that the duration of earthquake
event varies significantly; The other is that the proposals generated are
temporally correlated. To address these challenges, we propose a novel cascaded
region-based convolutional neural network to capture earthquake events in
different sizes, while incorporating contextual information to enrich features
for each individual proposal. To achieve a better generalization performance,
we use densely connected blocks as the backbone of our network. Because of the
fact that some positive events are not correctly annotated, we further
formulate the detection problem as a learning-from-noise problem. To verify the
performance of our detection methods, we employ our methods to seismic data
generated from a bi-axial "earthquake machine" located at Rock Mechanics
Laboratory, and we acquire labels with the help of experts. Through our
numerical tests, we show that our novel detection techniques yield high
accuracy. Therefore, our novel deep-learning-based detection methods can
potentially be powerful tools for locating events from time series data in
various applications.
|
Yue Wu and Youzuo Lin and Zheng Zhou and David Chas Bolton and Ji Liu
and Paul Johnson
|
10.1109/TGRS.2018.2852302
|
1709.07943
| null | null |
Multi-task Learning with Gradient Guided Policy Specialization
|
cs.RO cs.AI cs.LG
|
We present a method for efficient learning of control policies for multiple
related robotic motor skills. Our approach consists of two stages, joint
training and specialization training. During the joint training stage, a neural
network policy is trained with minimal information to disambiguate the motor
skills. This forces the policy to learn a common representation of the
different tasks. Then, during the specialization training stage we selectively
split the weights of the policy based on a per-weight metric that measures the
disagreement among the multiple tasks. By splitting part of the control policy,
it can be further trained to specialize to each task. To update the control
policy during learning, we use Trust Region Policy Optimization with
Generalized Advantage Function (TRPOGAE). We propose a modification to the
gradient update stage of TRPO to better accommodate multi-task learning
scenarios. We evaluate our approach on three continuous motor skill learning
problems in simulation: 1) a locomotion task where three single legged robots
with considerable difference in shape and size are trained to hop forward, 2) a
manipulation task where three robot manipulators with different sizes and joint
types are trained to reach different locations in 3D space, and 3) locomotion
of a two-legged robot, whose range of motion of one leg is constrained in
different ways. We compare our training method to three baselines. The first
baseline uses only joint training for the policy, the second trains independent
policies for each task, and the last randomly selects weights to split. We show
that our approach learns more efficiently than each of the baseline methods.
|
Wenhao Yu, C. Karen Liu, Greg Turk
| null |
1709.07979
| null | null |
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