pwc-archive/papers-with-abstracts huggingface dataset:
On the Expressive Power of Overlapping Architectures of Deep Learning
Authors: Or Sharir, Amnon Shashua
Date: 2017-03-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Attribute, Deep Learning

Expressive efficiency refers to the relation between two architectures A and
B, whereby any function realized by B could be replicated by A, but there
exists functions realized by A, which cannot be replicated by B unless its size
grows significantly larger. For example, it is known that deep networks are
exponentially efficient with respect to shallow networks, in the sense that a
shallow network must grow exponentially large in order to approximate the
functions represented by a deep network of polynomial size. In this work, we
extend the study of expressive efficiency to the attribute of network
connectivity and in particular to the effect of "overlaps" in the convolutional
process, i.e., when the stride of the convolution is smaller than its filter
size (receptive field). To theoretically analyze this aspect of network's
design, we focus on a well-established surrogate for ConvNets called
Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically
that our results hold for standard ConvNets as well. Specifically, our analysis
shows that having overlapping local receptive fields, and more broadly denser
connectivity, results in an exponential increase in the expressive capacity of
neural networks. Moreover, while denser connectivity can increase the
expressive capacity, we show that the most common types of modern architectures
already exhibit exponential increase in expressivity, without relying on
fully-connected layers.
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
Authors: Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
Date: 2018-02-24 00:00:00
Tasks: Atari Games, Benchmarking, MuJoCo, Reinforcement Learning, Reinforcement Learning (RL)

Evolution Strategies (ES) have recently been demonstrated to be a viable
alternative to reinforcement learning (RL) algorithms on a set of challenging
deep RL problems, including Atari games and MuJoCo humanoid locomotion
benchmarks. While the ES algorithms in that work belonged to the specialized
class of natural evolution strategies (which resemble approximate gradient RL
algorithms, such as REINFORCE), we demonstrate that even a very basic canonical
ES algorithm can achieve the same or even better performance. This success of a
basic ES algorithm suggests that the state-of-the-art can be advanced further
by integrating the many advances made in the field of ES in the last decades.
  We also demonstrate qualitatively that ES algorithms have very different
performance characteristics than traditional RL algorithms: on some games, they
learn to exploit the environment and perform much better while on others they
can get stuck in suboptimal local minima. Combining their strengths with those
of traditional RL algorithms is therefore likely to lead to new advances in the
state of the art.
The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes
Authors: Stephane Le Roux, Guillermo A. Perez
Date: 2017-10-22 00:00:00
Tasks: Relation

We study the never-worse relation (NWR) for Markov decision processes with an
infinite-horizon reachability objective. A state q is never worse than a state
p if the maximal probability of reaching the target set of states from p is at
most the same value from q, regard- less of the probabilities labelling the
transitions. Extremal-probability states, end components, and essential states
are all special cases of the equivalence relation induced by the NWR. Using the
NWR, states in the same equivalence class can be collapsed. Then, actions
leading to sub- optimal states can be removed. We show the natural decision
problem associated to computing the NWR is coNP-complete. Finally, we ex- tend
a previously known incomplete polynomial-time iterative algorithm to
under-approximate the NWR.
Adaptive Deep Learning through Visual Domain Localization
Authors: Gabriele Angeletti, Barbara Caputo, Tatiana Tommasi
Date: 2018-02-24 00:00:00
Tasks: Deep Learning, Domain Adaptation

A commercial robot, trained by its manufacturer to recognize a predefined
number and type of objects, might be used in many settings, that will in
general differ in their illumination conditions, background, type and degree of
clutter, and so on. Recent computer vision works tackle this generalization
issue through domain adaptation methods, assuming as source the visual domain
where the system is trained and as target the domain of deployment. All
approaches assume to have access to images from all classes of the target
during training, an unrealistic condition in robotics applications. We address
this issue proposing an algorithm that takes into account the specific needs of
robot vision. Our intuition is that the nature of the domain shift experienced
mostly in robotics is local. We exploit this through the learning of maps that
spatially ground the domain and quantify the degree of shift, embedded into an
end-to-end deep domain adaptation architecture. By explicitly localizing the
roots of the domain shift we significantly reduce the number of parameters of
the architecture to tune, we gain the flexibility necessary to deal with subset
of categories in the target domain at training time, and we provide a clear
feedback on the rationale behind any classification decision, which can be
exploited in human-robot interactions. Experiments on two different settings of
the iCub World database confirm the suitability of our method for robot vision.
PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application
Authors: Chang-Shing Lee, Mei-Hui Wang, Chi-Shiang Wang, Olivier Teytaud, Jialin Liu, Su-Wei Lin, Pi-Hsia Hung
Date: 2018-02-24 00:00:00
Tasks: parameter estimation

This paper proposes an agent with particle swarm optimization (PSO) based on
a Fuzzy Markup Language (FML) for students learning performance evaluation and
educational applications, and the proposed agent is according to the response
data from a conventional test and an item response theory. First, we apply a
GS-based parameter estimation mechanism to estimate the items parameters
according to the response data, and then to compare its results with those of
an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a
static-IRT test assembly mechanism to assemble a form for the conventional
test. The presented FML-based dynamic assessment mechanism infers the
probability of making a correct response to the item for a student with various
abilities. Moreover, this paper also proposes a novel PFML learning mechanism
for optimizing the parameters between items and students. Finally, we adopt a
K-fold cross validation mechanism to evaluate the performance of the proposed
agent. Experimental results show that the novel PFML learning mechanism for the
parameter estimation and learning optimization performs favorably. We believe
the proposed PFML will be a reference for education research and pedagogy and
an important co-learning mechanism for future human-machine educational
applications.
Quantum cognition goes beyond-quantum: modeling the collective participant in psychological measurements
Authors: Diederik Aerts, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2018-02-24 00:00:00
Tasks: valid

In psychological measurements, two levels should be distinguished: the
'individual level', relative to the different participants in a given cognitive
situation, and the 'collective level', relative to the overall statistics of
their outcomes, which we propose to associate with a notion of 'collective
participant'. When the distinction between these two levels is properly
formalized, it reveals why the modeling of the collective participant generally
requires beyond-quantum - non-Bornian - probabilistic models, when sequential
measurements at the individual level are considered, and this though a pure
quantum description remains valid for single measurement situations.
A Twofold Siamese Network for Real-Time Object Tracking
Authors: Anfeng He, Chong Luo, Xinmei Tian, Wen-Jun Zeng
Date: 2018-02-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Object, Object Tracking

Observing that Semantic features learned in an image classification task and
Appearance features learned in a similarity matching task complement each
other, we build a twofold Siamese network, named SA-Siam, for real-time object
tracking. SA-Siam is composed of a semantic branch and an appearance branch.
Each branch is a similarity-learning Siamese network. An important design
choice in SA-Siam is to separately train the two branches to keep the
heterogeneity of the two types of features. In addition, we propose a channel
attention mechanism for the semantic branch. Channel-wise weights are computed
according to the channel activations around the target position. While the
inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate
beyond real-time, the twofold design and the attention mechanism significantly
improve the tracking performance. The proposed SA-Siam outperforms all other
real-time trackers by a large margin on OTB-2013/50/100 benchmarks.
Towards a Continuous Knowledge Learning Engine for Chatbots
Authors: Sahisnu Mazumder, Nianzu Ma, Bing Liu
Date: 2018-02-16 00:00:00
Tasks: General Knowledge, Knowledge Base Completion, World Knowledge

Although chatbots have been very popular in recent years, they still have
some serious weaknesses which limit the scope of their applications. One major
weakness is that they cannot learn new knowledge during the conversation
process, i.e., their knowledge is fixed beforehand and cannot be expanded or
updated during conversation. In this paper, we propose to build a general
knowledge learning engine for chatbots to enable them to continuously and
interactively learn new knowledge during conversations. As time goes by, they
become more and more knowledgeable and better and better at learning and
conversation. We model the task as an open-world knowledge base completion
problem and propose a novel technique called lifelong interactive learning and
inference (LiLi) to solve it. LiLi works by imitating how humans acquire
knowledge and perform inference during an interactive conversation. Our
experimental results show LiLi is highly promising.
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
Authors: Raghuram Bharadwaj Diddigi, Prabuchandran K. J., Shalabh Bhatnagar
Date: 2017-08-27 00:00:00
Tasks: Intrusion Detection, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We consider the problem of tracking an intruder using a network of wireless
sensors. For tracking the intruder at each instant, the optimal number and the
right configuration of sensors has to be powered. As powering the sensors
consumes energy, there is a trade off between accurately tracking the position
of the intruder at each instant and the energy consumption of sensors. This
problem has been formulated in the framework of Partially Observable Markov
Decision Process (POMDP). Even for the state-of-the-art algorithm in the
literature, the curse of dimensionality renders the problem intractable. In
this paper, we formulate the Intrusion Detection (ID) problem with a suitable
state-action space in the framework of POMDP and develop a Reinforcement
Learning (RL) algorithm utilizing the Upper Confidence Tree Search (UCT) method
to solve the ID problem. Through simulations, we show that our algorithm
performs and scales well with the increasing state and action spaces.
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network
Authors: Yanting Hu, Xinbo Gao, Jie Li, Yuanfei Huang, Hanzi Wang
Date: 2018-02-24 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

The deep convolutional neural networks have achieved significant improvements
in accuracy and speed for single image super-resolution. However, as the depth
of network grows, the information flow is weakened and the training becomes
harder and harder. On the other hand, most of the models adopt a single-stream
structure with which integrating complementary contextual information under
different receptive fields is difficult. To improve information flow and to
capture sufficient knowledge for reconstructing the high-frequency details, we
propose a cascaded multi-scale cross network (CMSC) in which a sequence of
subnetworks is cascaded to infer high resolution features in a coarse-to-fine
manner. In each cascaded subnetwork, we stack multiple multi-scale cross (MSC)
modules to fuse complementary multi-scale information in an efficient way as
well as to improve information flow across the layers. Meanwhile, by
introducing residual-features learning in each stage, the relative information
between high-resolution and low-resolution features is fully utilized to
further boost reconstruction performance. We train the proposed network with
cascaded-supervision and then assemble the intermediate predictions of the
cascade to achieve high quality image reconstruction. Extensive quantitative
and qualitative evaluations on benchmark datasets illustrate the superiority of
our proposed method over state-of-the-art super-resolution methods.
A Denoising Loss Bound for Neural Network based Universal Discrete Denoisers
Authors: Taesup Moon
Date: 2017-09-12 00:00:00
Tasks: Denoising, Learning Theory

We obtain a denoising loss bound of the recently proposed neural network
based universal discrete denoiser, Neural DUDE, which can adaptively learn its
parameters solely from the noise-corrupted data, by minimizing the
\emph{empirical estimated loss}. The resulting bound resembles the
generalization error bound of the standard empirical risk minimizers (ERM) in
supervised learning, and we show that the well-known bias-variance tradeoff
also exists in our loss bound. The key tool we develop is the concentration of
the unbiased estimated loss on the true denoising loss, which is shown to hold
\emph{uniformly} for \emph{all} bounded network parameters and \emph{all}
underlying clean sequences. For proving our main results, we make a novel
application of the tools from the statistical learning theory. Finally, we show
that the hyperparameters of Neural DUDE can be chosen from a small validation
set to significantly improve the denoising performance, as predicted by the
theoretical result of this paper.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Authors: Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang
Date: 2018-02-24 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) agents improve through trial-and-error, but when
reward is sparse and the agent cannot discover successful action sequences,
learning stagnates. This has been a notable problem in training deep RL agents
to perform web-based tasks, such as booking flights or replying to emails,
where a single mistake can ruin the entire sequence of actions. A common remedy
is to "warm-start" the agent by pre-training it to mimic expert demonstrations,
but this is prone to overfitting. Instead, we propose to constrain exploration
using demonstrations. From each demonstration, we induce high-level "workflows"
which constrain the allowable actions at each time step to be similar to those
in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some
text"). Our exploration policy then learns to identify successful workflows and
samples actions that satisfy these workflows. Workflows prune out bad
exploration directions and accelerate the agent's ability to discover rewards.
We use our approach to train a novel neural policy designed to handle the
semi-structured nature of websites, and evaluate on a suite of web tasks,
including the recent World of Bits benchmark. We achieve new state-of-the-art
results, and show that workflow-guided exploration improves sample efficiency
over behavioral cloning by more than 100x.
Stochastic Gradient Descent on Highly-Parallel Architectures
Authors: Yujing Ma, Florin Rusu, Martin Torres
Date: 2018-02-24 00:00:00
Tasks: CPU, GPU

There is an increased interest in building data analytics frameworks with
advanced algebraic capabilities both in industry and academia. Many of these
frameworks, e.g., TensorFlow and BIDMach, implement their compute-intensive
primitives in two flavors---as multi-thread routines for multi-core CPUs and as
highly-parallel kernels executed on GPU. Stochastic gradient descent (SGD) is
the most popular optimization method for model training implemented extensively
on modern data analytics platforms. While the data-intensive properties of SGD
are well-known, there is an intense debate on which of the many SGD variants is
better in practice. In this paper, we perform a comprehensive study of parallel
SGD for training generalized linear models. We consider the impact of three
factors -- computing architecture (multi-core CPU or GPU), synchronous or
asynchronous model updates, and data sparsity -- on three measures---hardware
efficiency, statistical efficiency, and time to convergence. In the process, we
design an optimized asynchronous SGD algorithm for GPU that leverages warp
shuffling and cache coalescing for data and model access. We draw several
interesting findings from our extensive experiments with logistic regression
(LR) and support vector machines (SVM) on five real datasets. For synchronous
SGD, GPU always outperforms parallel CPU---they both outperform a sequential
CPU solution by more than 400X. For asynchronous SGD, parallel CPU is the
safest choice while GPU with data replication is better in certain situations.
The choice between synchronous GPU and asynchronous CPU depends on the task and
the characteristics of the data. As a reference, our best implementation
outperforms TensorFlow and BIDMach consistently. We hope that our insights
provide a useful guide for applying parallel SGD to generalized linear models.
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
Authors: Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
Date: 2017-10-17 00:00:00

Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS.
Constrained Image Generation Using Binarized Neural Networks with Decision Procedures
Authors: Svyatoslav Korneev, Nina Narodytska, Luca Pulina, Armando Tacchella, Nikolaj Bjorner, Mooly Sagiv
Date: 2018-02-24 00:00:00
Tasks: Image Generation

We consider the problem of binary image generation with given properties.
This problem arises in a number of practical applications, including generation
of artificial porous medium for an electrode of lithium-ion batteries, for
composed materials, etc. A generated image represents a porous medium and, as
such, it is subject to two sets of constraints: topological constraints on the
structure and process constraints on the physical process over this structure.
To perform image generation we need to define a mapping from a porous medium to
its physical process parameters. For a given geometry of a porous medium, this
mapping can be done by solving a partial differential equation (PDE). However,
embedding a PDE solver into the search procedure is computationally expensive.
We use a binarized neural network to approximate a PDE solver. This allows us
to encode the entire problem as a logical formula. Our main contribution is
that, for the first time, we show that this problem can be tackled using
decision procedures. Our experiments show that our model is able to produce
random constrained images that satisfy both topological and process
constraints.
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
Authors: Qian Huang, Weixin Zhu, Yang Zhao, Linsen Chen, Yao Wang, Tao Yue, Xun Cao
Date: 2018-02-24 00:00:00

Multispectral images contain many clues of surface characteristics of the
objects, thus can be widely used in many computer vision tasks, e.g.,
recolorization and segmentation. However, due to the complex illumination and
the geometry structure of natural scenes, the spectra curves of a same surface
can look very different. In this paper, a Low Rank Multispectral Image
Intrinsic Decomposition model (LRIID) is presented to decompose the shading and
reflectance from a single multispectral image. We extend the Retinex model,
which is proposed for RGB image intrinsic decomposition, for multispectral
domain. Based on this, a low rank constraint is proposed to reduce the
ill-posedness of the problem and make the algorithm solvable. A dataset of 12
images is given with the ground truth of shadings and reflectance, so that the
objective evaluations can be conducted. The experiments demonstrate the
effectiveness of proposed method.
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis
Authors: Yuanhang Su, Yuzhong Huang, C. -C. Jay Kuo
Date: 2018-01-20 00:00:00
Tasks: Dimensionality Reduction, General Classification, text-classification, Text Classification

A novel text data dimension reduction technique, called the tree-structured
multi-linear principal component anal- ysis (TMPCA), is proposed in this work.
Being different from traditional text dimension reduction methods that deal
with the word-level representation, the TMPCA technique reduces the dimension
of input sequences and sentences to simplify the following text classification
tasks. It is shown mathematically and experimentally that the TMPCA tool
demands much lower complexity (and, hence, less computing power) than the
ordinary principal component analysis (PCA). Furthermore, it is demon- strated
by experimental results that the support vector machine (SVM) method applied to
the TMPCA-processed data achieves commensurable or better performance than the
state-of-the-art recurrent neural network (RNN) approach.