pwc-archive/papers-with-abstracts huggingface dataset:
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
Authors: Daniel McDuff, Ashish Kapoor
Date: 2018-05-25 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As people learn to navigate the world, autonomic nervous system (e.g., "fight
or flight") responses provide intrinsic feedback about the potential
consequence of action choices (e.g., becoming nervous when close to a cliff
edge or driving fast around a bend.) Physiological changes are correlated with
these biological preparations to protect one-self from danger. We present a
novel approach to reinforcement learning that leverages a task-independent
intrinsic reward function trained on peripheral pulse measurements that are
correlated with human autonomic nervous system responses. Our hypothesis is
that such reward functions can circumvent the challenges associated with sparse
and skewed rewards in reinforcement learning settings and can help improve
sample efficiency. We test this in a simulated driving environment and show
that it can increase the speed of learning and reduce the number of collisions
during the learning stage.
Part-based Visual Tracking via Structural Support Correlation Filter
Authors: Zhangjian Ji, Kai Feng, Yuhua Qian
Date: 2018-05-25 00:00:00
Tasks: Visual Tracking

Recently, part-based and support vector machines (SVM) based trackers have
shown favorable performance. Nonetheless, the time-consuming online training
and updating process limit their real-time applications. In order to better
deal with the partial occlusion issue and improve their efficiency, we propose
a novel part-based structural support correlation filter tracking method, which
absorbs the strong discriminative ability from SVM and the excellent property
of part-based tracking methods which is less sensitive to partial occlusion.
Then, our proposed model can learn the support correlation filter of each part
jointly by a star structure model, which preserves the spatial layout structure
among parts and tolerates outliers of parts. In addition, to mitigate the issue
of drift away from object further, we introduce inter-frame consistencies of
local parts into our model. Finally, in our model, we accurately estimate the
scale changes of object by the relative distance change among reliable parts.
The extensive empirical evaluations on three benchmark datasets: OTB2015,
TempleColor128 and VOT2015 demonstrate that the proposed method performs
superiorly against several state-of-the-art trackers in terms of tracking
accuracy, speed and robustness.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Authors: Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian
Date: 2018-05-25 00:00:00
Proceeding: ICML 2018 7

Recently, the decentralized optimization problem is attracting growing
attention. Most existing methods are deterministic with high per-iteration cost
and have a convergence rate quadratically depending on the problem condition
number. Besides, the dense communication is necessary to ensure the convergence
even if the dataset is sparse. In this paper, we generalize the decentralized
optimization problem to a monotone operator root finding problem, and propose a
stochastic algorithm named DSBA that (i) converges geometrically with a rate
linearly depending on the problem condition number, and (ii) can be implemented
using sparse communication only. Additionally, DSBA handles learning problems
like AUC-maximization which cannot be tackled efficiently in the decentralized
setting. Experiments on convex minimization and AUC-maximization validate the
efficiency of our method.
Cooking State Recognition from Images Using Inception Architecture
Authors: Md Sirajus Salekin, Ahmad Babaeian Jelodar, Rafsanjany Kushol
Date: 2018-05-25 00:00:00
Tasks: object-detection, Object Detection

A kitchen robot properly needs to understand the cooking environment to
continue any cooking activities. But object's state detection has not been
researched well so far as like object detection. In this paper, we propose a
deep learning approach to identify different cooking states from images for a
kitchen robot. In our research, we investigate particularly the performance of
Inception architecture and propose a modified architecture based on Inception
model to classify different cooking states. The model is analyzed robustly in
terms of different layers, and optimizers. Experimental results on a cooking
datasets demonstrate that proposed model can be a potential solution to the
cooking state recognition problem.
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
Authors: Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Date: 2018-05-25 00:00:00
Tasks: Bayesian Inference, Multi-Armed Bandits, Probabilistic Programming, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

We design a new myopic strategy for a wide class of sequential design of
experiment (DOE) problems, where the goal is to collect data in order to to
fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling
(MPS), is inspired by the classical posterior (Thompson) sampling algorithm for
multi-armed bandits and leverages the flexibility of probabilistic programming
and approximate Bayesian inference to address a broad set of problems.
Empirically, this general-purpose strategy is competitive with more specialised
methods in a wide array of DOE tasks, and more importantly, enables addressing
complex DOE goals where no existing method seems applicable. On the theoretical
side, we leverage ideas from adaptive submodularity and reinforcement learning
to derive conditions under which MPS achieves sublinear regret against natural
benchmark policies.
Phrase Table as Recommendation Memory for Neural Machine Translation
Authors: Yang Zhao, Yining Wang, Jiajun Zhang, Cheng-qing Zong
Date: 2018-05-25 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural Machine Translation (NMT) has drawn much attention due to its
promising translation performance recently. However, several studies indicate
that NMT often generates fluent but unfaithful translations. In this paper, we
propose a method to alleviate this problem by using a phrase table as
recommendation memory. The main idea is to add bonus to words worthy of
recommendation, so that NMT can make correct predictions. Specifically, we
first derive a prefix tree to accommodate all the candidate target phrases by
searching the phrase translation table according to the source sentence. Then,
we construct a recommendation word set by matching between candidate target
phrases and previously translated target words by NMT. After that, we determine
the specific bonus value for each recommendable word by using the attention
vector and phrase translation probability. Finally, we integrate this bonus
value into NMT to improve the translation results. The extensive experiments
demonstrate that the proposed methods obtain remarkable improvements over the
strong attentionbased NMT.
A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter
Authors: Eric M. Clark, Ted James, Chris A. Jones, Amulya Alapati, Promise Ukandu, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2018-05-25 00:00:00
Tasks: Decision Making, Sentiment Analysis

Background: Social media has the capacity to afford the healthcare industry
with valuable feedback from patients who reveal and express their medical
decision-making process, as well as self-reported quality of life indicators
both during and post treatment. In prior work, [Crannell et. al.], we have
studied an active cancer patient population on Twitter and compiled a set of
tweets describing their experience with this disease. We refer to these online
public testimonies as "Invisible Patient Reported Outcomes" (iPROs), because
they carry relevant indicators, yet are difficult to capture by conventional
means of self-report. Methods: Our present study aims to identify tweets
related to the patient experience as an additional informative tool for
monitoring public health. Using Twitter's public streaming API, we compiled
over 5.3 million "breast cancer" related tweets spanning September 2016 until
mid December 2017. We combined supervised machine learning methods with natural
language processing to sift tweets relevant to breast cancer patient
experiences. We analyzed a sample of 845 breast cancer patient and survivor
accounts, responsible for over 48,000 posts. We investigated tweet content with
a hedonometric sentiment analysis to quantitatively extract emotionally charged
topics. Results: We found that positive experiences were shared regarding
patient treatment, raising support, and spreading awareness. Further
discussions related to healthcare were prevalent and largely negative focusing
on fear of political legislation that could result in loss of coverage.
Conclusions: Social media can provide a positive outlet for patients to discuss
their needs and concerns regarding their healthcare coverage and treatment
needs. Capturing iPROs from online communication can help inform healthcare
professionals and lead to more connected and personalized treatment regimens.
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Authors: Minhyuk Sung, Hao Su, Ronald Yu, Leonidas Guibas
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Segmentation

Various 3D semantic attributes such as segmentation masks, geometric
features, keypoints, and materials can be encoded as per-point probe functions
on 3D geometries. Given a collection of related 3D shapes, we consider how to
jointly analyze such probe functions over different shapes, and how to discover
common latent structures using a neural network --- even in the absence of any
correspondence information. Our network is trained on point cloud
representations of shape geometry and associated semantic functions on that
point cloud. These functions express a shared semantic understanding of the
shapes but are not coordinated in any way. For example, in a segmentation task,
the functions can be indicator functions of arbitrary sets of shape parts, with
the particular combination involved not known to the network. Our network is
able to produce a small dictionary of basis functions for each shape, a
dictionary whose span includes the semantic functions provided for that shape.
Even though our shapes have independent discretizations and no functional
correspondences are provided, the network is able to generate latent bases, in
a consistent order, that reflect the shared semantic structure among the
shapes. We demonstrate the effectiveness of our technique in various
segmentation and keypoint selection applications.
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks
Authors: Jiefeng Chen, Xi Wu, Vaibhav Rastogi, YIngyu Liang, Somesh Jha
Date: 2018-05-20 00:00:00

Wide adoption of artificial neural networks in various domains has led to an increasing interest in defending adversarial attacks against them. Preprocessing defense methods such as pixel discretization are particularly attractive in practice due to their simplicity, low computational overhead, and applicability to various systems. It is observed that such methods work well on simple datasets like MNIST, but break on more complicated ones like ImageNet under recently proposed strong white-box attacks. To understand the conditions for success and potentials for improvement, we study the pixel discretization defense method, including more sophisticated variants that take into account the properties of the dataset being discretized. Our results again show poor resistance against the strong attacks. We analyze our results in a theoretical framework and offer strong evidence that pixel discretization is unlikely to work on all but the simplest of the datasets. Furthermore, our arguments present insights why some other preprocessing defenses may be insecure.
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
Authors: Suraj Maharjan, Sudipta Kar, Manuel Montes-y-Gomez, Fabio A. Gonzalez, Thamar Solorio
Date: 2018-05-24 00:00:00
Proceeding: NAACL 2018 6

Books have the power to make us feel happiness, sadness, pain, surprise, or
sorrow. An author's dexterity in the use of these emotions captivates readers
and makes it difficult for them to put the book down. In this paper, we model
the flow of emotions over a book using recurrent neural networks and quantify
its usefulness in predicting success in books. We obtained the best weighted
F1-score of 69% for predicting books' success in a multitask setting
(simultaneously predicting success and genre of books).
Deep Visual Domain Adaptation: A Survey
Authors: Mei Wang, Weihong Deng
Date: 2018-02-10 00:00:00
Tasks: Domain Adaptation, Face Recognition, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation, Survey

Deep domain adaption has emerged as a new learning technique to address the
lack of massive amounts of labeled data. Compared to conventional methods,
which learn shared feature subspaces or reuse important source instances with
shallow representations, deep domain adaption methods leverage deep networks to
learn more transferable representations by embedding domain adaptation in the
pipeline of deep learning. There have been comprehensive surveys for shallow
domain adaption, but few timely reviews the emerging deep learning based
methods. In this paper, we provide a comprehensive survey of deep domain
adaptation methods for computer vision applications with four major
contributions. First, we present a taxonomy of different deep domain adaption
scenarios according to the properties of data that define how two domains are
diverged. Second, we summarize deep domain adaption approaches into several
categories based on training loss, and analyze and compare briefly the
state-of-the-art methods under these categories. Third, we overview the
computer vision applications that go beyond image classification, such as face
recognition, semantic segmentation and object detection. Fourth, some potential
deficiencies of current methods and several future directions are highlighted.
A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios
Authors: Hossein Rastgoftar, Bingxin Zhang, Ella M. Atkins
Date: 2018-05-25 00:00:00
Tasks: Autonomous Driving, Motion Planning

This paper presents a novel data-driven approach to vehicle motion planning
and control in off-road driving scenarios. For autonomous off-road driving,
environmental conditions impact terrain traversability as a function of
weather, surface composition, and slope. Geographical information system (GIS)
and National Centers for Environmental Information datasets are processed to
provide this information for interactive planning and control system elements.
A top-level global route planner (GRP) defines optimal waypoints using dynamic
programming (DP). A local path planner (LPP) computes a desired trajectory
between waypoints such that infeasible control states and collisions with
obstacles are avoided. The LPP also updates the GRP with real-time sensing and
control data. A low-level feedback controller applies feedback linearization to
asymptotically track the specified LPP trajectory. Autonomous driving
simulation results are presented for traversal of terrains in Oregon and
Indiana case studies.
Topological Data Analysis of Decision Boundaries with Application to Model Selection
Authors: Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody
Date: 2018-05-25 00:00:00
Tasks: General Classification, Model Selection, Topological Data Analysis

We propose the labeled \v{C}ech complex, the plain labeled Vietoris-Rips
complex, and the locally scaled labeled Vietoris-Rips complex to perform
persistent homology inference of decision boundaries in classification tasks.
We provide theoretical conditions and analysis for recovering the homology of a
decision boundary from samples. Our main objective is quantification of deep
neural network complexity to enable matching of datasets to pre-trained models;
we report results for experiments using MNIST, FashionMNIST, and CIFAR10.
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?
Authors: Meimei Liu, Zuofeng Shang, Guang Cheng
Date: 2018-05-25 00:00:00
Tasks: regression, Two-sample testing

This paper aims to solve a basic problem in distributed statistical
inference: how many machines can we use in parallel computing? In kernel ridge
regression, we address this question in two important settings: nonparametric
estimation and hypothesis testing. Specifically, we find a range for the number
of machines under which optimal estimation/testing is achievable. The employed
empirical processes method provides a unified framework, that allows us to
handle various regression problems (such as thin-plate splines and
nonparametric additive regression) under different settings (such as
univariate, multivariate and diverging-dimensional designs). It is worth noting
that the upper bounds of the number of machines are proven to be un-improvable
(upto a logarithmic factor) in two important cases: smoothing spline regression
and Gaussian RKHS regression. Our theoretical findings are backed by thorough
numerical studies.
Meta Transfer Learning for Facial Emotion Recognition
Authors: Dung Nguyen, Kien Nguyen, Sridha Sridharan, Iman Abbasnejad, David Dean, Clinton Fookes
Date: 2018-05-25 00:00:00
Tasks: Deep Learning, Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Transfer Learning

The use of deep learning techniques for automatic facial expression
recognition has recently attracted great interest but developed models are
still unable to generalize well due to the lack of large emotion datasets for
deep learning. To overcome this problem, in this paper, we propose utilizing a
novel transfer learning approach relying on PathNet and investigate how
knowledge can be accumulated within a given dataset and how the knowledge
captured from one emotion dataset can be transferred into another in order to
improve the overall performance. To evaluate the robustness of our system, we
have conducted various sets of experiments on two emotion datasets: SAVEE and
eNTERFACE. The experimental results demonstrate that our proposed system leads
to improvement in performance of emotion recognition and performs significantly
better than the recent state-of-the-art schemes adopting fine-\
tuning/pre-trained approaches.
Deep Reinforcement Learning For Sequence to Sequence Models
Authors: Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K. Reddy
Date: 2018-05-24 00:00:00
Tasks: Abstractive Text Summarization, Caption Generation, Decision Making, Decoder, Deep Reinforcement Learning, Headline Generation, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Speech-to-Text, Text Summarization

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of
popularity and provide state-of-the-art performance in a wide variety of tasks
such as machine translation, headline generation, text summarization, speech to
text conversion, and image caption generation. The underlying framework for all
these models is usually a deep neural network comprising an encoder and a
decoder. Although simple encoder-decoder models produce competitive results,
many researchers have proposed additional improvements over these
sequence-to-sequence models, e.g., using an attention-based model over the
input, pointer-generation models, and self-attention models. However, such
seq2seq models suffer from two common problems: 1) exposure bias and 2)
inconsistency between train/test measurement. Recently, a completely novel
point of view has emerged in addressing these two problems in seq2seq models,
leveraging methods from reinforcement learning (RL). In this survey, we
consider seq2seq problems from the RL point of view and provide a formulation
combining the power of RL methods in decision-making with sequence-to-sequence
models that enable remembering long-term memories. We present some of the most
recent frameworks that combine concepts from RL and deep neural networks and
explain how these two areas could benefit from each other in solving complex
seq2seq tasks. Our work aims to provide insights into some of the problems that
inherently arise with current approaches and how we can address them with
better RL models. We also provide the source code for implementing most of the
RL models discussed in this paper to support the complex task of abstractive
text summarization.
Learning what and where to attend
Authors: Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre
Date: 2018-05-22 00:00:00
Tasks: Diagnostic, Image Categorization, Object Recognition

Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.
Training of photonic neural networks through in situ backpropagation
Authors: Tyler W. Hughes, Momchil Minkov, Yu Shi, Shanhui Fan
Date: 2018-05-25 00:00:00
Tasks: BIG-bench Machine Learning

Recently, integrated optics has gained interest as a hardware platform for
implementing machine learning algorithms. Of particular interest are artificial
neural networks, since matrix-vector multi- plications, which are used heavily
in artificial neural networks, can be done efficiently in photonic circuits.
The training of an artificial neural network is a crucial step in its
application. However, currently on the integrated photonics platform there is
no efficient protocol for the training of these networks. In this work, we
introduce a method that enables highly efficient, in situ training of a
photonic neural network. We use adjoint variable methods to derive the photonic
analogue of the backpropagation algorithm, which is the standard method for
computing gradients of conventional neural networks. We further show how these
gradients may be obtained exactly by performing intensity measurements within
the device. As an application, we demonstrate the training of a numerically
simulated photonic artificial neural network. Beyond the training of photonic
machine learning implementations, our method may also be of broad interest to
experimental sensitivity analysis of photonic systems and the optimization of
reconfigurable optics platforms.