pwc-archive/papers-with-abstracts huggingface dataset:
Properties on n-dimensional convolution for image deconvolution
Authors: Song Yizhi, Xu Cheng, Ding Daoxin, Zhou Hang, Quan Tingwei, Li Shiwei
Date: 2017-11-30 00:00:00
Tasks: Denoising, GPU, Image Deconvolution, Image Denoising, Image Enhancement

Convolution system is linear and time invariant, and can describe the optical
imaging process. Based on convolution system, many deconvolution techniques
have been developed for optical image analysis, such as boosting the space
resolution of optical images, image denoising, image enhancement and so on.
Here, we gave properties on N-dimensional convolution. By using these
properties, we proposed image deconvolution method. This method uses a series
of convolution operations to deconvolute image. We demonstrated that the method
has the similar deconvolution results to the state-of-art method. The core
calculation of the proposed method is image convolution, and thus our method
can easily be integrated into GPU mode for large-scale image deconvolution.
Riemannian Stein Variational Gradient Descent for Bayesian Inference
Authors: Chang Liu, Jun Zhu
Date: 2017-11-30 00:00:00
Tasks: Bayesian Inference

We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian
inference method that generalizes Stein Variational Gradient Descent (SVGD) to
Riemann manifold. The benefits are two-folds: (i) for inference tasks in
Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information
geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the
unique advantages of SVGD to the Riemannian world. To appropriately transfer to
Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD,
which are required by the intrinsically different characteristics of general
Riemann manifolds from Euclidean spaces. We also discover Riemannian Stein's
Identity and Riemannian Kernelized Stein Discrepancy. Experimental results show
the advantages over SVGD of exploring distribution geometry and the advantages
of particle-efficiency, iteration-effectiveness and approximation flexibility
over other inference methods on Riemann manifolds.
Development of Statewide AADT Estimation Model from Short-Term Counts: A Comparative Study for South Carolina
Authors: Sakib Mahmud Khan, Sababa Islam, MD Zadid Khan, Kakan Dey, Mashrur Chowdhury, Nathan Huynh
Date: 2017-11-30 00:00:00
Tasks: regression

Annual Average Daily Traffic (AADT) is an important parameter used in traffic
engineering analysis. Departments of Transportation (DOTs) continually collect
traffic count using both permanent count stations (i.e., Automatic Traffic
Recorders or ATRs) and temporary short-term count stations. In South Carolina,
87% of the ATRs are located on interstates and arterial highways. For most
secondary highways (i.e., collectors and local roads), AADT is estimated based
on short-term counts. This paper develops AADT estimation models for different
roadway functional classes with two machine learning techniques: Artificial
Neural Network (ANN) and Support Vector Regression (SVR). The models aim to
predict AADT from short-term counts. The results are first compared against
each other to identify the best model. Then, the results of the best model are
compared against a regression method and factor-based method. The comparison
reveals the superiority of SVR for AADT estimation for different roadway
functional classes over all other methods. Among all developed models for
different functional roadway classes, the SVR-based model shows a minimum root
mean square error (RMSE) of 0.22 and a mean absolute percentage error (MAPE) of
11.3% for the interstate/expressway functional class. This model also shows a
higher R-squared value compared to the traditional factor-based model and
regression model. SVR models are validated for each roadway functional class
using the 2016 ATR data and selected short-term count data collected by the
South Carolina Department of Transportation (SCDOT). The validation results
show that the SVR-based AADT estimation models can be used by the SCDOT as a
reliable option to predict AADT from the short-term counts.
Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care
Authors: Hyunwoo Lee, Jooyoung Kim, Dojun Yang, Joon-Ho Kim
Date: 2017-11-30 00:00:00

This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recognize a much greater variety of falls than the existing
datasets that existed before and solved privacy issues using the DVS. Secondly,
we introduce the DVS-TN : optimized deep learning network to detect falls using
DVS. Finally, we implemented a fall detection system which can run on
low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
into account various falls situations. Our approach achieved 95.5% on the
F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
Predicting the Popularity of Online Videos via Deep Neural Networks
Authors: Yue Mao, Yi Shen, Gang Qin, Longjun Cai
Date: 2017-11-29 00:00:00
Tasks: Multi-Task Learning, Relation Network

Predicting the popularity of online videos is important for video streaming
content providers. This is a challenging problem because of the following two
reasons. First, the problem is both "wide" and "deep". That is, it not only
depends on a wide range of features, but also be highly non-linear and complex.
Second, multiple competitors may be involved. In this paper, we propose a
general prediction model using the multi-task learning (MTL) module and the
relation network (RN) module, where MTL can reduce over-fitting and RN can
model the relations of multiple competitors. Experimental results show that our
proposed approach significantly increases the accuracy on predicting the total
view counts of TV series with RN and MTL modules.
Neural Response Generation with Dynamic Vocabularies
Authors: Yu Wu, Wei Wu, Dejian Yang, Can Xu, Zhoujun Li, Ming Zhou
Date: 2017-11-30 00:00:00
Tasks: Response Generation

We study response generation for open domain conversation in chatbots.
Existing methods assume that words in responses are generated from an identical
vocabulary regardless of their inputs, which not only makes them vulnerable to
generic patterns and irrelevant noise, but also causes a high cost in decoding.
We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows
each input to possess their own vocabulary in decoding. In training, vocabulary
construction and response generation are jointly learned by maximizing a lower
bound of the true objective with a Monte Carlo sampling method. In inference,
the model dynamically allocates a small vocabulary for an input with the word
prediction model, and conducts decoding only with the small vocabulary. Because
of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and
irrelevant words in generation, and enjoys efficient decoding at the same time.
Experimental results on both automatic metrics and human annotations show that
DVS2S can significantly outperform state-of-the-art methods in terms of
response quality, but only requires 60% decoding time compared to the most
efficient baseline.
Phase Transitions in Approximate Ranking
Authors: Chao Gao
Date: 2017-11-30 00:00:00

We study the problem of approximate ranking from observations of pairwise interactions. The goal is to estimate the underlying ranks of $n$ objects from data through interactions of comparison or collaboration. Under a general framework of approximate ranking models, we characterize the exact optimal statistical error rates of estimating the underlying ranks. We discover important phase transition boundaries of the optimal error rates. Depending on the value of the signal-to-noise ratio (SNR) parameter, the optimal rate, as a function of SNR, is either trivial, polynomial, exponential or zero. The four corresponding regimes thus have completely different error behaviors. To the best of our knowledge, this phenomenon, especially the phase transition between the polynomial and the exponential rates, has not been discovered before.
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies
Authors: Dhaval Adjodah, Dan Calacci, Yan Leng, Peter Krafft, Esteban Moro, Alex Pentland
Date: 2017-11-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We draw upon a previously largely untapped literature on human collective
intelligence as a source of inspiration for improving deep learning. Implicit
in many algorithms that attempt to solve Deep Reinforcement Learning (DRL)
tasks is the network of processors along which parameter values are shared. So
far, existing approaches have implicitly utilized fully-connected networks, in
which all processors are connected. However, the scientific literature on human
collective intelligence suggests that complete networks may not always be the
most effective information network structures for distributed search through
complex spaces. Here we show that alternative topologies can improve deep
neural network training: we find that sparser networks learn higher rewards
faster, leading to learning improvements at lower communication costs.
State Space LSTM Models with Particle MCMC Inference
Authors: Xun Zheng, Manzil Zaheer, Amr Ahmed, Yu-An Wang, Eric P. Xing, Alexander J. Smola
Date: 2017-11-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: State Space Models, Topic Models

Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Despite the strong performance, however, it lacks the nice interpretability as
in state space models. In this paper, we present a way to combine the best of
both worlds by introducing State Space LSTM (SSL) models that generalizes the
earlier work \cite{zaheer2017latent} of combining topic models with LSTM.
However, unlike \cite{zaheer2017latent}, we do not make any factorization
assumptions in our inference algorithm. We present an efficient sampler based
on sequential Monte Carlo (SMC) method that draws from the joint posterior
directly. Experimental results confirms the superiority and stability of this
SMC inference algorithm on a variety of domains.
Towards Data Quality Assessment in Online Advertising
Authors: Sahin Cem Geyik, Jianqiang Shen, Shahriar Shariat, Ali Dasdan, Santanu Kolay
Date: 2017-11-30 00:00:00

In online advertising, our aim is to match the advertisers with the most
relevant users to optimize the campaign performance. In the pursuit of
achieving this goal, multiple data sources provided by the advertisers or
third-party data providers are utilized to choose the set of users according to
the advertisers' targeting criteria. In this paper, we present a framework that
can be applied to assess the quality of such data sources in large scale. This
framework efficiently evaluates the similarity of a specific data source
categorization to that of the ground truth, especially for those cases when the
ground truth is accessible only in aggregate, and the user-level information is
anonymized or unavailable due to privacy reasons. We propose multiple
methodologies within this framework, present some preliminary assessment
results, and evaluate how the methodologies compare to each other. We also
present two use cases where we can utilize the data quality assessment results:
the first use case is targeting specific user categories, and the second one is
forecasting the desirable audiences we can reach for an online advertising
campaign with pre-set targeting criteria.
Valid Inference Corrected for Outlier Removal
Authors: Shuxiao Chen, Jacob Bien
Date: 2017-11-29 00:00:00
Tasks: Outlier Detection, valid

Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data collected. This standard "detect-and-forget" approach has been shown to be problematic, and in this paper we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real data sets to illustrate how our inferential results can differ from the traditional detect-and-forget strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R.
Learnings Options End-to-End for Continuous Action Tasks
Authors: Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup
Date: 2017-11-30 00:00:00
Tasks: MuJoCo

We present new results on learning temporally extended actions for
continuoustasks, using the options framework (Suttonet al.[1999b], Precup
[2000]). In orderto achieve this goal we work with the option-critic
architecture (Baconet al.[2017])using a deliberation cost and train it with
proximal policy optimization (Schulmanet al.[2017]) instead of vanilla policy
gradient. Results on Mujoco domains arepromising, but lead to interesting
questions aboutwhena given option should beused, an issue directly connected to
the use of initiation sets.
Safe Exploration for Identifying Linear Systems via Robust Optimization
Authors: Tyler Lu, Martin Zinkevich, Craig Boutilier, Binz Roy, Dale Schuurmans
Date: 2017-11-30 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

Safely exploring an unknown dynamical system is critical to the deployment of
reinforcement learning (RL) in physical systems where failures may have
catastrophic consequences. In scenarios where one knows little about the
dynamics, diverse transition data covering relevant regions of state-action
space is needed to apply either model-based or model-free RL. Motivated by the
cooling of Google's data centers, we study how one can safely identify the
parameters of a system model with a desired accuracy and confidence level. In
particular, we focus on learning an unknown linear system with Gaussian noise
assuming only that, initially, a nominal safe action is known. Define safety as
satisfying specific linear constraints on the state space (e.g., requirements
on process variable) that must hold over the span of an entire trajectory, and
given a Probably Approximately Correct (PAC) style bound on the estimation
error of model parameters, we show how to compute safe regions of action space
by gradually growing a ball around the nominal safe action. One can apply any
exploration strategy where actions are chosen from such safe regions.
Experiments on a stylized model of data center cooling dynamics show how
computing proper safe regions can increase the sample efficiency of safe
exploration.
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion
Authors: Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang
Date: 2017-11-29 00:00:00
Tasks: Feature Engineering

We present our preliminary work to determine if patient's vocal acoustic,
linguistic, and facial patterns could predict clinical ratings of depression
severity, namely Patient Health Questionnaire depression scale (PHQ-8). We
proposed a multi modal fusion model that combines three different modalities:
audio, video , and text features. By training over AVEC 2017 data set, our
proposed model outperforms each single modality prediction model, and surpasses
the data set baseline with ice margin.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
Authors: Shuyang Sun, Zhanghui Kuang, Wanli Ouyang, Lu Sheng, Wei zhang
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Action Recognition In Videos, Optical Flow Estimation, Temporal Action Localization

Motion representation plays a vital role in human action recognition in
videos. In this study, we introduce a novel compact motion representation for
video action recognition, named Optical Flow guided Feature (OFF), which
enables the network to distill temporal information through a fast and robust
approach. The OFF is derived from the definition of optical flow and is
orthogonal to the optical flow. The derivation also provides theoretical
support for using the difference between two frames. By directly calculating
pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be
embedded in any existing CNN based video action recognition framework with only
a slight additional cost. It enables the CNN to extract spatiotemporal
information, especially the temporal information between frames simultaneously.
This simple but powerful idea is validated by experimental results. The network
with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on
UCF-101, which is comparable with the result obtained by two streams (RGB and
optical flow), but is 15 times faster in speed. Experimental results also show
that OFF is complementary to other motion modalities such as optical flow. When
the proposed method is plugged into the state-of-the-art video action
recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51
respectively. The code for this project is available at
https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.
Structured learning and detailed interpretation of minimal object images
Authors: Guy Ben-Yosef, Liav Assif, Shimon Ullman
Date: 2017-11-29 00:00:00

We model the process of human full interpretation of object images, namely
the ability to identify and localize all semantic features and parts that are
recognized by human observers. The task is approached by dividing the
interpretation of the complete object to the interpretation of multiple reduced
but interpretable local regions. We model interpretation by a structured
learning framework, in which there are primitive components and relations that
play a useful role in local interpretation by humans. To identify useful
components and relations used in the interpretation process, we consider the
interpretation of minimal configurations, namely reduced local regions that are
minimal in the sense that further reduction will turn them unrecognizable and
uninterpretable. We show experimental results of our model, and results of
predicting and testing relations that were useful to the model via transformed
minimal images.
Modeling Information Flow Through Deep Neural Networks
Authors: Ahmad Chaddad, Behnaz Naisiri, Marco Pedersoli, Eric Granger, Christian Desrosiers, Matthew Toews
Date: 2017-11-29 00:00:00
Tasks: Classification, General Classification, Task 2, Transfer Learning

This paper proposes a principled information theoretic analysis of
classification for deep neural network structures, e.g. convolutional neural
networks (CNN). The output of convolutional filters is modeled as a random
variable Y conditioned on the object class C and network filter bank F. The
conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a
highly compact and class-informative code, that can be computed from the filter
outputs throughout an existing CNN and used to obtain higher classification
results than the original CNN itself. Experiments demonstrate the effectiveness
of CENT feature analysis in two separate CNN classification contexts. 1) In the
classification of neurodegeneration due to Alzheimer's disease (AD) and natural
aging from 3D magnetic resonance image (MRI) volumes, 3 CENT features result in
an AUC=94.6% for whole-brain AD classification, the highest reported accuracy
on the public OASIS dataset used and 12% higher than the softmax output of the
original CNN trained for the task. 2) In the context of visual object
classification from 2D photographs, transfer learning based on a small set of
CENT features identified throughout an existing CNN leads to AUC values
comparable to the 1000-feature softmax output of the original network when
classifying previously unseen object categories. The general information
theoretical analysis explains various recent CNN design successes, e.g. densely
connected CNN architectures, and provides insights for future research
directions in deep learning.
Easy High-Dimensional Likelihood-Free Inference
Authors: Vinay Jethava, Devdatt Dubhashi
Date: 2017-11-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We introduce a framework using Generative Adversarial Networks (GANs) for
likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC)
where we replace the black-box simulator model with an approximator network and
generate a rich set of summary features in a data driven fashion. On benchmark
data sets, our approach improves on others with respect to scalability, ability
to handle high dimensional data and complex probability distributions.
Enhancing Robustness of Machine Learning Systems via Data Transformations
Authors: Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal
Date: 2017-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Dimensionality Reduction, General Classification, image-classification, Image Classification

We propose the use of data transformations as a defense against evasion
attacks on ML classifiers. We present and investigate strategies for
incorporating a variety of data transformations including dimensionality
reduction via Principal Component Analysis and data `anti-whitening' to enhance
the resilience of machine learning, targeting both the classification and the
training phase. We empirically evaluate and demonstrate the feasibility of
linear transformations of data as a defense mechanism against evasion attacks
using multiple real-world datasets. Our key findings are that the defense is
(i) effective against the best known evasion attacks from the literature,
resulting in a two-fold increase in the resources required by a white-box
adversary with knowledge of the defense for a successful attack, (ii)
applicable across a range of ML classifiers, including Support Vector Machines
and Deep Neural Networks, and (iii) generalizable to multiple application
domains, including image classification and human activity classification.
Predicting and Explaining Human Semantic Search in a Cognitive Model
Authors: Filip Miscevic, Aida Nematzadeh, Suzanne Stevenson
Date: 2017-11-29 00:00:00
Proceeding: WS 2018 1
Tasks: Language Acquisition

Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive plausibility of their implementation. Existing work has also
neglected to consider the constraints that the incremental process of language
acquisition must place on the structure of semantic memory. Here we present a
model that incrementally updates a semantic network, with limited computational
steps, and replicates many patterns found in human semantic fluency using a
simple random walk. We also perform thorough analyses showing that a
combination of both structural and semantic features are correlated with human
performance patterns.