pwc-archive/papers-with-abstracts huggingface dataset:
Prediction and Localization of Student Engagement in the Wild
Authors: Amanjot Kaur, Aamir Mustafa, Love Mehta, Abhinav Dhall
Date: 2018-04-03 00:00:00
Tasks: Multiple Instance Learning, Weakly-supervised Learning

In this paper, we introduce a new dataset for student engagement detection
and localization. Digital revolution has transformed the traditional teaching
procedure and a result analysis of the student engagement in an e-learning
environment would facilitate effective task accomplishment and learning. Well
known social cues of engagement/disengagement can be inferred from facial
expressions, body movements and gaze pattern. In this paper, student's response
to various stimuli videos are recorded and important cues are extracted to
estimate variations in engagement level. In this paper, we study the
association of a subject's behavioral cues with his/her engagement level, as
annotated by labelers. We then localize engaging/non-engaging parts in the
stimuli videos using a deep multiple instance learning based framework, which
can give useful insight into designing Massive Open Online Courses (MOOCs)
video material. Recognizing the lack of any publicly available dataset in the
domain of user engagement, a new `in the wild' dataset is created to study the
subject engagement problem. The dataset contains 195 videos captured from 78
subjects which is about 16.5 hours of recording. We present detailed baseline
results using different classifiers ranging from traditional machine learning
to deep learning based approaches. The subject independent analysis is
performed so that it can be generalized to new users. The problem of engagement
prediction is modeled as a weakly supervised learning problem. The dataset is
manually annotated by different labelers for four levels of engagement
independently and the correlation studies between annotated and predicted
labels of videos by different classifiers is reported. This dataset creation is
an effort to facilitate research in various e-learning environments such as
intelligent tutoring systems, MOOCs, and others.
A computational perspective of the role of Thalamus in cognition
Authors: Nima Dehghani, Ralf D. Wimmer
Date: 2018-03-02 00:00:00

Thalamus has traditionally been considered as only a relay source of cortical
inputs, with hierarchically organized cortical circuits serially transforming
thalamic signals to cognitively-relevant representations. Given the absence of
local excitatory connections within the thalamus, the notion of thalamic
`relay' seemed like a reasonable description over the last several decades.
Recent advances in experimental approaches and theory provide a broader
perspective on the role of the thalamus in cognitively-relevant cortical
computations, and suggest that only a subset of thalamic circuit motifs fit the
relay description. Here, we discuss this perspective and highlight the
potential role for the thalamus -- and specifically mediodorsal (MD) nucleus --
in dynamic selection of cortical representations through a combination of
intrinsic thalamic computations and output signals that change cortical network
functional parameters. We suggest that through the contextual modulation of
cortical computation, thalamus and cortex jointly optimize the information/cost
tradeoff in an emergent fashion. We emphasize that coordinated experimental and
theoretical efforts will provide a path to understanding the role of the
thalamus in cognition, along with an understanding to augment cognitive
capacity in health and disease.
Automatic Dataset Augmentation
Authors: Yalong Bai, Kuiyuan Yang, Tao Mei, Wei-Ying Ma, Tiejun Zhao
Date: 2017-08-28 00:00:00
Tasks: Object Recognition

Large scale image dataset and deep convolutional neural network (DCNN) are
two primary driving forces for the rapid progress made in generic object
recognition tasks in recent years. While lots of network architectures have
been continuously designed to pursue lower error rates, few efforts are devoted
to enlarge existing datasets due to high labeling cost and unfair comparison
issues. In this paper, we aim to achieve lower error rate by augmenting
existing datasets in an automatic manner. Our method leverages both Web and
DCNN, where Web provides massive images with rich contextual information, and
DCNN replaces human to automatically label images under guidance of Web
contextual information. Experiments show our method can automatically scale up
existing datasets significantly from billions web pages with high accuracy, and
significantly improve the performance on object recognition tasks by using the
automatically augmented datasets, which demonstrates that more supervisory
information has been automatically gathered from the Web. Both the dataset and
models trained on the dataset are made publicly available.
Sparsely Aggregated Convolutional Networks
Authors: Ligeng Zhu, Ruizhi Deng, Michael Maire, Zhiwei Deng, Greg Mori, Ping Tan
Date: 2018-01-18 00:00:00
Proceeding: ECCV 2018 9

We explore a key architectural aspect of deep convolutional neural networks:
the pattern of internal skip connections used to aggregate outputs of earlier
layers for consumption by deeper layers. Such aggregation is critical to
facilitate training of very deep networks in an end-to-end manner. This is a
primary reason for the widespread adoption of residual networks, which
aggregate outputs via cumulative summation. While subsequent works investigate
alternative aggregation operations (e.g. concatenation), we focus on an
orthogonal question: which outputs to aggregate at a particular point in the
network. We propose a new internal connection structure which aggregates only a
sparse set of previous outputs at any given depth. Our experiments demonstrate
this simple design change offers superior performance with fewer parameters and
lower computational requirements. Moreover, we show that sparse aggregation
allows networks to scale more robustly to 1000+ layers, thereby opening future
avenues for training long-running visual processes.
Community Member Retrieval on Social Media using Textual Information
Authors: Aaron Jaech, Shobhit Hathi, Mari Ostendorf
Date: 2018-04-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Retrieval

This paper addresses the problem of community membership detection using only
text features in a scenario where a small number of positive labeled examples
defines the community. The solution introduces an unsupervised proxy task for
learning user embeddings: user re-identification. Experiments with 16 different
communities show that the resulting embeddings are more effective for community
membership identification than common unsupervised representations.
Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
Authors: Jaekoo Lee, Byunghan Lee, Jongyoon Song, Jaesik Yoon, Yongsik Lee, Dong-hun Lee, Sungroh Yoon
Date: 2018-04-16 00:00:00
Tasks: Management

With a new era of cloud and big data, Database Management Systems (DBMSs)
have become more crucial in numerous enterprise business applications in all
the industries. Accordingly, the importance of their proactive and preventive
maintenance has also increased. However, detecting problems by predefined rules
or stochastic modeling has limitations, particularly when analyzing the data on
high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent
years, Deep Learning (DL) has opened new opportunities for this complex
analysis. In this paper, we present two complementary DL approaches to detect
anomalies in SAP HANA. A temporal learning approach is used to detect abnormal
patterns based on unlabeled historical data, whereas a spatial learning
approach is used to classify known anomalies based on labeled data. We
implement a system in SAP HANA integrated with Google TensorFlow. The
experimental results with real-world data confirm the effectiveness of the
system and models.
conformalClassification: A Conformal Prediction R Package for Classification
Authors: Niharika Gauraha, Ola Spjuth
Date: 2018-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Conformal Prediction, Diagnostic, General Classification, Prediction

The conformalClassification package implements Transductive Conformal
Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification
problems. Conformal Prediction (CP) is a framework that complements the
predictions of machine learning algorithms with reliable measures of
confidence. TCP gives results with higher validity than ICP, however ICP is
computationally faster than TCP. The package conformalClassification is built
upon the random forest method, where votes of the random forest for each class
are considered as the conformity scores for each data point. Although the main
aim of the conformalClassification package is to generate CP errors (p-values)
for classification problems, the package also implements various diagnostic
measures such as deviation from validity, error rate, efficiency, observed
fuzziness and calibration plots. In future releases, we plan to extend the
package to use other machine learning algorithms, (e.g. support vector
machines) for model fitting.
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Authors: Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser
Date: 2018-03-27 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Skilled robotic manipulation benefits from complex synergies between
non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing
can help rearrange cluttered objects to make space for arms and fingers;
likewise, grasping can help displace objects to make pushing movements more
precise and collision-free. In this work, we demonstrate that it is possible to
discover and learn these synergies from scratch through model-free deep
reinforcement learning. Our method involves training two fully convolutional
networks that map from visual observations to actions: one infers the utility
of pushes for a dense pixel-wise sampling of end effector orientations and
locations, while the other does the same for grasping. Both networks are
trained jointly in a Q-learning framework and are entirely self-supervised by
trial and error, where rewards are provided from successful grasps. In this
way, our policy learns pushing motions that enable future grasps, while
learning grasps that can leverage past pushes. During picking experiments in
both simulation and real-world scenarios, we find that our system quickly
learns complex behaviors amid challenging cases of clutter, and achieves better
grasping success rates and picking efficiencies than baseline alternatives
after only a few hours of training. We further demonstrate that our method is
capable of generalizing to novel objects. Qualitative results (videos), code,
pre-trained models, and simulation environments are available at
http://vpg.cs.princeton.edu
Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
Authors: Florentina Bunea, Christophe Giraud, Xi Luo, Martin Royer, Nicolas Verzelen
Date: 2015-08-08 00:00:00
Tasks: Clustering

Model-based clustering defines population level clusters relative to a model
that embeds notions of similarity. Algorithms tailored to such models yield
estimated clusters with a clear statistical interpretation. We take this view
here and introduce the class of G-block covariance models as a background model
for variable clustering. In such models, two variables in a cluster are deemed
similar if they have similar associations will all other variables. This can
arise, for instance, when groups of variables are noise corrupted versions of
the same latent factor. We quantify the difficulty of clustering data generated
from a G-block covariance model in terms of cluster proximity, measured with
respect to two related, but different, cluster separation metrics. We derive
minimax cluster separation thresholds, which are the metric values below which
no algorithm can recover the model-defined clusters exactly, and show that they
are different for the two metrics. We therefore develop two algorithms, COD and
PECOK, tailored to G-block covariance models, and study their
minimax-optimality with respect to each metric. Of independent interest is the
fact that the analysis of the PECOK algorithm, which is based on a corrected
convex relaxation of the popular K-means algorithm, provides the first
statistical analysis of such algorithms for variable clustering. Additionally,
we contrast our methods with another popular clustering method, spectral
clustering, specialized to variable clustering, and show that ensuring exact
cluster recovery via this method requires clusters to have a higher separation,
relative to the minimax threshold. Extensive simulation studies, as well as our
data analyses, confirm the applicability of our approach.
Locally Adaptive Learning Loss for Semantic Image Segmentation
Authors: Jinjiang Guo, Pengyuan Ren, Aiguo Gu, Jian Xu, Weixin Wu
Date: 2018-02-23 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We propose a novel locally adaptive learning estimator for enhancing the
inter- and intra- discriminative capabilities of Deep Neural Networks, which
can be used as improved loss layer for semantic image segmentation tasks. Most
loss layers compute pixel-wise cost between feature maps and ground truths,
ignoring spatial layouts and interactions between neighboring pixels with same
object category, and thus networks cannot be effectively sensitive to
intra-class connections. Stride by stride, our method firstly conducts adaptive
pooling filter operating over predicted feature maps, aiming to merge predicted
distributions over a small group of neighboring pixels with same category, and
then it computes cost between the merged distribution vector and their category
label. Such design can make groups of neighboring predictions from same
category involved into estimations on predicting correctness with respect to
their category, and hence train networks to be more sensitive to regional
connections between adjacent pixels based on their categories. In the
experiments on Pascal VOC 2012 segmentation datasets, the consistently improved
results show that our proposed approach achieves better segmentation masks
against previous counterparts.
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
Authors: Hongyu Xu, Jingjing Zheng, Azadeh Alavi, Rama Chellappa
Date: 2018-04-12 00:00:00
Tasks: Dictionary Learning, Domain Adaptation

In real-world visual recognition problems, the assumption that the training
data (source domain) and test data (target domain) are sampled from the same
distribution is often violated. This is known as the domain adaptation problem.
In this work, we propose a novel domain-adaptive dictionary learning framework
for cross-domain visual recognition. Our method generates a set of intermediate
domains. These intermediate domains form a smooth path and bridge the gap
between the source and target domains. Specifically, we not only learn a common
dictionary to encode the domain-shared features, but also learn a set of
domain-specific dictionaries to model the domain shift. The separation of the
common and domain-specific dictionaries enables us to learn more compact and
reconstructive dictionaries for domain adaptation. These dictionaries are
learned by alternating between domain-adaptive sparse coding and dictionary
updating steps. Meanwhile, our approach gradually recovers the feature
representations of both source and target data along the domain path. By
aligning all the recovered domain data, we derive the final domain-adaptive
features for cross-domain visual recognition. Extensive experiments on three
public datasets demonstrates that our approach outperforms most
state-of-the-art methods.
Review on Optical Image Hiding and Watermarking Techniques
Authors: Shuming Jiao, Changyuan Zhou, Yishi Shi, Wenbin Zou, Xia Li
Date: 2018-04-16 00:00:00

Information security is a critical issue in modern society and image
watermarking can effectively prevent unauthorized information access. Optical
image watermarking techniques generally have advantages of parallel high-speed
processing and multi-dimensional capabilities compared with digital approaches.
This paper provides a comprehensive review on the research works related to
optical image hiding and watermarking techniques conducted in the past decade.
The past research works are focused on two major aspects, various optical
systems for image hiding and the methods for embedding optical system output
into a host image. A summary of the state-of-the-art works is made from these
two perspectives.
Binary Matrix Factorization via Dictionary Learning
Authors: Ignacio Ramirez
Date: 2018-04-16 00:00:00
Tasks: Dictionary Learning, Model Selection, Recommendation Systems

Matrix factorization is a key tool in data analysis; its applications include
recommender systems, correlation analysis, signal processing, among others.
Binary matrices are a particular case which has received significant attention
for over thirty years, especially within the field of data mining. Dictionary
learning refers to a family of methods for learning overcomplete basis (also
called frames) in order to efficiently encode samples of a given type; this
area, now also about twenty years old, was mostly developed within the signal
processing field. In this work we propose two binary matrix factorization
methods based on a binary adaptation of the dictionary learning paradigm to
binary matrices. The proposed algorithms focus on speed and scalability; they
work with binary factors combined with bit-wise operations and a few auxiliary
integer ones. Furthermore, the methods are readily applicable to online binary
matrix factorization. Another important issue in matrix factorization is the
choice of rank for the factors; we address this model selection problem with an
efficient method based on the Minimum Description Length principle. Our
preliminary results show that the proposed methods are effective at producing
interpretable factorizations of various data types of different nature.
A Direct Sum Result for the Information Complexity of Learning
Authors: Ido Nachum, Jonathan Shafer, Amir Yehudayoff
Date: 2018-04-16 00:00:00

How many bits of information are required to PAC learn a class of hypotheses
of VC dimension $d$? The mathematical setting we follow is that of Bassily et
al. (2018), where the value of interest is the mutual information
$\mathrm{I}(S;A(S))$ between the input sample $S$ and the hypothesis outputted
by the learning algorithm $A$. We introduce a class of functions of VC
dimension $d$ over the domain $\mathcal{X}$ with information complexity at
least $\Omega\left(d\log \log \frac{|\mathcal{X}|}{d}\right)$ bits for any
consistent and proper algorithm (deterministic or random). Bassily et al.
proved a similar (but quantitatively weaker) result for the case $d=1$.
  The above result is in fact a special case of a more general phenomenon we
explore. We define the notion of information complexity of a given class of
functions $\mathcal{H}$. Intuitively, it is the minimum amount of information
that an algorithm for $\mathcal{H}$ must retain about its input to ensure
consistency and properness. We prove a direct sum result for information
complexity in this context; roughly speaking, the information complexity sums
when combining several classes.
Optimizing Video Object Detection via a Scale-Time Lattice
Authors: Kai Chen, Jiaqi Wang, Shuo Yang, Xingcheng Zhang, Yuanjun Xiong, Chen Change Loy, Dahua Lin
Date: 2018-04-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection, Video Object Detection

High-performance object detection relies on expensive convolutional networks
to compute features, often leading to significant challenges in applications,
e.g. those that require detecting objects from video streams in real time. The
key to this problem is to trade accuracy for efficiency in an effective way,
i.e. reducing the computing cost while maintaining competitive performance. To
seek a good balance, previous efforts usually focus on optimizing the model
architectures. This paper explores an alternative approach, that is, to
reallocate the computation over a scale-time space. The basic idea is to
perform expensive detection sparsely and propagate the results across both
scales and time with substantially cheaper networks, by exploiting the strong
correlations among them. Specifically, we present a unified framework that
integrates detection, temporal propagation, and across-scale refinement on a
Scale-Time Lattice. On this framework, one can explore various strategies to
balance performance and cost. Taking advantage of this flexibility, we further
develop an adaptive scheme with the detector invoked on demand and thus obtain
improved tradeoff. On ImageNet VID dataset, the proposed method can achieve a
competitive mAP 79.6% at 20 fps, or 79.0% at 62 fps as a performance/speed
tradeoff.
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Authors: Jongchan Park, Joon-Young Lee, Donggeun Yoo, In So Kweon
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning-based color enhancement approaches typically learn to map from input
images to retouched images. Most of existing methods require expensive pairs of
input-retouched images or produce results in a non-interpretable way. In this
paper, we present a deep reinforcement learning (DRL) based method for color
enhancement to explicitly model the step-wise nature of human retouching
process. We cast a color enhancement process as a Markov Decision Process where
actions are defined as global color adjustment operations. Then we train our
agent to learn the optimal global enhancement sequence of the actions. In
addition, we present a 'distort-and-recover' training scheme which only
requires high-quality reference images for training instead of input and
retouched image pairs. Given high-quality reference images, we distort the
images' color distribution and form distorted-reference image pairs for
training. Through extensive experiments, we show that our method produces
decent enhancement results and our DRL approach is more suitable for the
'distort-and-recover' training scheme than previous supervised approaches.
Supplementary material and code are available at
https://sites.google.com/view/distort-and-recover/
Composable Unpaired Image to Image Translation
Authors: Laura Graesser, Anant Gupta
Date: 2018-04-16 00:00:00
Tasks: Image-to-Image Translation, Translation

There has been remarkable recent work in unpaired image-to-image translation.
However, they're restricted to translation on single pairs of distributions,
with some exceptions. In this study, we extend one of these works to a scalable
multidistribution translation mechanism. Our translation models not only
converts from one distribution to another but can be stacked to create
composite translation functions. We show that this composite property makes it
possible to generate images with characteristics not seen in the training set.
We also propose a decoupled training mechanism to train multiple distributions
separately, which we show, generates better samples than isolated joint
training. Further, we do a qualitative and quantitative analysis to assess the
plausibility of the samples. The code is made available at
https://github.com/lgraesser/im2im2im.