pwc-archive/papers-with-abstracts huggingface dataset:
Private Model Compression via Knowledge Distillation
Authors: Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu
Date: 2018-11-13 00:00:00
Tasks: Knowledge Distillation, model, Model Compression, Self-Learning

The soaring demand for intelligent mobile applications calls for deploying
powerful deep neural networks (DNNs) on mobile devices. However, the
outstanding performance of DNNs notoriously relies on increasingly complex
models, which in turn is associated with an increase in computational expense
far surpassing mobile devices' capacity. What is worse, app service providers
need to collect and utilize a large volume of users' data, which contain
sensitive information, to build the sophisticated DNN models. Directly
deploying these models on public mobile devices presents prohibitive privacy
risk. To benefit from the on-device deep learning without the capacity and
privacy concerns, we design a private model compression framework RONA.
Following the knowledge distillation paradigm, we jointly use hint learning,
distillation learning, and self learning to train a compact and fast neural
network. The knowledge distilled from the cumbersome model is adaptively
bounded and carefully perturbed to enforce differential privacy. We further
propose an elegant query sample selection method to reduce the number of
queries and control the privacy loss. A series of empirical evaluations as well
as the implementation on an Android mobile device show that RONA can not only
compress cumbersome models efficiently but also provide a strong privacy
guarantee. For example, on SVHN, when a meaningful
$(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained
by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with
merely 0.97% accuracy loss.
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation
Authors: John Benhardt, Peter Hase, Liuyi Zhu, Cynthia Rudin
Date: 2018-11-13 00:00:00
Tasks: Sonnet Generation

We provide an approach for generating beautiful poetry. Our sonnet-generation algorithm includes several novel elements that improve over the state of the art, leading to metrical, rhyming poetry with many human-like qualities. These novel elements include in-line punctuation, part of speech restrictions, and more appropriate training corpora. Our work is the winner of the 2018 PoetiX Literary Turing Test Award for computer-generated poetry.
Dynamic Feature Scaling for K-Nearest Neighbor Algorithm
Authors: Chandrasekaran Anirudh Bhardwaj, Megha Mishra, Kalyani Desikan
Date: 2018-11-13 00:00:00

Nearest Neighbors Algorithm is a Lazy Learning Algorithm, in which the
algorithm tries to approximate the predictions with the help of similar
existing vectors in the training dataset. The predictions made by the K-Nearest
Neighbors algorithm is based on averaging the target values of the spatial
neighbors. The selection process for neighbors in the Hermitian space is done
with the help of distance metrics such as Euclidean distance, Minkowski
distance, Mahalanobis distance etc. A majority of the metrics such as Euclidean
distance are scale variant, meaning that the results could vary for different
range of values used for the features. Standard techniques used for the
normalization of scaling factors are feature scaling method such as Z-score
normalization technique, Min-Max scaling etc. Scaling methods uniformly assign
equal weights to all the features, which might result in a non-ideal situation.
This paper proposes a novel method to assign weights to individual feature with
the help of out of bag errors obtained from constructing multiple decision tree
models.
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
Authors: Jun Wen, Risheng Liu, Nenggan Zheng, Qian Zheng, Zhefeng Gong, Junsong Yuan
Date: 2018-11-12 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation methods aim to alleviate performance
degradation caused by domain-shift by learning domain-invariant
representations. Existing deep domain adaptation methods focus on holistic
feature alignment by matching source and target holistic feature distributions,
without considering local features and their multi-mode statistics. We show
that the learned local feature patterns are more generic and transferable and a
further local feature distribution matching enables fine-grained feature
alignment. In this paper, we present a method for learning domain-invariant
local feature patterns and jointly aligning holistic and local feature
statistics. Comparisons to the state-of-the-art unsupervised domain adaptation
methods on two popular benchmark datasets demonstrate the superiority of our
approach and its effectiveness on alleviating negative transfer.
Finding All Bayesian Network Structures within a Factor of Optimal
Authors: Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek
Date: 2018-11-12 00:00:00
Tasks: All

A Bayesian network is a widely used probabilistic graphical model with
applications in knowledge discovery and prediction. Learning a Bayesian network
(BN) from data can be cast as an optimization problem using the well-known
score-and-search approach. However, selecting a single model (i.e., the best
scoring BN) can be misleading or may not achieve the best possible accuracy. An
alternative to committing to a single model is to perform some form of Bayesian
or frequentist model averaging, where the space of possible BNs is sampled or
enumerated in some fashion. Unfortunately, existing approaches for model
averaging either severely restrict the structure of the Bayesian network or
have only been shown to scale to networks with fewer than 30 random variables.
In this paper, we propose a novel approach to model averaging inspired by
performance guarantees in approximation algorithms. Our approach has two
primary advantages. First, our approach only considers credible models in that
they are optimal or near-optimal in score. Second, our approach is more
efficient and scales to significantly larger Bayesian networks than existing
approaches.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
Authors: Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello
Date: 2018-11-12 00:00:00
Tasks: Denoising, Super-Resolution

Motivated by augmented and virtual reality applications such as telepresence,
there has been a recent focus in real-time performance capture of humans under
motion. However, given the real-time constraint, these systems often suffer
from artifacts in geometry and texture such as holes and noise in the final
rendering, poor lighting, and low-resolution textures. We take the novel
approach to augment such real-time performance capture systems with a deep
architecture that takes a rendering from an arbitrary viewpoint, and jointly
performs completion, super resolution, and denoising of the imagery in
real-time. We call this approach neural (re-)rendering, and our live system
"LookinGood". Our deep architecture is trained to produce high resolution and
high quality images from a coarse rendering in real-time. First, we propose a
self-supervised training method that does not require manual ground-truth
annotation. We contribute a specialized reconstruction error that uses semantic
information to focus on relevant parts of the subject, e.g. the face. We also
introduce a salient reweighing scheme of the loss function that is able to
discard outliers. We specifically design the system for virtual and augmented
reality headsets where the consistency between the left and right eye plays a
crucial role in the final user experience. Finally, we generate temporally
stable results by explicitly minimizing the difference between two consecutive
frames. We tested the proposed system in two different scenarios: one involving
a single RGB-D sensor, and upper body reconstruction of an actor, the second
consisting of full body 360 degree capture. Through extensive experimentation,
we demonstrate how our system generalizes across unseen sequences and subjects.
The supplementary video is available at http://youtu.be/Md3tdAKoLGU.
Deep Neural Network Augmentation: Generating Faces for Affect Analysis
Authors: Dimitrios Kollias, Shiyang Cheng, Evangelos Ververas, Irene Kotsia, Stefanos Zafeiriou
Date: 2018-11-12 00:00:00
Tasks: Data Augmentation, Face Generation, Facial Expression Recognition (FER)

This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). The proposed approach accepts the following inputs: i) a neutral 2D image of a person; ii) a basic facial expression or a pair of valence-arousal (VA) emotional state descriptors to be generated, or a path of affect in the 2D VA Space to be generated as an image sequence. In order to synthesize affect in terms of VA, for this person, $600,000$ frames from the 4DFAB database were annotated. The affect synthesis is implemented by fitting a 3D Morphable Model on the neutral image, then deforming the reconstructed face and adding the inputted affect, and blending the new face with the given affect into the original image. Qualitative experiments illustrate the generation of realistic images, when the neutral image is sampled from thirteen well known lab-controlled or in-the-wild databases, including Aff-Wild, AffectNet, RAF-DB; comparisons with Generative Adversarial Networks (GANs) show the higher quality achieved by the proposed approach. Then, quantitative experiments are conducted, in which the synthesized images are used for data augmentation in training Deep Neural Networks to perform affect recognition over all databases; greatly improved performances are achieved when compared with state-of-the-art methods, as well as with GAN-based data augmentation, in all cases.
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training
Authors: Yao Wan, Wenqiang Yan, Jianwei Gao, Zhou Zhao, Jian Wu, Philip S. Yu
Date: 2018-11-12 00:00:00
Tasks: Classification, Dialogue Act Classification, Dialogue Interpretation, General Classification, Structured Prediction

Dialogue Act (DA) classification is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DA classification problem ranging from multi-classification to
structured prediction, which suffer from two limitations: a) these methods are
either handcrafted feature-based or have limited memories. b) adversarial
examples can't be correctly classified by traditional training methods. To
address these issues, in this paper we first cast the problem into a question
and answering problem and proposed an improved dynamic memory networks with
hierarchical pyramidal utterance encoder. Moreover, we apply adversarial
training to train our proposed model. We evaluate our model on two public
datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus.
Extensive experiments show that our proposed model is not only robust, but also
achieves better performance when compared with some state-of-the-art baselines.
Learning Temporal Point Processes via Reinforcement Learning
Authors: Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song
Date: 2018-11-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Point Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time. The generative processes of these event data can be very complex, requiring flexible models to capture their dynamics. Temporal point processes offer an elegant framework for modeling event data without discretizing the time. However, the existing maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting the intensity function beforehand and cannot directly monitor the goodness-of-fit of the estimated model in the process of training. To alleviate the risk of model-misspecification in MLE, we propose to generate samples from the generative model and monitor the quality of the samples in the process of training until the samples and the real data are indistinguishable. We take inspiration from reinforcement learning (RL) and treat the generation of each event as the action taken by a stochastic policy. We parameterize the policy as a flexible recurrent neural network and gradually improve the policy to mimic the observed event distribution. Since the reward function is unknown in this setting, we uncover an analytic and nonparametric form of the reward function using an inverse reinforcement learning formulation. This new RL framework allows us to derive an efficient policy gradient algorithm for learning flexible point process models, and we show that it performs well in both synthetic and real data.
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
Authors: Rongcheng Lin, Jing Xiao, Jianping Fan
Date: 2018-11-12 00:00:00
Tasks: Efficient Neural Network, General Classification, Video Classification, Video Understanding

This paper introduces a fast and efficient network architecture, NeXtVLAD, to
aggregate frame-level features into a compact feature vector for large-scale
video classification. Briefly speaking, the basic idea is to decompose a
high-dimensional feature into a group of relatively low-dimensional vectors
with attention before applying NetVLAD aggregation over time. This NeXtVLAD
approach turns out to be both effective and parameter efficient in aggregating
temporal information. In the 2nd Youtube-8M video understanding challenge, a
single NeXtVLAD model with less than 80M parameters achieves a GAP score of
0.87846 in private leaderboard. A mixture of 3 NeXtVLAD models results in
0.88722, which is ranked 3rd over 394 teams. The code is publicly available at
https://github.com/linrongc/youtube-8m.
Blindfold Baselines for Embodied QA
Authors: Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron Courville
Date: 2018-11-12 00:00:00
Tasks: Embodied Question Answering, Question Answering

We explore blindfold (question-only) baselines for Embodied Question
Answering. The EmbodiedQA task requires an agent to answer a question by
intelligently navigating in a simulated environment, gathering necessary visual
information only through first-person vision before finally answering.
Consequently, a blindfold baseline which ignores the environment and visual
information is a degenerate solution, yet we show through our experiments on
the EQAv1 dataset that a simple question-only baseline achieves
state-of-the-art results on the EmbodiedQA task in all cases except when the
agent is spawned extremely close to the object.
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
Authors: Long Nguyen, Zhou Yang, Jiazhen Zhu, Jia Li, Fang Jin
Date: 2018-11-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

A crucial and time-sensitive task when any disaster occurs is to rescue
victims and distribute resources to the right groups and locations. This task
is challenging in populated urban areas, due to the huge burst of help requests
generated in a very short period. To improve the efficiency of the emergency
response in the immediate aftermath of a disaster, we propose a heuristic
multi-agent reinforcement learning scheduling algorithm, named as ResQ, which
can effectively schedule the rapid deployment of volunteers to rescue victims
in dynamic settings. The core concept is to quickly identify victims and
volunteers from social network data and then schedule rescue parties with an
adaptive learning algorithm. This framework performs two key functions: 1)
identify trapped victims and rescue volunteers, and 2) optimize the volunteers'
rescue strategy in a complex time-sensitive environment. The proposed ResQ
algorithm can speed up the training processes through a heuristic function
which reduces the state-action space by identifying the set of particular
actions over others. Experimental results showed that the proposed heuristic
multi-agent reinforcement learning based scheduling outperforms several
state-of-art methods, in terms of both reward rate and response times.
A new approach for pedestrian density estimation using moving sensors and computer vision
Authors: Eric K. Tokuda, Yitzchak Lockerman, Gabriel B. A. Ferreira, Ethan Sorrelgreen, David Boyle, Roberto M. Cesar-Jr., Claudio T. Silva
Date: 2018-11-12 00:00:00
Tasks: Density Estimation, Pedestrian Density Estimation

An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state of the art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error.
OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
Authors: Chenxu Luo, Xiao Chu, Alan Yuille
Date: 2018-11-12 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

In this paper, we propose a fully convolutional network for 3D human pose
estimation from monocular images. We use limb orientations as a new way to
represent 3D poses and bind the orientation together with the bounding box of
each limb region to better associate images and predictions. The 3D
orientations are modeled jointly with 2D keypoint detections. Without
additional constraints, this simple method can achieve good results on several
large-scale benchmarks. Further experiments show that our method can generalize
well to novel scenes and is robust to inaccurate bounding boxes.
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
Authors: Samyak Parajuli, Aswin Raghavan, Sek Chai
Date: 2018-11-12 00:00:00
Tasks: Edge-computing, General Classification, Neural Network Compression

The use of deep neural networks in edge computing devices hinges on the
balance between accuracy and complexity of computations. Ternary Connect (TC)
\cite{lin2015neural} addresses this issue by restricting the parameters to
three levels $-1, 0$, and $+1$, thus eliminating multiplications in the forward
pass of the network during prediction. We propose Generalized Ternary Connect
(GTC), which allows an arbitrary number of levels while at the same time
eliminating multiplications by restricting the parameters to integer powers of
two. The primary contribution is that GTC learns the number of levels and their
values for each layer, jointly with the weights of the network in an end-to-end
fashion. Experiments on MNIST and CIFAR-10 show that GTC naturally converges to
an `almost binary' network for deep classification networks (e.g. VGG-16) and
deep variational auto-encoders, with negligible loss of classification accuracy
and comparable visual quality of generated samples respectively. We demonstrate
superior compression and similar accuracy of GTC in comparison to several
state-of-the-art methods for neural network compression. We conclude with
simulations showing the potential benefits of GTC in hardware.
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Authors: Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lio, Nigel Collier
Date: 2018-11-12 00:00:00
Tasks: General Classification, Graph Embedding, text-classification, Text Classification, Word Similarity

Word embedding techniques heavily rely on the abundance of training data for
individual words. Given the Zipfian distribution of words in natural language
texts, a large number of words do not usually appear frequently or at all in
the training data. In this paper we put forward a technique that exploits the
knowledge encoded in lexical resources, such as WordNet, to induce embeddings
for unseen words. Our approach adapts graph embedding and cross-lingual vector
space transformation techniques in order to merge lexical knowledge encoded in
ontologies with that derived from corpus statistics. We show that the approach
can provide consistent performance improvements across multiple evaluation
benchmarks: in-vitro, on multiple rare word similarity datasets, and in-vivo,
in two downstream text classification tasks.
Eliminating Latent Discrimination: Train Then Mask
Authors: Soheil Ghili, Ehsan Kazemi, Amin Karbasi
Date: 2018-11-12 00:00:00
Tasks: Decision Making, Econometrics, Fairness

How can we control for latent discrimination in predictive models? How can we
provably remove it? Such questions are at the heart of algorithmic fairness and
its impacts on society. In this paper, we define a new operational fairness
criteria, inspired by the well-understood notion of omitted variable-bias in
statistics and econometrics. Our notion of fairness effectively controls for
sensitive features and provides diagnostics for deviations from fair decision
making. We then establish analytical and algorithmic results about the
existence of a fair classifier in the context of supervised learning. Our
results readily imply a simple, but rather counter-intuitive, strategy for
eliminating latent discrimination. In order to prevent other features proxying
for sensitive features, we need to include sensitive features in the training
phase, but exclude them in the test/evaluation phase while controlling for
their effects. We evaluate the performance of our algorithm on several
real-world datasets and show how fairness for these datasets can be improved
with a very small loss in accuracy.
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Authors: Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, Sen Wang
Date: 2018-11-12 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Intent Detection, Muscular Movement Recognition

Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.