pwc-archive/papers-with-abstracts huggingface dataset:
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Authors: Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Date: 2017-09-02 00:00:00
Tasks: General Classification, Knowledge Distillation

There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information learned from a large
and accurate teacher network. We propose to use conditional adversarial
networks to learn the loss function to transfer knowledge from teacher to
student. The proposed method is particularly effective for relatively small
student networks. Moreover, experimental results show the effect of network
size when the modern networks are used as student. We empirically study the
trade-off between inference time and classification accuracy, and provide
suggestions on choosing a proper student network.
Egocentric 6-DoF Tracking of Small Handheld Objects
Authors: Rohit Pandey, Pavel Pidlypenskyi, Shuoran Yang, Christine Kaeser-Chen
Date: 2018-04-16 00:00:00
Tasks: CPU

Virtual and augmented reality technologies have seen significant growth in
the past few years. A key component of such systems is the ability to track the
pose of head mounted displays and controllers in 3D space. We tackle the
problem of efficient 6-DoF tracking of a handheld controller from egocentric
camera perspectives. We collected the HMD Controller dataset which consist of
over 540,000 stereo image pairs labelled with the full 6-DoF pose of the
handheld controller. Our proposed SSD-AF-Stereo3D model achieves a mean average
error of 33.5 millimeters in 3D keypoint prediction and is used in conjunction
with an IMU sensor on the controller to enable 6-DoF tracking. We also present
results on approaches for model based full 6-DoF tracking. All our models
operate under the strict constraints of real time mobile CPU inference.
Towards High Performance Video Object Detection for Mobiles
Authors: Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan
Date: 2018-04-16 00:00:00
Tasks: Object, object-detection, Object Detection, Video Object Detection, Vocal Bursts Intensity Prediction

Despite the recent success of video object detection on Desktop GPUs, its
architecture is still far too heavy for mobiles. It is also unclear whether the
key principles of sparse feature propagation and multi-frame feature
aggregation apply at very limited computational resources. In this paper, we
present a light weight network architecture for video object detection on
mobiles. Light weight image object detector is applied on sparse key frames. A
very small network, Light Flow, is designed for establishing correspondence
across frames. A flow-guided GRU module is designed to effectively aggregate
features on key frames. For non-key frames, sparse feature propagation is
performed. The whole network can be trained end-to-end. The proposed system
achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).
Learning a Generative Model for Validity in Complex Discrete Structures
Authors: David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Date: 2017-12-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning, valid

Deep generative models have been successfully used to learn representations
for high-dimensional discrete spaces by representing discrete objects as
sequences and employing powerful sequence-based deep models. Unfortunately,
these sequence-based models often produce invalid sequences: sequences which do
not represent any underlying discrete structure; invalid sequences hinder the
utility of such models. As a step towards solving this problem, we propose to
learn a deep recurrent validator model, which can estimate whether a partial
sequence can function as the beginning of a full, valid sequence. This
validator provides insight as to how individual sequence elements influence the
validity of the overall sequence, and can be used to constrain sequence based
models to generate valid sequences -- and thus faithfully model discrete
objects. Our approach is inspired by reinforcement learning, where an oracle
which can evaluate validity of complete sequences provides a sparse reward
signal. We demonstrate its effectiveness as a generative model of Python 3
source code for mathematical expressions, and in improving the ability of a
variational autoencoder trained on SMILES strings to decode valid molecular
structures.
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation
Authors: Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gkhan Uzunbas, Tom Goldstein, Ser Nam Lim, Larry S. Davis
Date: 2018-04-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Semantic Segmentation

Harvesting dense pixel-level annotations to train deep neural networks for
semantic segmentation is extremely expensive and unwieldy at scale. While
learning from synthetic data where labels are readily available sounds
promising, performance degrades significantly when testing on novel realistic
data due to domain discrepancies. We present Dual Channel-wise Alignment
Networks (DCAN), a simple yet effective approach to reduce domain shift at both
pixel-level and feature-level. Exploring statistics in each channel of CNN
feature maps, our framework performs channel-wise feature alignment, which
preserves spatial structures and semantic information, in both an image
generator and a segmentation network. In particular, given an image from the
source domain and unlabeled samples from the target domain, the generator
synthesizes new images on-the-fly to resemble samples from the target domain in
appearance and the segmentation network further refines high-level features
before predicting semantic maps, both of which leverage feature statistics of
sampled images from the target domain. Unlike much recent and concurrent work
relying on adversarial training, our framework is lightweight and easy to
train. Extensive experiments on adapting models trained on synthetic
segmentation benchmarks to real urban scenes demonstrate the effectiveness of
the proposed framework.
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes
Authors: Tanay Kumar Saha, Thomas Williams, Mohammad Al Hasan, Shafiq Joty, Nicholas K. Varberg
Date: 2018-04-16 00:00:00
Tasks: Link Prediction, Representation Learning

In a dynamic network, the neighborhood of the vertices evolve across
different temporal snapshots of the network. Accurate modeling of this temporal
evolution can help solve complex tasks involving real-life social and
interaction networks. However, existing models for learning latent
representation are inadequate for obtaining the representation vectors of the
vertices for different time-stamps of a dynamic network in a meaningful way. In
this paper, we propose latent representation learning models for dynamic
networks which overcome the above limitation by considering two different kinds
of temporal smoothness: (i) retrofitted, and (ii) linear transformation. The
retrofitted model tracks the representation vector of a vertex over time,
facilitating vertex-based temporal analysis of a network. On the other hand,
linear transformation based model provides a smooth transition operator which
maps the representation vectors of all vertices from one temporal snapshot to
the next (unobserved) snapshot-this facilitates prediction of the state of a
network in a future time-stamp. We validate the performance of our proposed
models by employing them for solving the temporal link prediction task.
Experiments on 9 real-life networks from various domains validate that the
proposed models are significantly better than the existing models for
predicting the dynamics of an evolving network.
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Authors: Shang-Tse Chen, Cory Cornelius, Jason Martin, Duen Horng Chau
Date: 2018-04-16 00:00:00
Tasks: Adversarial Attack, Autonomous Vehicles, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection

Given the ability to directly manipulate image pixels in the digital input
space, an adversary can easily generate imperceptible perturbations to fool a
Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In
this work, we propose ShapeShifter, an attack that tackles the more challenging
problem of crafting physical adversarial perturbations to fool image-based
object detectors like Faster R-CNN. Attacking an object detector is more
difficult than attacking an image classifier, as it needs to mislead the
classification results in multiple bounding boxes with different scales.
Extending the digital attack to the physical world adds another layer of
difficulty, because it requires the perturbation to be robust enough to survive
real-world distortions due to different viewing distances and angles, lighting
conditions, and camera limitations. We show that the Expectation over
Transformation technique, which was originally proposed to enhance the
robustness of adversarial perturbations in image classification, can be
successfully adapted to the object detection setting. ShapeShifter can generate
adversarially perturbed stop signs that are consistently mis-detected by Faster
R-CNN as other objects, posing a potential threat to autonomous vehicles and
other safety-critical computer vision systems.
Deep Embedding Kernel
Authors: Linh Le, Ying Xie
Date: 2018-04-16 00:00:00
Tasks: Deep Learning, Dimensionality Reduction, General Classification, regression, Transfer Learning

In this paper, we propose a novel supervised learning method that is called
Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and
kernel methods in a unified framework. More specifically, DEK is a learnable
kernel represented by a newly designed deep architecture. Compared with
pre-defined kernels, this kernel can be explicitly trained to map data to an
optimized high-level feature space where data may have favorable features
toward the application. Compared with typical deep learning using SoftMax or
logistic regression as the top layer, DEK is expected to be more generalizable
to new data. Experimental results show that DEK has superior performance than
typical machine learning methods in identity detection, classification,
regression, dimension reduction, and transfer learning.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm
Authors: Wenjie Ruan, Min Wu, Youcheng Sun, Xiaowei Huang, Daniel Kroening, Marta Kwiatkowska
Date: 2018-04-16 00:00:00
Tasks: GPU

Deployment of deep neural networks (DNNs) in safety- or security-critical
systems requires provable guarantees on their correct behaviour. A common
requirement is robustness to adversarial perturbations in a neighbourhood
around an input. In this paper we focus on the $L_0$ norm and aim to compute,
for a trained DNN and an input, the maximal radius of a safe norm ball around
the input within which there are no adversarial examples. Then we define global
robustness as an expectation of the maximal safe radius over a test data set.
We first show that the problem is NP-hard, and then propose an approximate
approach to iteratively compute lower and upper bounds on the network's
robustness. The approach is \emph{anytime}, i.e., it returns intermediate
bounds and robustness estimates that are gradually, but strictly, improved as
the computation proceeds; \emph{tensor-based}, i.e., the computation is
conducted over a set of inputs simultaneously, instead of one by one, to enable
efficient GPU computation; and has \emph{provable guarantees}, i.e., both the
bounds and the robustness estimates can converge to their optimal values.
Finally, we demonstrate the utility of the proposed approach in practice to
compute tight bounds by applying and adapting the anytime algorithm to a set of
challenging problems, including global robustness evaluation, competitive $L_0$
attacks, test case generation for DNNs, and local robustness evaluation on
large-scale ImageNet DNNs. We release the code of all case studies via GitHub.
Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies
Authors: Lucas Janson, Tommy Hu, Marco Pavone
Date: 2018-04-16 00:00:00
Tasks: Motion Planning

This paper addresses the problem of planning a safe (i.e., collision-free)
trajectory from an initial state to a goal region when the obstacle space is
a-priori unknown and is incrementally revealed online, e.g., through
line-of-sight perception. Despite its ubiquitous nature, this formulation of
motion planning has received relatively little theoretical investigation, as
opposed to the setup where the environment is assumed known. A fundamental
challenge is that, unlike motion planning with known obstacles, it is not even
clear what an optimal policy to strive for is. Our contribution is threefold.
First, we present a notion of optimality for safe planning in unknown
environments in the spirit of comparative (as opposed to competitive) analysis,
with the goal of obtaining a benchmark that is, at least conceptually,
attainable. Second, by leveraging this theoretical benchmark, we derive a
pseudo-optimal class of policies that can seamlessly incorporate any amount of
prior or learned information while still guaranteeing the robot never collides.
Finally, we demonstrate the practicality of our algorithmic approach in
numerical experiments using a range of environment types and dynamics,
including a comparison with a state of the art method. A key aspect of our
framework is that it automatically and implicitly weighs exploration versus
exploitation in a way that is optimal with respect to the information
available.
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Authors: Sudha Rao, Joel Tetreault
Date: 2018-03-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Formality Style Transfer, Machine Translation, Style Transfer, Translation

Style transfer is the task of automatically transforming a piece of text in
one particular style into another. A major barrier to progress in this field
has been a lack of training and evaluation datasets, as well as benchmarks and
automatic metrics. In this work, we create the largest corpus for a particular
stylistic transfer (formality) and show that techniques from the machine
translation community can serve as strong baselines for future work. We also
discuss challenges of using automatic metrics.
An AI-driven Malfunction Detection Concept for NFV Instances in 5G
Authors: Julian Ahrens, Mathias Strufe, Lia Ahrens, Hans D. Schotten
Date: 2018-04-16 00:00:00
Tasks: Management

Efficient network management is one of the key challenges of the constantly
growing and increasingly complex wide area networks (WAN). The paradigm shift
towards virtualized (NFV) and software defined networks (SDN) in the next
generation of mobile networks (5G), as well as the latest scientific insights
in the field of Artificial Intelligence (AI) enable the transition from
manually managed networks nowadays to fully autonomic and dynamic
self-organized networks (SON). This helps to meet the KPIs and reduce at the
same time operational costs (OPEX). In this paper, an AI driven concept is
presented for the malfunction detection in NFV applications with the help of
semi-supervised learning. For this purpose, a profile of the application under
test is created. This profile then is used as a reference to detect abnormal
behaviour. For example, if there is a bug in the updated version of the app, it
is now possible to react autonomously and roll-back the NFV app to a previous
version in order to avoid network outages.
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
Authors: Peter L. Bartlett, Steven N. Evans, Philip M. Long
Date: 2018-04-13 00:00:00

We show that any smooth bi-Lipschitz $h$ can be represented exactly as a
composition $h_m \circ ... \circ h_1$ of functions $h_1,...,h_m$ that are close
to the identity in the sense that each $\left(h_i-\mathrm{Id}\right)$ is
Lipschitz, and the Lipschitz constant decreases inversely with the number $m$
of functions composed. This implies that $h$ can be represented to any accuracy
by a deep residual network whose nonlinear layers compute functions with a
small Lipschitz constant. Next, we consider nonlinear regression with a
composition of near-identity nonlinear maps. We show that, regarding Fr\'echet
derivatives with respect to the $h_1,...,h_m$, any critical point of a
quadratic criterion in this near-identity region must be a global minimizer. In
contrast, if we consider derivatives with respect to parameters of a fixed-size
residual network with sigmoid activation functions, we show that there are
near-identity critical points that are suboptimal, even in the realizable case.
Informally, this means that functional gradient methods for residual networks
cannot get stuck at suboptimal critical points corresponding to near-identity
layers, whereas parametric gradient methods for sigmoidal residual networks
suffer from suboptimal critical points in the near-identity region.
Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
Authors: Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker
Date: 2018-04-16 00:00:00
Proceeding: ECCV 2018 9

We propose a material acquisition approach to recover the spatially-varying
BRDF and normal map of a near-planar surface from a single image captured by a
handheld mobile phone camera. Our method images the surface under arbitrary
environment lighting with the flash turned on, thereby avoiding shadows while
simultaneously capturing high-frequency specular highlights. We train a CNN to
regress an SVBRDF and surface normals from this image. Our network is trained
using a large-scale SVBRDF dataset and designed to incorporate physical
insights for material estimation, including an in-network rendering layer to
model appearance and a material classifier to provide additional supervision
during training. We refine the results from the network using a dense CRF
module whose terms are designed specifically for our task. The framework is
trained end-to-end and produces high quality results for a variety of
materials. We provide extensive ablation studies to evaluate our network on
both synthetic and real data, while demonstrating significant improvements in
comparisons with prior works.
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning
Authors: Samarth Tripathi, Sarthak Tripathi, Homayoon Beigi
Date: 2018-04-16 00:00:00
Tasks: Deep Learning, Emotion Recognition, Multimodal Emotion Recognition

Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.
Ranking CGANs: Subjective Control over Semantic Image Attributes
Authors: Yassir Saquil, Kwang In Kim, Peter Hall
Date: 2018-04-11 00:00:00
Tasks: Attribute, Image Generation

In this paper, we investigate the use of generative adversarial networks in
the task of image generation according to subjective measures of semantic
attributes. Unlike the standard (CGAN) that generates images from discrete
categorical labels, our architecture handles both continuous and discrete
scales. Given pairwise comparisons of images, our model, called RankCGAN,
performs two tasks: it learns to rank images using a subjective measure; and it
learns a generative model that can be controlled by that measure. RankCGAN
associates each subjective measure of interest to a distinct dimension of some
latent space. We perform experiments on UT-Zap50K, PubFig and OSR datasets and
demonstrate that the model is expressive and diverse enough to conduct
two-attribute exploration and image editing.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
Authors: Sergio Ramírez-Gallego, Salvador García, Ning Xiong, Francisco Herrera
Date: 2018-04-16 00:00:00
Tasks: feature selection

With the advent of Big Data era, data reduction methods are highly demanded
given its ability to simplify huge data, and ease complex learning processes.
Concretely, algorithms that are able to filter relevant dimensions from a set
of millions are of huge importance. Although effective, these techniques suffer
from the "scalability" curse as well.
  In this work, we propose a distributed feature weighting algorithm, which is
able to rank millions of features in parallel using large samples. This method,
inspired by the well-known RELIEF algorithm, introduces a novel redundancy
elimination measure that provides similar schemes to those based on entropy at
a much lower cost. It also allows smooth scale up when more instances are
demanded in feature estimations. Empirical tests performed on our method show
its estimation ability in manifold huge sets --both in number of features and
instances--, as well as its simplified runtime cost (specially, at the
redundancy detection step).
Distributionally Ambiguous Optimization Techniques for Batch Bayesian Optimization
Authors: Nikitas Rontsis, Michael A. Osborne, Paul J. Goulart
Date: 2017-07-13 00:00:00
Tasks: Bayesian Optimization

We propose a novel, theoretically-grounded, acquisition function for Batch
Bayesian optimization informed by insights from distributionally ambiguous
optimization. Our acquisition function is a lower bound on the well-known
Expected Improvement function, which requires evaluation of a Gaussian
Expectation over a multivariate piecewise affine function. Our bound is
computed instead by evaluating the best-case expectation over all probability
distributions consistent with the same mean and variance as the original
Gaussian distribution. Unlike alternative approaches, including Expected
Improvement, our proposed acquisition function avoids multi-dimensional
integrations entirely, and can be computed exactly - even on large batch sizes
- as the solution of a tractable convex optimization problem. Our suggested
acquisition function can also be optimized efficiently, since first and second
derivative information can be calculated inexpensively as by-products of the
acquisition function calculation itself. We derive various novel theorems that
ground our work theoretically and we demonstrate superior performance via
simple motivating examples, benchmark functions and real-world problems.