pwc-archive/papers-with-abstracts huggingface dataset:
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle
Authors: Rana Ali Amjad, Bernhard C. Geiger
Date: 2018-02-27 00:00:00
Tasks: General Classification

In this theory paper, we investigate training deep neural networks (DNNs) for
classification via minimizing the information bottleneck (IB) functional. We
show that the resulting optimization problem suffers from two severe issues:
First, for deterministic DNNs, either the IB functional is infinite for almost
all values of network parameters, making the optimization problem ill-posed, or
it is piecewise constant, hence not admitting gradient-based optimization
methods. Second, the invariance of the IB functional under bijections prevents
it from capturing properties of the learned representation that are desirable
for classification, such as robustness and simplicity. We argue that these
issues are partly resolved for stochastic DNNs, DNNs that include a (hard or
soft) decision rule, or by replacing the IB functional with related, but more
well-behaved cost functions. We conclude that recent successes reported about
training DNNs using the IB framework must be attributed to such solutions. As a
side effect, our results indicate limitations of the IB framework for the
analysis of DNNs. We also note that rather than trying to repair the inherent
problems in the IB functional, a better approach may be to design regularizers
on latent representation enforcing the desired properties directly.
Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition
Authors: Danilo Avola, Marco Bernardi, Luigi Cinque, Gian Luca Foresti, Cristiano Massaroni
Date: 2018-03-28 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Temporal Sequences, valid

In human interactions, hands are a powerful way of expressing information
that, in some cases, can be used as a valid substitute for voice, as it happens
in Sign Language. Hand gesture recognition has always been an interesting topic
in the areas of computer vision and multimedia. These gestures can be
represented as sets of feature vectors that change over time. Recurrent Neural
Networks (RNNs) are suited to analyse this type of sets thanks to their ability
to model the long term contextual information of temporal sequences. In this
paper, a RNN is trained by using as features the angles formed by the finger
bones of human hands. The selected features, acquired by a Leap Motion
Controller (LMC) sensor, have been chosen because the majority of human
gestures produce joint movements that generate truly characteristic corners. A
challenging subset composed by a large number of gestures defined by the
American Sign Language (ASL) is used to test the proposed solution and the
effectiveness of the selected angles. Moreover, the proposed method has been
compared to other state of the art works on the SHREC dataset, thus
demonstrating its superiority in hand gesture recognition accuracy.
Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework
Authors: Jie Chen, Cheen-Hau Tan, Junhui Hou, Lap-Pui Chau, He Li
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Rain Removal

Rain removal is important for improving the robustness of outdoor vision
based systems. Current rain removal methods show limitations either for complex
dynamic scenes shot from fast moving cameras, or under torrential rain fall
with opaque occlusions. We propose a novel derain algorithm, which applies
superpixel (SP) segmentation to decompose the scene into depth consistent
units. Alignment of scene contents are done at the SP level, which proves to be
robust towards rain occlusion and fast camera motion. Two alignment output
tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match
tensor, provide informative clues for rain streak location and occluded
background contents to generate an intermediate derain output. These tensors
will be subsequently prepared as input features for a convolutional neural
network to restore high frequency details to the intermediate output for
compensation of mis-alignment blur. Extensive evaluations show that up to 5 dB
reconstruction PSNR advantage is achieved over state-of-the-art methods. Visual
inspection shows that much cleaner rain removal is achieved especially for
highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
Authors: Yoseob Han, Jong Chul Ye
Date: 2017-08-28 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.
A General Dichotomy of Evolutionary Algorithms on Monotone Functions
Authors: Johannes Lengler
Date: 2018-03-25 00:00:00
Tasks: Evolutionary Algorithms

It is known that the evolutionary algorithm $(1+1)$-EA with mutation rate
$c/n$ optimises every monotone function efficiently if $c<1$, and needs
exponential time on some monotone functions (HotTopic functions) if $c\geq
2.2$. We study the same question for a large variety of algorithms,
particularly for $(1+\lambda)$-EA, $(\mu+1)$-EA, $(\mu+1)$-GA, their fast
counterparts like fast $(1+1)$-EA, and for $(1+(\lambda,\lambda))$-GA. We find
that all considered mutation-based algorithms show a similar dichotomy for
HotTopic functions, or even for all monotone functions. For the
$(1+(\lambda,\lambda))$-GA, this dichotomy is in the parameter $c\gamma$, which
is the expected number of bit flips in an individual after mutation and
crossover, neglecting selection. For the fast algorithms, the dichotomy is in
$m_2/m_1$, where $m_1$ and $m_2$ are the first and second falling moment of the
number of bit flips. Surprisingly, the range of efficient parameters is not
affected by either population size $\mu$ nor by the offspring population size
$\lambda$.
  The picture changes completely if crossover is allowed. The genetic
algorithms $(\mu+1)$-GA and fast $(\mu+1)$-GA are efficient for arbitrary
mutations strengths if $\mu$ is large enough.
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
Authors: Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer
Date: 2018-03-26 00:00:00
Tasks: Drug Design, Drug Discovery

The new wave of successful generative models in machine learning has
increased the interest in deep learning driven de novo drug design. However,
assessing the performance of such generative models is notoriously difficult.
Metrics that are typically used to assess the performance of such generative
models are the percentage of chemically valid molecules or the similarity to
real molecules in terms of particular descriptors, such as the partition
coefficient (logP) or druglikeness. However, method comparison is difficult
because of the inconsistent use of evaluation metrics, the necessity for
multiple metrics, and the fact that some of these measures can easily be
tricked by simple rule-based systems. We propose a novel distance measure
between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that
can be used as an evaluation metric for generative models. The FCD is similar
to a recently established performance metric for comparing image generation
methods, the Fr\'echet Inception Distance (FID). Whereas the FID uses one of
the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
deep neural network called ChemNet, which was trained to predict drug
activities. Thus, the FCD metric takes into account chemically and biologically
relevant information about molecules, and also measures the diversity of the
set via the distribution of generated molecules. The FCD's advantage over
previous metrics is that it can detect if generated molecules are a) diverse
and have similar b) chemical and c) biological properties as real molecules. We
further provide an easy-to-use implementation that only requires the SMILES
representation of the generated molecules as input to calculate the FCD.
Implementations are available at: https://www.github.com/bioinf-jku/FCD
Eye movement simulation and detector creation to reduce laborious parameter adjustments
Authors: Wolfgang Fuhl, Thiago Santini, Thomas Kuebler, Nora Castner, Wolfgang Rosenstiel, Enkelejda Kasneci
Date: 2018-03-28 00:00:00
Tasks: BIG-bench Machine Learning

Eye movements hold information about human perception, intention and
cognitive state. Various algorithms have been proposed to identify and
distinguish eye movements, particularly fixations, saccades, and smooth
pursuits. A major drawback of existing algorithms is that they rely on accurate
and constant sampling rates, impeding straightforward adaptation to new
movements such as micro saccades. We propose a novel eye movement simulator
that i) probabilistically simulates saccade movements as gamma distributions
considering different peak velocities and ii) models smooth pursuit onsets with
the sigmoid function. This simulator is combined with a machine learning
approach to create detectors for general and specific velocity profiles.
Additionally, our approach is capable of using any sampling rate, even with
fluctuations. The machine learning approach consists of different binary
patterns combined using conditional distributions. The simulation is evaluated
against publicly available real data using a squared error, and the detectors
are evaluated against state-of-the-art algorithms.
Handling Verb Phrase Anaphora with Dependent Types and Events
Authors: Daniyar Itegulov, Ekaterina Lebedeva
Date: 2018-03-28 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper studies how dependent typed events can be used to treat verb
phrase anaphora. We introduce a framework that extends Dependent Type Semantics
(DTS) with a new atomic type for neo-Davidsonian events and an extended
@-operator that can return new events that share properties of events
referenced by verb phrase anaphora. The proposed framework, along with
illustrative examples of its use, are presented after a brief overview of the
necessary background and of the major challenges posed by verb phrase anaphora.
Handling Homographs in Neural Machine Translation
Authors: Frederick Liu, Han Lu, Graham Neubig
Date: 2017-08-22 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Translation, Word Embeddings, Word Sense Disambiguation

Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.
A Better Resource Allocation Algorithm with Semi-Bandit Feedback
Authors: Yuval Dagan, Koby Crammer
Date: 2018-03-28 00:00:00

We study a sequential resource allocation problem between a fixed number of
arms. On each iteration the algorithm distributes a resource among the arms in
order to maximize the expected success rate. Allocating more of the resource to
a given arm increases the probability that it succeeds, yet with a cut-off. We
follow Lattimore et al. (2014) and assume that the probability increases
linearly until it equals one, after which allocating more of the resource is
wasteful. These cut-off values are fixed and unknown to the learner. We present
an algorithm for this problem and prove a regret upper bound of $O(\log n)$
improving over the best known bound of $O(\log^2 n)$. Lower bounds we prove
show that our upper bound is tight. Simulations demonstrate the superiority of
our algorithm.
Hashing as Tie-Aware Learning to Rank
Authors: Kun He, Fatih Cakir, Sarah Adel Bargal, Stan Sclaroff
Date: 2017-05-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Retrieval, Learning-To-Rank, Retrieval

Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.
Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network
Authors: Hai X. Pham, Yuting Wang, Vladimir Pavlovic
Date: 2018-03-21 00:00:00
Tasks: Face Model

This paper presents Generative Adversarial Talking Head (GATH), a novel deep
generative neural network that enables fully automatic facial expression
synthesis of an arbitrary portrait with continuous action unit (AU)
coefficients. Specifically, our model directly manipulates image pixels to make
the unseen subject in the still photo express various emotions controlled by
values of facial AU coefficients, while maintaining her personal
characteristics, such as facial geometry, skin color and hair style, as well as
the original surrounding background. In contrast to prior work, GATH is purely
data-driven and it requires neither a statistical face model nor image
processing tricks to enact facial deformations. Additionally, our model is
trained from unpaired data, where the input image, with its auxiliary identity
label taken from abundance of still photos in the wild, and the target frame
are from different persons. In order to effectively learn such model, we
propose a novel weakly supervised adversarial learning framework that consists
of a generator, a discriminator, a classifier and an action unit estimator. Our
work gives rise to template-and-target-free expression editing, where still
faces can be effortlessly animated with arbitrary AU coefficients provided by
the user.
3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
Authors: Angela Dai, Matthias Nießner
Date: 2018-03-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Architecture, Scene Segmentation, Segmentation, Semantic Segmentation

We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D
scans in indoor environments using a joint 3D-multi-view prediction network. In
contrast to existing methods that either use geometry or RGB data as input for
this task, we combine both data modalities in a joint, end-to-end network
architecture. Rather than simply projecting color data into a volumetric grid
and operating solely in 3D -- which would result in insufficient detail -- we
first extract feature maps from associated RGB images. These features are then
mapped into the volumetric feature grid of a 3D network using a differentiable
backprojection layer. Since our target is 3D scanning scenarios with possibly
many frames, we use a multi-view pooling approach in order to handle a varying
number of RGB input views. This learned combination of RGB and geometric
features with our joint 2D-3D architecture achieves significantly better
results than existing baselines. For instance, our final result on the ScanNet
3D segmentation benchmark increases from 52.8\% to 75\% accuracy compared to
existing volumetric architectures.
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Authors: Sambit Ghadai, Aditya Balu, Adarsh Krishnamurthy, Soumik Sarkar
Date: 2017-11-13 00:00:00
Tasks: 3D Object Recognition, Decision Making, Object, Object Recognition

3D Convolutional Neural Networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. However, interpreting
the decision making process of these 3D-CNNs is still an infeasible task. In
this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
Mapping method (3D-GradCAM) for visual explanations of the distinct local
geometric features of interest within an object. To enable efficient learning
of 3D geometries, we augment the voxel data with surface normals of the object
boundary. We then train a 3D-CNN with this augmented data and identify the
local features critical for decision-making using 3D GradCAM. An application of
this feature identification framework is to recognize difficult-to-manufacture
drilled hole features in a complex CAD geometry. The framework can be extended
to identify difficult-to-manufacture features at multiple spatial scales
leading to a real-time design for manufacturability decision support system.
Supervising Unsupervised Learning with Evolutionary Algorithm in Deep Neural Network
Authors: Takeshi Inagaki
Date: 2018-03-28 00:00:00
Tasks: Document Classification, General Classification

A method to control results of gradient descent unsupervised learning in a
deep neural network by using evolutionary algorithm is proposed. To process
crossover of unsupervisedly trained models, the algorithm evaluates pointwise
fitness of individual nodes in neural network. Labeled training data is
randomly sampled and breeding process selects nodes by calculating degree of
their consistency on different sets of sampled data. This method supervises
unsupervised training by evolutionary process. We also introduce modified
Restricted Boltzmann Machine which contains repulsive force among nodes in a
neural network and it contributes to isolate network nodes each other to avoid
accidental degeneration of nodes by evolutionary process. These new methods are
applied to document classification problem and it results better accuracy than
a traditional fully supervised classifier implemented with linear regression
algorithm.
Predictions of short-term driving intention using recurrent neural network on sequential data
Authors: Zhou Xing, Fei Xiao
Date: 2018-03-28 00:00:00
Tasks: Autonomous Driving, Decision Making

Predictions of driver's intentions and their behaviors using the road is of
great importance for planning and decision making processes of autonomous
driving vehicles. In particular, relatively short-term driving intentions are
the fundamental units that constitute more sophisticated driving goals,
behaviors, such as overtaking the slow vehicle in front, exit or merge onto a
high way, etc. While it is not uncommon that most of the time human driver can
rationalize, in advance, various on-road behaviors, intentions, as well as the
associated risks, aggressiveness, reciprocity characteristics, etc., such
reasoning skills can be challenging and difficult for an autonomous driving
system to learn. In this article, we demonstrate a disciplined methodology that
can be used to build and train a predictive drive system, therefore to learn
the on-road characteristics aforementioned.
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Authors: Jun Hatori, Yuta Kikuchi, Sosuke Kobayashi, Kuniyuki Takahashi, Yuta Tsuboi, Yuya Unno, Wilson Ko, Jethro Tan
Date: 2017-10-17 00:00:00
Tasks: object-detection, Object Detection

Comprehension of spoken natural language is an essential component for robots
to communicate with human effectively. However, handling unconstrained spoken
instructions is challenging due to (1) complex structures including a wide
variety of expressions used in spoken language and (2) inherent ambiguity in
interpretation of human instructions. In this paper, we propose the first
comprehensive system that can handle unconstrained spoken language and is able
to effectively resolve ambiguity in spoken instructions. Specifically, we
integrate deep-learning-based object detection together with natural language
processing technologies to handle unconstrained spoken instructions, and
propose a method for robots to resolve instruction ambiguity through dialogue.
Through our experiments on both a simulated environment as well as a physical
industrial robot arm, we demonstrate the ability of our system to understand
natural instructions from human operators effectively, and how higher success
rates of the object picking task can be achieved through an interactive
clarification process.
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval
Authors: Xuefei Zhe, Shifeng Chen, Hong Yan
Date: 2018-02-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, Metric Learning, Retrieval, Triplet

Deep distance metric learning (DDML), which is proposed to learn image
similarity metrics in an end-to-end manner based on the convolution neural
network, has achieved encouraging results in many computer vision
tasks.$L2$-normalization in the embedding space has been used to improve the
performance of several DDML methods. However, the commonly used Euclidean
distance is no longer an accurate metric for $L2$-normalized embedding space,
i.e., a hyper-sphere. Another challenge of current DDML methods is that their
loss functions are usually based on rigid data formats, such as the triplet
tuple. Thus, an extra process is needed to prepare data in specific formats. In
addition, their losses are obtained from a limited number of samples, which
leads to a lack of the global view of the embedding space. In this paper, we
replace the Euclidean distance with the cosine similarity to better utilize the
$L2$-normalization, which is able to attenuate the curse of dimensionality.
More specifically, a novel loss function based on the von Mises-Fisher
distribution is proposed to learn a compact hyper-spherical embedding space.
Moreover, a new efficient learning algorithm is developed to better capture the
global structure of the embedding space. Experiments for both classification
and retrieval tasks on several standard datasets show that our method achieves
state-of-the-art performance with a simpler training procedure. Furthermore, we
demonstrate that, even with a small number of convolutional layers, our model
can still obtain significantly better classification performance than the
widely used softmax loss.