pwc-archive/papers-with-abstracts huggingface dataset:
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Authors: Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis
Date: 2017-09-15 00:00:00

Information Cascades Model captures dynamical properties of user activity in
a social network. In this work, we develop a novel framework for activity
shaping under the Continuous-Time Information Cascades Model which allows the
administrator for local control actions by allocating targeted resources that
can alter the spread of the process. Our framework employs the optimization of
the spectral radius of the Hazard matrix, a quantity that has been shown to
drive the maximum influence in a network, while enjoying a simple convex
relaxation when used to minimize the influence of the cascade. In addition,
use-cases such as quarantine and node immunization are discussed to highlight
the generality of the proposed activity shaping framework. Finally, we present
the NetShape influence minimization method which is compared favorably to
baseline and state-of-the-art approaches through simulations on real social
networks.
Transcribing Against Time
Authors: Matthias Sperber, Graham Neubig, Jan Niehues, Satoshi Nakamura, Alex Waibel
Date: 2017-09-15 00:00:00

We investigate the problem of manually correcting errors from an automatic
speech transcript in a cost-sensitive fashion. This is done by specifying a
fixed time budget, and then automatically choosing location and size of
segments for correction such that the number of corrected errors is maximized.
The core components, as suggested by previous research [1], are a utility model
that estimates the number of errors in a particular segment, and a cost model
that estimates annotation effort for the segment. In this work we propose a
dynamic updating framework that allows for the training of cost models during
the ongoing transcription process. This removes the need for transcriber
enrollment prior to the actual transcription, and improves correction
efficiency by allowing highly transcriber-adaptive cost modeling. We first
confirm and analyze the improvements afforded by this method in a simulated
study. We then conduct a realistic user study, observing efficiency
improvements of 15% relative on average, and 42% for the participants who
deviated most strongly from our initial, transcriber-agnostic cost model.
Moreover, we find that our updating framework can capture dynamically changing
factors, such as transcriber fatigue and topic familiarity, which we observe to
have a large influence on the transcriber's working behavior.
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings
Authors: Pawel Cyrta, Tomasz Trzciński, Wojciech Stokowiec
Date: 2017-08-09 00:00:00
Tasks: speaker-diarization, Speaker Diarization

In this paper we propose a new method of speaker diarization that employs a
deep learning architecture to learn speaker embeddings. In contrast to the
traditional approaches that build their speaker embeddings using manually
hand-crafted spectral features, we propose to train for this purpose a
recurrent convolutional neural network applied directly on magnitude
spectrograms. To compare our approach with the state of the art, we collect and
release for the public an additional dataset of over 6 hours of fully annotated
broadcast material. The results of our evaluation on the new dataset and three
other benchmark datasets show that our proposed method significantly
outperforms the competitors and reduces diarization error rate by a large
margin of over 30% with respect to the baseline.
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Authors: Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Caimin Wei, Qingfu Zhang, Kalyanmoy Deb, Erik D. Goodman
Date: 2017-09-15 00:00:00

This paper proposes a push and pull search (PPS) framework for solving
constrained multi-objective optimization problems (CMOPs). To be more specific,
the proposed PPS divides the search process into two different stages,
including the push and pull search stages. In the push stage, a multi-objective
evolutionary algorithm (MOEA) is adopted to explore the search space without
considering any constraints, which can help to get across infeasible regions
very fast and approach the unconstrained Pareto front. Furthermore, the
landscape of CMOPs with constraints can be probed and estimated in the push
stage, which can be utilized to conduct the parameters setting for
constraint-handling approaches applied in the pull stage. Then, a constrained
multi-objective evolutionary algorithm (CMOEA) equipped with an improved
epsilon constraint-handling is applied to pull the infeasible individuals
achieved in the push stage to the feasible and non-dominated regions. Compared
with other CMOEAs, the proposed PPS method can more efficiently get across
infeasible regions and converge to the feasible and non-dominated regions by
applying push and pull search strategies at different stages. To evaluate the
performance regarding convergence and diversity, a set of benchmark CMOPs is
used to test the proposed PPS and compare with other five CMOEAs, including
MOEA/D-CDP, MOEA/D-SR, C-MOEA/D, MOEA/D-Epsilon and MOEA/D-IEpsilon. The
comprehensive experimental results demonstrate that the proposed PPS achieves
significantly better or competitive performance than the other five CMOEAs on
most of the benchmark set.
Unsupervised state representation learning with robotic priors: a robustness benchmark
Authors: Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat
Date: 2017-09-15 00:00:00
Tasks: Position, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning, Transfer Learning

Our understanding of the world depends highly on our capacity to produce
intuitive and simplified representations which can be easily used to solve
problems. We reproduce this simplification process using a neural network to
build a low dimensional state representation of the world from images acquired
by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way
using prior knowledge about the world as loss functions called robotic priors
and extend this approach to high dimension richer images to learn a 3D
representation of the hand position of a robot from RGB images. We propose a
quantitative evaluation of the learned representation using nearest neighbors
in the state space that allows to assess its quality and show both the
potential and limitations of robotic priors in realistic environments. We
augment image size, add distractors and domain randomization, all crucial
components to achieve transfer learning to real robots. Finally, we also
contribute a new prior to improve the robustness of the representation. The
applications of such low dimensional state representation range from easing
reinforcement learning (RL) and knowledge transfer across tasks, to
facilitating learning from raw data with more efficient and compact high level
representations. The results show that the robotic prior approach is able to
extract high level representation as the 3D position of an arm and organize it
into a compact and coherent space of states in a challenging dataset.
Deep Reinforcement Learning: An Overview
Authors: Yuxi Li
Date: 2017-01-25 00:00:00
Tasks: Deep Reinforcement Learning, Machine Translation, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation, Transfer Learning, Translation

We give an overview of recent exciting achievements of deep reinforcement
learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (DQN), policy, reward,
model, planning, and exploration. After that, we discuss important mechanisms
for RL, including attention and memory, unsupervised learning, transfer
learning, multi-agent RL, hierarchical RL, and learning to learn. Then we
discuss various applications of RL, including games, in particular, AlphaGo,
robotics, natural language processing, including dialogue systems, machine
translation, and text generation, computer vision, neural architecture design,
business management, finance, healthcare, Industry 4.0, smart grid, intelligent
transportation systems, and computer systems. We mention topics not reviewed
yet, and list a collection of RL resources. After presenting a brief summary,
we close with discussions.
  Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant
update.
Efficient Deformable Shape Correspondence via Kernel Matching
Authors: Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers
Date: 2017-07-25 00:00:00

We present a method to match three dimensional shapes under non-isometric
deformations, topology changes and partiality. We formulate the problem as
matching between a set of pair-wise and point-wise descriptors, imposing a
continuity prior on the mapping, and propose a projected descent optimization
procedure inspired by difference of convex functions (DC) programming.
Surprisingly, in spite of the highly non-convex nature of the resulting
quadratic assignment problem, our method converges to a semantically meaningful
and continuous mapping in most of our experiments, and scales well. We provide
preliminary theoretical analysis and several interpretations of the method.
Function Assistant: A Tool for NL Querying of APIs
Authors: Kyle Richardson, Jonas Kuhn
Date: 2017-06-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Natural Language Queries, Semantic Parsing, Translation

In this paper, we describe Function Assistant, a lightweight Python-based
toolkit for querying and exploring source code repositories using natural
language. The toolkit is designed to help end-users of a target API quickly
find information about functions through high-level natural language queries
and descriptions. For a given text query and background API, the tool finds
candidate functions by performing a translation from the text to known
representations in the API using the semantic parsing approach of Richardson
and Kuhn (2017). Translations are automatically learned from example text-code
pairs in example APIs. The toolkit includes features for building translation
pipelines and query engines for arbitrary source code projects. To explore this
last feature, we perform new experiments on 27 well-known Python projects
hosted on Github.
Multi-scale Deep Learning Architectures for Person Re-identification
Authors: Xuelin Qian, Yanwei Fu, Yu-Gang Jiang, Tao Xiang, xiangyang xue
Date: 2017-09-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deep Learning, Person Re-Identification

Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location and scales. Existing re-id models, particularly the recently
proposed deep learning based ones match people at a single scale. In contrast,
in this paper, a novel multi-scale deep learning model is proposed. Our model
is able to learn deep discriminative feature representations at different
scales and automatically determine the most suitable scales for matching. The
importance of different spatial locations for extracting discriminative
features is also learned explicitly. Experiments are carried out to demonstrate
that the proposed model outperforms the state-of-the art on a number of
benchmarks
Trend Detection based Regret Minimization for Bandit Problems
Authors: Paresh Nakhe, Rebecca Reiffenhäuser
Date: 2017-09-15 00:00:00
Tasks: Multi-Armed Bandits

We study a variation of the classical multi-armed bandits problem. In this
problem, the learner has to make a sequence of decisions, picking from a fixed
set of choices. In each round, she receives as feedback only the loss incurred
from the chosen action. Conventionally, this problem has been studied when
losses of the actions are drawn from an unknown distribution or when they are
adversarial. In this paper, we study this problem when the losses of the
actions also satisfy certain structural properties, and especially, do show a
trend structure. When this is true, we show that using \textit{trend
detection}, we can achieve regret of order $\tilde{O} (N \sqrt{TK})$ with
respect to a switching strategy for the version of the problem where a single
action is chosen in each round and $\tilde{O} (Nm \sqrt{TK})$ when $m$ actions
are chosen each round. This guarantee is a significant improvement over the
conventional benchmark. Our approach can, as a framework, be applied in
combination with various well-known bandit algorithms, like Exp3. For both
versions of the problem, we give regret guarantees also for the
\textit{anytime} setting, i.e. when the length of the choice-sequence is not
known in advance. Finally, we pinpoint the advantages of our method by
comparing it to some well-known other strategies.
Multilingual Hierarchical Attention Networks for Document Classification
Authors: Nikolaos Pappas, Andrei Popescu-Belis
Date: 2017-07-04 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Classification, Computational Efficiency, Document Classification, General Classification, Multi-Task Learning, Transfer Learning

Hierarchical attention networks have recently achieved remarkable performance
for document classification in a given language. However, when multilingual
document collections are considered, training such models separately for each
language entails linear parameter growth and lack of cross-language transfer.
Learning a single multilingual model with fewer parameters is therefore a
challenging but potentially beneficial objective. To this end, we propose
multilingual hierarchical attention networks for learning document structures,
with shared encoders and/or shared attention mechanisms across languages, using
multi-task learning and an aligned semantic space as input. We evaluate the
proposed models on multilingual document classification with disjoint label
sets, on a large dataset which we provide, with 600k news documents in 8
languages, and 5k labels. The multilingual models outperform monolingual ones
in low-resource as well as full-resource settings, and use fewer parameters,
thus confirming their computational efficiency and the utility of
cross-language transfer.
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
Authors: Matan Sela, Elad Richardson, Ron Kimmel
Date: 2017-03-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Face Reconstruction, Image-to-Image Translation, Translation

It has been recently shown that neural networks can recover the geometric
structure of a face from a single given image. A common denominator of most
existing face geometry reconstruction methods is the restriction of the
solution space to some low-dimensional subspace. While such a model
significantly simplifies the reconstruction problem, it is inherently limited
in its expressiveness. As an alternative, we propose an Image-to-Image
translation network that jointly maps the input image to a depth image and a
facial correspondence map. This explicit pixel-based mapping can then be
utilized to provide high quality reconstructions of diverse faces under extreme
expressions, using a purely geometric refinement process. In the spirit of
recent approaches, the network is trained only with synthetic data, and is then
evaluated on in-the-wild facial images. Both qualitative and quantitative
analyses demonstrate the accuracy and the robustness of our approach.
Feed-forward approximations to dynamic recurrent network architectures
Authors: Dylan Richard Muir
Date: 2017-04-21 00:00:00

Recurrent neural network architectures can have useful computational
properties, with complex temporal dynamics and input-sensitive attractor
states. However, evaluation of recurrent dynamic architectures requires
solution of systems of differential equations, and the number of evaluations
required to determine their response to a given input can vary with the input,
or can be indeterminate altogether in the case of oscillations or instability.
In feed-forward networks, by contrast, only a single pass through the network
is needed to determine the response to a given input. Modern machine-learning
systems are designed to operate efficiently on feed-forward architectures. We
hypothesised that two-layer feedforward architectures with simple,
deterministic dynamics could approximate the responses of single-layer
recurrent network architectures. By identifying the fixed-point responses of a
given recurrent network, we trained two-layer networks to directly approximate
the fixed-point response to a given input. These feed-forward networks then
embodied useful computations, including competitive interactions, information
transformations and noise rejection. Our approach was able to find useful
approximations to recurrent networks, which can then be evaluated in linear and
deterministic time complexity.
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
Authors: Natalia Neverova, Christian Wolf, Florian Nebout, Graham Taylor
Date: 2015-11-20 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation, Segmentation, Weakly-supervised Learning

We propose a method for hand pose estimation based on a deep regressor
trained on two different kinds of input. Raw depth data is fused with an
intermediate representation in the form of a segmentation of the hand into
parts. This intermediate representation contains important topological
information and provides useful cues for reasoning about joint locations. The
mapping from raw depth to segmentation maps is learned in a
semi/weakly-supervised way from two different datasets: (i) a synthetic dataset
created through a rendering pipeline including densely labeled ground truth
(pixelwise segmentations); and (ii) a dataset with real images for which ground
truth joint positions are available, but not dense segmentations. Loss for
training on real images is generated from a patch-wise restoration process,
which aligns tentative segmentation maps with a large dictionary of synthetic
poses. The underlying premise is that the domain shift between synthetic and
real data is smaller in the intermediate representation, where labels carry
geometric and topological meaning, than in the raw input domain. Experiments on
the NYU dataset show that the proposed training method decreases error on
joints over direct regression of joints from depth data by 15.7%.
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso
Authors: Dominik Müller, Claudia Czado
Date: 2017-09-15 00:00:00

To model high dimensional data, Gaussian methods are widely used since they
remain tractable and yield parsimonious models by imposing strong assumptions
on the data. Vine copulas are more flexible by combining arbitrary marginal
distributions and (conditional) bivariate copulas. Yet, this adaptability is
accompanied by sharply increasing computational effort as the dimension
increases. The approach proposed in this paper overcomes this burden and makes
the first step into ultra high dimensional non-Gaussian dependence modeling by
using a divide-and-conquer approach. First, we apply Gaussian methods to split
datasets into feasibly small subsets and second, apply parsimonious and
flexible vine copulas thereon. Finally, we reconcile them into one joint model.
We provide numerical results demonstrating the feasibility of our approach in
moderate dimensions and showcase its ability to estimate ultra high dimensional
non-Gaussian dependence models in thousands of dimensions.
A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications
Authors: Yuan Du, Li Du, Yilei Li, Junjie Su, Mau-Chung Frank Chang
Date: 2017-09-15 00:00:00

Deep convolutional neural networks (CNN) are widely used in modern artificial
intelligence (AI) and smart vision systems but also limited by computation
latency, throughput, and energy efficiency on a resource-limited scenario, such
as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV),
and so on. A hardware streaming architecture is proposed to accelerate
convolution and pooling computations for state-of-the-art deep CNNs. It is
optimized for energy efficiency by maximizing local data reuse to reduce
off-chip DRAM data access. In addition, image and feature decomposition
techniques are introduced to optimize memory access pattern for an arbitrary
size of image and number of features within limited on-chip SRAM capacity. A
prototype accelerator was implemented in TSMC 65 nm CMOS technology with 2.3 mm
x 0.8 mm core area, which achieves 144 GOPS peak throughput and 0.8 TOPS/W peak
energy efficiency.
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding
Authors: Qian Wang, Ke Chen
Date: 2017-09-15 00:00:00
Tasks: Action Recognition, Multi-Label Learning, Multi-label zero-shot learning, Temporal Action Localization, Transfer Learning, Zero-Shot Learning

Human action recognition refers to automatic recognizing human actions from a
video clip. In reality, there often exist multiple human actions in a video
stream. Such a video stream is often weakly-annotated with a set of relevant
human action labels at a global level rather than assigning each label to a
specific video episode corresponding to a single action, which leads to a
multi-label learning problem. Furthermore, there are many meaningful human
actions in reality but it would be extremely difficult to collect/annotate
video clips regarding all of various human actions, which leads to a zero-shot
learning scenario. To the best of our knowledge, there is no work that has
addressed all the above issues together in human action recognition. In this
paper, we formulate a real-world human action recognition task as a multi-label
zero-shot learning problem and propose a framework to tackle this problem in a
holistic way. Our framework holistically tackles the issue of unknown temporal
boundaries between different actions for multi-label learning and exploits the
side information regarding the semantic relationship between different human
actions for knowledge transfer. Consequently, our framework leads to a joint
latent ranking embedding for multi-label zero-shot human action recognition. A
novel neural architecture of two component models and an alternate learning
algorithm are proposed to carry out the joint latent ranking embedding
learning. Thus, multi-label zero-shot recognition is done by measuring
relatedness scores of action labels to a test video clip in the joint latent
visual and semantic embedding spaces. We evaluate our framework with different
settings, including a novel data split scheme designed especially for
evaluating multi-label zero-shot learning, on two datasets: Breakfast and
Charades. The experimental results demonstrate the effectiveness of our
framework.