pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification
Authors: Gautam Bhattacharya, Joao Monteiro, Jahangir Alam, Patrick Kenny
Date: 2018-11-07 00:00:00
Tasks: Dimensionality Reduction, Speaker Verification

This article presents a novel approach for learning domain-invariant speaker
embeddings using Generative Adversarial Networks. The main idea is to confuse a
domain discriminator so that is can't tell if embeddings are from the source or
target domains. We train several GAN variants using our proposed framework and
apply them to the speaker verification task. On the challenging NIST-SRE 2016
dataset, we are able to match the performance of a strong baseline x-vector
system. In contrast to the the baseline systems which are dependent on
dimensionality reduction (LDA) and an external classifier (PLDA), our proposed
speaker embeddings can be scored using simple cosine distance. This is achieved
by optimizing our models end-to-end, using an angular margin loss function.
Furthermore, we are able to significantly boost verification performance by
averaging our different GAN models at the score level, achieving a relative
improvement of 7.2% over the baseline.
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks
Authors: Raphael Tang, Ashutosh Adhikari, Jimmy Lin
Date: 2018-11-07 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: GPU, image-classification, Image Classification, Model Compression

There exists a plethora of techniques for inducing structured sparsity in
parametric models during the optimization process, with the final goal of
resource-efficient inference. However, few methods target a specific number of
floating-point operations (FLOPs) as part of the optimization objective,
despite many reporting FLOPs as part of the results. Furthermore, a
one-size-fits-all approach ignores realistic system constraints, which differ
significantly between, say, a GPU and a mobile phone -- FLOPs on the former
incur less latency than on the latter; thus, it is important for practitioners
to be able to specify a target number of FLOPs during model compression. In
this work, we extend a state-of-the-art technique to directly incorporate FLOPs
as part of the optimization objective and show that, given a desired FLOPs
requirement, different neural networks can be successfully trained for image
classification.
Policy Certificates: Towards Accountable Reinforcement Learning
Authors: Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill
Date: 2018-11-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality of their current policy before executing it, and thus have limited use in high-stakes applications like healthcare. We address this lack of accountability by proposing that algorithms output policy certificates. These certificates bound the sub-optimality and return of the policy in the next episode, allowing humans to intervene when the certified quality is not satisfactory. We further introduce two new algorithms with certificates and present a new framework for theoretical analysis that guarantees the quality of their policies and certificates. For tabular MDPs, we show that computing certificates can even improve the sample-efficiency of optimism-based exploration. As a result, one of our algorithms is the first to achieve minimax-optimal PAC bounds up to lower-order terms, and this algorithm also matches (and in some settings slightly improves upon) existing minimax regret bounds.
Adapting End-to-End Neural Speaker Verification to New Languages and Recording Conditions with Adversarial Training
Authors: Gautam Bhattacharya, Jahangir Alam, Patrick Kenny
Date: 2018-11-07 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this article we propose a novel approach for adapting speaker embeddings
to new domains based on adversarial training of neural networks. We apply our
embeddings to the task of text-independent speaker verification, a challenging,
real-world problem in biometric security. We further the development of
end-to-end speaker embedding models by combing a novel 1-dimensional,
self-attentive residual network, an angular margin loss function and
adversarial training strategy. Our model is able to learn extremely compact,
64-dimensional speaker embeddings that deliver competitive performance on a
number of popular datasets using simple cosine distance scoring. One the
NIST-SRE 2016 task we are able to beat a strong i-vector baseline, while on the
Speakers in the Wild task our model was able to outperform both i-vector and
x-vector baselines, showing an absolute improvement of 2.19% over the latter.
Additionally, we show that the integration of adversarial training consistently
leads to a significant improvement over an unadapted model.
Data Selection with Feature Decay Algorithms Using an Approximated Target Side
Authors: Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
Date: 2018-11-07 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, NMT, Sentence, Transductive Learning

Data selection techniques applied to neural machine translation (NMT) aim to
increase the performance of a model by retrieving a subset of sentences for use
as training data.
  One of the possible data selection techniques are transductive learning
methods, which select the data based on the test set, i.e. the document to be
translated. A limitation of these methods to date is that using the source-side
test set does not by itself guarantee that sentences are selected with correct
translations, or translations that are suitable given the test-set domain. Some
corpora, such as subtitle corpora, may contain parallel sentences with
inaccurate translations caused by localization or length restrictions.
  In order to try to fix this problem, in this paper we propose to use an
approximated target-side in addition to the source-side when selecting suitable
sentence-pairs for training a model. This approximated target-side is built by
pre-translating the source-side.
  In this work, we explore the performance of this general idea for one
specific data selection approach called Feature Decay Algorithms (FDA).
  We train German-English NMT models on data selected by using the test set
(source), the approximated target side, and a mixture of both. Our findings
reveal that models built using a combination of outputs of FDA (using the test
set and an approximated target side) perform better than those solely using the
test set. We obtain a statistically significant improvement of more than 1.5
BLEU points over a model trained with all data, and more than 0.5 BLEU points
over a strong FDA baseline that uses source-side information only.
IMS at the PolEval 2018: A Bulky Ensemble Depedency Parser meets 12 Simple Rules for Predicting Enhanced Dependencies in Polish
Authors: Agnieszka Falenska, Anders Björkelund, Xiang Yu, Jonas Kuhn
Date: 2018-11-07 00:00:00
Tasks: Dependency Parsing, POS

This paper presents the IMS contribution to the PolEval 2018 Shared Task. We
submitted systems for both of the Subtasks of Task 1. In Subtask (A), which was
about dependency parsing, we used our ensemble system from the CoNLL 2017 UD
Shared Task. The system first preprocesses the sentences with a CRF
POS/morphological tagger and predicts supertags with a neural tagger. Then, it
employs multiple instances of three different parsers and merges their outputs
by applying blending. The system achieved the second place out of four
participating teams. In this paper we show which components of the system were
the most responsible for its final performance.
  The goal of Subtask (B) was to predict enhanced graphs. Our approach
consisted of two steps: parsing the sentences with our ensemble system from
Subtask (A), and applying 12 simple rules to obtain the final dependency
graphs. The rules introduce additional enhanced arcs only for tokens with
"conj" heads (conjuncts). They do not predict semantic relations at all. The
system ranked first out of three participating teams. In this paper we show
examples of rules we designed and analyze the relation between the quality of
automatically parsed trees and the accuracy of the enhanced graphs.
Computing the Value of Computation for Planning
Authors: Can Eren Sezener
Date: 2018-11-07 00:00:00

An intelligent agent performs actions in order to achieve its goals. Such
actions can either be externally directed, such as opening a door, or
internally directed, such as writing data to a memory location or strengthening
a synaptic connection. Some internal actions, to which we refer as
computations, potentially help the agent choose better actions. Considering
that (external) actions and computations might draw upon the same resources,
such as time and energy, deciding when to act or compute, as well as what to
compute, are detrimental to the performance of an agent.
  In an environment that provides rewards depending on an agent's behavior, an
action's value is typically defined as the sum of expected long-term rewards
succeeding the action (itself a complex quantity that depends on what the agent
goes on to do after the action in question). However, defining the value of a
computation is not as straightforward, as computations are only valuable in a
higher order way, through the alteration of actions.
  This thesis offers a principled way of computing the value of a computation
in a planning setting formalized as a Markov decision process. We present two
different definitions of computation values: static and dynamic. They address
two extreme cases of the computation budget: affording calculation of zero or
infinitely many steps in the future. We show that these values have desirable
properties, such as temporal consistency and asymptotic convergence.
  Furthermore, we propose methods for efficiently computing and approximating
the static and dynamic computation values. We describe a sense in which the
policies that greedily maximize these values can be optimal. We utilize these
principles to construct Monte Carlo tree search algorithms that outperform most
of the state-of-the-art in terms of finding higher quality actions given the
same simulation resources.
A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes
Authors: Ahmad Khaliq, Shoaib Ehsan, Zetao Chen, Michael Milford, Klaus McDonald-Maier
Date: 2018-11-07 00:00:00
Tasks: Visual Place Recognition

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods.
Estimating Network Structure from Incomplete Event Data
Authors: Benjamin Mark, Garvesh Raskutti, Rebecca Willett
Date: 2018-11-07 00:00:00
Tasks: Time Series, Time Series Analysis

Multivariate Bernoulli autoregressive (BAR) processes model time series of
events in which the likelihood of current events is determined by the times and
locations of past events. These processes can be used to model nonlinear
dynamical systems corresponding to criminal activity, responses of patients to
different medical treatment plans, opinion dynamics across social networks,
epidemic spread, and more. Past work examines this problem under the assumption
that the event data is complete, but in many cases only a fraction of events
are observed. Incomplete observations pose a significant challenge in this
setting because the unobserved events still govern the underlying dynamical
system. In this work, we develop a novel approach to estimating the parameters
of a BAR process in the presence of unobserved events via an unbiased estimator
of the complete data log-likelihood function. We propose a computationally
efficient estimation algorithm which approximates this estimator via Taylor
series truncation and establish theoretical results for both the statistical
error and optimization error of our algorithm. We further justify our approach
by testing our method on both simulated data and a real data set consisting of
crimes recorded by the city of Chicago.
Instantly Deployable Expert Knowledge - Networks of Knowledge Engines
Authors: Bernhard Bergmair, Thomas Buchegger, Johann Hoffelner, Gerald Schatz, Siegfried Silber, Johannes Klinglmayr
Date: 2018-11-07 00:00:00

Knowledge and information are becoming the primary resources of the emerging
information society. To exploit the potential of available expert knowledge,
comprehension and application skills (i.e. expert competences) are necessary.
The ability to acquire these skills is limited for any individual human.
Consequently, the capacities to solve problems based on human knowledge in a
manual (i.e. mental) way are strongly limited. We envision a new systemic
approach to enable scalable knowledge deployment without expert competences.
Eventually, the system is meant to instantly deploy humanity's total knowledge
in full depth for every individual challenge. To this end, we propose a
socio-technical framework that transforms expert knowledge into a solution
creation system. Knowledge is represented by automated algorithms (knowledge
engines). Executable compositions of knowledge engines (networks of knowledge
engines) generate requested individual information at runtime. We outline how
these knowledge representations could yield legal, ethical and social
challenges and nurture new business and remuneration models on knowledge. We
identify major technological and economic concepts that are already pushing the
boundaries in knowledge utilisation: e.g. in artificial intelligence, knowledge
bases, ontologies, advanced search tools, automation of knowledge work, the API
economy. We indicate impacts on society, economy and labour. Existing
developments are linked, including a specific use case in engineering design.
Compositional Language Understanding with Text-based Relational Reasoning
Authors: Koustuv Sinha, Shagun Sodhani, William L. Hamilton, Joelle Pineau
Date: 2018-11-07 00:00:00
Tasks: Common Sense Reasoning, Inductive Bias, Language Modeling, Language Modelling, Question Answering, Relational Reasoning

Neural networks for natural language reasoning have largely focused on
extractive, fact-based question-answering (QA) and common-sense inference.
However, it is also crucial to understand the extent to which neural networks
can perform relational reasoning and combinatorial generalization from natural
language---abilities that are often obscured by annotation artifacts and the
dominance of language modeling in standard QA benchmarks. In this work, we
present a novel benchmark dataset for language understanding that isolates
performance on relational reasoning. We also present a neural message-passing
baseline and show that this model, which incorporates a relational inductive
bias, is superior at combinatorial generalization compared to a traditional
recurrent neural network approach.
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
Authors: Roshanak Zakizadeh, Yu Qian, Michele Sasdelli, Eduard Vazquez
Date: 2018-11-07 00:00:00
Tasks: Attribute, Retrieval

In this paper, we present a method for instance ranking and retrieval at
fine-grained level based on the global features extracted from a
multi-attribute recognition model which is not dependent on landmarks
information or part-based annotations. Further, we make this architecture
suitable for mobile-device application by adopting the bilinear CNN to make the
multi-attribute recognition model smaller (in terms of the number of
parameters). The experiments run on the Dress category of DeepFashion In-Shop
Clothes Retrieval and CUB200 datasets show that the results of instance
retrieval at fine-grained level are promising for these datasets, specially in
terms of texture and color.
SurReal: enhancing Surgical simulation Realism using style transfer
Authors: Imanol Luengo, Evangello Flouty, Petros Giataganas, Piyamate Wisanuvej, Jean Nehme, Danail Stoyanov
Date: 2018-11-07 00:00:00
Tasks: Style Transfer

Surgical simulation is an increasingly important element of surgical
education. Using simulation can be a means to address some of the significant
challenges in developing surgical skills with limited time and resources. The
photo-realistic fidelity of simulations is a key feature that can improve the
experience and transfer ratio of trainees. In this paper, we demonstrate how we
can enhance the visual fidelity of existing surgical simulation by performing
style transfer of multi-class labels from real surgical video onto synthetic
content. We demonstrate our approach on simulations of cataract surgery using
real data labels from an existing public dataset. Our results highlight the
feasibility of the approach and also the powerful possibility to extend this
technique to incorporate additional temporal constraints and to different
applications.
Behavioural Repertoire via Generative Adversarial Policy Networks
Authors: Marija Jegorova, Stéphane Doncieux, Timothy Hospedales
Date: 2018-11-07 00:00:00
Tasks: Diversity

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this paper, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment. We demonstrate this idea with an application of robust ball-throwing in the presence of obstacles. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of obstacles.
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
Authors: Shahab Aslani, Michael Dayan, Loredana Storelli, Massimo Filippi, Vittorio Murino, Maria A. Rocca, Diego Sona
Date: 2018-11-07 00:00:00
Tasks: Feature Upsampling, Lesion Segmentation, Segmentation

In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Authors: Hongwei Yong, Deyu Meng, Jinxing Li, WangMeng Zuo, Lei Zhang
Date: 2018-11-07 00:00:00

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of
the data obtained from multiple views. Different from single view case, MSL
should take both common and specific knowledge among different views into
consideration. To enhance the robustness of model, the complexity,
non-consistency and similarity of noise in multi-view data should be fully
taken into consideration. Most current MSL methods only assume a simple
Gaussian or Laplacian distribution for the noise while neglect the complex
noise configurations in each view and noise correlations among different views
of practical data. To this issue, this work initiates a MSL method by encoding
the multi-view-shared and single-view-specific noise knowledge in data.
Specifically, we model data noise in each view as a separated Mixture of
Gaussians (MoG), which can fit a wider range of complex noise types than
conventional Gaussian/Laplacian. Furthermore, we link all single-view-noise as
a whole by regularizing them by a common MoG component, encoding the shared
noise knowledge among them. Such regularization component can be formulated as
a concise KL-divergence regularization term under a MAP framework, leading to
good interpretation of our model and simple EM-based solving strategy to the
problem. Experimental results substantiate the superiority of our method.
Emerging Applications of Reversible Data Hiding
Authors: Dongdong Hou, Weiming Zhang, Jiayang Liu, Siyan Zhou, Dong-Dong Chen, Nenghai Yu
Date: 2018-11-07 00:00:00

Reversible data hiding (RDH) is one special type of information hiding, by
which the host sequence as well as the embedded data can be both restored from
the marked sequence without loss. Beside media annotation and integrity
authentication, recently some scholars begin to apply RDH in many other fields
innovatively. In this paper, we summarize these emerging applications,
including steganography, adversarial example, visual transformation, image
processing, and give out the general frameworks to make these operations
reversible. As far as we are concerned, this is the first paper to summarize
the extended applications of RDH.
DOD-CNN: Doubly-injecting Object Information for Event Recognition
Authors: Hyungtae Lee, Sungmin Eum, Heesung Kwon
Date: 2018-11-07 00:00:00
Tasks: Object, object-detection, Object Detection

Recognizing an event in an image can be enhanced by detecting relevant
objects in two ways: 1) indirectly utilizing object detection information
within the unified architecture or 2) directly making use of the object
detection output results. We introduce a novel approach, referred to as
Doubly-injected Object Detection CNN (DOD-CNN), exploiting the object
information in both ways for the task of event recognition. The structure of
this network is inspired by the Integrated Object Detection CNN (IOD-CNN) where
object information is indirectly exploited by the event recognition module
through the shared portion of the network. In the DOD-CNN architecture, the
intermediate object detection outputs are directly injected into the event
recognition network while keeping the indirect sharing structure inherited from
the IOD-CNN, thus being `doubly-injected'. We also introduce a batch pooling
layer which constructs one representative feature map from multiple object
hypotheses. We have demonstrated the effectiveness of injecting the object
detection information in two different ways in the task of malicious event
recognition.