pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Virtual and Real Person for Unsupervised Person Re-identification
Authors: Fengxiang Yang, Zhun Zhong, Zhiming Luo, Sheng Lian, Shaozi Li
Date: 2018-11-05 00:00:00
Tasks: Collaborative Filtering, Person Re-Identification, Style Transfer, Unsupervised Person Re-Identification

Person re-identification (re-ID) is a challenging problem especially when no
labels are available for training. Although recent deep re-ID methods have
achieved great improvement, it is still difficult to optimize deep re-ID model
without annotations in training data. To address this problem, this study
introduces a novel approach for unsupervised person re-ID by leveraging virtual
and real data. Our approach includes two components: virtual person generation
and training of deep re-ID model. For virtual person generation, we learn a
person generation model and a camera style transfer model using unlabeled real
data to generate virtual persons with different poses and camera styles. The
virtual data is formed as labeled training data, enabling subsequently training
deep re-ID model in supervision. For training of deep re-ID model, we divide it
into three steps: 1) pre-training a coarse re-ID model by using virtual data;
2) collaborative filtering based positive pair mining from the real data; and
3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs
and virtual data. The final re-ID model is achieved by iterating between step 2
and step 3 until convergence. Experimental results on two large-scale datasets,
Market-1501 and DukeMTMC-reID, demonstrate the effectiveness of our approach
and shows that the state of the art is achieved in unsupervised person re-ID.
QUOTA: The Quantile Option Architecture for Reinforcement Learning
Authors: Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao
Date: 2018-11-05 00:00:00
Tasks: Decision Making, Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose the Quantile Option Architecture (QUOTA) for
exploration based on recent advances in distributional reinforcement learning
(RL). In QUOTA, decision making is based on quantiles of a value distribution,
not only the mean. QUOTA provides a new dimension for exploration via making
use of both optimism and pessimism of a value distribution. We demonstrate the
performance advantage of QUOTA in both challenging video games and physical
robot simulators.
Sample Compression, Support Vectors, and Generalization in Deep Learning
Authors: Christopher Snyder, Sriram Vishwanath
Date: 2018-11-05 00:00:00
Tasks: Deep Learning

Even though Deep Neural Networks (DNNs) are widely celebrated for their practical performance, they possess many intriguing properties related to depth that are difficult to explain both theoretically and intuitively. Understanding how weights in deep networks coordinate together across layers to form useful learners has proven challenging, in part because the repeated composition of nonlinearities has proved intractable. This paper presents a reparameterization of DNNs as a linear function of a feature map that is locally independent of the weights. This feature map transforms depth-dependencies into simple tensor products and maps each input to a discrete subset of the feature space. Then, using a max-margin assumption, the paper develops a sample compression representation of the neural network in terms of the discrete activation state of neurons induced by s ``support vectors". The paper shows that the number of support vectors s relates with learning guarantees for neural networks through sample compression bounds, yielding a sample complexity of O(ns/epsilon) for networks with n neurons. Finally, the number of support vectors s is found to monotonically increase with width and label noise but decrease with depth.
How to Improve Your Speaker Embeddings Extractor in Generic Toolkits
Authors: Hossein Zeinali, Lukas Burget, Johan Rohdin, Themos Stafylakis, Jan Cernocky
Date: 2018-11-05 00:00:00
Tasks: Speaker Verification

Recently, speaker embeddings extracted with deep neural networks became the
state-of-the-art method for speaker verification. In this paper we aim to
facilitate its implementation on a more generic toolkit than Kaldi, which we
anticipate to enable further improvements on the method. We examine several
tricks in training, such as the effects of normalizing input features and
pooled statistics, different methods for preventing overfitting as well as
alternative non-linearities that can be used instead of Rectifier Linear Units.
In addition, we investigate the difference in performance between TDNN and CNN,
and between two types of attention mechanism. Experimental results on Speaker
in the Wild, SRE 2016 and SRE 2018 datasets demonstrate the effectiveness of
the proposed implementation.
When CTC Training Meets Acoustic Landmarks
Authors: Di He, Xuesong Yang, Boon Pang Lim, Yi Liang, Mark Hasegawa-Johnson, Deming Chen
Date: 2018-11-05 00:00:00
Tasks: Automatic Speech Recognition (ASR), Speech Recognition

Connectionist temporal classification (CTC) provides an end-to-end acoustic
model (AM) training strategy. CTC learns accurate AMs without time-aligned
phonetic transcription, but sometimes fails to converge, especially in
resource-constrained scenarios. In this paper, the convergence properties of
CTC are improved by incorporating acoustic landmarks. We tailored a new set of
acoustic landmarks to help CTC training converge more rapidly and smoothly
while also reducing recognition error rates. We leveraged new target label
sequences mixed with both phone and manner changes to guide CTC training.
Experiments on TIMIT demonstrated that CTC based acoustic models converge
significantly faster and smoother when they are augmented by acoustic
landmarks. The models pretrained with mixed target labels can be further
finetuned, resulting in phone error rates 8.72% below baseline on TIMIT.
Consistent performance gain is also observed on WSJ (a larger corpus) and
reduced TIMIT (smaller). With WSJ, we are the first to succeed in verifying the
effectiveness of acoustic landmark theory on a mid-sized ASR task.
End-to-End Monaural Multi-speaker ASR System without Pretraining
Authors: Xuankai Chang, Yanmin Qian, Kai Yu, Shinji Watanabe
Date: 2018-11-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

Recently, end-to-end models have become a popular approach as an alternative
to traditional hybrid models in automatic speech recognition (ASR). The
multi-speaker speech separation and recognition task is a central task in
cocktail party problem. In this paper, we present a state-of-the-art monaural
multi-speaker end-to-end automatic speech recognition model. In contrast to
previous studies on the monaural multi-speaker speech recognition, this
end-to-end framework is trained to recognize multiple label sequences
completely from scratch. The system only requires the speech mixture and
corresponding label sequences, without needing any indeterminate supervisions
obtained from non-mixture speech or corresponding labels/alignments. Moreover,
we exploited using the individual attention module for each separated speaker
and the scheduled sampling to further improve the performance. Finally, we
evaluate the proposed model on the 2-speaker mixed speech generated from the
WSJ corpus and the wsj0-2mix dataset, which is a speech separation and
recognition benchmark. The experiments demonstrate that the proposed methods
can improve the performance of the end-to-end model in separating the
overlapping speech and recognizing the separated streams. From the results, the
proposed model leads to ~10.0% relative performance gains in terms of CER and
WER respectively.
The Marchex 2018 English Conversational Telephone Speech Recognition System
Authors: Seongjun Hahm, Iroro Orife, Shane Walker, Jason Flaks
Date: 2018-11-05 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper, we describe recent performance improvements to the production
Marchex speech recognition system for our spontaneous customer-to-business
telephone conversations. In our previous work, we focused on in-domain language
and acoustic model training. In this work we employ state-of-the-art
semi-supervised lattice-free maximum mutual information (LF-MMI) training
process which can supervise over full lattices from unlabeled audio. On Marchex
English (ME), a modern evaluation set of conversational North American English,
we observed a 3.3% (3.2% for agent, 3.6% for caller) reduction in absolute word
error rate (WER) with 3x faster decoding speed over the performance of the 2017
production system. We expect this improvement boost Marchex Call Analytics
system performance especially for natural language processing pipeline.
Exploring Connections Between Active Learning and Model Extraction
Authors: Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan
Date: 2018-11-05 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, model, Model extraction

Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, such MLaaS systems raise privacy concerns such as model extraction. In model extraction attacks, adversaries maliciously exploit the query interface to steal the model. More precisely, in a model extraction attack, a good approximation of a sensitive or proprietary model held by the server is extracted (i.e. learned) by a dishonest user who interacts with the server only via the query interface. This attack was introduced by Tramer et al. at the 2016 USENIX Security Symposium, where practical attacks for various models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount to designing secure MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and discussing possible defense strategies, and (b) drawing parallels between model extraction and established area of active learning. In particular, we show that recent advancements in the active learning domain can be used to implement powerful model extraction attacks, and investigate possible defense strategies.
Managing engineering systems with large state and action spaces through deep reinforcement learning
Authors: C. P. Andriotis, K. G. Papakonstantinou
Date: 2018-11-05 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision-making for engineering systems can be efficiently formulated as a
Markov Decision Process (MDP) or a Partially Observable MDP (POMDP). Typical
MDP and POMDP solution procedures utilize offline knowledge about the
environment and provide detailed policies for relatively small systems with
tractable state and action spaces. However, in large multi-component systems
the sizes of these spaces easily explode, as system states and actions scale
exponentially with the number of components, whereas environment dynamics are
difficult to be described in explicit forms for the entire system and may only
be accessible through numerical simulators. In this work, to address these
issues, an integrated Deep Reinforcement Learning (DRL) framework is
introduced. The Deep Centralized Multi-agent Actor Critic (DCMAC) is developed,
an off-policy actor-critic DRL approach, providing efficient life-cycle
policies for large multi-component systems operating in high-dimensional
spaces. Apart from deep function approximations that parametrize large state
spaces, DCMAC also adopts a factorized representation of the system actions,
being able to designate individualized component- and subsystem-level
decisions, while maintaining a centralized value function for the entire
system. DCMAC compares well against Deep Q-Network (DQN) solutions and exact
policies, where applicable, and outperforms optimized baselines that are based
on time-based, condition-based and periodic policies.
Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation
Authors: Ye Jia, Melvin Johnson, Wolfgang Macherey, Ron J. Weiss, Yuan Cao, Chung-Cheng Chiu, Naveen Ari, Stella Laurenzo, Yonghui Wu
Date: 2018-11-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Multi-Task Learning, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, text-to-speech, Text to Speech, Translation

End-to-end Speech Translation (ST) models have many potential advantages when
compared to the cascade of Automatic Speech Recognition (ASR) and text Machine
Translation (MT) models, including lowered inference latency and the avoidance
of error compounding. However, the quality of end-to-end ST is often limited by
a paucity of training data, since it is difficult to collect large parallel
corpora of speech and translated transcript pairs. Previous studies have
proposed the use of pre-trained components and multi-task learning in order to
benefit from weakly supervised training data, such as speech-to-transcript or
text-to-foreign-text pairs. In this paper, we demonstrate that using
pre-trained MT or text-to-speech (TTS) synthesis models to convert weakly
supervised data into speech-to-translation pairs for ST training can be more
effective than multi-task learning. Furthermore, we demonstrate that a high
quality end-to-end ST model can be trained using only weakly supervised
datasets, and that synthetic data sourced from unlabeled monolingual text or
speech can be used to improve performance. Finally, we discuss methods for
avoiding overfitting to synthetic speech with a quantitative ablation study.
Non-Local Compressive Sensing Based SAR Tomography
Authors: Yilei Shi, Xiao Xiang Zhu, Richard Bamler
Date: 2018-11-05 00:00:00
Tasks: Compressive Sensing

Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
Authors: Liu Yang, Dongkun Zhang, George Em. Karniadakis
Date: 2018-11-05 00:00:00
Tasks: Gaussian Processes

We developed a new class of physics-informed generative adversarial networks
(PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic
problems based on a limited number of scattered measurements. Unlike standard
GANs relying only on data for training, here we encoded into the architecture
of GANs the governing physical laws in the form of stochastic differential
equations (SDEs) using automatic differentiation. In particular, we applied
Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability
compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian
processes of different correlation lengths based on data realizations collected
from simultaneous reads at sparsely placed sensors. We obtained good
approximation of the generated stochastic processes to the target ones even for
a mismatch between the input noise dimensionality and the effective
dimensionality of the target stochastic processes. We also studied the
overfitting issue for both the discriminator and generator, and we found that
overfitting occurs also in the generator in addition to the discriminator as
previously reported. Subsequently, we considered the solution of elliptic SDEs
requiring approximations of three stochastic processes, namely the solution,
the forcing, and the diffusion coefficient. We used three generators for the
PI-GANs, two of them were feed forward deep neural networks (DNNs) while the
other one was the neural network induced by the SDE. Depending on the data, we
employed one or multiple feed forward DNNs as the discriminators in PI-GANs.
Here, we have demonstrated the accuracy and effectiveness of PI-GANs in solving
SDEs for up to 30 dimensions, but in principle, PI-GANs could tackle very high
dimensional problems given more sensor data with low-polynomial growth in
computational cost.
A General Theory of Equivariant CNNs on Homogeneous Spaces
Authors: Taco Cohen, Mario Geiger, Maurice Weiler
Date: 2018-11-05 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: General Classification

We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere. Feature maps in these networks represent fields on a homogeneous base space, and layers are equivariant maps between spaces of fields. The theory enables a systematic classification of all existing G-CNNs in terms of their symmetry group, base space, and field type. We also consider a fundamental question: what is the most general kind of equivariant linear map between feature spaces (fields) of given types? Following Mackey, we show that such maps correspond one-to-one with convolutions using equivariant kernels, and characterize the space of such kernels.
A Unified Perspective of Evolutionary Game Dynamics Using Generalized Growth Transforms
Authors: Oindrila Chatterjee, Shantanu Chakrabartty
Date: 2018-11-05 00:00:00

In this paper, we show that different types of evolutionary game dynamics
are, in principle, special cases of a dynamical system model based on our
previously reported framework of generalized growth transforms. The framework
shows that different dynamics arise as a result of minimizing a population
energy such that the population as a whole evolves to reach the most stable
state. By introducing a population dependent time-constant in the generalized
growth transform model, the proposed framework can be used to explain a vast
repertoire of evolutionary dynamics, including some novel forms of game
dynamics with non-linear payoffs.
Compact Personalized Models for Neural Machine Translation
Authors: Joern Wuebker, Patrick Simianer, John DeNero
Date: 2018-11-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Domain Adaptation, Machine Translation, Translation

We propose and compare methods for gradient-based domain adaptation of
self-attentive neural machine translation models. We demonstrate that a large
proportion of model parameters can be frozen during adaptation with minimal or
no reduction in translation quality by encouraging structured sparsity in the
set of offset tensors during learning via group lasso regularization. We
evaluate this technique for both batch and incremental adaptation across
multiple data sets and language pairs. Our system architecture - combining a
state-of-the-art self-attentive model with compact domain adaptation - provides
high quality personalized machine translation that is both space and time
efficient.
A Differential Volumetric Approach to Multi-View Photometric Stereo
Authors: Fotios Logothetis, Roberto Mecca, Roberto Cipolla
Date: 2018-11-05 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Volumetric Reconstruction

Highly accurate 3D volumetric reconstruction is still an open research topic where the main difficulty is usually related to merging some rough estimations with high frequency details. One of the most promising methods is the fusion between multi-view stereo and photometric stereo images. Beside the intrinsic difficulties that multi-view stereo and photometric stereo in order to work reliably, supplementary problems arise when considered together. In this work, we present a volumetric approach to the multi-view photometric stereo problem. The key point of our method is the signed distance field parameterisation and its relation to the surface normal. This is exploited in order to obtain a linear partial differential equation which is solved in a variational framework, that combines multiple images from multiple points of view in a single system. In addition, the volumetric approach is naturally implemented on an octree, which allows for fast ray-tracing that reliably alleviates occlusions and cast shadows. Our approach is evaluated on synthetic and real data-sets and achieves state-of-the-art results.
Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
Authors: Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II
Date: 2018-11-05 00:00:00

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm
inspired by the convergence mechanism to the center of potential generated
within a single well of a spatially co-located double-delta well setup. It
mimics the wave nature of candidate positions in solution spaces and draws upon
quantum mechanical interpretations much like other quantum-inspired
computational intelligence paradigms. In this work, we introduce a Chebyshev
map driven chaotic perturbation in the optimization phase of the algorithm to
diversify weights placed on contemporary and historical, socially-optimal
agents' solutions. We follow this up with a characterization of solution
quality on a suite of 23 single-objective functions and carry out a comparative
analysis with eight other related nature-inspired approaches. By comparing
solution quality and successful runs over dynamic solution ranges, insights
about the nature of convergence are obtained. A two-tailed t-test establishes
the statistical significance of the solution data whereas Cohen's d and Hedge's
g values provide a measure of effect sizes. We trace the trajectory of the
fittest pseudo-agent over all function evaluations to comment on the dynamics
of the system and prove that the proposed algorithm is theoretically globally
convergent under the assumptions adopted for proofs of other closely-related
random search algorithms.
Self-Attention Linguistic-Acoustic Decoder
Authors: Santiago Pascual, Antonio Bonafonte, Joan Serrà
Date: 2018-08-31 00:00:00
Tasks: CPU, Decoder, Speech Synthesis, text-to-speech, Text to Speech

The conversion from text to speech relies on the accurate mapping from
linguistic to acoustic symbol sequences, for which current practice employs
recurrent statistical models like recurrent neural networks. Despite the good
performance of such models (in terms of low distortion in the generated
speech), their recursive structure tends to make them slow to train and to
sample from. In this work, we try to overcome the limitations of recursive
structure by using a module based on the transformer decoder network, designed
without recurrent connections but emulating them with attention and positioning
codes. Our results show that the proposed decoder network is competitive in
terms of distortion when compared to a recurrent baseline, whilst being
significantly faster in terms of CPU inference time. On average, it increases
Mel cepstral distortion between 0.1 and 0.3 dB, but it is over an order of
magnitude faster on average. Fast inference is important for the deployment of
speech synthesis systems on devices with restricted resources, like mobile
phones or embedded systems, where speaking virtual assistants are gaining
importance.