pwc-archive/papers-with-abstracts huggingface dataset:
Query Adaptive Late Fusion for Image Retrieval
Authors: Zhongdao Wang, Liang Zheng, Shengjin Wang
Date: 2018-10-31 00:00:00
Tasks: Image Retrieval, Person Recognition, Person Re-Identification, Retrieval

Feature fusion is a commonly used strategy in image retrieval tasks, which
aggregates the matching responses of multiple visual features. Feasible sets of
features can be either descriptors (SIFT, HSV) for an entire image or the same
descriptor for different local parts (face, body). Ideally, the to-be-fused
heterogeneous features are pre-assumed to be discriminative and complementary
to each other. However, the effectiveness of different features varies
dramatically according to different queries. That is to say, for some queries,
a feature may be neither discriminative nor complementary to existing ones,
while for other queries, the feature suffices. As a result, it is important to
estimate the effectiveness of features in a query-adaptive manner. To this end,
this article proposes a new late fusion scheme at the score level. We base our
method on the observation that the sorted score curves contain patterns that
describe their effectiveness. For example, an "L"-shaped curve indicates that
the feature is discriminative while a gradually descending curve suggests a bad
feature. As such, this paper introduces a query-adaptive late fusion pipeline.
In the hand-crafted version, it can be an unsupervised approach to tasks like
particular object retrieval. In the learning version, it can also be applied to
supervised tasks like person recognition and pedestrian retrieval, based on a
trainable neural module. Extensive experiments are conducted on two object
retrieval datasets and one person recognition dataset. We show that our method
is able to highlight the good features and suppress the bad ones, is resilient
to distractor features, and achieves very competitive retrieval accuracy
compared with the state of the art. In an additional person re-identification
dataset, the application scope and limitation of the proposed method are
studied.
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
Authors: Chao Li, Zhun Sun, Jinshi Yu, Ming Hou, Qibin Zhao
Date: 2018-10-31 00:00:00
Tasks: General Classification, Tensor Decomposition

Although the convolutional neural networks (CNNs) have become popular for
various image processing and computer vision task recently, it remains a
challenging problem to reduce the storage cost of the parameters for
resource-limited platforms. In the previous studies, tensor decomposition (TD)
has achieved promising compression performance by embedding the kernel of a
convolutional layer into a low-rank subspace. However the employment of TD is
naively on the kernel or its specified variants. Unlike the conventional
approaches, this paper shows that the kernel can be embedded into more general
or even random low-rank subspaces. We demonstrate this by compressing the
convolutional layers via randomly-shuffled tensor decomposition (RsTD) for a
standard classification task using CIFAR-10. In addition, we analyze how the
spatial similarity of the training data influences the low-rank structure of
the kernels. The experimental results show that the CNN can be significantly
compressed even if the kernels are randomly shuffled. Furthermore, the
RsTD-based method yields more stable classification accuracy than the
conventional TD-based methods in a large range of compression ratios.
Attentive Neural Network for Named Entity Recognition in Vietnamese
Authors: Kim Anh Nguyen, Ngan Dong, Cam-Tu Nguyen
Date: 2018-10-31 00:00:00
Tasks: Decoder, named-entity-recognition, Named Entity Recognition, Named Entity Recognition In Vietnamese, Named Entity Recognition (NER), Word Embeddings

We propose an attentive neural network for the task of named entity recognition in Vietnamese. The proposed attentive neural model makes use of character-based language models and word embeddings to encode words as vector representations. A neural network architecture of encoder, attention, and decoder layers is then utilized to encode knowledge of input sentences and to label entity tags. The experimental results show that the proposed attentive neural network achieves the state-of-the-art results on the benchmark named entity recognition datasets in Vietnamese in comparison to both hand-crafted features based models and neural models.
Towards End-to-End Code-Switching Speech Recognition
Authors: Ne Luo, Dongwei Jiang, Shuaijiang Zhao, Caixia Gong, Wei Zou, Xiangang Li
Date: 2018-10-31 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, speech-recognition, Speech Recognition

Code-switching speech recognition has attracted an increasing interest
recently, but the need for expert linguistic knowledge has always been a big
issue. End-to-end automatic speech recognition (ASR) simplifies the building of
ASR systems considerably by predicting graphemes or characters directly from
acoustic input. In the mean time, the need of expert linguistic knowledge is
also eliminated, which makes it an attractive choice for code-switching ASR.
This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English
code-switching (CS) speech recognition system and studies the effect of hybrid
CTC-Attention based models, different modeling units, the inclusion of language
identification and different decoding strategies on the task of code-switching
ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of
34.24%.
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English
Authors: Yan Yin, Ramon Prieto, Bin Wang, Jianwei Zhou, Yiwei Gu, Yang Liu, Hui Lin
Date: 2018-10-31 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent research has shown that attention-based sequence-to-sequence models
such as Listen, Attend, and Spell (LAS) yield comparable results to
state-of-the-art ASR systems on various tasks. In this paper, we describe the
development of such a system and demonstrate its performance on two tasks:
first we achieve a new state-of-the-art word error rate of 3.43% on the test
clean subset of LibriSpeech English data; second on non-native English speech,
including both read speech and spontaneous speech, we obtain very competitive
results compared to a conventional system built with the most updated Kaldi
recipe.
Provably Accelerated Randomized Gossip Algorithms
Authors: Nicolas Loizou, Michael Rabbat, Peter Richtárik
Date: 2018-10-31 00:00:00

In this work we present novel provably accelerated gossip algorithms for
solving the average consensus problem. The proposed protocols are inspired from
the recently developed accelerated variants of the randomized Kaczmarz method -
a popular method for solving linear systems. In each gossip iteration all nodes
of the network update their values but only a pair of them exchange their
private information. Numerical experiments on popular wireless sensor networks
showing the benefits of our protocols are also presented.
GraphIE: A Graph-Based Framework for Information Extraction
Authors: Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo, Regina Barzilay
Date: 2018-10-31 00:00:00
Proceeding: NAACL 2019 6

Most modern Information Extraction (IE) systems are implemented as sequential
taggers and only model local dependencies. Non-local and non-sequential context
is, however, a valuable source of information to improve predictions. In this
paper, we introduce GraphIE, a framework that operates over a graph
representing a broad set of dependencies between textual units (i.e. words or
sentences). The algorithm propagates information between connected nodes
through graph convolutions, generating a richer representation that can be
exploited to improve word-level predictions. Evaluation on three different
tasks --- namely textual, social media and visual information extraction ---
shows that GraphIE consistently outperforms the state-of-the-art sequence
tagging model by a significant margin.
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
Authors: Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar
Date: 2018-10-31 00:00:00
Tasks: Computational chemistry

This paper proposes a physics-guided recurrent neural network model (PGRNN)
that combines RNNs and physics-based models to leverage their complementary
strengths and improve the modeling of physical processes. Specifically, we show
that a PGRNN can improve prediction accuracy over that of physical models,
while generating outputs consistent with physical laws, and achieving good
generalizability. Standard RNNs, even when producing superior prediction
accuracy, often produce physically inconsistent results and lack
generalizability. We further enhance this approach by using a pre-training
method that leverages the simulated data from a physics-based model to address
the scarcity of observed data. The PGRNN has the flexibility to incorporate
additional physical constraints and we incorporate a density-depth
relationship. Both enhancements further improve PGRNN performance. Although we
present and evaluate this methodology in the context of modeling the dynamics
of temperature in lakes, it is applicable more widely to a range of scientific
and engineering disciplines where mechanistic (also known as process-based)
models are used, e.g., power engineering, climate science, materials science,
computational chemistry, and biomedicine.
Formal Verification of Neural Network Controlled Autonomous Systems
Authors: Xiaowu Sun, Haitham Khedr, Yasser Shoukry
Date: 2018-10-31 00:00:00

In this paper, we consider the problem of formally verifying the safety of an
autonomous robot equipped with a Neural Network (NN) controller that processes
LiDAR images to produce control actions. Given a workspace that is
characterized by a set of polytopic obstacles, our objective is to compute the
set of safe initial conditions such that a robot trajectory starting from these
initial conditions is guaranteed to avoid the obstacles. Our approach is to
construct a finite state abstraction of the system and use standard
reachability analysis over the finite state abstraction to compute the set of
the safe initial states. The first technical problem in computing the finite
state abstraction is to mathematically model the imaging function that maps the
robot position to the LiDAR image. To that end, we introduce the notion of
imaging-adapted sets as partitions of the workspace in which the imaging
function is guaranteed to be affine. We develop a polynomial-time algorithm to
partition the workspace into imaging-adapted sets along with computing the
corresponding affine imaging functions. Given this workspace partitioning, a
discrete-time linear dynamics of the robot, and a pre-trained NN controller
with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge
is to analyze the behavior of the neural network. To that end, we utilize a
Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible
segments of different ReLUs. SMC solvers then use a Boolean satisfiability
solver and a convex programming solver and decompose the problem into smaller
subproblems. To accelerate this process, we develop a pre-processing algorithm
that could rapidly prune the space feasible ReLU segments. Finally, we
demonstrate the efficiency of the proposed algorithms using numerical
simulations with increasing complexity of the neural network controller.
Dynamic Assortment Optimization with Changing Contextual Information
Authors: Xi Chen, Yining Wang, Yuan Zhou
Date: 2018-10-31 00:00:00
Tasks: Assortment Optimization, Combinatorial Optimization

In this paper, we study the dynamic assortment optimization problem under a
finite selling season of length $T$. At each time period, the seller offers an
arriving customer an assortment of substitutable products under a cardinality
constraint, and the customer makes the purchase among offered products
according to a discrete choice model. Most existing work associates each
product with a real-valued fixed mean utility and assumes a multinomial logit
choice (MNL) model. In many practical applications, feature/contexutal
information of products is readily available. In this paper, we incorporate the
feature information by assuming a linear relationship between the mean utility
and the feature. In addition, we allow the feature information of products to
change over time so that the underlying choice model can also be
non-stationary. To solve the dynamic assortment optimization under this
changing contextual MNL model, we need to simultaneously learn the underlying
unknown coefficient and makes the decision on the assortment. To this end, we
develop an upper confidence bound (UCB) based policy and establish the regret
bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of
the feature and $\widetilde O$ suppresses logarithmic dependence. We further
established the lower bound $\Omega(d\sqrt{T}/K)$ where $K$ is the cardinality
constraint of an offered assortment, which is usually small. When $K$ is a
constant, our policy is optimal up to logarithmic factors. In the exploitation
phase of the UCB algorithm, we need to solve a combinatorial optimization for
assortment optimization based on the learned information. We further develop an
approximation algorithm and an efficient greedy heuristic. The effectiveness of
the proposed policy is further demonstrated by our numerical studies.
Visual Attention Network for Low Dose CT
Authors: Wenchao Du, Hu Chen, Peixi Liao, Hongyu Yang, Ge Wang, Yi Zhang
Date: 2018-10-31 00:00:00
Tasks: Generative Adversarial Network, Image Restoration

Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intractable in the context of LDCT due to the statistical and technical uncertainties. In this paper, we apply the generative adversarial network (GAN) framework with a visual attention mechanism to deal with this problem in a data-driven/machine learning fashion. Our main idea is to inject visual attention knowledge into the learning process of GAN to provide a powerful prior of the noise distribution. By doing this, both the generator and discriminator networks are empowered with visual attention information so they will not only pay special attention to noisy regions and surrounding structures but also explicitly assess the local consistency of the recovered regions. Our experiments qualitatively and quantitatively demonstrate the effectiveness of the proposed method with clinic CT images.
User Constrained Thumbnail Generation using Adaptive Convolutions
Authors: Perla Sai Raj Kishore, Ayan Kumar Bhunia, Shuvozit Ghose, Partha Pratim Roy
Date: 2018-10-31 00:00:00
Tasks: Region Proposal, User Constrained Thumbnail Generation

Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques.
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
Authors: Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Date: 2018-10-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 3D Object Detection, Camera Pose Estimation, Monocular 3D Object Detection, Object, object-detection, Object Detection, Pose Estimation, Room Layout Estimation, Scene Understanding

Holistic 3D indoor scene understanding refers to jointly recovering the i)
object bounding boxes, ii) room layout, and iii) camera pose, all in 3D. The
existing methods either are ineffective or only tackle the problem partially.
In this paper, we propose an end-to-end model that simultaneously solves all
three tasks in real-time given only a single RGB image. The essence of the
proposed method is to improve the prediction by i) parametrizing the targets
(e.g., 3D boxes) instead of directly estimating the targets, and ii)
cooperative training across different modules in contrast to training these
modules individually. Specifically, we parametrize the 3D object bounding boxes
by the predictions from several modules, i.e., 3D camera pose and object
attributes. The proposed method provides two major advantages: i) The
parametrization helps maintain the consistency between the 2D image and the 3D
world, thus largely reducing the prediction variances in 3D coordinates. ii)
Constraints can be imposed on the parametrization to train different modules
simultaneously. We call these constraints "cooperative losses" as they enable
the joint training and inference. We employ three cooperative losses for 3D
bounding boxes, 2D projections, and physical constraints to estimate a
geometrically consistent and physically plausible 3D scene. Experiments on the
SUN RGB-D dataset shows that the proposed method significantly outperforms
prior approaches on 3D object detection, 3D layout estimation, 3D camera pose
estimation, and holistic scene understanding.
Attentive Filtering Networks for Audio Replay Attack Detection
Authors: Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King
Date: 2018-10-31 00:00:00
Tasks: Speaker Verification

An attacker may use a variety of techniques to fool an automatic speaker
verification system into accepting them as a genuine user. Anti-spoofing
methods meanwhile aim to make the system robust against such attacks. The
ASVspoof 2017 Challenge focused specifically on replay attacks, with the
intention of measuring the limits of replay attack detection as well as
developing countermeasures against them. In this work, we propose our replay
attacks detection system - Attentive Filtering Network, which is composed of an
attention-based filtering mechanism that enhances feature representations in
both the frequency and time domains, and a ResNet-based classifier. We show
that the network enables us to visualize the automatically acquired feature
representations that are helpful for spoofing detection. Attentive Filtering
Network attains an evaluation EER of 8.99$\%$ on the ASVspoof 2017 Version 2.0
dataset. With system fusion, our best system further obtains a 30$\%$ relative
improvement over the ASVspoof 2017 enhanced baseline system.
Scalable Laplacian K-modes
Authors: Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed
Date: 2018-10-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, valid

We advocate Laplacian K-modes for joint clustering and density mode finding,
and propose a concave-convex relaxation of the problem, which yields a parallel
algorithm that scales up to large datasets and high dimensions. We optimize a
tight bound (auxiliary function) of our relaxation, which, at each iteration,
amounts to computing an independent update for each cluster-assignment
variable, with guaranteed convergence. Therefore, our bound optimizer can be
trivially distributed for large-scale data sets. Furthermore, we show that the
density modes can be obtained as byproducts of the assignment variables via
simple maximum-value operations whose additional computational cost is linear
in the number of data points. Our formulation does not need storing a full
affinity matrix and computing its eigenvalue decomposition, neither does it
perform expensive projection steps and Lagrangian-dual inner iterates for the
simplex constraints of each point. Furthermore, unlike mean-shift, our
density-mode estimation does not require inner-loop gradient-ascent iterates.
It has a complexity independent of feature-space dimension, yields modes that
are valid data points in the input set and is applicable to discrete domains as
well as arbitrary kernels. We report comprehensive experiments over various
data sets, which show that our algorithm yields very competitive performances
in term of optimization quality (i.e., the value of the discrete-variable
objective at convergence) and clustering accuracy.
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences
Authors: Atticus Geiger, Ignacio Cases, Lauri Karttunen, Christopher Potts
Date: 2018-10-30 00:00:00
Tasks: Natural Language Inference

Standard evaluations of deep learning models for semantics using naturalistic
corpora are limited in what they can tell us about the fidelity of the learned
representations, because the corpora rarely come with good measures of semantic
complexity. To overcome this limitation, we present a method for generating
data sets of multiply-quantified natural language inference (NLI) examples in
which semantic complexity can be precisely characterized, and we use this
method to show that a variety of common architectures for NLI inevitably fail
to encode crucial information; only a model with forced lexical alignments
avoids this damaging information loss.
Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks
Authors: Anton Ragni, Qiujia Li, Mark Gales, Yu Wang
Date: 2018-10-30 00:00:00

The standard approach to assess reliability of automatic speech
transcriptions is through the use of confidence scores. If accurate, these
scores provide a flexible mechanism to flag transcription errors for upstream
and downstream applications. One challenging type of errors that recognisers
make are deletions. These errors are not accounted for by the standard
confidence estimation schemes and are hard to rectify in the upstream and
downstream processing. High deletion rates are prominent in limited resource
and highly mismatched training/testing conditions studied under IARPA Babel and
Material programs. This paper looks at the use of bidirectional recurrent
neural networks to yield confidence estimates in predicted as well as deleted
words. Several simple schemes are examined for combination. To assess
usefulness of this approach, the combined confidence score is examined for
untranscribed data selection that favours transcriptions with lower deletion
errors. Experiments are conducted using IARPA Babel/Material program languages.