pwc-archive/papers-with-abstracts huggingface dataset:
High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition
Authors: David Gamarnik, Ilias Zadik
Date: 2017-01-16 00:00:00
Tasks: 2k, regression

We consider a sparse linear regression model Y=X\beta^{*}+W where X has a Gaussian entries, W is the noise vector with mean zero Gaussian entries, and \beta^{*} is a binary vector with support size (sparsity) k. Using a novel conditional second moment method we obtain a tight up to a multiplicative constant approximation of the optimal squared error \min_{\beta}\|Y-X\beta\|_{2}, where the minimization is over all k-sparse binary vectors \beta. The approximation reveals interesting structural properties of the underlying regression problem. In particular, a) We establish that n^*=2k\log p/\log (2k/\sigma^{2}+1) is a phase transition point with the following "all-or-nothing" property. When n exceeds n^{*}, (2k)^{-1}\|\beta_{2}-\beta^*\|_0\approx 0, and when n is below n^{*}, (2k)^{-1}\|\beta_{2}-\beta^*\|_0\approx 1, where \beta_2 is the optimal solution achieving the smallest squared error. With this we prove that n^{*} is the asymptotic threshold for recovering \beta^* information theoretically. b) We compute the squared error for an intermediate problem \min_{\beta}\|Y-X\beta\|_{2} where minimization is restricted to vectors \beta with \|\beta-\beta^{*}\|_0=2k \zeta, for \zeta\in [0,1]. We show that a lower bound part \Gamma(\zeta) of the estimate, which corresponds to the estimate based on the first moment method, undergoes a phase transition at three different thresholds, namely n_{\text{inf,1}}=\sigma^2\log p, which is information theoretic bound for recovering \beta^* when k=1 and \sigma is large, then at n^{*} and finally at n_{\text{LASSO/CS}}. c) We establish a certain Overlap Gap Property (OGP) on the space of all binary vectors \beta when n\le ck\log p for sufficiently small constant c. We conjecture that OGP is the source of algorithmic hardness of solving the minimization problem \min_{\beta}\|Y-X\beta\|_{2} in the regime n<n_{\text{LASSO/CS}}.
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
Authors: Shanshan Zhao, Xi Li, Omar El Farouk Bourahla
Date: 2017-07-23 00:00:00
Tasks: Optical Flow Estimation

As an important and challenging problem in computer vision, learning based
optical flow estimation aims to discover the intrinsic correspondence structure
between two adjacent video frames through statistical learning. Therefore, a
key issue to solve in this area is how to effectively model the multi-scale
correspondence structure properties in an adaptive end-to-end learning fashion.
Motivated by this observation, we propose an end-to-end multi-scale
correspondence structure learning (MSCSL) approach for optical flow estimation.
In principle, the proposed MSCSL approach is capable of effectively capturing
the multi-scale inter-image-correlation correspondence structures within a
multi-level feature space from deep learning. Moreover, the proposed MSCSL
approach builds a spatial Conv-GRU neural network model to adaptively model the
intrinsic dependency relationships among these multi-scale correspondence
structures. Finally, the above procedures for correspondence structure learning
and multi-scale dependency modeling are implemented in a unified end-to-end
deep learning framework. Experimental results on several benchmark datasets
demonstrate the effectiveness of the proposed approach.
Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning
Authors: H. Ghanbari, H. Zayyani, E. Yazdian
Date: 2017-07-23 00:00:00
Tasks: Dictionary Learning

This letter proposes a multiple parametric dictionary learning algorithm for
direction of arrival (DOA) estimation in presence of array gain-phase error and
mutual coupling. It jointly solves both the DOA estimation and array
imperfection problems to yield a robust DOA estimation in presence of array
imperfection errors and off-grid. In the proposed method, a multiple parametric
dictionary learning-based algorithm with an steepest-descent iteration is used
for learning the parametric perturbation matrices and the steering matrix
simultaneously. It also exploits the multiple snapshots information to enhance
the performance of DOA estimation. Simulation results show the efficiency of
the proposed algorithm when both off-grid problem and array imperfection exist.
Using Argument-based Features to Predict and Analyse Review Helpfulness
Authors: Haijing Liu, Yang Gao, Pin Lv, Mengxue Li, Shiqiang Geng, Minglan Li, Hao Wang
Date: 2017-07-23 00:00:00
Proceeding: EMNLP 2017 9

We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusions ratios, are good indicators of helpful reviews. To
validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
and investigate the effectiveness of several combinations of argument-based
features. Experiments suggest that, when being used together with the
argument-based features, the state-of-the-art baseline features can enjoy a
performance boost (in terms of F1) of 11.01\% in average.
Fine Grained Citation Span for References in Wikipedia
Authors: Besnik Fetahu, Katja Markert, Avishek Anand
Date: 2017-07-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Articles

\emph{Verifiability} is one of the core editing principles in Wikipedia,
editors being encouraged to provide citations for the added content. For a
Wikipedia article, determining the \emph{citation span} of a citation, i.e.
what content is covered by a citation, is important as it helps decide for
which content citations are still missing.
  We are the first to address the problem of determining the \emph{citation
span} in Wikipedia articles. We approach this problem by classifying which
textual fragments in an article are covered by a citation. We propose a
sequence classification approach where for a paragraph and a citation, we
determine the citation span at a fine-grained level.
  We provide a thorough experimental evaluation and compare our approach
against baselines adopted from the scientific domain, where we show improvement
for all evaluation metrics.
End-to-end Learning of Driving Models from Large-scale Video Datasets
Authors: Huazhe Xu, Yang Gao, Fisher Yu, Trevor Darrell
Date: 2016-12-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Scene Segmentation

Robust perception-action models should be learned from training data with
diverse visual appearances and realistic behaviors, yet current approaches to
deep visuomotor policy learning have been generally limited to in-situ models
learned from a single vehicle or a simulation environment. We advocate learning
a generic vehicle motion model from large scale crowd-sourced video data, and
develop an end-to-end trainable architecture for learning to predict a
distribution over future vehicle egomotion from instantaneous monocular camera
observations and previous vehicle state. Our model incorporates a novel
FCN-LSTM architecture, which can be learned from large-scale crowd-sourced
vehicle action data, and leverages available scene segmentation side tasks to
improve performance under a privileged learning paradigm.
Hierarchical Embeddings for Hypernymy Detection and Directionality
Authors: Kim Anh Nguyen, Maximilian Köper, Sabine Schulte im Walde, Ngoc Thang Vu
Date: 2017-07-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Lexical Entailment

We present a novel neural model HyperVec to learn hierarchical embeddings for
hypernymy detection and directionality. While previous embeddings have shown
limitations on prototypical hypernyms, HyperVec represents an unsupervised
measure where embeddings are learned in a specific order and capture the
hypernym$-$hyponym distributional hierarchy. Moreover, our model is able to
generalize over unseen hypernymy pairs, when using only small sets of training
data, and by mapping to other languages. Results on benchmark datasets show
that HyperVec outperforms both state$-$of$-$the$-$art unsupervised measures and
embedding models on hypernymy detection and directionality, and on predicting
graded lexical entailment.
MatchZoo: A Toolkit for Deep Text Matching
Authors: Yixing Fan, Liang Pang, Jianpeng Hou, Jiafeng Guo, Yanyan Lan, Xue-Qi Cheng
Date: 2017-07-23 00:00:00
Tasks: Ad-Hoc Information Retrieval, Information Retrieval, Question Answering, Retrieval, Text Matching

In recent years, deep neural models have been widely adopted for text
matching tasks, such as question answering and information retrieval, showing
improved performance as compared with previous methods. In this paper, we
introduce the MatchZoo toolkit that aims to facilitate the designing, comparing
and sharing of deep text matching models. Specifically, the toolkit provides a
unified data preparation module for different text matching problems, a
flexible layer-based model construction process, and a variety of training
objectives and evaluation metrics. In addition, the toolkit has implemented two
schools of representative deep text matching models, namely
representation-focused models and interaction-focused models. Finally, users
can easily modify existing models, create and share their own models for text
matching in MatchZoo.
Large sample analysis of the median heuristic
Authors: Damien Garreau, Wittawat Jitkrittum, Motonobu Kanagawa
Date: 2017-07-23 00:00:00

In kernel methods, the median heuristic has been widely used as a way of
setting the bandwidth of RBF kernels. While its empirical performances make it
a safe choice under many circumstances, there is little theoretical
understanding of why this is the case. Our aim in this paper is to advance our
understanding of the median heuristic by focusing on the setting of kernel
two-sample test. We collect new findings that may be of interest for both
theoreticians and practitioners. In theory, we provide a convergence analysis
that shows the asymptotic normality of the bandwidth chosen by the median
heuristic in the setting of kernel two-sample test. Systematic empirical
investigations are also conducted in simple settings, comparing the
performances based on the bandwidths chosen by the median heuristic and those
by the maximization of test power.
Programming with a Differentiable Forth Interpreter
Authors: Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel
Date: 2016-05-21 00:00:00
Proceeding: ICML 2017 8

Given that in practice training data is scarce for all but a small set of
problems, a core question is how to incorporate prior knowledge into a model.
In this paper, we consider the case of prior procedural knowledge for neural
networks, such as knowing how a program should traverse a sequence, but not
what local actions should be performed at each step. To this end, we present an
end-to-end differentiable interpreter for the programming language Forth which
enables programmers to write program sketches with slots that can be filled
with behaviour trained from program input-output data. We can optimise this
behaviour directly through gradient descent techniques on user-specified
objectives, and also integrate the program into any larger neural computation
graph. We show empirically that our interpreter is able to effectively leverage
different levels of prior program structure and learn complex behaviours such
as sequence sorting and addition. When connected to outputs of an LSTM and
trained jointly, our interpreter achieves state-of-the-art accuracy for
end-to-end reasoning about quantities expressed in natural language stories.
Composing Distributed Representations of Relational Patterns
Authors: Sho Takase, Naoaki Okazaki, Kentaro Inui
Date: 2017-07-23 00:00:00
Proceeding: ACL 2016 8
Tasks: General Classification, Relation, Relation Classification

Learning distributed representations for relation instances is a central
technique in downstream NLP applications. In order to address semantic modeling
of relational patterns, this paper constructs a new dataset that provides
multiple similarity ratings for every pair of relational patterns on the
existing dataset. In addition, we conduct a comparative study of different
encoders including additive composition, RNN, LSTM, and GRU for composing
distributed representations of relational patterns. We also present Gated
Additive Composition, which is an enhancement of additive composition with the
gating mechanism. Experiments show that the new dataset does not only enable
detailed analyses of the different encoders, but also provides a gauge to
predict successes of distributed representations of relational patterns in the
relation classification task.
Deeply-Learned Part-Aligned Representations for Person Re-Identification
Authors: Liming Zhao, Xi Li, Jingdong Wang, Yueting Zhuang
Date: 2017-07-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Person Re-Identification, Triplet

In this paper, we address the problem of person re-identification, which
refers to associating the persons captured from different cameras. We propose a
simple yet effective human part-aligned representation for handling the body
part misalignment problem. Our approach decomposes the human body into regions
(parts) which are discriminative for person matching, accordingly computes the
representations over the regions, and aggregates the similarities computed
between the corresponding regions of a pair of probe and gallery images as the
overall matching score. Our formulation, inspired by attention models, is a
deep neural network modeling the three steps together, which is learnt through
minimizing the triplet loss function without requiring body part labeling
information. Unlike most existing deep learning algorithms that learn a global
or spatial partition-based local representation, our approach performs human
body partition, and thus is more robust to pose changes and various human
spatial distributions in the person bounding box. Our approach shows
state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$,
CUHK$01$ and VIPeR.
Detecting and Grouping Identical Objects for Region Proposal and Classification
Authors: Wim Abbeloos, Sergio Caccamo, Esra Ataer-Cansizoglu, Yuichi Taguchi, Chen Feng, Teng-Yok Lee
Date: 2017-07-23 00:00:00
Tasks: Classification, General Classification, Object, Object Discovery, Region Proposal

Often multiple instances of an object occur in the same scene, for example in
a warehouse. Unsupervised multi-instance object discovery algorithms are able
to detect and identify such objects. We use such an algorithm to provide object
proposals to a convolutional neural network (CNN) based classifier. This
results in fewer regions to evaluate, compared to traditional region proposal
algorithms. Additionally, it enables using the joint probability of multiple
instances of an object, resulting in improved classification accuracy. The
proposed technique can also split a single class into multiple sub-classes
corresponding to the different object types, enabling hierarchical
classification.
Efficient Low Rank Tensor Ring Completion
Authors: Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Date: 2017-07-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Matrix Completion

Using the matrix product state (MPS) representation of the recently proposed
tensor ring decompositions, in this paper we propose a tensor completion
algorithm, which is an alternating minimization algorithm that alternates over
the factors in the MPS representation. This development is motivated in part by
the success of matrix completion algorithms that alternate over the (low-rank)
factors. In this paper, we propose a spectral initialization for the tensor
ring completion algorithm and analyze the computational complexity of the
proposed algorithm. We numerically compare it with existing methods that employ
a low rank tensor train approximation for data completion and show that our
method outperforms the existing ones for a variety of real computer vision
settings, and thus demonstrate the improved expressive power of tensor ring as
compared to tensor train.
Tensor Fusion Network for Multimodal Sentiment Analysis
Authors: Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis-Philippe Morency
Date: 2017-07-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Multimodal Sentiment Analysis, Sentiment Analysis

Multimodal sentiment analysis is an increasingly popular research area, which
extends the conventional language-based definition of sentiment analysis to a
multimodal setup where other relevant modalities accompany language. In this
paper, we pose the problem of multimodal sentiment analysis as modeling
intra-modality and inter-modality dynamics. We introduce a novel model, termed
Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed
approach is tailored for the volatile nature of spoken language in online
videos as well as accompanying gestures and voice. In the experiments, our
model outperforms state-of-the-art approaches for both multimodal and unimodal
sentiment analysis.
Exploiting Cross-Sentence Context for Neural Machine Translation
Authors: Long-Yue Wang, Zhaopeng Tu, Andy Way, Qun Liu
Date: 2017-04-14 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

In translation, considering the document as a whole can help to resolve
ambiguities and inconsistencies. In this paper, we propose a cross-sentence
context-aware approach and investigate the influence of historical contextual
information on the performance of neural machine translation (NMT). First, this
history is summarized in a hierarchical way. We then integrate the historical
representation into NMT in two strategies: 1) a warm-start of encoder and
decoder states, and 2) an auxiliary context source for updating decoder states.
Experimental results on a large Chinese-English translation task show that our
approach significantly improves upon a strong attention-based NMT system by up
to +2.1 BLEU points.
Towards Good Practices for Deep 3D Hand Pose Estimation
Authors: Hengkai Guo, Guijin Wang, Xinghao Chen, Cairong Zhang
Date: 2017-07-23 00:00:00
Tasks: 3D Hand Pose Estimation, Data Augmentation, Fingertip Detection, Hand Pose Estimation, Pose Estimation

3D hand pose estimation from single depth image is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional random forest based methods is not so
apparent. To exploit the good practice and promote the performance for hand
pose estimation, we propose a tree-structured Region Ensemble Network (REN) for
directly 3D coordinate regression. It first partitions the last convolution
outputs of ConvNet into several grid regions. The results from separate
fully-connected (FC) regressors on each regions are then integrated by another
FC layer to perform the estimation. By exploitation of several training
strategies including data augmentation and smooth $L_1$ loss, proposed REN can
significantly improve the performance of ConvNet to localize hand joints. The
experimental results demonstrate that our approach achieves the best
performance among state-of-the-art algorithms on three public hand pose
datasets. We also experiment our methods on fingertip detection and human pose
datasets and obtain state-of-the-art accuracy.
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison
Authors: Théo Trouillon, Maximilian Nickel
Date: 2017-07-05 00:00:00
Tasks: Articles, Entity Resolution, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Embeddings of knowledge graphs have received significant attention due to
their excellent performance for tasks like link prediction and entity
resolution. In this short paper, we are providing a comparison of two
state-of-the-art knowledge graph embeddings for which their equivalence has
recently been established, i.e., ComplEx and HolE [Nickel, Rosasco, and Poggio,
2016; Trouillon et al., 2016; Hayashi and Shimbo, 2017]. First, we briefly
review both models and discuss how their scoring functions are equivalent. We
then analyze the discrepancy of results reported in the original articles, and
show experimentally that they are likely due to the use of different loss
functions. In further experiments, we evaluate the ability of both models to
embed symmetric and antisymmetric patterns. Finally, we discuss advantages and
disadvantages of both models and under which conditions one would be preferable
to the other.
Team Applied Robotics: A closer look at our robotic picking system
Authors: Wim Abbeloos, Fabian Gouwens, Simon Jansen, Berend Küpers, Maurice Ramaker, Toon Goedemé
Date: 2017-07-23 00:00:00
Tasks: Object, object-detection, Object Detection

This paper describes the vision based robotic picking system that was
developed by our team, Team Applied Robotics, for the Amazon Picking Challenge
2016. This competition challenged teams to develop a robotic system that is
able to pick a large variety of products from a shelve or a tote. We discuss
the design considerations and our strategy, the high resolution 3D vision
system, the use of a combination of texture and shape-based object detection
algorithms, the robot path planning and object manipulators that were
developed.
A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks
Authors: Washington LC dos-Santos, Angelo A Duarte, Luiz AR de Freitas
Date: 2017-07-23 00:00:00
Tasks: Kidney Function, Prediction

This letter presente a comment on the paper Prediction of Kidney Function
from Biopsy Images using Convolutional Neural Networks by Ledbetter et al.
(2017)
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Authors: Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
Date: 2017-04-10 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Task-Completion Dialogue Policy Learning

Building a dialogue agent to fulfill complex tasks, such as travel planning,
is challenging because the agent has to learn to collectively complete multiple
subtasks. For example, the agent needs to reserve a hotel and book a flight so
that there leaves enough time for commute between arrival and hotel check-in.
This paper addresses this challenge by formulating the task in the mathematical
framework of options over Markov Decision Processes (MDPs), and proposing a
hierarchical deep reinforcement learning approach to learning a dialogue
manager that operates at different temporal scales. The dialogue manager
consists of: (1) a top-level dialogue policy that selects among subtasks or
options, (2) a low-level dialogue policy that selects primitive actions to
complete the subtask given by the top-level policy, and (3) a global state
tracker that helps ensure all cross-subtask constraints be satisfied.
Experiments on a travel planning task with simulated and real users show that
our approach leads to significant improvements over three baselines, two based
on handcrafted rules and the other based on flat deep reinforcement learning.