pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Synthesize a 4D RGBD Light Field from a Single Image
Authors: Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng
Date: 2017-08-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Depth Estimation

We present a machine learning algorithm that takes as input a 2D RGB image
and synthesizes a 4D RGBD light field (color and depth of the scene in each ray
direction). For training, we introduce the largest public light field dataset,
consisting of over 3300 plenoptic camera light fields of scenes containing
flowers and plants. Our synthesis pipeline consists of a convolutional neural
network (CNN) that estimates scene geometry, a stage that renders a Lambertian
light field using that geometry, and a second CNN that predicts occluded rays
and non-Lambertian effects. Our algorithm builds on recent view synthesis
methods, but is unique in predicting RGBD for each light field ray and
improving unsupervised single image depth estimation by enforcing consistency
of ray depths that should intersect the same scene point. Please see our
supplementary video at https://youtu.be/yLCvWoQLnms
A Portfolio Approach to Algorithm Selection for Discrete Time-Cost Trade-off Problem
Authors: Santosh Mungle
Date: 2014-12-05 00:00:00
Tasks: Evolutionary Algorithms

It is a known fact that the performance of optimization algorithms for
NP-Hard problems vary from instance to instance. We observed the same trend
when we comprehensively studied multi-objective evolutionary algorithms (MOEAs)
on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in
a construction project. In this paper, instead of using a single algorithm to
solve DTCTP, we use a portfolio approach that takes multiple algorithms as its
constituent. We proposed portfolio comprising of four MOEAs, Non-dominated
Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary
Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched
Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the
portfolio approach is computationally fast and qualitatively superior to its
constituent algorithms for all benchmark instances. Moreover, portfolio
approach provides an insight in selecting the best algorithm for all benchmark
instances of DTCTP.
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
Authors: Rahul Mazumder, Peter Radchenko, Antoine Dedieu
Date: 2017-08-10 00:00:00
Tasks: regression, Sparse Learning

We study a seemingly unexpected and relatively less understood overfitting aspect of a fundamental tool in sparse linear modeling - best subset selection, which minimizes the residual sum of squares subject to a constraint on the number of nonzero coefficients. While the best subset selection procedure is often perceived as the "gold standard" in sparse learning when the signal to noise ratio (SNR) is high, its predictive performance deteriorates when the SNR is low. In particular, it is outperformed by continuous shrinkage methods, such as ridge regression and the Lasso. We investigate the behavior of best subset selection in the high-noise regimes and propose an alternative approach based on a regularized version of the least-squares criterion. Our proposed estimators (a) mitigate, to a large extent, the poor predictive performance of best subset selection in the high-noise regimes; and (b) perform favorably, while generally delivering substantially sparser models, relative to the best predictive models available via ridge regression and the Lasso. We conduct an extensive theoretical analysis of the predictive properties of the proposed approach and provide justification for its superior predictive performance relative to best subset selection when the noise-level is high. Our estimators can be expressed as solutions to mixed integer second order conic optimization problems and, hence, are amenable to modern computational tools from mathematical optimization.
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
Authors: Xinghao Chen, Hengkai Guo, Guijin Wang, Li Zhang
Date: 2017-08-10 00:00:00
Tasks: General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Skeleton Based Action Recognition

Dynamic hand gesture recognition has attracted increasing interests because
of its importance for human computer interaction. In this paper, we propose a
new motion feature augmented recurrent neural network for skeleton-based
dynamic hand gesture recognition. Finger motion features are extracted to
describe finger movements and global motion features are utilized to represent
the global movement of hand skeleton. These motion features are then fed into a
bidirectional recurrent neural network (RNN) along with the skeleton sequence,
which can augment the motion features for RNN and improve the classification
performance. Experiments demonstrate that our proposed method is effective and
outperforms start-of-the-art methods.
Document Image Binarization with Fully Convolutional Neural Networks
Authors: Chris Tensmeyer, Tony Martinez
Date: 2017-08-10 00:00:00
Tasks: Binarization, Diversity, General Classification

Binarization of degraded historical manuscript images is an important
pre-processing step for many document processing tasks. We formulate
binarization as a pixel classification learning task and apply a novel Fully
Convolutional Network (FCN) architecture that operates at multiple image
scales, including full resolution. The FCN is trained to optimize a continuous
version of the Pseudo F-measure metric and an ensemble of FCNs outperform the
competition winners on 4 of 7 DIBCO competitions. This same binarization
technique can also be applied to different domains such as Palm Leaf
Manuscripts with good performance. We analyze the performance of the proposed
model w.r.t. the architectural hyperparameters, size and diversity of training
data, and the input features chosen.
Analysis of Convolutional Neural Networks for Document Image Classification
Authors: Chris Tensmeyer, Tony Martinez
Date: 2017-08-10 00:00:00
Tasks: Classification, Data Augmentation, document-image-classification, Document Image Classification, General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) are state-of-the-art models for document
image classification tasks. However, many of these approaches rely on
parameters and architectures designed for classifying natural images, which
differ from document images. We question whether this is appropriate and
conduct a large empirical study to find what aspects of CNNs most affect
performance on document images. Among other results, we exceed the
state-of-the-art on the RVL-CDIP dataset by using shear transform data
augmentation and an architecture designed for a larger input image.
Additionally, we analyze the learned features and find evidence that CNNs
trained on RVL-CDIP learn region-specific layout features.
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Authors: Leonard Dahlmann, Evgeny Matusov, Pavel Petrushkov, Shahram Khadivi
Date: 2017-08-10 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Translation

In this paper, we introduce a hybrid search for attention-based neural
machine translation (NMT). A target phrase learned with statistical MT models
extends a hypothesis in the NMT beam search when the attention of the NMT model
focuses on the source words translated by this phrase. Phrases added in this
way are scored with the NMT model, but also with SMT features including
phrase-level translation probabilities and a target language model.
Experimental results on German->English news domain and English->Russian
e-commerce domain translation tasks show that using phrase-based models in NMT
search improves MT quality by up to 2.3% BLEU absolute as compared to a strong
NMT baseline.
Robust polynomial regression up to the information theoretic limit
Authors: Daniel Kane, Sushrut Karmalkar, Eric Price
Date: 2017-08-10 00:00:00
Tasks: regression

We consider the problem of robust polynomial regression, where one receives
samples $(x_i, y_i)$ that are usually within $\sigma$ of a polynomial $y =
p(x)$, but have a $\rho$ chance of being arbitrary adversarial outliers.
Previously, it was known how to efficiently estimate $p$ only when $\rho <
\frac{1}{\log d}$. We give an algorithm that works for the entire feasible
range of $\rho < 1/2$, while simultaneously improving other parameters of the
problem. We complement our algorithm, which gives a factor 2 approximation,
with impossibility results that show, for example, that a $1.09$ approximation
is impossible even with infinitely many samples.
SESA: Supervised Explicit Semantic Analysis
Authors: Dasha Bogdanova, Majid Yazdani
Date: 2017-08-10 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable.
MAT: A Multimodal Attentive Translator for Image Captioning
Authors: Chang Liu, Fuchun Sun, Changhu Wang, Feng Wang, Alan Yuille
Date: 2017-02-18 00:00:00
Tasks: Caption Generation, Image Captioning, Machine Translation, Translation

In this work we formulate the problem of image captioning as a multimodal
translation task. Analogous to machine translation, we present a
sequence-to-sequence recurrent neural networks (RNN) model for image caption
generation. Different from most existing work where the whole image is
represented by convolutional neural network (CNN) feature, we propose to
represent the input image as a sequence of detected objects which feeds as the
source sequence of the RNN model. In this way, the sequential representation of
an image can be naturally translated to a sequence of words, as the target
sequence of the RNN model. To represent the image in a sequential way, we
extract the objects features in the image and arrange them in a order using
convolutional neural networks. To further leverage the visual information from
the encoded objects, a sequential attention layer is introduced to selectively
attend to the objects that are related to generate corresponding words in the
sentences. Extensive experiments are conducted to validate the proposed
approach on popular benchmark dataset, i.e., MS COCO, and the proposed model
surpasses the state-of-the-art methods in all metrics following the dataset
splits of previous work. The proposed approach is also evaluated by the
evaluation server of MS COCO captioning challenge, and achieves very
competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).
Automatic Selection of t-SNE Perplexity
Authors: Yanshuai Cao, Luyu Wang
Date: 2017-08-10 00:00:00
Tasks: Data Visualization, Dimensionality Reduction, Model Selection

t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely
used dimensionality reduction methods for data visualization, but it has a
perplexity hyperparameter that requires manual selection. In practice, proper
tuning of t-SNE perplexity requires users to understand the inner working of
the method as well as to have hands-on experience. We propose a model selection
objective for t-SNE perplexity that requires negligible extra computation
beyond that of the t-SNE itself. We empirically validate that the perplexity
settings found by our approach are consistent with preferences elicited from
human experts across a number of datasets. The similarities of our approach to
Bayesian information criteria (BIC) and minimum description length (MDL) are
also analyzed.
Consistent Multitask Learning with Nonlinear Output Relations
Authors: Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Prediction, Structured Prediction

Key to multitask learning is exploiting relationships between different tasks
to improve prediction performance. If the relations are linear, regularization
approaches can be used successfully. However, in practice assuming the tasks to
be linearly related might be restrictive, and allowing for nonlinear structures
is a challenge. In this paper, we tackle this issue by casting the problem
within the framework of structured prediction. Our main contribution is a novel
algorithm for learning multiple tasks which are related by a system of
nonlinear equations that their joint outputs need to satisfy. We show that the
algorithm is consistent and can be efficiently implemented. Experimental
results show the potential of the proposed method.
Tosca: Operationalizing Commitments Over Information Protocols
Authors: Thomas C. King, Akın Günay, Amit K. Chopra, Munindar P. Singh
Date: 2017-08-10 00:00:00

The notion of commitment is widely studied as a high-level abstraction for
modeling multiagent interaction. An important challenge is supporting flexible
decentralized enactments of commitment specifications. In this paper, we
combine recent advances on specifying commitments and information protocols.
Specifically, we contribute Tosca, a technique for automatically synthesizing
information protocols from commitment specifications. Our main result is that
the synthesized protocols support commitment alignment, which is the idea that
agents must make compatible inferences about their commitments despite
decentralization.
Neural and Statistical Methods for Leveraging Meta-information in Machine Translation
Authors: Shahram Khadivi, Patrick Wilken, Leonard Dahlmann, Evgeny Matusov
Date: 2017-08-10 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Machine Translation, Translation

In this paper, we discuss different methods which use meta information and
richer context that may accompany source language input to improve machine
translation quality. We focus on category information of input text as meta
information, but the proposed methods can be extended to all textual and
non-textual meta information that might be available for the input text or
automatically predicted using the text content. The main novelty of this work
is to use state-of-the-art neural network methods to tackle this problem within
a statistical machine translation (SMT) framework. We observe translation
quality improvements up to 3% in terms of BLEU score in some text categories.
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Authors: Zhao Meng, Lili Mou, Zhi Jin
Date: 2017-08-10 00:00:00
Proceeding: LREC 2018 5
Tasks: Classification, General Classification

Neural network-based dialog systems are attracting increasing attention in
both academia and industry. Recently, researchers have begun to realize the
importance of speaker modeling in neural dialog systems, but there lacks
established tasks and datasets. In this paper, we propose speaker
classification as a surrogate task for general speaker modeling, and collect
massive data to facilitate research in this direction. We further investigate
temporal-based and content-based models of speakers, and propose several
hybrids of them. Experiments show that speaker classification is feasible, and
that hybrid models outperform each single component.
The Static and Stochastic VRPTW with both random Customers and Reveal Times: algorithms and recourse strategies
Authors: Michael Saint-Guillain, Christine Solnon, Yves Deville
Date: 2017-08-10 00:00:00

Unlike its deterministic counterpart, static and stochastic vehicle routing
problems (SS-VRP) aim at modeling and solving real-life operational problems by
considering uncertainty on data. We consider the SS-VRPTW-CR introduced in
Saint-Guillain et al. (2017). Like the SS-VRP introduced by Bertsimas (1992),
we search for optimal first stage routes for a fleet of vehicles to handle a
set of stochastic customer demands, i.e., demands are uncertain and we only
know their probabilities. In addition to capacity constraints, customer demands
are also constrained by time windows. Unlike all SS-VRP variants, the
SS-VRPTW-CR does not make any assumption on the time at which a stochastic
demand is revealed, i.e., the reveal time is stochastic as well. To handle this
new problem, we introduce waiting locations: Each vehicle is assigned a
sequence of waiting locations from which it may serve some associated demands,
and the objective is to minimize the expected number of demands that cannot be
satisfied in time. In this paper, we propose two new recourse strategies for
the SS-VRPTW-CR, together with their closed-form expressions for efficiently
computing their expectations: The first one allows us to take vehicle
capacities into account; The second one allows us to optimize routes by
avoiding some useless trips. We propose two algorithms for searching for routes
with optimal expected costs: The first one is an extended branch-and-cut
algorithm, based on a stochastic integer formulation, and the second one is a
local search based heuristic method. We also introduce a new public benchmark
for the SS-VRPTW-CR, based on real-world data coming from the city of Lyon. We
evaluate our two algorithms on this benchmark and empirically demonstrate the
expected superiority of the SS-VRPTW-CR anticipative actions over a basic
"wait-and-serve" policy.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Authors: Wenhao Jiang, Cheng Deng, Wei Liu, Feiping Nie, Fu-Lai Chung, Heng Huang
Date: 2015-09-05 00:00:00
Tasks: Domain Adaptation

Domain adaptation problems arise in a variety of applications, where a
training dataset from the \textit{source} domain and a test dataset from the
\textit{target} domain typically follow different distributions. The primary
difficulty in designing effective learning models to solve such problems lies
in how to bridge the gap between the source and target distributions. In this
paper, we provide comprehensive analysis of feature learning algorithms used in
conjunction with linear classifiers for domain adaptation. Our analysis shows
that in order to achieve good adaptation performance, the second moments of the
source domain distribution and target domain distribution should be similar.
Based on our new analysis, a novel extremely easy feature learning algorithm
for domain adaptation is proposed. Furthermore, our algorithm is extended by
leveraging multiple layers, leading to a deep linear model. We evaluate the
effectiveness of the proposed algorithms in terms of domain adaptation tasks on
the Amazon review dataset and the spam dataset from the ECML/PKDD 2006
discovery challenge.
On the Local Structure of Stable Clustering Instances
Authors: Vincent Cohen-Addad, Chris Schwiegelshohn
Date: 2017-01-29 00:00:00
Tasks: Clustering

We study the classic $k$-median and $k$-means clustering objectives in the
beyond-worst-case scenario. We consider three well-studied notions of
structured data that aim at characterizing real-world inputs: Distribution
Stability (introduced by Awasthi, Blum, and Sheffet, FOCS 2010), Spectral
Separability (introduced by Kumar and Kannan, FOCS 2010), Perturbation
Resilience (introduced by Bilu and Linial, ICS 2010).
  We prove structural results showing that inputs satisfying at least one of
the conditions are inherently "local". Namely, for any such input, any local
optimum is close both in term of structure and in term of objective value to
the global optima.
  As a corollary we obtain that the widely-used Local Search algorithm has
strong performance guarantees for both the tasks of recovering the underlying
optimal clustering and obtaining a clustering of small cost. This is a
significant step toward understanding the success of local search heuristics in
clustering applications.
Distributed Training Large-Scale Deep Architectures
Authors: Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang
Date: 2017-08-10 00:00:00
Tasks: Benchmarking, Deep Learning

Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training.
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Authors: Qingxing Cao, Liang Lin, Yukai Shi, Xiaodan Liang, Guanbin Li
Date: 2017-08-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deep Reinforcement Learning, Face Hallucination, Hallucination, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Super-Resolution

Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations.