pwc-archive/papers-with-abstracts huggingface dataset:
Recognizing Cuneiform Signs Using Graph Based Methods
Authors: Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert
Date: 2018-02-16 00:00:00

The cuneiform script constitutes one of the earliest systems of writing and
is realized by wedge-shaped marks on clay tablets. A tremendous number of
cuneiform tablets have already been discovered and are incrementally
digitalized and made available to automated processing. As reading cuneiform
script is still a manual task, we address the real-world application of
recognizing cuneiform signs by two graph based methods with complementary
runtime characteristics. We present a graph model for cuneiform signs together
with a tailored distance measure based on the concept of the graph edit
distance. We propose efficient heuristics for its computation and demonstrate
its effectiveness in classification tasks experimentally. To this end, the
distance measure is used to implement a nearest neighbor classifier leading to
a high computational cost for the prediction phase with increasing training set
size. In order to overcome this issue, we propose to use CNNs adapted to graphs
as an alternative approach shifting the computational cost to the training
phase. We demonstrate the practicability of both approaches in an extensive
experimental comparison regarding runtime and prediction accuracy. Although
currently available annotated real-world data is still limited, we obtain a
high accuracy using CNNs, in particular, when the training set is enriched by
augmented examples.
Single View Stereo Matching
Authors: Yue Luo, Jimmy Ren, Mude Lin, Jiahao Pang, Wenxiu Sun, Hongsheng Li, Liang Lin
Date: 2018-03-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Monocular Depth Estimation, Stereo Matching, Stereo Matching Hand

Previous monocular depth estimation methods take a single view and directly
regress the expected results. Though recent advances are made by applying
geometrically inspired loss functions during training, the inference procedure
does not explicitly impose any geometrical constraint. Therefore these models
purely rely on the quality of data and the effectiveness of learning to
generalize. This either leads to suboptimal results or the demand of huge
amount of expensive ground truth labelled data to generate reasonable results.
In this paper, we show for the first time that the monocular depth estimation
problem can be reformulated as two sub-problems, a view synthesis procedure
followed by stereo matching, with two intriguing properties, namely i)
geometrical constraints can be explicitly imposed during inference; ii) demand
on labelled depth data can be greatly alleviated. We show that the whole
pipeline can still be trained in an end-to-end fashion and this new formulation
plays a critical role in advancing the performance. The resulting model
outperforms all the previous monocular depth estimation methods as well as the
stereo block matching method in the challenging KITTI dataset by only using a
small number of real training data. The model also generalizes well to other
monocular depth estimation benchmarks. We also discuss the implications and the
advantages of solving monocular depth estimation using stereo methods.
LSTM Pose Machines
Authors: Yue Luo, Jimmy Ren, Zhouxia Wang, Wenxiu Sun, Jinshan Pan, Jianbo Liu, Jiahao Pang, Liang Lin
Date: 2017-12-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: 2D Human Pose Estimation, Pose Estimation

We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Authors: Jian Shen, Yanru Qu, Wei-Nan Zhang, Yong Yu
Date: 2017-07-05 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Representation Learning

Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.
The Challenge of Crafting Intelligible Intelligence
Authors: Daniel S. Weld, Gagan Bansal
Date: 2018-03-09 00:00:00
Tasks: Stochastic Optimization

Since Artificial Intelligence (AI) software uses techniques like deep
lookahead search and stochastic optimization of huge neural networks to fit
mammoth datasets, it often results in complex behavior that is difficult for
people to understand. Yet organizations are deploying AI algorithms in many
mission-critical settings. To trust their behavior, we must make AI
intelligible, either by using inherently interpretable models or by developing
new methods for explaining and controlling otherwise overwhelmingly complex
decisions using local approximation, vocabulary alignment, and interactive
explanation. This paper argues that intelligibility is essential, surveys
recent work on building such systems, and highlights key directions for
research.
Image Registration Based Flicker Solving in Video Face Replacement and Analysis Based Sub-pixel Image Registration
Authors: Xiaofang Wang, Guoqiang Xiang, Xinyue Zhang, Wei Wei
Date: 2018-03-09 00:00:00
Tasks: Image Registration

In this paper, a framework of video face replacement is proposed and it deals
with the flicker of swapped face in video sequence. This framework contains two
main innovations: 1) the technique of image registration is exploited to align
the source and target video faces for eliminating the flicker or jitter of the
segmented video face sequence; 2) a fast subpixel image registration method is
proposed for farther accuracy and efficiency. Unlike the priori works, it
minimizes the overlapping region and takes spatiotemporal coherence into
account. Flicker in resulted videos is usually caused by the frequently changed
bound of the blending target face and unregistered faces between and along
video sequences. The subpixel image registration method is proposed to solve
the flicker problem. During the alignment process, integer pixel registration
is formulated by maximizing the similarity of images with down sampling
strategy speeding up the process and sub-pixel image registration is a
single-step image match via analytic method. Experimental results show the
proposed algorithm reduces the computation time and gets a high accuracy when
conducting experiments on different data sets.
Review of Visual Saliency Detection with Comprehensive Information
Authors: Runmin Cong, Jianjun Lei, Huazhu Fu, Ming-Ming Cheng, Weisi Lin, Qingming Huang
Date: 2018-03-09 00:00:00
Tasks: Co-Salient Object Detection, Saliency Detection, Video Saliency Detection

Visual saliency detection model simulates the human visual system to perceive
the scene, and has been widely used in many vision tasks. With the acquisition
technology development, more comprehensive information, such as depth cue,
inter-image correspondence, or temporal relationship, is available to extend
image saliency detection to RGBD saliency detection, co-saliency detection, or
video saliency detection. RGBD saliency detection model focuses on extracting
the salient regions from RGBD images by combining the depth information.
Co-saliency detection model introduces the inter-image correspondence
constraint to discover the common salient object in an image group. The goal of
video saliency detection model is to locate the motion-related salient object
in video sequences, which considers the motion cue and spatiotemporal
constraint jointly. In this paper, we review different types of saliency
detection algorithms, summarize the important issues of the existing methods,
and discuss the existent problems and future works. Moreover, the evaluation
datasets and quantitative measurements are briefly introduced, and the
experimental analysis and discission are conducted to provide a holistic
overview of different saliency detection methods.
High-Accuracy Low-Precision Training
Authors: Christopher De Sa, Megan Leszczynski, Jian Zhang, Alana Marzoev, Christopher R. Aberger, Kunle Olukotun, Christopher Ré
Date: 2018-03-09 00:00:00
Tasks: CPU, Quantization, Vocal Bursts Intensity Prediction

Low-precision computation is often used to lower the time and energy cost of
machine learning, and recently hardware accelerators have been developed to
support it. Still, it has been used primarily for inference - not training.
Previous low-precision training algorithms suffered from a fundamental
tradeoff: as the number of bits of precision is lowered, quantization noise is
added to the model, which limits statistical accuracy. To address this issue,
we describe a simple low-precision stochastic gradient descent variant called
HALP. HALP converges at the same theoretical rate as full-precision algorithms
despite the noise introduced by using low precision throughout execution. The
key idea is to use SVRG to reduce gradient variance, and to combine this with a
novel technique called bit centering to reduce quantization error. We show that
on the CPU, HALP can run up to $4 \times$ faster than full-precision SVRG and
can match its convergence trajectory. We implemented HALP in TensorQuant, and
show that it exceeds the validation performance of plain low-precision SGD on
two deep learning tasks.
Efficient Algorithms for Outlier-Robust Regression
Authors: Adam Klivans, Pravesh K. Kothari, Raghu Meka
Date: 2018-03-08 00:00:00
Tasks: regression

We give the first polynomial-time algorithm for performing linear or polynomial regression resilient to adversarial corruptions in both examples and labels. Given a sufficiently large (polynomial-size) training set drawn i.i.d. from distribution D and subsequently corrupted on some fraction of points, our algorithm outputs a linear function whose squared error is close to the squared error of the best-fitting linear function with respect to D, assuming that the marginal distribution of D over the input space is \emph{certifiably hypercontractive}. This natural property is satisfied by many well-studied distributions such as Gaussian, strongly log-concave distributions and, uniform distribution on the hypercube among others. We also give a simple statistical lower bound showing that some distributional assumption is necessary to succeed in this setting. These results are the first of their kind and were not known to be even information-theoretically possible prior to our work. Our approach is based on the sum-of-squares (SoS) method and is inspired by the recent applications of the method for parameter recovery problems in unsupervised learning. Our algorithm can be seen as a natural convex relaxation of the following conceptually simple non-convex optimization problem: find a linear function and a large subset of the input corrupted sample such that the least squares loss of the function over the subset is minimized over all possible large subsets.
Learning Approximate Inference Networks for Structured Prediction
Authors: Lifu Tu, Kevin Gimpel
Date: 2018-03-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Part-Of-Speech Tagging, Prediction, Structured Prediction

Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use
neural network architectures to define energy functions that can capture
arbitrary dependencies among parts of structured outputs. Prior work used
gradient descent for inference, relaxing the structured output to a set of
continuous variables and then optimizing the energy with respect to them. We
replace this use of gradient descent with a neural network trained to
approximate structured argmax inference. This "inference network" outputs
continuous values that we treat as the output structure. We develop
large-margin training criteria for joint training of the structured energy
function and inference network. On multi-label classification we report
speed-ups of 10-60x compared to (Belanger et al, 2017) while also improving
accuracy. For sequence labeling with simple structured energies, our approach
performs comparably to exact inference while being much faster at test time. We
then demonstrate improved accuracy by augmenting the energy with a "label
language model" that scores entire output label sequences, showing it can
improve handling of long-distance dependencies in part-of-speech tagging.
Finally, we show how inference networks can replace dynamic programming for
test-time inference in conditional random fields, suggestive for their general
use for fast inference in structured settings.
Non-Autoregressive Neural Machine Translation
Authors: Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, Richard Socher
Date: 2017-11-07 00:00:00
Proceeding: ICLR 2018 1
Tasks: Knowledge Distillation, Machine Translation, Translation

Existing approaches to neural machine translation condition each output word
on previously generated outputs. We introduce a model that avoids this
autoregressive property and produces its outputs in parallel, allowing an order
of magnitude lower latency during inference. Through knowledge distillation,
the use of input token fertilities as a latent variable, and policy gradient
fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative
to the autoregressive Transformer network used as a teacher. We demonstrate
substantial cumulative improvements associated with each of the three aspects
of our training strategy, and validate our approach on IWSLT 2016
English-German and two WMT language pairs. By sampling fertilities in parallel
at inference time, our non-autoregressive model achieves near-state-of-the-art
performance of 29.8 BLEU on WMT 2016 English-Romanian.
Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
Authors: Huan Gui, Qi Zhu, Liyuan Liu, Aston Zhang, Jiawei Han
Date: 2018-03-09 00:00:00

Expert finding is an important task in both industry and academia. It is
challenging to rank candidates with appropriate expertise for various queries.
In addition, different types of objects interact with one another, which
naturally forms heterogeneous information networks. We study the task of expert
finding in heterogeneous bibliographical networks based on two aspects: textual
content analysis and authority ranking. Regarding the textual content analysis,
we propose a new method for query expansion via locally-trained embedding
learning with concept hierarchy as guidance, which is particularly tailored for
specific queries with narrow semantic meanings. Compared with global embedding
learning, locally-trained embedding learning projects the terms into a latent
semantic space constrained on relevant topics, therefore it preserves more
precise and subtle information for specific queries. Considering the candidate
ranking, the heterogeneous information network structure, while being largely
ignored in the previous studies of expert finding, provides additional
information. Specifically, different types of interactions among objects play
different roles. We propose a ranking algorithm to estimate the authority of
objects in the network, treating each strongly-typed edge type individually. To
demonstrate the effectiveness of the proposed framework, we apply the proposed
method to a large-scale bibliographical dataset with over two million entries
and one million researcher candidates. The experiment results show that the
proposed framework outperforms existing methods for both general and specific
queries.
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-03-09 00:00:00
Tasks: Saliency Prediction

Visual saliency patterns are the result of a variety of factors aside from
the image being parsed, however existing approaches have ignored these. To
address this limitation, we propose a novel saliency estimation model which
leverages the semantic modelling power of conditional generative adversarial
networks together with memory architectures which capture the subject's
behavioural patterns and task dependent factors. We make contributions aiming
to bridge the gap between bottom-up feature learning capabilities in modern
deep learning architectures and traditional top-down hand-crafted features
based methods for task specific saliency modelling. The conditional nature of
the proposed framework enables us to learn contextual semantics and
relationships among different tasks together, instead of learning them
separately for each task. Our studies not only shed light on a novel
application area for generative adversarial networks, but also emphasise the
importance of task specific saliency modelling and demonstrate the plausibility
of fully capturing this context via an augmented memory architecture.
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
Authors: Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
Date: 2017-10-13 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

In human-in-the-loop machine learning, the user provides information beyond
that in the training data. Many algorithms and user interfaces have been
designed to optimize and facilitate this human--machine interaction; however,
fewer studies have addressed the potential defects the designs can cause.
Effective interaction often requires exposing the user to the training data or
its statistics. The design of the system is then critical, as this can lead to
double use of data and overfitting, if the user reinforces noisy patterns in
the data. We propose a user modelling methodology, by assuming simple rational
behaviour, to correct the problem. We show, in a user study with 48
participants, that the method improves predictive performance in a sparse
linear regression sentiment analysis task, where graded user knowledge on
feature relevance is elicited. We believe that the key idea of inferring user
knowledge with probabilistic user models has general applicability in guarding
against overfitting and improving interactive machine learning.
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey
Authors: Muzammal Naseer, Salman H. Khan, Fatih Porikli
Date: 2018-03-09 00:00:00
Tasks: 3D Reconstruction, object-detection, Object Detection, Pose Estimation, Saliency Detection, Scene Classification, Scene Understanding, Semantic Segmentation, Survey

With the availability of low-cost and compact 2.5/3D visual sensing devices,
computer vision community is experiencing a growing interest in visual scene
understanding of indoor environments. This survey paper provides a
comprehensive background to this research topic. We begin with a historical
perspective, followed by popular 3D data representations and a comparative
analysis of available datasets. Before delving into the application specific
details, this survey provides a succinct introduction to the core technologies
that are the underlying methods extensively used in the literature. Afterwards,
we review the developed techniques according to a taxonomy based on the scene
understanding tasks. This covers holistic indoor scene understanding as well as
subtasks such as scene classification, object detection, pose estimation,
semantic segmentation, 3D reconstruction, saliency detection, physics-based
reasoning and affordance prediction. Later on, we summarize the performance
metrics used for evaluation in different tasks and a quantitative comparison
among the recent state-of-the-art techniques. We conclude this review with the
current challenges and an outlook towards the open research problems requiring
further investigation.
A Labelling Framework for Probabilistic Argumentation
Authors: Regis Riveret, Pietro Baroni, Yang Gao, Guido Governatori, Antonino Rotolo, Giovanni Sartor
Date: 2017-08-01 00:00:00
Tasks: Abstract Argumentation

The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature.