pwc-archive/papers-with-abstracts huggingface dataset:
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
Authors: Chen Zhu, HengShu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, Pan Li
Date: 2018-10-08 00:00:00
Tasks: Data Visualization, Representation Learning

Person-Job Fit is the process of matching the right talent for the right job
by identifying talent competencies that are required for the job. While many
qualitative efforts have been made in related fields, it still lacks of
quantitative ways of measuring talent competencies as well as the job's talent
requirements. To this end, in this paper, we propose a novel end-to-end
data-driven model based on Convolutional Neural Network (CNN), namely
Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to
the requirements of a job. To be specific, PJFNN is a bipartite neural network
which can effectively learn the joint representation of Person-Job fitness from
historical job applications. In particular, due to the design of a hierarchical
representation structure, PJFNN can not only estimate whether a candidate fits
a job, but also identify which specific requirement items in the job posting
are satisfied by the candidate by measuring the distances between corresponding
latent representations. Finally, the extensive experiments on a large-scale
real-world dataset clearly validate the performance of PJFNN in terms of
Person-Job Fit prediction. Also, we provide effective data visualization to
show some job and talent benchmark insights obtained by PJFNN.
On Learning and Learned Data Representation by Capsule Networks
Authors: Ancheng Lin, Jun Li, Zhenyuan Ma
Date: 2018-10-08 00:00:00

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Authors: Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian Lane, Xiaobing Liu
Date: 2018-10-07 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.
Conversational Group Detection With Deep Convolutional Networks
Authors: Mason Swofford, John Peruzzi, Marynel Vázquez
Date: 2018-10-07 00:00:00

Detection of interacting and conversational groups from images has applications in video surveillance and social robotics. In this paper we build on prior attempts to find conversational groups by detection of social gathering spaces called o-spaces used to assign people to groups. As our contributions to the task, we are the first paper to incorporate features extracted from the room layout image, and the first to incorporate a deep network to generate an image representation of the proposed o-spaces. Specifically, this novel network builds on the PointNet architecture which allows unordered inputs of variable sizes. We present accuracies which demonstrate the ability to rival and sometimes outperform the best models, but due to a data imbalance issue we do not yet outperform existing models in our test results.
Hartley Spectral Pooling for Deep Learning
Authors: Hao Zhang, Jianwei Ma
Date: 2018-10-07 00:00:00
Tasks: Deep Learning, Dimensionality Reduction

In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-called pooling. Maximation and averaging over sliding windows (max/average pooling), and plain downsampling in the form of strided convolution are popular pooling methods. Since the pooling is a lossy procedure, a motivation of our work is to design a new pooling approach for less lossy in the dimensionality reduction. Inspired by the Fourier spectral pooling(FSP) proposed by Rippel et. al. [1], we present the Hartley transform based spectral pooling method in CNNs. Compared with FSP, the proposed spectral pooling avoids the use of complex arithmetic for frequency representation and reduces the computation. Spectral pooling preserves more structure features for network's discriminability than max and average pooling. We empirically show that Hartley spectral pooling gives rise to the convergence of training CNNs on MNIST and CIFAR-10 datasets.
Deep Geodesic Learning for Segmentation and Anatomical Landmarking
Authors: Neslisah Torosdagli, Denise K. Liberton, Payal Verma, Murat Sincan, Janice S. Lee, Ulas Bagci
Date: 2018-10-06 00:00:00
Tasks: Anatomy, Data Augmentation, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a novel deep learning framework for anatomy
segmentation and automatic landmark- ing. Specifically, we focus on the
challenging problem of mandible segmentation from cone-beam computed tomography
(CBCT) scans and identification of 9 anatomical landmarks of the mandible on
the geodesic space. The overall approach employs three inter-related steps. In
step 1, we propose a deep neu- ral network architecture with carefully designed
regularization, and network hyper-parameters to perform image segmentation
without the need for data augmentation and complex post- processing refinement.
In step 2, we formulate the landmark localization problem directly on the
geodesic space for sparsely- spaced anatomical landmarks. In step 3, we propose
to use a long short-term memory (LSTM) network to identify closely- spaced
landmarks, which is rather difficult to obtain using other standard detection
networks. The proposed fully automated method showed superior efficacy compared
to the state-of-the- art mandible segmentation and landmarking approaches in
craniofacial anomalies and diseased states. We used a very challenging CBCT
dataset of 50 patients with a high-degree of craniomaxillofacial (CMF)
variability that is realistic in clinical practice. Complementary to the
quantitative analysis, the qualitative visual inspection was conducted for
distinct CBCT scans from 250 patients with high anatomical variability. We have
also shown feasibility of the proposed work in an independent dataset from
MICCAI Head-Neck Challenge (2015) achieving the state-of-the-art performance.
Lastly, we present an in-depth analysis of the proposed deep networks with
respect to the choice of hyper-parameters such as pooling and activation
functions.
Solving the clustered traveling salesman problem with d-relaxed priority rule
Authors: Hoa Nguyen Phuong, Huyen Tran Ngoc Nhat, Minh Hoàng Hà, André Langevin, Martin Trépanier
Date: 2018-10-06 00:00:00
Tasks: Traveling Salesman Problem

The Clustered Traveling Salesman Problem with a Prespecified Order on the
Clusters, a variant of the well-known traveling salesman problem is studied in
literature. In this problem, delivery locations are divided into clusters with
different urgency levels and more urgent locations must be visited before less
urgent ones. However, this could lead to an inefficient route in terms of
traveling cost. This priority-oriented constraint can be relaxed by a rule
called d-relaxed priority that provides a trade-off between transportation cost
and emergency level. Our research proposes two approaches to solve the problem
with d-relaxed priority rule. We improve the mathematical formulation proposed
in the literature to construct an exact solution method. A meta-heuristic
method based on the framework of Iterated Local Search with problem-tailored
operators is also introduced to find approximate solutions. Experimental
results show the effectiveness of our methods.
A Comprehensive Survey of Deep Learning for Image Captioning
Authors: Md. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, Hamid Laga
Date: 2018-10-06 00:00:00
Tasks: Deep Learning, Image Captioning, Survey

Generating a description of an image is called image captioning. Image
captioning requires to recognize the important objects, their attributes and
their relationships in an image. It also needs to generate syntactically and
semantically correct sentences. Deep learning-based techniques are capable of
handling the complexities and challenges of image captioning. In this survey
paper, we aim to present a comprehensive review of existing deep learning-based
image captioning techniques. We discuss the foundation of the techniques to
analyze their performances, strengths and limitations. We also discuss the
datasets and the evaluation metrics popularly used in deep learning based
automatic image captioning.
Model Cards for Model Reporting
Authors: Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru
Date: 2018-10-05 00:00:00
Tasks: BIG-bench Machine Learning, model

Trained machine learning models are increasingly used to perform high-impact
tasks in areas such as law enforcement, medicine, education, and employment. In
order to clarify the intended use cases of machine learning models and minimize
their usage in contexts for which they are not well suited, we recommend that
released models be accompanied by documentation detailing their performance
characteristics. In this paper, we propose a framework that we call model
cards, to encourage such transparent model reporting. Model cards are short
documents accompanying trained machine learning models that provide benchmarked
evaluation in a variety of conditions, such as across different cultural,
demographic, or phenotypic groups (e.g., race, geographic location, sex,
Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex
and Fitzpatrick skin type) that are relevant to the intended application
domains. Model cards also disclose the context in which models are intended to
be used, details of the performance evaluation procedures, and other relevant
information. While we focus primarily on human-centered machine learning models
in the application fields of computer vision and natural language processing,
this framework can be used to document any trained machine learning model. To
solidify the concept, we provide cards for two supervised models: One trained
to detect smiling faces in images, and one trained to detect toxic comments in
text. We propose model cards as a step towards the responsible democratization
of machine learning and related AI technology, increasing transparency into how
well AI technology works. We hope this work encourages those releasing trained
machine learning models to accompany model releases with similar detailed
evaluation numbers and other relevant documentation.
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
Authors: Eric Dodds, Huy Nguyen, Simao Herdade, Jack Culpepper, Andrew Kae, Pierre Garrigues
Date: 2018-10-10 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Triplet

In this paper, we propose learning an embedding function for content-based
image retrieval within the e-commerce domain using the triplet loss and an
online sampling method that constructs triplets from within a minibatch. We
compare our method to several strong baselines as well as recent works on the
DeepFashion and Stanford Online Product datasets. Our approach significantly
outperforms the state-of-the-art on the DeepFashion dataset. With a
modification to favor sampling minibatches from a single product category, the
same approach demonstrates competitive results when compared to the
state-of-the-art for the Stanford Online Products dataset.
Principal component-guided sparse regression
Authors: J. Kenneth Tay, Jerome Friedman, Robert Tibshirani
Date: 2018-10-10 00:00:00
Tasks: regression

We propose a new method for supervised learning, especially suited to wide
data where the number of features is much greater than the number of
observations. The method combines the lasso ($\ell_1$) sparsity penalty with a
quadratic penalty that shrinks the coefficient vector toward the leading
principal components of the feature matrix. We call the proposed method the
"principal components lasso" ("pcLasso"). The method can be especially powerful
if the features are pre-assigned to groups (such as cell-pathways, assays or
protein interaction networks). In that case, pcLasso shrinks each group-wise
component of the solution toward the leading principal components of that
group. In the process, it also carries out selection of the feature groups. We
provide some theory for this method and illustrate it on a number of simulated
and real data examples.
Multi-Task Learning as Multi-Objective Optimization
Authors: Ozan Sener, Vladlen Koltun
Date: 2018-10-10 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Depth Estimation, General Classification, Inductive Bias, Instance Segmentation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Task Learning, Scene Understanding, Semantic Segmentation, valid

In multi-task learning, multiple tasks are solved jointly, sharing inductive
bias between them. Multi-task learning is inherently a multi-objective problem
because different tasks may conflict, necessitating a trade-off. A common
compromise is to optimize a proxy objective that minimizes a weighted linear
combination of per-task losses. However, this workaround is only valid when the
tasks do not compete, which is rarely the case. In this paper, we explicitly
cast multi-task learning as multi-objective optimization, with the overall
objective of finding a Pareto optimal solution. To this end, we use algorithms
developed in the gradient-based multi-objective optimization literature. These
algorithms are not directly applicable to large-scale learning problems since
they scale poorly with the dimensionality of the gradients and the number of
tasks. We therefore propose an upper bound for the multi-objective loss and
show that it can be optimized efficiently. We further prove that optimizing
this upper bound yields a Pareto optimal solution under realistic assumptions.
We apply our method to a variety of multi-task deep learning problems including
digit classification, scene understanding (joint semantic segmentation,
instance segmentation, and depth estimation), and multi-label classification.
Our method produces higher-performing models than recent multi-task learning
formulations or per-task training.
Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder
Authors: Indrasis Chakraborty, Sai Pushpak Nandanoori, Soumya Kundu
Date: 2018-10-10 00:00:00
Tasks: Transfer Learning

Recent studies have shown that the aggregated dynamic flexibility of an
ensemble of thermostatic loads can be modeled in the form of a virtual battery.
The existing methods for computing the virtual battery parameters require the
knowledge of the first-principle models and parameter values of the loads in
the ensemble. In real-world applications, however, it is likely that the only
available information are end-use measurements such as power consumption, room
temperature, device on/off status, etc., while very little about the individual
load models and parameters are known. We propose a transfer learning based deep
network framework for calculating virtual battery state of a given ensemble of
flexible thermostatic loads, from the available end-use measurements. This
proposed framework extracts first order virtual battery model parameters for
the given ensemble. We illustrate the effectiveness of this novel framework on
different ensembles of ACs and WHs.
Multimodal Speech Emotion Recognition Using Audio and Text
Authors: Seunghyun Yoon, Seokhyun Byun, Kyomin Jung
Date: 2018-10-10 00:00:00
Tasks: Emotion Classification, Emotion Recognition, Multimodal Emotion Recognition, Multimodal Sentiment Analysis, Speech Emotion Recognition

Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.
Non-linear process convolutions for multi-output Gaussian processes
Authors: Mauricio A. Álvarez, Wil O. C. Ward, Cristian Guarnizo
Date: 2018-10-10 00:00:00
Tasks: Gaussian Processes

The paper introduces a non-linear version of the process convolution
formalism for building covariance functions for multi-output Gaussian
processes. The non-linearity is introduced via Volterra series, one series per
each output. We provide closed-form expressions for the mean function and the
covariance function of the approximated Gaussian process at the output of the
Volterra series. The mean function and covariance function for the joint
Gaussian process are derived using formulae for the product moments of Gaussian
variables. We compare the performance of the non-linear model against the
classical process convolution approach in one synthetic dataset and two real
datasets.
Extracting Arguments from Korean Question and Command: An Annotated Corpus for Structured Paraphrasing
Authors: Won Ik Cho, Young Ki Moon, Woo Hyun Kang, Nam Soo Kim
Date: 2018-10-10 00:00:00
Tasks: Argument Mining, Management, Sentence, Slot Filling

Intention identification is a core issue in dialog management. However, due to the non-canonicality of the spoken language, it is difficult to extract the content automatically from the conversation-style utterances. This is much more challenging for languages like Korean and Japanese since the agglutination between morphemes make it difficult for the machines to parse the sentence and understand the intention. To suggest a guideline for this problem, and to merge the issue flexibly with the neural paraphrasing systems introduced recently, we propose a structured annotation scheme for Korean question/commands and the resulting corpus which are widely applicable to the field of argument mining. The scheme and dataset are expected to help machines understand the intention of natural language and grasp the core meaning of conversation-style instructions.
A Closer Look at Structured Pruning for Neural Network Compression
Authors: Elliot J. Crowley, Jack Turner, Amos Storkey, Michael O'Boyle
Date: 2018-10-10 00:00:00
Tasks: Network Pruning, Neural Network Compression

Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network. However, the efficacy of structured pruning has largely evaded scrutiny. In this paper, we examine ResNets and DenseNets obtained through structured pruning-and-tuning and make two interesting observations: (i) reduced networks---smaller versions of the original network trained from scratch---consistently outperform pruned networks; (ii) if one takes the architecture of a pruned network and then trains it from scratch it is significantly more competitive. Furthermore, these architectures are easy to approximate: we can prune once and obtain a family of new, scalable network architectures that can simply be trained from scratch. Finally, we compare the inference speed of reduced and pruned networks on hardware, and show that reduced networks are significantly faster. Code is available at https://github.com/BayesWatch/pytorch-prunes.
A Similarity Measure for Weaving Patterns in Textiles
Authors: Sven Helmer, Vuong M. Ngo
Date: 2018-10-10 00:00:00

We propose a novel approach for measuring the similarity between weaving
patterns that can provide similarity-based search functionality for textile
archives. We represent textile structures using hypergraphs and extract
multisets of k-neighborhoods from these graphs. The resulting multisets are
then compared using Jaccard coefficients, Hamming distances, and cosine
measures. We evaluate the different variants of our similarity measure
experimentally, showing that it can be implemented efficiently and illustrating
its quality using it to cluster and query a data set containing more than a
thousand textile samples.
The Laplacian in RL: Learning Representations with Efficient Approximations
Authors: Yifan Wu, George Tucker, Ofir Nachum
Date: 2018-10-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

The smallest eigenvectors of the graph Laplacian are well-known to provide a
succinct representation of the geometry of a weighted graph. In reinforcement
learning (RL), where the weighted graph may be interpreted as the state
transition process induced by a behavior policy acting on the environment,
approximating the eigenvectors of the Laplacian provides a promising approach
to state representation learning. However, existing methods for performing this
approximation are ill-suited in general RL settings for two main reasons:
First, they are computationally expensive, often requiring operations on large
matrices. Second, these methods lack adequate justification beyond simple,
tabular, finite-state settings. In this paper, we present a fully general and
scalable method for approximating the eigenvectors of the Laplacian in a
model-free RL context. We systematically evaluate our approach and empirically
show that it generalizes beyond the tabular, finite-state setting. Even in
tabular, finite-state settings, its ability to approximate the eigenvectors
outperforms previous proposals. Finally, we show the potential benefits of
using a Laplacian representation learned using our method in goal-achieving RL
tasks, providing evidence that our technique can be used to significantly
improve the performance of an RL agent.