pwc-archive/papers-with-abstracts huggingface dataset:
Predefined Sparseness in Recurrent Sequence Models
Authors: Thomas Demeester, Johannes Deleu, Fréderic Godin, Chris Develder
Date: 2018-08-27 00:00:00
Proceeding: CONLL 2018 10
Tasks: Language Modeling, Language Modelling, Word Embeddings

Inducing sparseness while training neural networks has been shown to yield
models with a lower memory footprint but similar effectiveness to dense models.
However, sparseness is typically induced starting from a dense model, and thus
this advantage does not hold during training. We propose techniques to enforce
sparseness upfront in recurrent sequence models for NLP applications, to also
benefit training. First, in language modeling, we show how to increase hidden
state sizes in recurrent layers without increasing the number of parameters,
leading to more expressive models. Second, for sequence labeling, we show that
word embeddings with predefined sparseness lead to similar performance as dense
embeddings, at a fraction of the number of trainable parameters.
Wide Activation for Efficient and Accurate Image Super-Resolution
Authors: Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, Thomas Huang
Date: 2018-08-27 00:00:00
Tasks: Image Super-Resolution, Multi-Frame Super-Resolution, Super-Resolution

Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
Generating Text through Adversarial Training using Skip-Thought Vectors
Authors: Afroz Ahamad
Date: 2018-08-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: Conditional Text Generation, Language Modelling, Sentence, Sentence Embeddings, Style Transfer, Text Generation, Word Embeddings

GANs have been shown to perform exceedingly well on tasks pertaining to image generation and style transfer. In the field of language modelling, word embeddings such as GLoVe and word2vec are state-of-the-art methods for applying neural network models on textual data. Attempts have been made to utilize GANs with word embeddings for text generation. This study presents an approach to text generation using Skip-Thought sentence embeddings with GANs based on gradient penalty functions and f-measures. The proposed architecture aims to reproduce writing style in the generated text by modelling the way of expression at a sentence level across all the works of an author. Extensive experiments were run in different embedding settings on a variety of tasks including conditional text generation and language generation. The model outperforms baseline text generation networks across several automated evaluation metrics like BLEU-n, METEOR and ROUGE. Further, wide applicability and effectiveness in real life tasks are demonstrated through human judgement scores.
Augmenting Bottleneck Features of Deep Neural Network Employing Motor State for Speech Recognition at Humanoid Robots
Authors: Moa Lee, Joon Hyuk Chang
Date: 2018-08-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

As for the humanoid robots, the internal noise, which is generated by motors,
fans and mechanical components when the robot is moving or shaking its body,
severely degrades the performance of the speech recognition accuracy. In this
paper, a novel speech recognition system robust to ego-noise for humanoid
robots is proposed, in which on/off state of the motor is employed as auxiliary
information for finding the relevant input features. For this, we consider the
bottleneck features, which have been successfully applied to deep neural
network (DNN) based automatic speech recognition (ASR) system. When learning
the bottleneck features to catch, we first exploit the motor on/off state data
as supplementary information in addition to the acoustic features as the input
of the first deep neural network (DNN) for preliminary acoustic modeling. Then,
the second DNN for primary acoustic modeling employs both the bottleneck
features tossed from the first DNN and the acoustics features. When the
proposed method is evaluated in terms of phoneme error rate (PER) on TIMIT
database, the experimental results show that achieve obvious improvement (11%
relative) is achieved by our algorithm over the conventional systems.
Stereo Computation for a Single Mixture Image
Authors: Yiran Zhong, Yuchao Dai, Hongdong Li
Date: 2018-08-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Stereo Matching, Stereo Matching Hand, valid

This paper proposes an original problem of \emph{stereo computation from a
single mixture image}-- a challenging problem that had not been researched
before. The goal is to separate (\ie, unmix) a single mixture image into two
constitute image layers, such that the two layers form a left-right stereo
image pair, from which a valid disparity map can be recovered. This is a
severely illposed problem, from one input image one effectively aims to recover
three (\ie, left image, right image and a disparity map). In this work we give
a novel deep-learning based solution, by jointly solving the two subtasks of
image layer separation as well as stereo matching. Training our deep net is a
simple task, as it does not need to have disparity maps. Extensive experiments
demonstrate the efficacy of our method.
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Authors: Xibin Song, Yuchao Dai, Xueying Qin
Date: 2018-08-27 00:00:00
Tasks: Benchmarking, Blocking, Depth Map Super-Resolution, Novel View Synthesis, Super-Resolution

Deep convolutional neural network (DCNN) has been successfully applied to
depth map super-resolution and outperforms existing methods by a wide margin.
However, there still exist two major issues with these DCNN based depth map
super-resolution methods that hinder the performance: i) The low-resolution
depth maps either need to be up-sampled before feeding into the network or
substantial deconvolution has to be used; and ii) The supervision
(high-resolution depth maps) is only applied at the end of the network, thus it
is difficult to handle large up-sampling factors, such as $\times 8, \times
16$. In this paper, we propose a new framework to tackle the above problems.
First, we propose to represent the task of depth map super-resolution as a
series of novel view synthesis sub-tasks. The novel view synthesis sub-task
aims at generating (synthesizing) a depth map from different camera pose, which
could be learned in parallel. Second, to handle large up-sampling factors, we
present a deeply supervised network structure to enforce strong supervision in
each stage of the network. Third, a multi-scale fusion strategy is proposed to
effectively exploit the feature maps at different scales and handle the
blocking effect. In this way, our proposed framework could deal with
challenging depth map super-resolution efficiently under large up-sampling
factors (e.g. $\times 8, \times 16$). Our method only uses the low-resolution
depth map as input, and the support of color image is not needed, which greatly
reduces the restriction of our method. Extensive experiments on various
benchmarking datasets demonstrate the superiority of our method over current
state-of-the-art depth map super-resolution methods.
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Authors: Zixuan Huang, Junming Fan, Shenggan Cheng, Shuai Yi, Xiaogang Wang, Hongsheng Li
Date: 2018-08-27 00:00:00
Tasks: Autonomous Driving, Decoder, Depth Completion

Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around the vehicle to perceive the surrounding environments. However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which aims at generating a dense depth map from an input sparse depth map. To effectively utilize multi-scale features, we propose three novel sparsity-invariant operations, based on which, a sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is also proposed. Additional RGB features could be incorporated to further improve the depth completion performance. Our extensive experiments and component analysis on two public benchmarks, KITTI depth completion benchmark and NYU-depth-v2 dataset, demonstrate the effectiveness of the proposed approach. As of Aug. 12th, 2018, on KITTI depth completion leaderboard, our proposed model without RGB guidance ranks first among all peer-reviewed methods without using RGB information, and our model with RGB guidance ranks second among all RGB-guided methods.
Exploring the Applications of Faster R-CNN and Single-Shot Multi-box Detection in a Smart Nursery Domain
Authors: Somnuk Phon-Amnuaisuk, Ken T. Murata, Praphan Pavarangkoon, Kazunori Yamamoto, Takamichi Mizuhara
Date: 2018-08-27 00:00:00
Tasks: Object, object-detection, Object Detection, Region Proposal

The ultimate goal of a baby detection task concerns detecting the presence of
a baby and other objects in a sequence of 2D images, tracking them and
understanding the semantic contents of the scene. Recent advances in deep
learning and computer vision offer various powerful tools in general object
detection and can be applied to a baby detection task. In this paper, the
Faster Region-based Convolutional Neural Network and the Single-Shot Multi-Box
Detection approaches are explored. They are the two state-of-the-art object
detectors based on the region proposal tactic and the multi-box tactic. The
presence of a baby in the scene obtained from these detectors, tested using
different pre-trained models, are discussed. This study is important since the
behaviors of these detectors in a baby detection task using different
pre-trained models are still not well understood. This exploratory study
reveals many useful insights into the applications of these object detectors in
the smart nursery domain.
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Authors: Jorge A. Balazs, Edison Marrese-Taylor, Yutaka Matsuo
Date: 2018-08-27 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, General Classification, Sentence

In this paper we describe our system designed for the WASSA 2018 Implicit
Emotion Shared Task (IEST), which obtained 2$^{\text{nd}}$ place out of 26
teams with a test macro F1 score of $0.710$. The system is composed of a single
pre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory
Network BiLSTM for enriching word representations with context, a max-pooling
operation for creating sentence representations from said word vectors, and a
Dense Layer for projecting the sentence representations into label space. Our
official submission was obtained by ensembling 6 of these models initialized
with different random seeds. The code for replicating this paper is available
at https://github.com/jabalazs/implicit_emotion.
Approach for Video Classification with Multi-label on YouTube-8M Dataset
Authors: Kwangsoo Shin, Junhyeong Jeon, Seungbin Lee, Boyoung Lim, Minsoo Jeong, Jongho Nang
Date: 2018-08-27 00:00:00
Tasks: General Classification, Video Classification

Video traffic is increasing at a considerable rate due to the spread of
personal media and advancements in media technology. Accordingly, there is a
growing need for techniques to automatically classify moving images. This paper
use NetVLAD and NetFV models and the Huber loss function for video
classification problem and YouTube-8M dataset to verify the experiment. We
tried various attempts according to the dataset and optimize hyperparameters,
ultimately obtain a GAP score of 0.8668.
Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
Authors: Jiahui Qiu, Qi. Wang, Yangming Zhou, Tong Ruan, Ju Gao
Date: 2018-08-27 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Task 2, Translation

Clinical Named Entity Recognition (CNER) aims to identify and classify
clinical terms such as diseases, symptoms, treatments, exams, and body parts in
electronic health records, which is a fundamental and crucial task for clinical
and translation research. In recent years, deep learning methods have achieved
significant success in CNER tasks. However, these methods depend greatly on
Recurrent Neural Networks (RNNs), which maintain a vector of hidden activations
that are propagated through time, thus causing too much time to train models.
In this paper, we propose a Residual Dilated Convolutional Neural Network with
Conditional Random Field (RD-CNN-CRF) to solve it. Specifically, Chinese
characters and dictionary features are first projected into dense vector
representations, then they are fed into the residual dilated convolutional
neural network to capture contextual features. Finally, a conditional random
field is employed to capture dependencies between neighboring tags.
Computational results on the CCKS-2017 Task 2 benchmark dataset show that our
proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based
methods both in terms of computational performance and training time.
The Disparate Effects of Strategic Manipulation
Authors: Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
Date: 2018-08-27 00:00:00
Tasks: General Classification

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness, termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of interventions in which a learner subsidizes members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off--even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.
Predicting Semantic Relations using Global Graph Properties
Authors: Yuval Pinter, Jacob Eisenstein
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Link Prediction

Semantic graphs, such as WordNet, are resources which curate natural language
on two distinguishable layers. On the local level, individual relations between
synsets (semantic building blocks) such as hypernymy and meronymy enhance our
understanding of the words used to express their meanings. Globally, analysis
of graph-theoretic properties of the entire net sheds light on the structure of
human language as a whole. In this paper, we combine global and local
properties of semantic graphs through the framework of Max-Margin Markov Graph
Models (M3GM), a novel extension of Exponential Random Graph Model (ERGM) that
scales to large multi-relational graphs. We demonstrate how such global
modeling improves performance on the local task of predicting semantic
relations between synsets, yielding new state-of-the-art results on the WN18RR
dataset, a challenging version of WordNet link prediction in which "easy"
reciprocal cases are removed. In addition, the M3GM model identifies
multirelational motifs that are characteristic of well-formed lexical semantic
ontologies.
Scientific Relation Extraction with Selectively Incorporated Concept Embeddings
Authors: Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2018-08-26 00:00:00
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction

This paper describes our submission for the SemEval 2018 Task 7 shared task
on semantic relation extraction and classification in scientific papers. We
extend the end-to-end relation extraction model of (Miwa and Bansal) with
enhancements such as a character-level encoding attention mechanism on
selecting pretrained concept candidate embeddings. Our official submission
ranked the second in relation classification task (Subtask 1.1 and Subtask 2
Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario
1).
Detecting Outliers in Data with Correlated Measures
Authors: Yu-Hsuan Kuo, Zhenhui Li, Daniel Kifer
Date: 2018-08-26 00:00:00
Tasks: Outlier Detection

Advances in sensor technology have enabled the collection of large-scale
datasets. Such datasets can be extremely noisy and often contain a significant
amount of outliers that result from sensor malfunction or human operation
faults. In order to utilize such data for real-world applications, it is
critical to detect outliers so that models built from these datasets will not
be skewed by outliers.
  In this paper, we propose a new outlier detection method that utilizes the
correlations in the data (e.g., taxi trip distance vs. trip time). Different
from existing outlier detection methods, we build a robust regression model
that explicitly models the outliers and detects outliers simultaneously with
the model fitting.
  We validate our approach on real-world datasets against methods specifically
designed for each dataset as well as the state of the art outlier detectors.
Our outlier detection method achieves better performances, demonstrating the
robustness and generality of our method. Last, we report interesting case
studies on some outliers that result from atypical events.
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
Authors: Jundong Li, Liang Wu, Huan Liu
Date: 2018-08-26 00:00:00
Proceeding: ASONAM 2019 2019 11
Tasks: Ensemble Learning, Feature Engineering, Network Embedding, Representation Learning

As opposed to manual feature engineering which is tedious and difficult to
scale, network representation learning has attracted a surge of research
interests as it automates the process of feature learning on graphs. The
learned low-dimensional node vector representation is generalizable and eases
the knowledge discovery process on graphs by enabling various off-the-shelf
machine learning tools to be directly applied. Recent research has shown that
the past decade of network embedding approaches either explicitly factorize a
carefully designed matrix to obtain the low-dimensional node vector
representation or are closely related to implicit matrix factorization, with
the fundamental assumption that the factorized node connectivity matrix is
low-rank. Nonetheless, the global low-rank assumption does not necessarily hold
especially when the factorized matrix encodes complex node interactions, and
the resultant single low-rank embedding matrix is insufficient to capture all
the observed connectivity patterns. In this regard, we propose a novel
multi-level network embedding framework BoostNE, which can learn multiple
network embedding representations of different granularity from coarse to fine
without imposing the prevalent global low-rank assumption. The proposed BoostNE
method is also in line with the successful gradient boosting method in ensemble
learning as multiple weak embeddings lead to a stronger and more effective one.
We assess the effectiveness of the proposed BoostNE framework by comparing it
with existing state-of-the-art network embedding methods on various datasets,
and the experimental results corroborate the superiority of the proposed
BoostNE network embedding framework.
Identifying Domain Adjacent Instances for Semantic Parsers
Authors: James Ferguson, Janara Christensen, Edward Li, Edgar Gonzàlez
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: domain classification, General Classification, Sentence

When the semantics of a sentence are not representable in a semantic parser's
output schema, parsing will inevitably fail. Detection of these instances is
commonly treated as an out-of-domain classification problem. However, there is
also a more subtle scenario in which the test data is drawn from the same
domain. In addition to formalizing this problem of domain-adjacency, we present
a comparison of various baselines that could be used to solve it. We also
propose a new simple sentence representation that emphasizes words which are
unexpected. This approach improves the performance of a downstream semantic
parser run on in-domain and domain-adjacent instances.
Semi-Supervised Event Extraction with Paraphrase Clusters
Authors: James Ferguson, Colin Lockard, Daniel S. Weld, Hannaneh Hajishirzi
Date: 2018-08-26 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Event Extraction

Supervised event extraction systems are limited in their accuracy due to the
lack of available training data. We present a method for self-training event
extraction systems by bootstrapping additional training data. This is done by
taking advantage of the occurrence of multiple mentions of the same event
instances across newswire articles from multiple sources. If our system can
make a highconfidence extraction of some mentions in such a cluster, it can
then acquire diverse training examples by adding the other mentions as well.
Our experiments show significant performance improvements on multiple event
extractors over ACE 2005 and TAC-KBP 2015 datasets.
Avoiding Disparity Amplification under Different Worldviews
Authors: Samuel Yeom, Michael Carl Tschantz
Date: 2018-08-26 00:00:00
Tasks: Fairness

We mathematically compare four competing definitions of group-level nondiscrimination: demographic parity, equalized odds, predictive parity, and calibration. Using the theoretical framework of Friedler et al., we study the properties of each definition under various worldviews, which are assumptions about how, if at all, the observed data is biased. We argue that different worldviews call for different definitions of fairness, and we specify the worldviews that, when combined with the desire to avoid a criterion for discrimination that we call disparity amplification, motivate demographic parity and equalized odds. We also argue that predictive parity and calibration are insufficient for avoiding disparity amplification because predictive parity allows an arbitrarily large inter-group disparity and calibration is not robust to post-processing. Finally, we define a worldview that is more realistic than the previously considered ones, and we introduce a new notion of fairness that corresponds to this worldview.
Deep Learning: Computational Aspects
Authors: Nicholas Polson, Vadim Sokolov
Date: 2018-08-26 00:00:00
Tasks: Deep Learning

In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear algebra libraries is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive data sets.