pwc-archive/papers-with-abstracts huggingface dataset:
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell
Authors: Pia Sommerauer, Antske Fokkens
Date: 2018-09-05 00:00:00
Proceeding: WS 2018 11
Tasks: Word Embeddings

This paper presents an approach for investigating the nature of semantic
information captured by word embeddings. We propose a method that extends an
existing human-elicited semantic property dataset with gold negative examples
using crowd judgments. Our experimental approach tests the ability of
supervised classifiers to identify semantic features in word embedding vectors
and com- pares this to a feature-identification method based on full vector
cosine similarity. The idea behind this method is that properties identified by
classifiers, but not through full vector comparison are captured by embeddings.
Properties that cannot be identified by either method are not. Our results
provide an initial indication that semantic properties relevant for the way
entities interact (e.g. dangerous) are captured, while perceptual information
(e.g. colors) is not represented. We conclude that, though preliminary, these
results show that our method is suitable for identifying which properties are
captured by embeddings.
Temporally Coherent Video Harmonization Using Adversarial Networks
Authors: Hao-Zhi Huang, Senzhe Xu, Junxiong Cai, Wei Liu, Shi-Min Hu
Date: 2018-09-05 00:00:00
Tasks: Video Harmonization

Compositing is one of the most important editing operations for images and
videos. The process of improving the realism of composite results is often
called harmonization. Previous approaches for harmonization mainly focus on
images. In this work, we take one step further to attack the problem of video
harmonization. Specifically, we train a convolutional neural network in an
adversarial way, exploiting a pixel-wise disharmony discriminator to achieve
more realistic harmonized results and introducing a temporal loss to increase
temporal consistency between consecutive harmonized frames. Thanks to the
pixel-wise disharmony discriminator, we are also able to relieve the need of
input foreground masks. Since existing video datasets which have ground-truth
foreground masks and optical flows are not sufficiently large, we propose a
simple yet efficient method to build up a synthetic dataset supporting
supervised training of the proposed adversarial network. Experiments show that
training on our synthetic dataset generalizes well to the real-world composite
dataset. Also, our method successfully incorporates temporal consistency during
training and achieves more harmonious results than previous methods.
Towards quantitative methods to assess network generative models
Authors: Vahid Mostofi, Sadegh Aliakbary
Date: 2018-09-05 00:00:00

Assessing generative models is not an easy task. Generative models should
synthesize graphs which are not replicates of real networks but show
topological features similar to real graphs. We introduce an approach for
assessing graph generative models using graph classifiers. The inability of an
established graph classifier for distinguishing real and synthesized graphs
could be considered as a performance measurement for graph generators.
Towards a Better Match in Siamese Network Based Visual Object Tracker
Authors: Anfeng He, Chong Luo, Xinmei Tian, Wen-Jun Zeng
Date: 2018-09-05 00:00:00
Tasks: Visual Object Tracking

Recently, Siamese network based trackers have received tremendous interest
for their fast tracking speed and high performance. Despite the great success,
this tracking framework still suffers from several limitations. First, it
cannot properly handle large object rotation. Second, tracking gets easily
distracted when the background contains salient objects. In this paper, we
propose two simple yet effective mechanisms, namely angle estimation and
spatial masking, to address these issues. The objective is to extract more
representative features so that a better match can be obtained between the same
object from different frames. The resulting tracker, named Siam-BM, not only
significantly improves the tracking performance, but more importantly maintains
the realtime capability. Evaluations on the VOT2017 dataset show that Siam-BM
achieves an EAO of 0.335, which makes it the best-performing realtime tracker
to date.
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
Authors: Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang
Date: 2018-09-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Domain Adaptation, Translation, Unsupervised Domain Adaptation

We present a novel and unified deep learning framework which is capable of
learning domain-invariant representation from data across multiple domains.
Realized by adversarial training with additional ability to exploit
domain-specific information, the proposed network is able to perform continuous
cross-domain image translation and manipulation, and produces desirable output
images accordingly. In addition, the resulting feature representation exhibits
superior performance of unsupervised domain adaptation, which also verifies the
effectiveness of the proposed model in learning disentangled features for
describing cross-domain data.
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
Authors: Mike Wu, Milan Mosse, Noah Goodman, Chris Piech
Date: 2018-09-05 00:00:00
Tasks: Misconceptions, Zero-Shot Learning

In modern computer science education, massive open online courses (MOOCs) log
thousands of hours of data about how students solve coding challenges. Being so
rich in data, these platforms have garnered the interest of the machine
learning community, with many new algorithms attempting to autonomously provide
feedback to help future students learn. But what about those first hundred
thousand students? In most educational contexts (i.e. classrooms), assignments
do not have enough historical data for supervised learning. In this paper, we
introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero
shot" feedback challenge. We are able to provide autonomous feedback for the
first students working on an introductory programming assignment with accuracy
that substantially outperforms data-hungry algorithms and approaches human
level fidelity. Rubric sampling requires minimal teacher effort, can associate
feedback with specific parts of a student's solution and can articulate a
student's misconceptions in the language of the instructor. Deep learning
inference enables rubric sampling to further improve as more assignment
specific student data is acquired. We demonstrate our results on a novel
dataset from Code.org, the world's largest programming education platform.
Semantic Human Matting
Authors: Quan Chen, Tiezheng Ge, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, Kun Gai
Date: 2018-09-05 00:00:00
Tasks: Image Matting

Human matting, high quality extraction of humans from natural images, is
crucial for a wide variety of applications. Since the matting problem is
severely under-constrained, most previous methods require user interactions to
take user designated trimaps or scribbles as constraints. This user-in-the-loop
nature makes them difficult to be applied to large scale data or time-sensitive
scenarios. In this paper, instead of using explicit user input constraints, we
employ implicit semantic constraints learned from data and propose an automatic
human matting algorithm (SHM). SHM is the first algorithm that learns to
jointly fit both semantic information and high quality details with deep
networks. In practice, simultaneously learning both coarse semantics and fine
details is challenging. We propose a novel fusion strategy which naturally
gives a probabilistic estimation of the alpha matte. We also construct a very
large dataset with high quality annotations consisting of 35,513 unique
foregrounds to facilitate the learning and evaluation of human matting.
Extensive experiments on this dataset and plenty of real images show that SHM
achieves comparable results with state-of-the-art interactive matting methods.
IKA: Independent Kernel Approximator
Authors: Matteo Ronchetti
Date: 2018-09-05 00:00:00

This paper describes a new method for low rank kernel approximation called
IKA. The main advantage of IKA is that it produces a function $\psi(x)$ defined
as a linear combination of arbitrarily chosen functions. In contrast the
approximation produced by Nystr\"om method is a linear combination of kernel
evaluations. The proposed method consistently outperformed Nystr\"om method in
a comparison on the STL-10 dataset. Numerical results are reproducible using
the source code available at https://gitlab.com/matteo-ronchetti/IKA
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
Authors: Avisek Lahiri, Vineet Jain, Arnab Mondal, Prabir Kumar Biswas
Date: 2018-09-05 00:00:00
Tasks: Decoder, Generative Adversarial Network, Image Segmentation, Medical Image Segmentation, Retinal Vessel Segmentation, Segmentation, Semantic Segmentation, Structured Prediction

Contemporary deep learning based medical image segmentation algorithms
require hours of annotation labor by domain experts. These data hungry deep
models perform sub-optimally in the presence of limited amount of labeled data.
In this paper, we present a data efficient learning framework using the recent
concept of Generative Adversarial Networks; this allows a deep neural network
to perform significantly better than its fully supervised counterpart in low
annotation regime. The proposed method is an extension of our previous work
with the addition of a new unsupervised adversarial loss and a structured
prediction based architecture. To the best of our knowledge, this work is the
first demonstration of an adversarial framework based structured prediction
model for medical image segmentation. Though generic, we apply our method for
segmentation of blood vessels in retinal fundus images. We experiment with
extreme low annotation budget (0.8 - 1.6% of contemporary annotation size). On
DRIVE and STARE datasets, the proposed method outperforms our previous method
and other fully supervised benchmark models by significant margins especially
with very low number of annotated examples. In addition, our systematic
ablation studies suggest some key recipes for successfully training GAN based
semi-supervised algorithms with an encoder-decoder style network architecture.
Embedding Multimodal Relational Data for Knowledge Base Completion
Authors: Pouya Pezeshkpour, Liyan Chen, Sameer Singh
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Imputation, Knowledge Base Completion, Link Prediction

Representing entities and relations in an embedding space is a well-studied
approach for machine learning on relational data. Existing approaches, however,
primarily focus on simple link structure between a finite set of entities,
ignoring the variety of data types that are often used in knowledge bases, such
as text, images, and numerical values. In this paper, we propose multimodal
knowledge base embeddings (MKBE) that use different neural encoders for this
variety of observed data, and combine them with existing relational models to
learn embeddings of the entities and multimodal data. Further, using these
learned embedings and different neural decoders, we introduce a novel
multimodal imputation model to generate missing multimodal values, like text
and images, from information in the knowledge base. We enrich existing
relational datasets to create two novel benchmarks that contain additional
information such as textual descriptions and images of the original entities.
We demonstrate that our models utilize this additional information effectively
to provide more accurate link prediction, achieving state-of-the-art results
with a considerable gap of 5-7% over existing methods. Further, we evaluate the
quality of our generated multimodal values via a user study. We have release
the datasets and the open-source implementation of our models at
https://github.com/pouyapez/mkbe
Localizing Moments in Video with Temporal Language
Authors: Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, Bryan Russell
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Queries, Retrieval, Video Understanding

Localizing moments in a longer video via natural language queries is a new,
challenging task at the intersection of language and video understanding.
Though moment localization with natural language is similar to other language
and vision tasks like natural language object retrieval in images, moment
localization offers an interesting opportunity to model temporal dependencies
and reasoning in text. We propose a new model that explicitly reasons about
different temporal segments in a video, and shows that temporal context is
important for localizing phrases which include temporal language. To benchmark
whether our model, and other recent video localization models, can effectively
reason about temporal language, we collect the novel TEMPOral reasoning in
video and language (TEMPO) dataset. Our dataset consists of two parts: a
dataset with real videos and template sentences (TEMPO - Template Language)
which allows for controlled studies on temporal language, and a human language
dataset which consists of temporal sentences annotated by humans (TEMPO - Human
Language).
Neural MultiVoice Models for Expressing Novel Personalities in Dialog
Authors: Shereen Oraby, Lena Reed, Sharath TS, Shubhangi Tandon, Marilyn Walker
Date: 2018-09-05 00:00:00
Tasks: Response Generation

Natural language generators for task-oriented dialog should be able to vary
the style of the output utterance while still effectively realizing the system
dialog actions and their associated semantics. While the use of neural
generation for training the response generation component of conversational
agents promises to simplify the process of producing high quality responses in
new domains, to our knowledge, there has been very little investigation of
neural generators for task-oriented dialog that can vary their response style,
and we know of no experiments on models that can generate responses that are
different in style from those seen during training, while still maintain- ing
semantic fidelity to the input meaning representation. Here, we show that a
model that is trained to achieve a single stylis- tic personality target can
produce outputs that combine stylistic targets. We carefully evaluate the
multivoice outputs for both semantic fidelity and for similarities to and
differences from the linguistic features that characterize the original
training style. We show that contrary to our predictions, the learned models do
not always simply interpolate model parameters, but rather produce styles that
are distinct, and novel from the personalities they were trained on.
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Authors: Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Date: 2018-09-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification

Convolutional neural networks (CNNs) have shown great capability of solving
various artificial intelligence tasks. However, the increasing model size has
raised challenges in employing them in resource-limited applications. In this
work, we propose to compress deep models by using channel-wise convolutions,
which re- place dense connections among feature maps with sparse ones in CNNs.
Based on this novel operation, we build light-weight CNNs known as ChannelNets.
Channel- Nets use three instances of channel-wise convolutions; namely group
channel-wise convolutions, depth-wise separable channel-wise convolutions, and
the convolu- tional classification layer. Compared to prior CNNs designed for
mobile devices, ChannelNets achieve a significant reduction in terms of the
number of parameters and computational cost without loss in accuracy. Notably,
our work represents the first attempt to compress the fully-connected
classification layer, which usually accounts for about 25% of total parameters
in compact CNNs. Experimental results on the ImageNet dataset demonstrate that
ChannelNets achieve consistently better performance compared to prior methods.
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
Authors: Richard Futrell, Ethan Wilcox, Takashi Morita, Roger Levy
Date: 2018-09-05 00:00:00
Tasks: Language Modeling, Language Modelling

Recurrent neural networks (RNNs) are the state of the art in sequence
modeling for natural language. However, it remains poorly understood what
grammatical characteristics of natural language they implicitly learn and
represent as a consequence of optimizing the language modeling objective. Here
we deploy the methods of controlled psycholinguistic experimentation to shed
light on to what extent RNN behavior reflects incremental syntactic state and
grammatical dependency representations known to characterize human linguistic
behavior. We broadly test two publicly available long short-term memory (LSTM)
English sequence models, and learn and test a new Japanese LSTM. We demonstrate
that these models represent and maintain incremental syntactic state, but that
they do not always generalize in the same way as humans. Furthermore, none of
our models learn the appropriate grammatical dependency configurations
licensing reflexive pronouns or negative polarity items.
Cross validation residuals for generalised least squares and other correlated data models
Authors: Ingrid Annette Baade
Date: 2018-09-05 00:00:00
Tasks: All

Cross validation residuals are well known for the ordinary least squares
model. Here leave-M-out cross validation is extended to generalised least
squares. The relationship between cross validation residuals and Cook's
distance is demonstrated, in terms of an approximation to the difference in the
generalised residual sum of squares for a model fit to all the data (training
and test) and a model fit to a reduced dataset (training data only). For
generalised least squares, as for ordinary least squares, there is no need to
refit the model to reduced size datasets as all the values for K fold cross
validation are available after fitting the model to all the data.
Reconstruction and Registration of Large-Scale Medical Scene Using Point Clouds Data from Different Modalities
Authors: Ke Wang, Han Song, Jiahui Zhang, Xinran Zhang, Hongen Liao
Date: 2018-09-05 00:00:00

Sensing the medical scenario can ensure the safety during the surgical
operations. So, in this regard, a monitor platform which can obtain the
accurate location information of the surgery room is desperately needed.
Compared to 2D camera image, 3D data contains more information of distance and
direction. Therefore, 3D sensors are more suitable to be used in surgical scene
monitoring. However, each 3D sensor has its own limitations. For example, Lidar
(Light Detection and Ranging) can detect large-scale environment with high
precision, but the point clouds or depth maps are very sparse. As for commodity
RGBD sensors, such as Kinect, can accurately capture denser data, but limited
to a small range from 0.5 to 4.5m. So, a proper method which can address these
problems for fusing different modalities data is important. In this paper, we
proposed a method which can fuse different modalities 3D data to get a
large-scale and dense point cloud. The key contributions of our work are as
follows. First, we proposed a 3D data collecting system to reconstruct the
medical scenes. By fusing the Lidar and Kinect data, a large-scale medical
scene with more details can be reconstructed. Second, we proposed a
location-based fast point clouds registration algorithm to deal with different
modality datasets.
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
Authors: Donghyeon Kim, Jinhyuk Lee, Donghee Choi, Jaehoon Choi, Jaewoo Kang
Date: 2018-09-05 00:00:00
Tasks: Scheduling

With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior. However, event scheduling is still time-consuming even with the development of online calendars. Although machine learning based event scheduling models have automated scheduling processes to some extent, they often fail to understand subtle user preferences and complex calendar contexts with event titles written in natural language. In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling. We leverage over 593K calendar events for NESA to learn scheduling personal events, and we further utilize NESA for multi-attendee event scheduling. NESA successfully incorporates deep neural networks such as Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Highway Network for learning the preferences of each user and understanding calendar context based on natural languages. The experimental results show that NESA significantly outperforms previous baseline models in terms of various evaluation metrics on both personal and multi-attendee event scheduling tasks. Our qualitative analysis demonstrates the effectiveness of each layer in NESA and learned user preferences.
BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis
Authors: Pamela Shapiro, Kevin Duh
Date: 2018-09-05 00:00:00
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) in low-resource settings and of
morphologically rich languages is made difficult in part by data sparsity of
vocabulary words. Several methods have been used to help reduce this sparsity,
notably Byte-Pair Encoding (BPE) and a character-based CNN layer (charCNN).
However, the charCNN has largely been neglected, possibly because it has only
been compared to BPE rather than combined with it. We argue for a
reconsideration of the charCNN, based on cross-lingual improvements on
low-resource data. We translate from 8 languages into English, using a
multi-way parallel collection of TED transcripts. We find that in most cases,
using both BPE and a charCNN performs best, while in Hebrew, using a charCNN
over words is best.
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Authors: Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau Yih
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Semantic Parsing

Semantic parsing from denotations faces two key challenges in model training:
(1) given only the denotations (e.g., answers), search for good candidate
semantic parses, and (2) choose the best model update algorithm. We propose
effective and general solutions to each of them. Using policy shaping, we bias
the search procedure towards semantic parses that are more compatible to the
text, which provide better supervision signals for training. In addition, we
propose an update equation that generalizes three different families of
learning algorithms, which enables fast model exploration. When experimented on
a recently proposed sequential question answering dataset, our framework leads
to a new state-of-the-art model that outperforms previous work by 5.0% absolute
on exact match accuracy.