pwc-archive/papers-with-abstracts huggingface dataset:
Towards Understanding Regularization in Batch Normalization
Authors: Ping Luo, Xinjiang Wang, Wenqi Shao, Zhanglin Peng
Date: 2018-09-04 00:00:00
Proceeding: ICLR 2019 5

Batch Normalization (BN) improves both convergence and generalization in
training neural networks. This work understands these phenomena theoretically.
We analyze BN by using a basic block of neural networks, consisting of a kernel
layer, a BN layer, and a nonlinear activation function. This basic network
helps us understand the impacts of BN in three aspects. First, by viewing BN as
an implicit regularizer, BN can be decomposed into population normalization
(PN) and gamma decay as an explicit regularization. Second, learning dynamics
of BN and the regularization show that training converged with large maximum
and effective learning rate. Third, generalization of BN is explored by using
statistical mechanics. Experiments demonstrate that BN in convolutional neural
networks share the same traits of regularization as the above analyses.
Metabolize Neural Network
Authors: Dan Dai, Zhiwen Yu, Yang Hu, Wenming Cao, Mingnan Luo
Date: 2018-09-04 00:00:00

The metabolism of cells is the most basic and important part of human
function. Neural networks in deep learning stem from neuronal activity. It is
self-evident that the significance of metabolize neuronal network(MetaNet) in
model construction. In this study, we explore neuronal metabolism for shallow
network from proliferation and autophagy two aspects. First, we propose
different neuron proliferate methods that constructive the selfgrowing network
in metabolism cycle. Proliferate neurons alleviate resources wasting and
insufficient model learning problem when network initializes more or less
parameters. Then combined with autophagy mechanism in the process of model self
construction to ablate under-expressed neurons. The MetaNet can automatically
determine the number of neurons during training, further, save more resource
consumption. We verify the performance of the proposed methods on datasets:
MNIST, Fashion-MNIST and CIFAR-10.
A Recurrent Neural Network for Sentiment Quantification
Authors: Andrea Esuli, Alejandro Moreo Fernández, Fabrizio Sebastiani
Date: 2018-09-04 00:00:00

Quantification is a supervised learning task that consists in predicting,
given a set of classes C and a set D of unlabelled items, the prevalence (or
relative frequency) p(c|D) of each class c in C. Quantification can in
principle be solved by classifying all the unlabelled items and counting how
many of them have been attributed to each class. However, this "classify and
count" approach has been shown to yield suboptimal quantification accuracy;
this has established quantification as a task of its own, and given rise to a
number of methods specifically devised for it. We propose a recurrent neural
network architecture for quantification (that we call QuaNet) that observes the
classification predictions to learn higher-order "quantification embeddings",
which are then refined by incorporating quantification predictions of simple
classify-and-count-like methods. We test {QuaNet on sentiment quantification on
text, showing that it substantially outperforms several state-of-the-art
baselines.
Bounded Rational Decision-Making with Adaptive Neural Network Priors
Authors: Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
Date: 2018-09-04 00:00:00
Tasks: Decision Making

Bounded rationality investigates utility-optimizing decision-makers with
limited information-processing power. In particular, information theoretic
bounded rationality models formalize resource constraints abstractly in terms
of relative Shannon information, namely the Kullback-Leibler Divergence between
the agents' prior and posterior policy. Between prior and posterior lies an
anytime deliberation process that can be instantiated by sample-based
evaluations of the utility function through Markov Chain Monte Carlo (MCMC)
optimization. The most simple model assumes a fixed prior and can relate
abstract information-theoretic processing costs to the number of sample
evaluations. However, more advanced models would also address the question of
learning, that is how the prior is adapted over time such that generated prior
proposals become more efficient. In this work we investigate generative neural
networks as priors that are optimized concurrently with anytime sample-based
decision-making processes such as MCMC. We evaluate this approach on toy
examples.
Improving the Expressiveness of Deep Learning Frameworks with Recursion
Authors: Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun
Date: 2018-09-04 00:00:00
Tasks: Deep Learning

Recursive neural networks have widely been used by researchers to handle
applications with recursively or hierarchically structured data. However,
embedded control flow deep learning frameworks such as TensorFlow, Theano,
Caffe2, and MXNet fail to efficiently represent and execute such neural
networks, due to lack of support for recursion. In this paper, we add recursion
to the programming model of existing frameworks by complementing their design
with recursive execution of dataflow graphs as well as additional APIs for
recursive definitions. Unlike iterative implementations, which can only
understand the topological index of each node in recursive data structures, our
recursive implementation is able to exploit the recursive relationships between
nodes for efficient execution based on parallel computation. We present an
implementation on TensorFlow and evaluation results with various recursive
neural network models, showing that our recursive implementation not only
conveys the recursive nature of recursive neural networks better than other
implementations, but also uses given resources more effectively to reduce
training and inference time.
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes
Authors: Semih Yagcioglu, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reading Comprehension

Understanding and reasoning about cooking recipes is a fruitful research
direction towards enabling machines to interpret procedural text. In this work,
we introduce RecipeQA, a dataset for multimodal comprehension of cooking
recipes. It comprises of approximately 20K instructional recipes with multiple
modalities such as titles, descriptions and aligned set of images. With over
36K automatically generated question-answer pairs, we design a set of
comprehension and reasoning tasks that require joint understanding of images
and text, capturing the temporal flow of events and making sense of procedural
knowledge. Our preliminary results indicate that RecipeQA will serve as a
challenging test bed and an ideal benchmark for evaluating machine
comprehension systems. The data and leaderboard are available at
http://hucvl.github.io/recipeqa.
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
Authors: Ahmed Ben Said, Abdelkarim Erradi, Azadeh Ghari Neiat, Athman Bouguettaya
Date: 2018-09-04 00:00:00
Tasks: Deep Learning, General Classification, Prediction, Time Series, Time Series Analysis

This papers presents a deep learning-based framework to predict crowdsourced
service availability spatially and temporally. A novel two-stage prediction
model is introduced based on historical spatio-temporal traces of mobile
crowdsourced services. The prediction model first clusters mobile crowdsourced
services into regions. The availability prediction of a mobile crowdsourced
service at a certain location and time is then formulated as a classification
problem. To determine the availability duration of predicted mobile
crowdsourced services, we formulate a forecasting task of time series using the
Gramian Angular Field. We validated the effectiveness of the proposed framework
through multiple experiments.
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Authors: Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Sentence, Sentence Embeddings, Translation

In this paper, we propose a new kernel-based co-occurrence measure that can
be applied to sparse linguistic expressions (e.g., sentences) with a very short
learning time, as an alternative to pointwise mutual information (PMI). As well
as deriving PMI from mutual information, we derive this new measure from the
Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure
the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of
PMI that allows various similarity metrics (e.g., sentence embeddings) to be
plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast
(linear in the size of the data) matrix calculations regardless of whether we
use linear or nonlinear kernels. Empirically, in a dialogue response selection
task, PHSIC is learned thousands of times faster than an RNN-based PMI while
outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is
beneficial as a criterion of a data selection task for machine translation
owing to its ability to give high (low) scores to a consistent (inconsistent)
pair with other pairs.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Authors: Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wangrong Zhu, Devendra Singh Sachan, Eric P. Xing
Date: 2018-09-04 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, Text Generation, Translation

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled and plugged in/swapped out. The toolkit also supports a rich set of large-scale pretrained models. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
Authors: Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, Yoav Artzi
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Action Generation, Conditional Image Generation, Image Generation, Instruction Following

We propose to decompose instruction execution to goal prediction and action
generation. We design a model that maps raw visual observations to goals using
LINGUNET, a language-conditioned image generation network, and then generates
the actions required to complete them. Our model is trained from demonstration
only without external resources. To evaluate our approach, we introduce two
benchmarks for instruction following: LANI, a navigation task; and CHAI, where
an agent executes household instructions. Our evaluation demonstrates the
advantages of our model decomposition, and illustrates the challenges posed by
our new benchmarks.
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Authors: Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William W. Cohen
Date: 2018-09-04 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Graph Representation Learning, Open-Domain Question Answering, Question Answering, Representation Learning

Open Domain Question Answering (QA) is evolving from complex pipelined
systems to end-to-end deep neural networks. Specialized neural models have been
developed for extracting answers from either text alone or Knowledge Bases
(KBs) alone. In this paper we look at a more practical setting, namely QA over
the combination of a KB and entity-linked text, which is appropriate when an
incomplete KB is available with a large text corpus. Building on recent
advances in graph representation learning we propose a novel model, GRAFT-Net,
for extracting answers from a question-specific subgraph containing text and KB
entities and relations. We construct a suite of benchmark tasks for this
problem, varying the difficulty of questions, the amount of training data, and
KB completeness. We show that GRAFT-Net is competitive with the
state-of-the-art when tested using either KBs or text alone, and vastly
outperforms existing methods in the combined setting. Source code is available
at https://github.com/OceanskySun/GraftNet .
Lipschitz Networks and Distributional Robustness
Authors: Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock
Date: 2018-09-04 00:00:00

Robust risk minimisation has several advantages: it has been studied with
regards to improving the generalisation properties of models and robustness to
adversarial perturbation. We bound the distributionally robust risk for a model
class rich enough to include deep neural networks by a regularised empirical
risk involving the Lipschitz constant of the model. This allows us to
interpretand quantify the robustness properties of a deep neural network. As an
application we show the distributionally robust risk upperbounds the
adversarial training risk.
PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
Authors: Takuya Akiba, Tommi Kerola, Yusuke Niitani, Toru Ogawa, Shotaro Sano, Shuji Suzuki
Date: 2018-09-04 00:00:00
Tasks: Object, object-detection, Object Detection

We present a large-scale object detection system by team PFDet. Our system
enables training with huge datasets using 512 GPUs, handles sparsely verified
classes, and massive class imbalance. Using our method, we achieved 2nd place
in the Google AI Open Images Object Detection Track 2018 on Kaggle.
Deep Smoke Segmentation
Authors: Feiniu Yuan, Lin Zhang, Xue Xia, Boyang Wan, Qinghua Huang, Xuelong. Li
Date: 2018-09-04 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Inspired by the recent success of fully convolutional networks (FCN) in
semantic segmentation, we propose a deep smoke segmentation network to infer
high quality segmentation masks from blurry smoke images. To overcome large
variations in texture, color and shape of smoke appearance, we divide the
proposed network into a coarse path and a fine path. The first path is an
encoder-decoder FCN with skip structures, which extracts global context
information of smoke and accordingly generates a coarse segmentation mask. To
retain fine spatial details of smoke, the second path is also designed as an
encoder-decoder FCN with skip structures, but it is shallower than the first
path network. Finally, we propose a very small network containing only add,
convolution and activation layers to fuse the results of the two paths. Thus,
we can easily train the proposed network end to end for simultaneous
optimization of network parameters. To avoid the difficulty in manually
labelling fuzzy smoke objects, we propose a method to generate synthetic smoke
images. According to results of our deep segmentation method, we can easily and
accurately perform smoke detection from videos. Experiments on three synthetic
smoke datasets and a realistic smoke dataset show that our method achieves much
better performance than state-of-the-art segmentation algorithms based on FCNs.
Test results of our method on videos are also appealing.
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
Authors: Bo Chen, Le Sun, Xianpei Han
Date: 2018-09-04 00:00:00
Proceeding: ACL 2018 7
Tasks: Graph Generation, Representation Learning, Semantic Parsing, Sentence

This paper proposes a neural semantic parsing approach -- Sequence-to-Action,
which models semantic parsing as an end-to-end semantic graph generation
process. Our method simultaneously leverages the advantages from two recent
promising directions of semantic parsing. Firstly, our model uses a semantic
graph to represent the meaning of a sentence, which has a tight-coupling with
knowledge bases. Secondly, by leveraging the powerful representation learning
and prediction ability of neural network models, we propose a RNN model which
can effectively map sentences to action sequences for semantic graph
generation. Experiments show that our method achieves state-of-the-art
performance on OVERNIGHT dataset and gets competitive performance on GEO and
ATIS datasets.
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation
Authors: Shu-Hsuan Hsu, I-Chao Shen, Bing-Yu Chen
Date: 2018-09-04 00:00:00
Tasks: Atari Games, Board Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the past few years, deep reinforcement learning has been proven to solve
problems which have complex states like video games or board games. The next
step of intelligent agents would be able to generalize between tasks, and using
prior experience to pick up new skills more quickly. However, most
reinforcement learning algorithms for now are often suffering from catastrophic
forgetting even when facing a very similar target task. Our approach enables
the agents to generalize knowledge from a single source task, and boost the
learning progress with a semisupervised learning method when facing a new task.
We evaluate this approach on Atari games, which is a popular reinforcement
learning benchmark, and show that it outperforms common baselines based on
pre-training and fine-tuning.
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
Authors: Cides S. Bezerra, Rayson Laroca, Diego R. Lucio, Evair Severo, Lucas F. Oliveira, Alceu S. Britto Jr., David Menotti
Date: 2018-09-04 00:00:00
Tasks: Iris Segmentation, Segmentation

The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two approaches for robust iris segmentation based on Fully Convolutional
Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Similar to a common convolutional network, but without the fully connected
layers (i.e., the classification layers), an FCN employs at its end a
combination of pooling layers from different convolutional layers. Based on the
game theory, a GAN is designed as two networks competing with each other to
generate the best segmentation. The proposed segmentation networks achieved
promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4,
IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in
both non-cooperative and cooperative domains, outperforming the baselines
techniques which are the best ones found so far in the literature, i.e., a new
state of the art for these datasets. Furthermore, we manually labeled 2,431
images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks
available for research purposes.
Spatial-Spectral Fusion by Combining Deep Learning and Variation Model
Authors: Huanfeng Shen, Menghui Jiang, Jie Li, Qiangqiang Yuan, Yanchong Wei, Liangpei Zhang
Date: 2018-09-04 00:00:00
Tasks: Deep Learning

In the field of spatial-spectral fusion, the model-based method and the deep
learning (DL)-based method are state-of-the-art. This paper presents a fusion
method that incorporates the deep neural network into the model-based method
for the most common case in the spatial-spectral fusion: PAN/multispectral (MS)
fusion. Specifically, we first map the gradient of the high spatial resolution
panchromatic image (HR-PAN) and the low spatial resolution multispectral image
(LR-MS) to the gradient of the high spatial resolution multispectral image
(HR-MS) via a deep residual convolutional neural network (CNN). Then we
construct a fusion framework by the LR-MS image, the gradient prior learned
from the gradient network, and the ideal fused image. Finally, an iterative
optimization algorithm is used to solve the fusion model. Both quantitative and
visual assessments on high-quality images from various sources demonstrate that
the proposed fusion method is superior to all the mainstream algorithms
included in the comparison in terms of overall fusion accuracy.
End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights
Authors: Myungsu Chae, Tae-Ho Kim, Young Hoon Shin, June-Woo Kim, Soo-Young Lee
Date: 2018-09-04 00:00:00
Tasks: Multi-Task Learning

Multi-task learning is a method for improving the generalizability of
multiple tasks. In order to perform multiple classification tasks with one
neural network model, the losses of each task should be combined. Previous
studies have mostly focused on multiple prediction tasks using joint loss with
static weights for training models, choosing the weights between tasks without
making sufficient considerations by setting them uniformly or empirically. In
this study, we propose a method to calculate joint loss using dynamic weights
to improve the total performance, instead of the individual performance, of
tasks. We apply this method to design an end-to-end multimodal emotion and
gender recognition model using audio and video data. This approach provides
proper weights for the loss of each task when the training process ends. In our
experiments, emotion and gender recognition with the proposed method yielded a
lower joint loss, which is computed as the negative log-likelihood, than using
static weights for joint loss. Moreover, our proposed model has better
generalizability than other models. To the best of our knowledge, this research
is the first to demonstrate the strength of using dynamic weights for joint
loss for maximizing overall performance in emotion and gender recognition
tasks.
"Read My Lips": Using Automatic Text Analysis to Classify Politicians by Party and Ideology
Authors: Eitan Sapiro-Gheiler
Date: 2018-09-03 00:00:00
Tasks: BIG-bench Machine Learning

The increasing digitization of political speech has opened the door to
studying a new dimension of political behavior using text analysis. This work
investigates the value of word-level statistical data from the US Congressional
Record--which contains the full text of all speeches made in the US
Congress--for studying the ideological positions and behavior of senators.
Applying machine learning techniques, we use this data to automatically
classify senators according to party, obtaining accuracy in the 70-95% range
depending on the specific method used. We also show that using text to predict
DW-NOMINATE scores, a common proxy for ideology, does not improve upon these
already-successful results. This classification deteriorates when applied to
text from sessions of Congress that are four or more years removed from the
training set, pointing to a need on the part of voters to dynamically update
the heuristics they use to evaluate party based on political speech. Text-based
predictions are less accurate than those based on voting behavior, supporting
the theory that roll-call votes represent greater commitment on the part of
politicians and are thus a more accurate reflection of their ideological
preferences. However, the overall success of the machine learning approaches
studied here demonstrates that political speeches are highly predictive of
partisan affiliation. In addition to these findings, this work also introduces
the computational tools and methods relevant to the use of political speech
data.