pwc-archive/papers-with-abstracts huggingface dataset:
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
Authors: Xiaojing Liu, Feiyu Gao, Qiong Zhang, Huasha Zhao
Date: 2019-03-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: document understanding, Entity Extraction using GAN

Visually rich documents (VRDs) are ubiquitous in daily business and life.
Examples are purchase receipts, insurance policy documents, custom declaration
forms and so on. In VRDs, visual and layout information is critical for
document understanding, and texts in such documents cannot be serialized into
the one-dimensional sequence without losing information. Classic information
extraction models such as BiLSTM-CRF typically operate on text sequences and do
not incorporate visual features. In this paper, we introduce a graph
convolution based model to combine textual and visual information presented in
VRDs. Graph embeddings are trained to summarize the context of a text segment
in the document, and further combined with text embeddings for entity
extraction. Extensive experiments have been conducted to show that our method
outperforms BiLSTM-CRF baselines by significant margins, on two real-world
datasets. Additionally, ablation studies are also performed to evaluate the
effectiveness of each component of our model.
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
Authors: Masataro Asai, Hiroshi Kajino
Date: 2019-03-27 00:00:00

While classical planning has been an active branch of AI, its applicability
is limited to the tasks precisely modeled by humans. Fully automated high-level
agents should be instead able to find a symbolic representation of an unknown
environment without supervision, otherwise it exhibits the knowledge
acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially
resolves the bottleneck with a neural network called State AutoEncoder (SAE).
SAE obtains the propositional representation of the image-based puzzle domains
with unsupervised learning, generates a state space and performs classical
planning. In this paper, we identify the problematic, stochastic behavior of
the SAE-produced propositions as a new sub-problem of symbol grounding problem,
the symbol stability problem. Informally, symbols are stable when their
referents (e.g. propositional values) do not change against small perturbation
of the observation, and unstable symbols are harmful for symbolic reasoning. We
analyze the problem in Latplan both formally and empirically, and propose
"Zero-Suppressed SAE", an enhancement that stabilizes the propositions using
the idea of closed-world assumption as a prior for NN optimization. We show
that it finds the more stable propositions and the more compact
representations, resulting in an improved success rate of Latplan. It is robust
against various hyperparameters and eases the tuning effort, and also provides
a weight pruning capability as a side effect.
Graded Relevance Assessments and Graded Relevance Measures of NTCIR: A Survey of the First Twenty Years
Authors: Tetsuya Sakai
Date: 2019-03-27 00:00:00
Tasks: Retrieval, Survey

NTCIR was the first large-scale IR evaluation conference to construct test
collections with graded relevance assessments: the NTCIR-1 test collections
from 1998 already featured relevant and partially relevant documents. In this
paper, I first describe a few graded-relevance measures that originated from
NTCIR (and a few variants) which are used across different NTCIR tasks. I then
provide a survey on the use of graded relevance assessments and of graded
relevance measures in the past NTCIR tasks which primarily tackled ranked
retrieval. My survey shows that the majority of the past tasks fully utilised
graded relevance by means of graded evaluation measures, but not all of them;
interestingly, even a few relatively recent tasks chose to adhere to binary
relevance measures. I conclude this paper by a summary of my survey in table
form, and a brief discussion on what may lie beyond graded relevance.
CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages
Authors: Kyubyong Park, Thomas Mulc
Date: 2019-03-27 00:00:00
Tasks: text-to-speech, Text to Speech

We describe our development of CSS10, a collection of single speaker speech datasets for ten languages. It is composed of short audio clips from LibriVox audiobooks and their aligned texts. To validate its quality we train two neural text-to-speech models on each dataset. Subsequently, we conduct Mean Opinion Score tests on the synthesized speech samples. We make our datasets, pre-trained models, and test resources publicly available. We hope they will be used for future speech tasks.
Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
Authors: Guo-Jun Qi, Jiebo Luo
Date: 2019-03-27 00:00:00
Tasks: Domain Adaptation, Representation Learning, Small Data Image Classification

Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems.
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
Authors: Subutai Ahmad, Luiz Scheinkman
Date: 2019-03-27 00:00:00
Tasks: Computational Efficiency

Most artificial networks today rely on dense representations, whereas
biological networks rely on sparse representations. In this paper we show how
sparse representations can be more robust to noise and interference, as long as
the underlying dimensionality is sufficiently high. A key intuition that we
develop is that the ratio of the operable volume around a sparse vector divided
by the volume of the representational space decreases exponentially with
dimensionality. We then analyze computationally efficient sparse networks
containing both sparse weights and activations. Simulations on MNIST and the
Google Speech Command Dataset show that such networks demonstrate significantly
improved robustness and stability compared to dense networks, while maintaining
competitive accuracy. We discuss the potential benefits of sparsity on
accuracy, noise robustness, hyperparameter tuning, learning speed,
computational efficiency, and power requirements.
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
Authors: Qun Liu, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala, Sanaz Saeidi, Alimire Nabijiang
Date: 2019-03-27 00:00:00
Tasks: Knowledge Distillation, Management

Route Choice Models predict the route choices of travelers traversing an
urban area. Most of the route choice models link route characteristics of
alternative routes to those chosen by the drivers. The models play an important
role in prediction of traffic levels on different routes and thus assist in
development of efficient traffic management strategies that result in
minimizing traffic delay and maximizing effective utilization of transport
system. High fidelity route choice models are required to predict traffic
levels with higher accuracy. Existing route choice models do not take into
account dynamic contextual conditions such as the occurrence of an accident,
the socio-cultural and economic background of drivers, other human behaviors,
the dynamic personal risk level, etc. As a result, they can only make
predictions at an aggregate level and for a fixed set of contextual factors.
For higher fidelity, it is highly desirable to use a model that captures
significance of subjective or contextual factors in route choice. This paper
presents a novel approach for developing high-fidelity route choice models with
increased predictive power by augmenting existing aggregate level baseline
models with information on drivers' responses to contextual factors obtained
from Stated Choice Experiments carried out in an Immersive Virtual Environment
through the use of knowledge distillation.
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
Authors: Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, Mingkui Tan
Date: 2019-03-27 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Scene Understanding, Vocal Bursts Intensity Prediction

Generating images via the generative adversarial network (GAN) has attracted
much attention recently. However, most of the existing GAN-based methods can
only produce low-resolution images of limited quality. Directly generating
high-resolution images using GANs is nontrivial, and often produces problematic
images with incomplete objects. To address this issue, we develop a novel GAN
called Auto-Embedding Generative Adversarial Network (AEGAN), which
simultaneously encodes the global structure features and captures the
fine-grained details. In our network, we use an autoencoder to learn the
intrinsic high-level structure of real images and design a novel denoiser
network to provide photo-realistic details for the generated images. In the
experiments, we are able to produce 512x512 images of promising quality
directly from the input noise. The resultant images exhibit better perceptual
photo-realism, i.e., with sharper structure and richer details, than other
baselines on several datasets, including Oxford-102 Flowers, Caltech-UCSD Birds
(CUB), High-Quality Large-scale CelebFaces Attributes (CelebA-HQ), Large-scale
Scene Understanding (LSUN) and ImageNet.
W-Net: Reinforced U-Net for Density Map Estimation
Authors: Varun Kannadi Valloli, Kinal Mehta
Date: 2019-03-27 00:00:00
Tasks: Crowd Counting, Decoder, Management, SSIM

Crowd management is of paramount importance when it comes to preventing
stampedes and saving lives, especially in a countries like China and India
where the combined population is a third of the global population. Millions of
people convene annually all around the nation to celebrate a myriad of events
and crowd count estimation is the linchpin of the crowd management system that
could prevent stampedes and save lives. We present a network for crowd counting
which reports state of the art results on crowd counting benchmarks. Our
contributions are, first, a U-Net inspired model which affords us to report
state of the art results. Second, we propose an independent decoding
Reinforcement branch which helps the network converge much earlier and also
enables the network to estimate density maps with high Structural Similarity
Index (SSIM). Third, we discuss the drawbacks of the contemporary architectures
and empirically show that even though our architecture achieves state of the
art results, the merit may be due to the encoder-decoder pipeline instead.
Finally, we report the error analysis which shows that the contemporary line of
work is at saturation and leaves certain prominent problems unsolved.
Mimicking the In-Camera Color Pipeline for Camera-Aware Object Compositing
Authors: Jun Gao, Xiao Li, Li-Wei Wang, Sanja Fidler, Stephen Lin
Date: 2019-03-27 00:00:00

We present a method for compositing virtual objects into a photograph such
that the object colors appear to have been processed by the photo's camera
imaging pipeline. Compositing in such a camera-aware manner is essential for
high realism, and it requires the color transformation in the photo's pipeline
to be inferred, which is challenging due to the inherent one-to-many mapping
that exists from a scene to a photo. To address this problem for the case of a
single photo taken from an unknown camera, we propose a dual-learning approach
in which the reverse color transformation (from the photo to the scene) is
jointly estimated. Learning of the reverse transformation is used to facilitate
learning of the forward mapping, by enforcing cycle consistency of the two
processes. We additionally employ a feature sharing schema to extract evidence
from the target photo in the reverse mapping to guide the forward color
transformation. Our dual-learning approach achieves object compositing results
that surpass those of alternative techniques.
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
Authors: Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, Xia Hu
Date: 2019-03-27 00:00:00
Tasks: Decision Making

RNN models have achieved the state-of-the-art performance in a wide range of
text mining tasks. However, these models are often regarded as black-boxes and
are criticized due to the lack of interpretability. In this paper, we enhance
the interpretability of RNNs by providing interpretable rationales for RNN
predictions. Nevertheless, interpreting RNNs is a challenging problem. Firstly,
unlike existing methods that rely on local approximation, we aim to provide
rationales that are more faithful to the decision making process of RNN models.
Secondly, a flexible interpretation method should be able to assign
contribution scores to text segments of varying lengths, instead of only to
individual words. To tackle these challenges, we propose a novel attribution
method, called REAT, to provide interpretations to RNN predictions. REAT
decomposes the final prediction of a RNN into additive contribution of each
word in the input text. This additive decomposition enables REAT to further
obtain phrase-level attribution scores. In addition, REAT is generally
applicable to various RNN architectures, including GRU, LSTM and their
bidirectional versions. Experimental results demonstrate the faithfulness and
interpretability of the proposed attribution method. Comprehensive analysis
shows that our attribution method could unveil the useful linguistic knowledge
captured by RNNs. Some analysis further demonstrates our method could be
utilized as a debugging tool to examine the vulnerability and failure reasons
of RNNs, which may lead to several promising future directions to promote
generalization ability of RNNs.
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
Authors: Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser
Date: 2019-03-27 00:00:00
Tasks: Friction

We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.edu
Training Quantized Neural Networks with a Full-precision Auxiliary Module
Authors: Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2020 6
Tasks: image-classification, Image Classification, object-detection, Object Detection, Quantization

In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a fullprecision auxiliary module. Specifically, during training, we construct a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. Then the augmented mix-precision network and the low-precision network are jointly optimized. This strategy creates additional full-precision routes to update the parameters of the low-precision model, thus making the gradient back-propagates more easily. At the inference time, we discard the auxiliary module without introducing any computational complexity to the low-precision network. We evaluate the proposed method on image classification and object detection over various quantization approaches and show consistent performance increase. In particular, we achieve near lossless performance to the full-precision model by using a 4-bit detector, which is of great practical value.
Deep Co-Training for Semi-Supervised Image Segmentation
Authors: Jizong Peng, Guillermo Estrada, Marco Pedersoli, Christian Desrosiers
Date: 2019-03-27 00:00:00
Tasks: Diversity, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images. We present a method based on an ensemble of deep segmentation models. Each model is trained on a subset of the annotated data, and uses the non-annotated images to exchange information with the other models, similar to co-training. Even if each model learns on the same non-annotated images, diversity is preserved with the use of adversarial samples. Our results show that this ability to simultaneously train models, which exchange knowledge while preserving diversity, leads to state-of-the-art results on two challenging medical image datasets.
Feature Selection for Data Integration with Mixed Multi-view Data
Authors: Yulia Baker, Tiffany M. Tang, Genevera I. Allen
Date: 2019-03-27 00:00:00
Tasks: Data Integration, feature selection

Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by the advantages of data integration in the era of "big data", we investigate feature selection for high-dimensional multi-view data with mixed data types (e.g. continuous, binary, count-valued). This heterogeneity of multi-view data poses numerous challenges for existing feature selection methods. However, after critically examining these issues through empirical and theoretically-guided lenses, we develop a practical solution, the Block Randomized Adaptive Iterative Lasso (B-RAIL), which combines the strengths of the randomized Lasso, adaptive weighting schemes, and stability selection. B-RAIL serves as a versatile data integration method for sparse regression and graph selection, and we demonstrate the effectiveness of B-RAIL through extensive simulations and a case study to infer the ovarian cancer gene regulatory network. In this case study, B-RAIL successfully identifies well-known biomarkers associated with ovarian cancer and hints at novel candidates for future ovarian cancer research.
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
Authors: Zhiqin Chen, Kangxue Yin, Matthew Fisher, Siddhartha Chaudhuri, Hao Zhang
Date: 2019-03-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder, One-Shot Learning, Representation Learning

We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with a collection of un-segmented shapes, using a shape reconstruction loss, without any ground-truth labels. Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape. Importantly, the decoder is branched: each branch learns a compact representation for one commonly recurring part of the shape collection, e.g., airplane wings. By complementing the shape reconstruction loss with a label loss, BAE-NET is easily tuned for one-shot learning. We show unsupervised, weakly supervised, and one-shot learning results by BAE-NET, demonstrating that using only a couple of exemplars, our network can generally outperform state-of-the-art supervised methods trained on hundreds of segmented shapes. Code is available at https://github.com/czq142857/BAE-NET.
ner and pos when nothing is capitalized
Authors: Stephen Mayhew, Tatiana Tsygankova, Dan Roth
Date: 2019-03-27 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Translation

For those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging. In fact, it is such a strong signal that model performance on these tasks drops sharply in common lowercased scenarios, such as noisy web text or machine translation outputs. In this work, we perform a systematic analysis of solutions to this problem, modifying only the casing of the train or test data using lowercasing and truecasing methods. While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text. As shown in our experiments, this result holds across tasks and input representations. Finally, we show that our proposed solution gives an 8% F1 improvement in mention detection on noisy out-of-domain Twitter data.