pwc-archive/papers-with-abstracts huggingface dataset:
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers
Authors: Nafise Sadat Moosavi, Michael Strube
Date: 2017-08-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: coreference-resolution, Coreference Resolution

Coreference resolution is an intermediate step for text understanding. It is
used in tasks and domains for which we do not necessarily have coreference
annotated corpora. Therefore, generalization is of special importance for
coreference resolution. However, while recent coreference resolvers have
notable improvements on the CoNLL dataset, they struggle to generalize properly
to new domains or datasets. In this paper, we investigate the role of
linguistic features in building more generalizable coreference resolvers. We
show that generalization improves only slightly by merely using a set of
additional linguistic features. However, employing features and subsets of
their values that are informative for coreference resolution, considerably
improves generalization. Thanks to better generalization, our system achieves
state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our
system, which is trained on CoNLL, achieves on-par performance with a system
designed for this dataset.
Image Denoising via CNNs: An Adversarial Approach
Authors: Nithish Divakar, R. Venkatesh Babu
Date: 2017-08-01 00:00:00
Tasks: Denoising, General Classification, Image Denoising, object-detection, Object Detection

Is it possible to recover an image from its noisy version using convolutional
neural networks? This is an interesting problem as convolutional layers are
generally used as feature detectors for tasks like classification, segmentation
and object detection. We present a new CNN architecture for blind image
denoising which synergically combines three architecture components, a
multi-scale feature extraction layer which helps in reducing the effect of
noise on feature maps, an l_p regularizer which helps in selecting only the
appropriate feature maps for the task of reconstruction, and finally a three
step training approach which leverages adversarial training to give the final
performance boost to the model. The proposed model shows competitive denoising
performance when compared to the state-of-the-art approaches.
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Authors: Piji Li, ZiHao Wang, Zhaochun Ren, Lidong Bing, Wai Lam
Date: 2017-08-01 00:00:00
Tasks: regression, Sentence

Recently, some E-commerce sites launch a new interaction box called Tips on
their mobile apps. Users can express their experience and feelings or provide
suggestions using short texts typically several words or one sentence. In
essence, writing some tips and giving a numerical rating are two facets of a
user's product assessment action, expressing the user experience and feelings.
Jointly modeling these two facets is helpful for designing a better
recommendation system. While some existing models integrate text information
such as item specifications or user reviews into user and item latent factors
for improving the rating prediction, no existing works consider tips for
improving recommendation quality. We propose a deep learning based framework
named NRT which can simultaneously predict precise ratings and generate
abstractive tips with good linguistic quality simulating user experience and
feelings. For abstractive tips generation, gated recurrent neural networks are
employed to "translate" user and item latent representations into a concise
sentence. Extensive experiments on benchmark datasets from different domains
show that NRT achieves significant improvements over the state-of-the-art
methods. Moreover, the generated tips can vividly predict the user experience
and feelings.
Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
Authors: Heng Fan, Haibin Ling
Date: 2017-08-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Visual Tracking

Being intensively studied, visual tracking has seen great recent advances in
either speed (e.g., with correlation filters) or accuracy (e.g., with deep
features). Real-time and high accuracy tracking algorithms, however, remain
scarce. In this paper we study the problem from a new perspective and present a
novel parallel tracking and verifying (PTAV) framework, by taking advantage of
the ubiquity of multi-thread techniques and borrowing from the success of
parallel tracking and mapping in visual SLAM. Our PTAV framework typically
consists of two components, a tracker T and a verifier V, working in parallel
on two separate threads. The tracker T aims to provide a super real-time
tracking inference and is expected to perform well most of the time; by
contrast, the verifier V checks the tracking results and corrects T when
needed. The key innovation is that, V does not work on every frame but only
upon the requests from T; on the other end, T may adjust the tracking according
to the feedback from V. With such collaboration, PTAV enjoys both the high
efficiency provided by T and the strong discriminative power by V. In our
extensive experiments on popular benchmarks including OTB2013, OTB2015, TC128
and UAV20L, PTAV achieves the best tracking accuracy among all real-time
trackers, and in fact performs even better than many deep learning based
solutions. Moreover, as a general framework, PTAV is very flexible and has
great rooms for improvement and generalization.
Unsupervised Learning of Depth and Ego-Motion from Video
Authors: Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe
Date: 2017-04-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Camera Pose Estimation, Depth And Camera Motion, Depth Estimation, Motion Estimation, Pose Estimation

We present an unsupervised learning framework for the task of monocular depth
and camera motion estimation from unstructured video sequences. We achieve this
by simultaneously training depth and camera pose estimation networks using the
task of view synthesis as the supervisory signal. The networks are thus coupled
via the view synthesis objective during training, but can be applied
independently at test time. Empirical evaluation on the KITTI dataset
demonstrates the effectiveness of our approach: 1) monocular depth performing
comparably with supervised methods that use either ground-truth pose or depth
for training, and 2) pose estimation performing favorably with established SLAM
systems under comparable input settings.
Grounding Language for Transfer in Deep Reinforcement Learning
Authors: Karthik Narasimhan, Regina Barzilay, Tommi Jaakkola
Date: 2017-08-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.
Predicting Session Length in Media Streaming
Authors: Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, Valery Rashkov
Date: 2017-08-01 00:00:00
Tasks: Scheduling, Survival Analysis

Session length is a very important aspect in determining a user's
satisfaction with a media streaming service. Being able to predict how long a
session will last can be of great use for various downstream tasks, such as
recommendations and ad scheduling. Most of the related literature on user
interaction duration has focused on dwell time for websites, usually in the
context of approximating post-click satisfaction either in search results, or
display ads. In this work we present the first analysis of session length in a
mobile-focused online service, using a real world data-set from a major music
streaming service. We use survival analysis techniques to show that the
characteristics of the length distributions can differ significantly between
users, and use gradient boosted trees with appropriate objectives to predict
the length of a session using only information available at its beginning. Our
evaluation on real world data illustrates that our proposed technique
outperforms the considered baseline.
Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis
Authors: Andy Kitchen, Jarrel Seah
Date: 2017-08-01 00:00:00

Generative Adversarial Neural Networks (GANs) are applied to the synthetic
generation of prostate lesion MRI images. GANs have been applied to a variety
of natural images, is shown show that the same techniques can be used in the
medical domain to create realistic looking synthetic lesion images. 16mm x 16mm
patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016
and used to train a Deep Convolutional Generative Adversarial Neural Network
(DCGAN) utilizing cutting edge techniques. Synthetic outputs are compared to
real images and the implicit latent representations induced by the GAN are
explored. Training techniques and successful neural network architectures are
explained in detail.
Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification
Authors: Jessica M. Rudd
Date: 2017-08-01 00:00:00
Tasks: General Classification, regression

Disease classification is a crucial element of biomedical research. Recent
studies have demonstrated that machine learning techniques, such as Support
Vector Machine (SVM) modeling, produce similar or improved predictive
capabilities in comparison to the traditional method of Logistic Regression. In
addition, it has been found that social network metrics can provide useful
predictive information for disease modeling. In this study, we combine
simulated social network metrics with SVM to predict diabetes in a sample of
data from the Behavioral Risk Factor Surveillance System. In this dataset,
Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models
with and without graph metrics, respectively. SVM with a polynomial kernel had
ROC index of 72.9 and 75.6 for models with and without graph metrics,
respectively. Although this did not perform as well as Logistic Regression, the
results are consistent with previous studies utilizing SVM to classify
diabetes.
Dual Path Networks
Authors: Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng
Date: 2017-07-06 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: image-classification, Image Classification

In this work, we present a simple, highly efficient and modularized Dual Path
Network (DPN) for image classification which presents a new topology of
connection paths internally. By revealing the equivalence of the
state-of-the-art Residual Network (ResNet) and Densely Convolutional Network
(DenseNet) within the HORNN framework, we find that ResNet enables feature
re-usage while DenseNet enables new features exploration which are both
important for learning good representations. To enjoy the benefits from both
path topologies, our proposed Dual Path Network shares common features while
maintaining the flexibility to explore new features through dual path
architectures. Extensive experiments on three benchmark datasets, ImagNet-1k,
Places365 and PASCAL VOC, clearly demonstrate superior performance of the
proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset,
a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model
size, 25% less computational cost and 8% lower memory consumption, and a deeper
DPN (DPN-131) further pushes the state-of-the-art single model performance with
about 2 times faster training speed. Experiments on the Places365 large-scale
scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation
dataset also demonstrate its consistently better performance than DenseNet,
ResNet and the latest ResNeXt model over various applications.
Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem
Authors: Satoshi Hara, Takayuki Katsuki, Hiroki Yanagisawa, Masaaki Imaizumi, Takafumi Ono, Ryo Okamoto, Shigeki Takeuchi
Date: 2017-07-31 00:00:00
Tasks: feature selection, Model Selection

Two-sample feature selection is the problem of finding features that describe
a difference between two probability distributions, which is a ubiquitous
problem in both scientific and engineering studies. However, existing methods
have limited applicability because of their restrictive assumptions on data
distributoins or computational difficulty. In this paper, we resolve these
difficulties by formulating the problem as a sparsest $k$-subgraph problem. The
proposed method is nonparametric and does not assume any specific parametric
models on the data distributions. We show that the proposed method is
computationally efficient and does not require any extra computation for model
selection. Moreover, we prove that the proposed method provides a consistent
estimator of features under mild conditions. Our experimental results show that
the proposed method outperforms the current method with regard to both accuracy
and computation time.
What Is Around The Camera?
Authors: Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars, Luc van Gool
Date: 2016-11-28 00:00:00
Proceeding: ICCV 2017 10

How much does a single image reveal about the environment it was taken in? In
this paper, we investigate how much of that information can be retrieved from a
foreground object, combined with the background (i.e. the visible part of the
environment). Assuming it is not perfectly diffuse, the foreground object acts
as a complexly shaped and far-from-perfect mirror. An additional challenge is
that its appearance confounds the light coming from the environment with the
unknown materials it is made of. We propose a learning-based approach to
predict the environment from multiple reflectance maps that are computed from
approximate surface normals. The proposed method allows us to jointly model the
statistics of environments and material properties. We train our system from
synthesized training data, but demonstrate its applicability to real-world
data. Interestingly, our analysis shows that the information obtained from
objects made out of multiple materials often is complementary and leads to
better performance.
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
Authors: Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura
Date: 2017-07-29 00:00:00
Proceeding: ICCV 2017 10

In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations of existing methods and incorporate the temporal
information of traffic video, we design a novel FCN-rLSTM network to jointly
estimate vehicle density and vehicle count by connecting fully convolutional
neural networks (FCN) with long short term memory networks (LSTM) in a residual
learning fashion. Such design leverages the strengths of FCN for pixel-level
prediction and the strengths of LSTM for learning complex temporal dynamics.
The residual learning connection reformulates the vehicle count regression as
learning residual functions with reference to the sum of densities in each
frame, which significantly accelerates the training of networks. To preserve
feature map resolution, we propose a Hyper-Atrous combination to integrate
atrous convolution in FCN and combine feature maps of different convolution
layers. FCN-rLSTM enables refined feature representation and a novel end-to-end
trainable mapping from pixels to vehicle count. We extensively evaluated the
proposed method on different counting tasks with three datasets, with
experimental results demonstrating their effectiveness and robustness. In
particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21
on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process
is accelerated by 5 times on average.
Learned in Translation: Contextualized Word Vectors
Authors: Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
Date: 2017-08-01 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification, Machine Translation, Question Answering, Sentiment Analysis, Text Classification, Translation

Computer vision has benefited from initializing multiple deep layers with
weights pretrained on large supervised training sets like ImageNet. Natural
language processing (NLP) typically sees initialization of only the lowest
layer of deep models with pretrained word vectors. In this paper, we use a deep
LSTM encoder from an attentional sequence-to-sequence model trained for machine
translation (MT) to contextualize word vectors. We show that adding these
context vectors (CoVe) improves performance over using only unsupervised word
and character vectors on a wide variety of common NLP tasks: sentiment analysis
(SST, IMDb), question classification (TREC), entailment (SNLI), and question
answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe
improves performance of our baseline models to the state of the art.
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
Authors: Lucas Lehnert, Stefanie Tellex, Michael L. Littman
Date: 2017-07-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One question central to Reinforcement Learning is how to learn a feature
representation that supports algorithm scaling and re-use of learned
information from different tasks. Successor Features approach this problem by
learning a feature representation that satisfies a temporal constraint. We
present an implementation of an approach that decouples the feature
representation from the reward function, making it suitable for transferring
knowledge between domains. We then assess the advantages and limitations of
using Successor Features for transfer.
The Code2Text Challenge: Text Generation in Source Code Libraries
Authors: Kyle Richardson, Sina Zarrieß, Jonas Kuhn
Date: 2017-07-31 00:00:00
Tasks: Data-to-Text Generation, Text Generation

We propose a new shared task for tactical data-to-text generation in the
domain of source code libraries. Specifically, we focus on text generation of
function descriptions from example software projects. Data is drawn from
existing resources used for studying the related problem of semantic parser
induction (Richardson and Kuhn, 2017b; Richardson and Kuhn, 2017a), and spans a
wide variety of both natural languages and programming languages. In this
paper, we describe these existing resources, which will serve as training and
development data for the task, and discuss plans for building new independent
test sets.
Context-aware Captions from Context-agnostic Supervision
Authors: Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik
Date: 2017-01-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Captioning, Language Modeling, Language Modelling

We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.
Learning Algorithms for Active Learning
Authors: Philip Bachman, Alessandro Sordoni, Adam Trischler
Date: 2017-07-31 00:00:00
Proceeding: ICML 2017 8
Tasks: Active Learning, Prediction

We introduce a model that learns active learning algorithms via metalearning.
For a distribution of related tasks, our model jointly learns: a data
representation, an item selection heuristic, and a method for constructing
prediction functions from labeled training sets. Our model uses the item
selection heuristic to gather labeled training sets from which to construct
prediction functions. Using the Omniglot and MovieLens datasets, we test our
model in synthetic and practical settings.
Bayesian Sparsification of Recurrent Neural Networks
Authors: Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov
Date: 2017-07-31 00:00:00
Tasks: Language Modeling, Language Modelling, Sentiment Analysis

Recurrent neural networks show state-of-the-art results in many text analysis
tasks but often require a lot of memory to store their weights. Recently
proposed Sparse Variational Dropout eliminates the majority of the weights in a
feed-forward neural network without significant loss of quality. We apply this
technique to sparsify recurrent neural networks. To account for recurrent
specifics we also rely on Binary Variational Dropout for RNN. We report 99.5%
sparsity level on sentiment analysis task without a quality drop and up to 87%
sparsity level on language modeling task with slight loss of accuracy.
Efficient Regret Minimization in Non-Convex Games
Authors: Elad Hazan, Karan Singh, Cyril Zhang
Date: 2017-07-31 00:00:00
Proceeding: ICML 2017 8

We consider regret minimization in repeated games with non-convex loss
functions. Minimizing the standard notion of regret is computationally
intractable. Thus, we define a natural notion of regret which permits efficient
optimization and generalizes offline guarantees for convergence to an
approximate local optimum. We give gradient-based methods that achieve optimal
regret, which in turn guarantee convergence to equilibrium in this framework.
Learning Robust Representations for Computer Vision
Authors: Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy, Jayaraman Jayaraman Thiagarajan
Date: 2017-07-31 00:00:00
Tasks: Clustering, Representation Learning

Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
  Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.