pwc-archive/papers-with-abstracts huggingface dataset:
Learning to segment with image-level supervision
Authors: Gaurav Pandey, Ambedkar Dukkipati
Date: 2017-05-03 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Deep convolutional networks have achieved the state-of-the-art for semantic
image segmentation tasks. However, training these networks requires access to
densely labeled images, which are known to be very expensive to obtain. On the
other hand, the web provides an almost unlimited source of images annotated at
the image level. How can one utilize this much larger weakly annotated set for
tasks that require dense labeling? Prior work often relied on localization
cues, such as saliency maps, objectness priors, bounding boxes etc., to address
this challenging problem. In this paper, we propose a model that generates
auxiliary labels for each image, while simultaneously forcing the output of the
CNN to satisfy the mean-field constraints imposed by a conditional random
field. We show that one can enforce the CRF constraints by forcing the
distribution at each pixel to be close to the distribution of its neighbors.
This is in stark contrast with methods that compute a recursive expansion of
the mean-field distribution using a recurrent architecture and train the
resultant distribution. Instead, the proposed model adds an extra loss term to
the output of the CNN, and hence, is faster than recursive implementations. We
achieve the state-of-the-art for weakly supervised semantic image segmentation
on VOC 2012 dataset, assuming no manually labeled pixel level information is
available. Furthermore, the incorporation of conditional random fields in CNN
incurs little extra time during training.
Super-Resolution of Wavelet-Encoded Images
Authors: Vildan Atalay Aydin, Hassan Foroosh
Date: 2017-05-03 00:00:00
Tasks: Image Reconstruction, Super-Resolution

Multiview super-resolution image reconstruction (SRIR) is often cast as a
resampling problem by merging non-redundant data from multiple low-resolution
(LR) images on a finer high-resolution (HR) grid, while inverting the effect of
the camera point spread function (PSF). One main problem with multiview methods
is that resampling from nonuniform samples (provided by LR images) and the
inversion of the PSF are highly nonlinear and ill-posed problems. Non-linearity
and ill-posedness are typically overcome by linearization and regularization,
often through an iterative optimization process, which essentially trade off
the very same information (i.e. high frequency) that we want to recover. We
propose a novel point of view for multiview SRIR: Unlike existing multiview
methods that reconstruct the entire spectrum of the HR image from the multiple
given LR images, we derive explicit expressions that show how the
high-frequency spectra of the unknown HR image are related to the spectra of
the LR images. Therefore, by taking any of the LR images as the reference to
represent the low-frequency spectra of the HR image, one can reconstruct the
super-resolution image by focusing only on the reconstruction of the
high-frequency spectra. This is very much like single-image methods, which
extrapolate the spectrum of one image, except that we rely on information
provided by all other views, rather than by prior constraints as in
single-image methods (which may not be an accurate source of information). This
is made possible by deriving and applying explicit closed-form expressions that
define how the local high frequency information that we aim to recover for the
reference high resolution image is related to the local low frequency
information in the sequence of views. Results and comparisons with recently
published state-of-the-art methods show the superiority of the proposed
solution.
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Authors: Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy
Date: 2017-04-19 00:00:00
Proceeding: ACL 2017 7
Tasks: Knowledge Base Completion, Transfer Learning

Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.
The Forgettable-Watcher Model for Video Question Answering
Authors: Hongyang Xue, Zhou Zhao, Deng Cai
Date: 2017-05-03 00:00:00
Tasks: model, Question Answering, Question Generation, Question-Generation, Video Question Answering, Visual Question Answering, Visual Question Answering (VQA)

A number of visual question answering approaches have been proposed recently,
aiming at understanding the visual scenes by answering the natural language
questions. While the image question answering has drawn significant attention,
video question answering is largely unexplored.
  Video-QA is different from Image-QA since the information and the events are
scattered among multiple frames. In order to better utilize the temporal
structure of the videos and the phrasal structures of the answers, we propose
two mechanisms: the re-watching and the re-reading mechanisms and combine them
into the forgettable-watcher model. Then we propose a TGIF-QA dataset for video
question answering with the help of automatic question generation. Finally, we
evaluate the models on our dataset. The experimental results show the
effectiveness of our proposed models.
Fine-graind Image Classification via Combining Vision and Language
Authors: Xiangteng He, Yuxin Peng
Date: 2017-04-10 00:00:00
Tasks: Attribute, Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obtain the semantic parts for better classification
accuracy. Despite achieving promising results, these methods mainly have two
limitations: (1) not all the parts which obtained through the part detection
models are beneficial and indispensable for classification, and (2)
fine-grained image classification requires more detailed visual descriptions
which could not be provided by the part locations or attribute annotations. For
addressing the above two limitations, this paper proposes the two-stream model
combining vision and language (CVL) for learning latent semantic
representations. The vision stream learns deep representations from the
original visual information via deep convolutional neural network. The language
stream utilizes the natural language descriptions which could point out the
discriminative parts or characteristics for each image, and provides a flexible
and compact way of encoding the salient visual aspects for distinguishing
sub-categories. Since the two streams are complementary, combining the two
streams can further achieves better classification accuracy. Comparing with 12
state-of-the-art methods on the widely used CUB-200-2011 dataset for
fine-grained image classification, the experimental results demonstrate our CVL
approach achieves the best performance.
A Versatile, Sound Tool for Simplifying Definitions
Authors: Alessandro Coglio, Matt Kaufmann, Eric W. Smith
Date: 2017-05-03 00:00:00

We present a tool, simplify-defun, that transforms the definition of a given
function into a simplified definition of a new function, providing a proof
checked by ACL2 that the old and new functions are equivalent. When appropriate
it also generates termination and guard proofs for the new function. We explain
how the tool is engineered so that these proofs will succeed. Examples
illustrate its utility, in particular for program transformation in synthesis
and verification.
Marine Animal Classification with Correntropy Loss Based Multi-view Learning
Authors: Zheng Cao, Shujian Yu, Bing Ouyang, Fraser Dalgleish, Anni Vuorenkoski, Gabriel Alsenas, Jose Principe
Date: 2017-05-03 00:00:00
Tasks: General Classification, MULTI-VIEW LEARNING

To analyze marine animals behavior, seasonal distribution and abundance,
digital imagery can be acquired by visual or Lidar camera. Depending on the
quantity and properties of acquired imagery, the animals are characterized as
either features (shape, color, texture, etc.), or dissimilarity matrices
derived from different shape analysis methods (shape context, internal distance
shape context, etc.). For both cases, multi-view learning is critical in
integrating more than one set of feature or dissimilarity matrix for higher
classification accuracy. This paper adopts correntropy loss as cost function in
multi-view learning, which has favorable statistical properties for rejecting
noise. For the case of features, the correntropy loss-based multi-view learning
and its entrywise variation are developed based on the multi-view intact space
learning algorithm. For the case of dissimilarity matrices, the robust
Euclidean embedding algorithm is extended to its multi-view form with the
correntropy loss function. Results from simulated data and real-world marine
animal imagery show that the proposed algorithms can effectively enhance
classification rate, as well as suppress noise under different noise
conditions.
MACA: A Modular Architecture for Conversational Agents
Authors: Hoai Phuoc Truong, Prasanna Parthasarathi, Joelle Pineau
Date: 2017-05-01 00:00:00
Proceeding: WS 2017 8

We propose a software architecture designed to ease the implementation of
dialogue systems. The Modular Architecture for Conversational Agents (MACA)
uses a plug-n-play style that allows quick prototyping, thereby facilitating
the development of new techniques and the reproduction of previous work. The
architecture separates the domain of the conversation from the agent's dialogue
strategy, and as such can be easily extended to multiple domains. MACA provides
tools to host dialogue agents on Amazon Mechanical Turk (mTurk) for data
collection and allows processing of other sources of training data. The current
version of the framework already incorporates several domains and existing
dialogue strategies from the recent literature.
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Authors: Zhao Kang, Chong Peng, Qiang Cheng
Date: 2017-05-01 00:00:00
Tasks: Clustering

Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise and outliers. Thus the learned similarity matrix is often not
suitable, let alone optimal, for the subsequent clustering. In addition,
nonlinear similarity often exists in many real world data which, however, has
not been effectively considered by most existing methods. To tackle these two
challenges, we propose a model to simultaneously learn cluster indicator matrix
and similarity information in kernel spaces in a principled way. We show
theoretical relationships to kernel k-means, k-means, and spectral clustering
methods. Then, to address the practical issue of how to select the most
suitable kernel for a particular clustering task, we further extend our model
with a multiple kernel learning ability. With this joint model, we can
automatically accomplish three subtasks of finding the best cluster indicator
matrix, the most accurate similarity relations and the optimal combination of
multiple kernels. By leveraging the interactions between these three subtasks
in a joint framework, each subtask can be iteratively boosted by using the
results of the others towards an overall optimal solution. Extensive
experiments are performed to demonstrate the effectiveness of our method.
A Rule-Based Computational Model of Cognitive Arithmetic
Authors: Ashis Pati, Kantwon Rogers, Hanqing Zhu
Date: 2017-05-03 00:00:00
Tasks: Math, model, Retrieval

Cognitive arithmetic studies the mental processes used in solving math
problems. This area of research explores the retrieval mechanisms and
strategies used by people during a common cognitive task. Past research has
shown that human performance in arithmetic operations is correlated to the
numerical size of the problem. Past research on cognitive arithmetic has
pinpointed this trend to either retrieval strength, error checking, or
strategy-based approaches when solving equations. This paper describes a
rule-based computational model that performs the four major arithmetic
operations (addition, subtraction, multiplication and division) on two
operands. We then evaluated our model to probe its validity in representing the
prevailing concepts observed in psychology experiments from the related works.
The experiments specifically explore the problem size effect, an
activation-based model for fact retrieval, backup strategies when retrieval
fails, and finally optimization strategies when faced with large operands. From
our experimental results, we concluded that our model's response times were
comparable to results observed when people performed similar tasks during
psychology experiments. The fit of our model in reproducing these results and
incorporating accuracy into our model are discussed.
Local Shrunk Discriminant Analysis (LSDA)
Authors: Zan Gao, Guotai Zhang, Feiping Nie, Hua Zhang
Date: 2017-05-03 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction

Dimensionality reduction is a crucial step for pattern recognition and data
mining tasks to overcome the curse of dimensionality. Principal component
analysis (PCA) is a traditional technique for unsupervised dimensionality
reduction, which is often employed to seek a projection to best represent the
data in a least-squares sense, but if the original data is nonlinear structure,
the performance of PCA will quickly drop. An supervised dimensionality
reduction algorithm called Linear discriminant analysis (LDA) seeks for an
embedding transformation, which can work well with Gaussian distribution data
or single-modal data, but for non-Gaussian distribution data or multimodal
data, it gives undesired results. What is worse, the dimension of LDA cannot be
more than the number of classes. In order to solve these issues, Local shrunk
discriminant analysis (LSDA) is proposed in this work to process the
non-Gaussian distribution data or multimodal data, which not only incorporate
both the linear and nonlinear structures of original data, but also learn the
pattern shrinking to make the data more flexible to fit the manifold structure.
Further, LSDA has more strong generalization performance, whose objective
function will become local LDA and traditional LDA when different extreme
parameters are utilized respectively. What is more, a new efficient
optimization algorithm is introduced to solve the non-convex objective function
with low computational cost. Compared with other related approaches, such as
PCA, LDA and local LDA, the proposed method can derive a subspace which is more
suitable for non-Gaussian distribution and real data. Promising experimental
results on different kinds of data sets demonstrate the effectiveness of the
proposed approach.
Spectral clustering in the dynamic stochastic block model
Authors: Marianna Pensky, Teng Zhang
Date: 2017-05-02 00:00:00
Tasks: Clustering, model, Stochastic Block Model

In the present paper, we studied a Dynamic Stochastic Block Model (DSBM)
under the assumptions that the connection probabilities, as functions of time,
are smooth and that at most $s$ nodes can switch their class memberships
between two consecutive time points. We estimate the edge probability tensor by
a kernel-type procedure and extract the group memberships of the nodes by
spectral clustering. The procedure is computationally viable, adaptive to the
unknown smoothness of the functional connection probabilities, to the rate $s$
of membership switching and to the unknown number of clusters. In addition, it
is accompanied by non-asymptotic guarantees for the precision of estimation and
clustering.
Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections
Authors: David Isele, Akansel Cosgun, Kikuo Fujimura
Date: 2017-05-02 00:00:00
Tasks: Deep Reinforcement Learning, Lifelong learning, Reinforcement Learning, Transfer Learning

We analyze how the knowledge to autonomously handle one type of intersection,
represented as a Deep Q-Network, translates to other types of intersections
(tasks). We view intersection handling as a deep reinforcement learning
problem, which approximates the state action Q function as a deep neural
network. Using a traffic simulator, we show that directly copying a network
trained for one type of intersection to another type of intersection decreases
the success rate. We also show that when a network that is pre-trained on Task
A and then is fine-tuned on a Task B, the resulting network not only performs
better on the Task B than an network exclusively trained on Task A, but also
retained knowledge on the Task A. Finally, we examine a lifelong learning
setting, where we train a single network on five different types of
intersections sequentially and show that the resulting network exhibited
catastrophic forgetting of knowledge on previous tasks. This result suggests a
need for a long-term memory component to preserve knowledge.
On weight initialization in deep neural networks
Authors: Siddharth Krishna Kumar
Date: 2017-04-28 00:00:00

A proper initialization of the weights in a neural network is critical to its
convergence. Current insights into weight initialization come primarily from
linear activation functions. In this paper, I develop a theory for weight
initializations with non-linear activations. First, I derive a general weight
initialization strategy for any neural network using activation functions
differentiable at 0. Next, I derive the weight initialization strategy for the
Rectified Linear Unit (RELU), and provide theoretical insights into why the
Xavier initialization is a poor choice with RELU activations. My analysis
provides a clear demonstration of the role of non-linearities in determining
the proper weight initializations.
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Authors: Akansel Cosgun, Lichao Ma, Jimmy Chiu, Jiawei Huang, Mahmut Demir, Alexandre Miranda Anon, Thang Lian, Hasan Tafish, Samir Al-Stouhi
Date: 2017-05-02 00:00:00

Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.
Understanding and Detecting Supporting Arguments of Diverse Types
Authors: Xinyu Hua, Lu Wang
Date: 2017-04-28 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Sentence

We investigate the problem of sentence-level supporting argument detection
from relevant documents for user-specified claims. A dataset containing claims
and associated citation articles is collected from online debate website
idebate.org. We then manually label sentence-level supporting arguments from
the documents along with their types as study, factual, opinion, or reasoning.
We further characterize arguments of different types, and explore whether
leveraging type information can facilitate the supporting arguments detection
task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses
features informed by argument types yields better performance than the same
ranker trained without type information.
Cascaded Boundary Regression for Temporal Action Detection
Authors: Jiyang Gao, Zhenheng Yang, Ram Nevatia
Date: 2017-05-02 00:00:00
Tasks: Action Detection, regression

Temporal action detection in long videos is an important problem.
State-of-the-art methods address this problem by applying action classifiers on
sliding windows. Although sliding windows may contain an identifiable portion
of the actions, they may not necessarily cover the entire action instance,
which would lead to inferior performance. We adapt a two-stage temporal action
detection pipeline with Cascaded Boundary Regression (CBR) model.
Class-agnostic proposals and specific actions are detected respectively in the
first and the second stage. CBR uses temporal coordinate regression to refine
the temporal boundaries of the sliding windows. The salient aspect of the
refinement process is that, inside each stage, the temporal boundaries are
adjusted in a cascaded way by feeding the refined windows back to the system
for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and
achieve state-of-the-art performance on both datasets. The performance gain is
especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14
is improved from 19.0% to 31.0%.
Predicting Native Language from Gaze
Authors: Yevgeni Berzak, Chie Nakamura, Suzanne Flynn, Boris Katz
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Acquisition

A fundamental question in language learning concerns the role of a speaker's
first language in second language acquisition. We present a novel methodology
for studying this question: analysis of eye-movement patterns in second
language reading of free-form text. Using this methodology, we demonstrate for
the first time that the native language of English learners can be predicted
from their gaze fixations when reading English. We provide analysis of
classifier uncertainty and learned features, which indicates that differences
in English reading are likely to be rooted in linguistic divergences across
native languages. The presented framework complements production studies and
offers new ground for advancing research on multilingualism.
Imagining Probabilistic Belief Change as Imaging (Technical Report)
Authors: Gavin Rens, Thomas Meyer
Date: 2017-05-02 00:00:00

Imaging is a form of probabilistic belief change which could be employed for
both revision and update. In this paper, we propose a new framework for
probabilistic belief change based on imaging, called Expected Distance Imaging
(EDI). EDI is sufficiently general to define Bayesian conditioning and other
forms of imaging previously defined in the literature. We argue that, and
investigate how, EDI can be used for both revision and update. EDI's definition
depends crucially on a weight function whose properties are studied and whose
effect on belief change operations is analysed. Finally, four EDI
instantiations are proposed, two for revision and two for update, and
probabilistic rationality postulates are suggested for their analysis.