pwc-archive/papers-with-abstracts huggingface dataset:
Learning Dynamic Hierarchical Models for Anytime Scene Labeling
Authors: Buyu Liu, Xuming He
Date: 2016-08-11 00:00:00
Tasks: Model Selection, Representation Learning, Scene Labeling, Scene Parsing, Semantic Segmentation

With increasing demand for efficient image and video analysis, test-time cost
of scene parsing becomes critical for many large-scale or time-sensitive vision
applications. We propose a dynamic hierarchical model for anytime scene
labeling that allows us to achieve flexible trade-offs between efficiency and
accuracy in pixel-level prediction. In particular, our approach incorporates
the cost of feature computation and model inference, and optimizes the model
performance for any given test-time budget by learning a sequence of
image-adaptive hierarchical models. We formulate this anytime representation
learning as a Markov Decision Process with a discrete-continuous state-action
space. A high-quality policy of feature and model selection is learned based on
an approximate policy iteration method with action proposal mechanism. We
demonstrate the advantages of our dynamic non-myopic anytime scene parsing on
three semantic segmentation datasets, which achieves $90\%$ of the
state-of-the-art performances by using $15\%$ of their overall costs.
Sex, drugs, and violence
Authors: Stefania Raimondo, Frank Rudzicz
Date: 2016-08-11 00:00:00
Tasks: regression

Automatically detecting inappropriate content can be a difficult NLP task,
requiring understanding context and innuendo, not just identifying specific
keywords. Due to the large quantity of online user-generated content, automatic
detection is becoming increasingly necessary. We take a largely unsupervised
approach using a large corpus of narratives from a community-based
self-publishing website and a small segment of crowd-sourced annotations. We
explore topic modelling using latent Dirichlet allocation (and a variation),
and use these to regress appropriateness ratings, effectively automating rating
for suitability. The results suggest that certain topics inferred may be useful
in detecting latent inappropriateness -- yielding recall up to 96% and low
regression errors.
Solving Visual Madlibs with Multiple Cues
Authors: Tatiana Tommasi, Arun Mallya, Bryan Plummer, Svetlana Lazebnik, Alexander C. Berg, Tamara L. Berg
Date: 2016-08-11 00:00:00
Tasks: Activity Prediction, Attribute, Multiple-choice, Question Answering, Scene Classification, Visual Question Answering, Visual Question Answering (VQA)

This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach employs features derived from networks trained for
specialized tasks of scene classification, person activity prediction, and
person and object attribute prediction. We also present a method for selecting
sub-regions of an image that are relevant for evaluating the appropriateness of
a putative answer. Visual features are computed both from the whole image and
from local regions, while sentences are mapped to a common space using a simple
normalized canonical correlation analysis (CCA) model. Our results show a
significant improvement over the previous state of the art, and indicate that
answering different question types benefits from examining a variety of image
cues and carefully choosing informative image sub-regions.
Q($λ$) with Off-Policy Corrections
Authors: Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Remi Munos
Date: 2016-02-16 00:00:00

We propose and analyze an alternate approach to off-policy multi-step
temporal difference learning, in which off-policy returns are corrected with
the current Q-function in terms of rewards, rather than with the target policy
in terms of transition probabilities. We prove that such approximate
corrections are sufficient for off-policy convergence both in policy evaluation
and control, provided certain conditions. These conditions relate the distance
between the target and behavior policies, the eligibility trace parameter and
the discount factor, and formalize an underlying tradeoff in off-policy
TD($\lambda$). We illustrate this theoretical relationship empirically on a
continuous-state control task.
A machine learning method for the large-scale evaluation of urban visual environment
Authors: Lun Liu, Hui Wang, Chunyang Wu
Date: 2016-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction

Given the size of modern cities in the urbanising age, it is beyond the
perceptual capacity of most people to develop a good knowledge about the beauty
and ugliness of the city at every street corner. Correspondingly, for planners,
it is also difficult to accurately answer questions like 'where are the
worst-looking places in the city that regeneration should give first
consideration', or 'in the fast urbanising cities, how is the city appearance
changing', etc. To address this issue, we here present a computer vision method
for the large-scale and automatic evaluation of the urban visual environment,
by leveraging state-of-the-art machine learning techniques and the
wide-coverage street view images. From the various factors that are at work, we
choose two key features, the visual quality of street facade and the continuity
of street wall, as the starting point of this line of analysis. In order to
test the validity of this method, we further compare the machine ratings with
ratings collected on site from 752 passers-by on fifty-six locations. We show
that the machine learning model can produce a good estimation of people's real
visual experience, and it holds much potential for various tasks in terms of
urban design evaluation, culture identification, etc.
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
Authors: Justin Fu, Sergey Levine, Pieter Abbeel
Date: 2015-09-23 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, One-Shot Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the key challenges in applying reinforcement learning to complex
robotic control tasks is the need to gather large amounts of experience in
order to find an effective policy for the task at hand. Model-based
reinforcement learning can achieve good sample efficiency, but requires the
ability to learn a model of the dynamics that is good enough to learn an
effective policy. In this work, we develop a model-based reinforcement learning
algorithm that combines prior knowledge from previous tasks with online
adaptation of the dynamics model. These two ingredients enable highly
sample-efficient learning even in regimes where estimating the true dynamics is
very difficult, since the online model adaptation allows the method to locally
compensate for unmodeled variation in the dynamics. We encode the prior
experience into a neural network dynamics model, adapt it online by
progressively refitting a local linear model of the dynamics, and use model
predictive control to plan under these dynamics. Our experimental results show
that this approach can be used to solve a variety of complex robotic
manipulation tasks in just a single attempt, using prior data from other
manipulation behaviors.
Möbius Invariants of Shapes and Images
Authors: Stephen Marsland, Robert McLachlan
Date: 2016-03-30 00:00:00
Tasks: Object

Identifying when different images are of the same object despite changes
caused by imaging technologies, or processes such as growth, has many
applications in fields such as computer vision and biological image analysis.
One approach to this problem is to identify the group of possible
transformations of the object and to find invariants to the action of that
group, meaning that the object has the same values of the invariants despite
the action of the group. In this paper we study the invariants of planar shapes
and images under the M\"obius group $\mathrm{PSL}(2,\mathbb{C})$, which arises
in the conformal camera model of vision and may also correspond to neurological
aspects of vision, such as grouping of lines and circles. We survey properties
of invariants that are important in applications, and the known M\"obius
invariants, and then develop an algorithm by which shapes can be recognised
that is M\"obius- and reparametrization-invariant, numerically stable, and
robust to noise. We demonstrate the efficacy of this new invariant approach on
sets of curves, and then develop a M\"obius-invariant signature of grey-scale
images.
Automatic text extraction and character segmentation using maximally stable extremal regions
Authors: Nitigya Sambyal, Pawanesh Abrol
Date: 2016-08-11 00:00:00
Tasks: Segmentation, Text Detection

Text detection and segmentation is an important prerequisite for many content
based image analysis tasks. The paper proposes a novel text extraction and
character segmentation algorithm using Maximally Stable Extremal Regions as
basic letter candidates. These regions are then subjected to thresholding and
thereafter various connected components are determined to identify separate
characters. The algorithm is tested along a set of various JPEG, PNG and BMP
images over four different character sets; English, Russian, Hindi and Urdu.
The algorithm gives good results for English and Russian character set; however
character segmentation in Urdu and Hindi language is not much accurate. The
algorithm is simple, efficient, involves no overhead as required in training
and gives good results for even low quality images. The paper also proposes
various challenges in text extraction and segmentation for multilingual inputs.
SPECFACE - A Dataset of Human Faces Wearing Spectacles
Authors: Anirban Dasgupta, Shubhobrata Bhattacharya, Aurobinda Routray
Date: 2015-09-16 00:00:00
Tasks: Face Detection

This paper presents a database of human faces for persons wearing spectacles.
The database consists of images of faces having significant variations with
respect to illumination, head pose, skin color, facial expressions and sizes,
and nature of spectacles. The database contains data of 60 subjects. This
database is expected to be a precious resource for the development and
evaluation of algorithms for face detection, eye detection, head tracking, eye
gaze tracking, etc., for subjects wearing spectacles. As such, this can be a
valuable contribution to the computer vision community.
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
Authors: Ravi Kiran Sarvadevabhatla, Jogendra Kundu, Babu R. Venkatesh
Date: 2016-08-11 00:00:00
Tasks: Object, Object Recognition, Sketch Recognition

Freehand sketching is an inherently sequential process. Yet, most approaches
for hand-drawn sketch recognition either ignore this sequential aspect or
exploit it in an ad-hoc manner. In our work, we propose a recurrent neural
network architecture for sketch object recognition which exploits the long-term
sequential and structural regularities in stroke data in a scalable manner.
Specifically, we introduce a Gated Recurrent Unit based framework which
leverages deep sketch features and weighted per-timestep loss to achieve
state-of-the-art results on a large database of freehand object sketches across
a large number of object categories. The inherently online nature of our
framework is especially suited for on-the-fly recognition of objects as they
are being drawn. Thus, our framework can enable interesting applications such
as camera-equipped robots playing the popular party game Pictionary with human
players and generating sparsified yet recognizable sketches of objects.
NESTA, The NICTA Energy System Test Case Archive
Authors: Carleton Coffrin, Dan Gordon, Paul Scott
Date: 2014-11-03 00:00:00

In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.
Multi-source Hierarchical Prediction Consolidation
Authors: Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, Philip S. Yu
Date: 2016-08-11 00:00:00
Tasks: Prediction

In big data applications such as healthcare data mining, due to privacy
concerns, it is necessary to collect predictions from multiple information
sources for the same instance, with raw features being discarded or withheld
when aggregating multiple predictions. Besides, crowd-sourced labels need to be
aggregated to estimate the ground truth of the data. Because of the imperfect
predictive models or human crowdsourcing workers, noisy and conflicting
information is ubiquitous and inevitable. Although state-of-the-art aggregation
methods have been proposed to handle label spaces with flat structures, as the
label space is becoming more and more complicated, aggregation under a label
hierarchical structure becomes necessary but has been largely ignored. These
label hierarchies can be quite informative as they are usually created by
domain experts to make sense of highly complex label correlations for many
real-world cases like protein functionality interactions or disease
relationships.
  We propose a novel multi-source hierarchical prediction consolidation method
to effectively exploits the complicated hierarchical label structures to
resolve the noisy and conflicting information that inherently originates from
multiple imperfect sources. We formulate the problem as an optimization problem
with a closed-form solution. The proposed method captures the smoothness
overall information sources as well as penalizing any consolidation result that
violates the constraints derived from the label hierarchy. The hierarchical
instance similarity, as well as the consolidation result, are inferred in a
totally unsupervised, iterative fashion. Experimental results on both synthetic
and real-world datasets show the effectiveness of the proposed method over
existing alternatives.
Behavior Discovery and Alignment of Articulated Object Classes from Unstructured Video
Authors: Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari
Date: 2015-11-30 00:00:00
Tasks: Retrieval

We propose an automatic system for organizing the content of a collection of
unstructured videos of an articulated object class (e.g. tiger, horse). By
exploiting the recurring motion patterns of the class across videos, our
system: 1) identifies its characteristic behaviors; and 2) recovers
pixel-to-pixel alignments across different instances. Our system can be useful
for organizing video collections for indexing and retrieval. Moreover, it can
be a platform for learning the appearance or behaviors of object classes from
Internet video. Traditional supervised techniques cannot exploit this wealth of
data directly, as they require a large amount of time-consuming manual
annotations.
  The behavior discovery stage generates temporal video intervals, each
automatically trimmed to one instance of the discovered behavior, clustered by
type. It relies on our novel motion representation for articulated motion based
on the displacement of ordered pairs of trajectories (PoTs). The alignment
stage aligns hundreds of instances of the class to a great accuracy despite
considerable appearance variations (e.g. an adult tiger and a cub). It uses a
flexible Thin Plate Spline deformation model that can vary through time. We
carefully evaluate each step of our system on a new, fully annotated dataset.
On behavior discovery, we outperform the state-of-the-art Improved DTF
descriptor. On spatial alignment, we outperform the popular SIFT Flow
algorithm.
Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
Authors: Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen
Date: 2016-06-10 00:00:00
Tasks: Relation, Relation Extraction

We propose a framework to improve performance of distantly-supervised
relation extraction, by jointly learning to solve two related tasks:
concept-instance extraction and relation extraction. We combine this with a
novel use of document structure: in some small, well-structured corpora,
sections can be identified that correspond to relation arguments, and
distantly-labeled examples from such sections tend to have good precision.
Using these as seeds we extract additional relation examples by applying label
propagation on a graph composed of noisy examples extracted from a large
unstructured testing corpus. Combined with the soft constraint that concept
examples should have the same type as the second argument of the relation, we
get significant improvements over several state-of-the-art approaches to
distantly-supervised relation extraction.
Temporal Learning and Sequence Modeling for a Job Recommender System
Authors: Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan
Date: 2016-08-11 00:00:00
Tasks: Recommendation Systems

We present our solution to the job recommendation task for RecSys Challenge
2016. The main contribution of our work is to combine temporal learning with
sequence modeling to capture complex user-item activity patterns to improve job
recommendations. First, we propose a time-based ranking model applied to
historical observations and a hybrid matrix factorization over time re-weighted
interactions. Second, we exploit sequence properties in user-items activities
and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$
place in the challenge among more than 100 participants. Notably, the strong
performance of our RNN approach shows a promising new direction in employing
sequence modeling for recommendation systems.
Automatic Dialect Detection in Arabic Broadcast Speech
Authors: Ahmed Ali, Najim Dehak, Patrick Cardinal, Sameer Khurana, Sree Harsha Yella, James Glass, Peter Bell, Steve Renals
Date: 2015-09-23 00:00:00
Tasks: Dialect Identification, Language Identification, speech-recognition, Speech Recognition, Spoken language identification

We investigate different approaches for dialect identification in Arabic
broadcast speech, using phonetic, lexical features obtained from a speech
recognition system, and acoustic features using the i-vector framework. We
studied both generative and discriminate classifiers, and we combined these
features using a multi-class Support Vector Machine (SVM). We validated our
results on an Arabic/English language identification task, with an accuracy of
100%. We used these features in a binary classifier to discriminate between
Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We
further report results using the proposed method to discriminate between the
five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine,
North African, and MSA, with an accuracy of 52%. We discuss dialect
identification errors in the context of dialect code-switching between
Dialectal Arabic and MSA, and compare the error pattern between manually
labeled data, and the output from our classifier. We also release the train and
test data as standard corpus for dialect identification.
Approximate search with quantized sparse representations
Authors: Himalaya Jain, Patrick Pérez, Rémi Gribonval, Joaquin Zepeda, Hervé Jégou
Date: 2016-08-10 00:00:00
Tasks: Quantization

This paper tackles the task of storing a large collection of vectors, such as
visual descriptors, and of searching in it. To this end, we propose to
approximate database vectors by constrained sparse coding, where possible atom
weights are restricted to belong to a finite subset. This formulation
encompasses, as particular cases, previous state-of-the-art methods such as
product or residual quantization. As opposed to traditional sparse coding
methods, quantized sparse coding includes memory usage as a design constraint,
thereby allowing us to index a large collection such as the BIGANN
billion-sized benchmark. Our experiments, carried out on standard benchmarks,
show that our formulation leads to competitive solutions when considering
different trade-offs between learning/coding time, index size and search
quality.
Multi-level Contextual RNNs with Attention Model for Scene Labeling
Authors: Heng Fan, Xue Mei, Danil Prokhorov, Haibin Ling
Date: 2016-07-08 00:00:00
Tasks: Scene Labeling

Context in image is crucial for scene labeling while existing methods only
exploit local context generated from a small surrounding area of an image patch
or a pixel, by contrast long-range and global contextual information is
ignored. To handle this issue, we in this work propose a novel approach for
scene labeling by exploring multi-level contextual recurrent neural networks
(ML-CRNNs). Specifically, we encode three kinds of contextual cues, i.e., local
context, global context and image topic context in structural recurrent neural
networks (RNNs) to model long-range local and global dependencies in image. In
this way, our method is able to `see' the image in terms of both long-range
local and holistic views, and make a more reliable inference for image
labeling. Besides, we integrate the proposed contextual RNNs into hierarchical
convolutional neural networks (CNNs), and exploit dependence relationships in
multiple levels to provide rich spatial and semantic information. Moreover, we
novelly adopt an attention model to effectively merge multiple levels and show
that it outperforms average- or max-pooling fusion strategies. Extensive
experiments demonstrate that the proposed approach achieves new
state-of-the-art results on the CamVid, SiftFlow and Stanford-background
datasets.
Deep vs. shallow networks : An approximation theory perspective
Authors: Hrushikesh Mhaskar, Tomaso Poggio
Date: 2016-08-10 00:00:00

The paper briefy reviews several recent results on hierarchical architectures
for learning from examples, that may formally explain the conditions under
which Deep Convolutional Neural Networks perform much better in function
approximation problems than shallow, one-hidden layer architectures. The paper
announces new results for a non-smooth activation function - the ReLU function
- used in present-day neural networks, as well as for the Gaussian networks. We
propose a new definition of relative dimension to encapsulate different notions
of sparsity of a function class that can possibly be exploited by deep networks
but not by shallow ones to drastically reduce the complexity required for
approximation and learning.