pwc-archive/papers-with-abstracts huggingface dataset:
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL
Authors: Marco Loog, Jesse H. Krijthe, Are C. Jensen
Date: 2017-07-13 00:00:00
Tasks: Active Learning, Domain Adaptation, General Classification

In various approaches to learning, notably in domain adaptation, active
learning, learning under covariate shift, semi-supervised learning, learning
with concept drift, and the like, one often wants to compare a baseline
classifier to one or more advanced (or at least different) strategies. In this
chapter, we basically argue that if such classifiers, in their respective
training phases, optimize a so-called surrogate loss that it may also be
valuable to compare the behavior of this loss on the test set, next to the
regular classification error rate. It can provide us with an additional view on
the classifiers' relative performances that error rates cannot capture. As an
example, limited but convincing empirical results demonstrates that we may be
able to find semi-supervised learning strategies that can guarantee performance
improvements with increasing numbers of unlabeled data in terms of
log-likelihood. In contrast, the latter may be impossible to guarantee for the
classification error rate.
Query-Aware Sparse Coding for Multi-Video Summarization
Authors: Zhong Ji, Yaru Ma, Yanwei Pang, Xuelong. Li
Date: 2017-07-13 00:00:00
Tasks: Video Summarization

Given the explosive growth of online videos, it is becoming increasingly
important to relieve the tedious work of browsing and managing the video
content of interest. Video summarization aims at providing such a technique by
transforming one or multiple videos into a compact one. However, conventional
multi-video summarization methods often fail to produce satisfying results as
they ignore the user's search intent. To this end, this paper proposes a novel
query-aware approach by formulating the multi-video summarization in a sparse
coding framework, where the web images searched by the query are taken as the
important preference information to reveal the query intent. To provide a
user-friendly summarization, this paper also develops an event-keyframe
presentation structure to present keyframes in groups of specific events
related to the query by using an unsupervised multi-graph fusion method. We
release a new public dataset named MVS1K, which contains about 1, 000 videos
from 10 queries and their video tags, manual annotations, and associated web
images. Extensive experiments on MVS1K dataset validate our approaches produce
superior objective and subjective results against several recently proposed
approaches.
Dependency Injection for Programming by Optimization
Authors: Zoltan A. Kocsis, Jerry Swan
Date: 2017-07-13 00:00:00

Programming by Optimization tools perform automatic software configuration
according to the specification supplied by a software developer. Developers
specify design spaces for program components, and the onerous task of
determining which configuration best suits a given use case is determined using
automated analysis tools and optimization heuristics. However, in current
approaches to Programming by Optimization, design space specification and
exploration relies on external configuration algorithms, executable wrappers
and fragile, preprocessed programming language extensions.
  Here we show that the architectural pattern of Dependency Injection provides
a superior alternative to the traditional Programming by Optimization pipeline.
We demonstrate that configuration tools based on Dependency Injection fit
naturally into the software development process, while requiring less overhead
than current wrapper-based mechanisms. Furthermore, the structural
correspondence between Dependency Injection and context-free grammars yields a
new class of evolutionary metaheuristics for automated algorithm configuration.
We found that the new heuristics significantly outperform existing
configuration algorithms on many problems of interest (in one case by two
orders of magnitude). We anticipate that these developments will make
Programming by Optimization immediately applicable to a large number of
enterprise software projects.
A Web-Based Tool for Analysing Normative Documents in English
Authors: John J. Camilleri, Mohammad Reza Haghshenas, Gerardo Schneider
Date: 2017-07-13 00:00:00

Our goal is to use formal methods to analyse normative documents written in
English, such as privacy policies and service-level agreements. This requires
the combination of a number of different elements, including information
extraction from natural language, formal languages for model representation,
and an interface for property specification and verification. We have worked on
a collection of components for this task: a natural language extraction tool, a
suitable formalism for representing such documents, an interface for building
models in this formalism, and methods for answering queries asked of a given
model. In this work, each of these concerns is brought together in a web-based
tool, providing a single interface for analysing normative texts in English.
Through the use of a running example, we describe each component and
demonstrate the workflow established by our tool.
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
Authors: Weixin Yang, Terry Lyons, Hao Ni, Cordelia Schmid, Lianwen Jin
Date: 2017-07-13 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Temporal Action Localization

Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynamics. To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events. We do not extract signature features from the raw path, rather we propose path disintegrations and path transformations as preprocessing steps. Path disintegrations turn a high-dimensional path linearly into a collection of lower-dimensional paths; some of these paths are in pose space while others are defined over a multiscale collection of temporal intervals. Path transformations decorate the paths with additional coordinates in standard ways to allow the truncated signatures of transformed paths to expose additional features. For spatial representation, we apply the signature transform to vectorize the paths that arise out of pose disintegration, and for temporal representation, we apply it again to describe this evolving vectorization. Finally, all the features are collected together to constitute the input vector of a linear single-hidden-layer fully-connected network for classification. Experimental results on four datasets demonstrated that the proposed feature set with only a linear shallow network and Dropconnect is effective and achieves comparable state-of-the-art results to the advanced deep networks, and meanwhile, is capable of interpretation.
Merge or Not? Learning to Group Faces via Imitation Learning
Authors: Yue He, Kaidi Cao, Cheng Li, Chen Change Loy
Date: 2017-07-13 00:00:00
Tasks: Clustering, Imitation Learning, Reinforcement Learning

Given a large number of unlabeled face images, face grouping aims at
clustering the images into individual identities present in the data. This task
remains a challenging problem despite the remarkable capability of deep
learning approaches in learning face representation. In particular, grouping
results can still be egregious given profile faces and a large number of
uninteresting faces and noisy detections. Often, a user needs to correct the
erroneous grouping manually. In this study, we formulate a novel face grouping
framework that learns clustering strategy from ground-truth simulated behavior.
This is achieved through imitation learning (a.k.a apprenticeship learning or
learning by watching) via inverse reinforcement learning (IRL). In contrast to
existing clustering approaches that group instances by similarity, our
framework makes sequential decision to dynamically decide when to merge two
face instances/groups driven by short- and long-term rewards. Extensive
experiments on three benchmark datasets show that our framework outperforms
unsupervised and supervised baselines.
Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks
Authors: Hui Li, Peng Wang, Chunhua Shen
Date: 2017-07-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Cropping, Text Detection, Text Spotting

In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feature re-calculation, word separation, or character
grouping. In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end, requiring only images, the ground-truth bounding boxes and
text labels. Through end-to-end training, the learned features can be more
informative, which improves the overall performance. The convolutional features
are calculated only once and shared by both detection and recognition, which
saves processing time. Our proposed method has achieved competitive performance
on several benchmark datasets.
The YouTube-8M Kaggle Competition: Challenges and Methods
Authors: Haosheng Zou, Kun Xu, Jialian Li, Jun Zhu
Date: 2017-06-28 00:00:00
Tasks: General Classification, Video Classification, Video Understanding

We took part in the YouTube-8M Video Understanding Challenge hosted on
Kaggle, and achieved the 10th place within less than one month's time. In this
paper, we present an extensive analysis and solution to the underlying
machine-learning problem based on frame-level data, where major challenges are
identified and corresponding preliminary methods are proposed. It's noteworthy
that, with merely the proposed strategies and uniformly-averaging multi-crop
ensemble was it sufficient for us to reach our ranking. We also report the
methods we believe to be promising but didn't have enough time to train to
convergence. We hope this paper could serve, to some extent, as a review and
guideline of the YouTube-8M multi-label video classification benchmark,
inspiring future attempts and research.
Learning Photography Aesthetics with Deep CNNs
Authors: Gautam Malu, Raju S. Bapi, Bipin Indurkhya
Date: 2017-07-13 00:00:00
Tasks: Diversity

Automatic photo aesthetic assessment is a challenging artificial intelligence
task. Existing computational approaches have focused on modeling a single
aesthetic score or a class (good or bad), however these do not provide any
details on why the photograph is good or bad, or which attributes contribute to
the quality of the photograph. To obtain both accuracy and human interpretation
of the score, we advocate learning the aesthetic attributes along with the
prediction of the overall score. For this purpose, we propose a novel multitask
deep convolution neural network, which jointly learns eight aesthetic
attributes along with the overall aesthetic score. We report near human
performance in the prediction of the overall aesthetic score. To understand the
internal representation of these attributes in the learned model, we also
develop the visualization technique using back propagation of gradients. These
visualizations highlight the important image regions for the corresponding
attributes, thus providing insights about model's representation of these
attributes. We showcase the diversity and complexity associated with different
attributes through a qualitative analysis of the activation maps.
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
Authors: Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar
Date: 2016-12-23 00:00:00
Tasks: counterfactual, Counterfactual Inference

This paper proposes a novel approach for constructing effective personalized
policies when the observed data lacks counter-factual information, is biased
and possesses many features. The approach is applicable in a wide variety of
settings from healthcare to advertising to education to finance. These settings
have in common that the decision maker can observe, for each previous instance,
an array of features of the instance, the action taken in that instance, and
the reward realized -- but not the rewards of actions that were not taken: the
counterfactual information. Learning in such settings is made even more
difficult because the observed data is typically biased by the existing policy
(that generated the data) and because the array of features that might affect
the reward in a particular instance -- and hence should be taken into account
in deciding on an action in each particular instance -- is often vast. The
approach presented here estimates propensity scores for the observed data,
infers counterfactuals, identifies a (relatively small) number of features that
are (most) relevant for each possible action and instance, and prescribes a
policy to be followed. Comparison of the proposed algorithm against the
state-of-art algorithm on actual datasets demonstrates that the proposed
algorithm achieves a significant improvement in performance.
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Authors: Marc Pickett, Ayush Sekhari, James Davidson
Date: 2017-07-13 00:00:00

Domain knowledge can often be encoded in the structure of a network, such as
convolutional layers for vision, which has been shown to increase
generalization and decrease sample complexity, or the number of samples
required for successful learning. In this study, we ask whether sample
complexity can be reduced for systems where the structure of the domain is
unknown beforehand, and the structure and parameters must both be learned from
the data. We show that sample complexity reduction through learning structure
is possible for at least two simple cases. In studying these cases, we also
gain insight into how this might be done for more complex domains.
Prediction and Control with Temporal Segment Models
Authors: Nikhil Mishra, Pieter Abbeel, Igor Mordatch
Date: 2017-03-12 00:00:00
Proceeding: ICML 2017 8
Tasks: Prediction

We introduce a method for learning the dynamics of complex nonlinear systems
based on deep generative models over temporal segments of states and actions.
Unlike dynamics models that operate over individual discrete timesteps, we
learn the distribution over future state trajectories conditioned on past
state, past action, and planned future action trajectories, as well as a latent
prior over action trajectories. Our approach is based on convolutional
autoregressive models and variational autoencoders. It makes stable and
accurate predictions over long horizons for complex, stochastic systems,
effectively expressing uncertainty and modeling the effects of collisions,
sensory noise, and action delays. The learned dynamics model and action prior
can be used for end-to-end, fully differentiable trajectory optimization and
model-based policy optimization, which we use to evaluate the performance and
sample-efficiency of our method.
Predicting Surgery Duration with Neural Heteroscedastic Regression
Authors: Nathan Ng, Rodney A Gabriel, Julian McAuley, Charles Elkan, Zachary C. Lipton
Date: 2017-02-17 00:00:00
Tasks: regression, Scheduling

Scheduling surgeries is a challenging task due to the fundamental uncertainty
of the clinical environment, as well as the risks and costs associated with
under- and over-booking. We investigate neural regression algorithms to
estimate the parameters of surgery case durations, focusing on the issue of
heteroscedasticity. We seek to simultaneously estimate the duration of each
surgery, as well as a surgery-specific notion of our uncertainty about its
duration. Estimating this uncertainty can lead to more nuanced and effective
scheduling strategies, as we are able to schedule surgeries more efficiently
while allowing an informed and case-specific margin of error. Using surgery
records %from the UC San Diego Health System, from a large United States health
system we demonstrate potential improvements on the order of 20% (in terms of
minutes overbooked) compared to current scheduling techniques. Moreover, we
demonstrate that surgery durations are indeed heteroscedastic. We show that
models that estimate case-specific uncertainty better fit the data (log
likelihood). Additionally, we show that the heteroscedastic predictions can
more optimally trade off between over and under-booking minutes, especially
when idle minutes and scheduling collisions confer disparate costs.
Predicting Causes of Reformulation in Intelligent Assistants
Authors: Shumpei Sano, Nobuhiro Kaji, Manabu Sassano
Date: 2017-07-13 00:00:00
Proceeding: WS 2017 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Natural Language Understanding, speech-recognition, Speech Recognition, Text Generation

Intelligent assistants (IAs) such as Siri and Cortana conversationally
interact with users and execute a wide range of actions (e.g., searching the
Web, setting alarms, and chatting). IAs can support these actions through the
combination of various components such as automatic speech recognition, natural
language understanding, and language generation. However, the complexity of
these components hinders developers from determining which component causes an
error. To remove this hindrance, we focus on reformulation, which is a useful
signal of user dissatisfaction, and propose a method to predict the
reformulation causes. We evaluate the method using the user logs of a
commercial IA. The experimental results have demonstrated that features
designed to detect the error of a specific component improve the performance of
reformulation cause detection.
Complexity of Manipulation with Partial Information in Voting
Authors: Palash Dey, Neeldhara Misra, Y. Narahari
Date: 2016-04-15 00:00:00

The Coalitional Manipulation problem has been studied extensively in the
literature for many voting rules. However, most studies have focused on the
complete information setting, wherein the manipulators know the votes of the
non-manipulators. While this assumption is reasonable for purposes of showing
intractability, it is unrealistic for algorithmic considerations. In most
real-world scenarios, it is impractical for the manipulators to have accurate
knowledge of all the other votes. In this paper, we investigate manipulation
with incomplete information. In our framework, the manipulators know a partial
order for each voter that is consistent with the true preference of that voter.
In this setting, we formulate three natural computational notions of
manipulation, namely weak, opportunistic, and strong manipulation. We say that
an extension of a partial order is if there exists a manipulative vote for that
extension.
  1. Weak Manipulation (WM): the manipulators seek to vote in a way that makes
their preferred candidate win in at least one extension of the partial votes of
the non-manipulators.
  2. Opportunistic Manipulation (OM): the manipulators seek to vote in a way
that makes their preferred candidate win in every viable extension of the
partial votes of the non-manipulators.
  3. Strong Manipulation (SM): the manipulators seek to vote in a way that
makes their preferred candidate win in every extension of the partial votes of
the non-manipulators.
  We consider several scenarios for which the traditional manipulation problems
are easy (for instance, Borda with a single manipulator). For many of them, the
corresponding manipulative questions that we propose turn out to be
computationally intractable. Our hardness results often hold even when very
little information is missing, or in other words, even when the instances are
quite close to the complete information setting.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Authors: Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, Zhi-Hua Zhou
Date: 2016-04-18 00:00:00
Tasks: Image Retrieval, Object Proposal Generation, Retrieval

Deep convolutional neural network models pre-trained for the ImageNet
classification task have been successfully adopted to tasks in other domains,
such as texture description and object proposal generation, but these tasks
require annotations for images in the new domain. In this paper, we focus on a
novel and challenging task in the pure unsupervised setting: fine-grained image
retrieval. Even with image labels, fine-grained images are difficult to
classify, let alone the unsupervised retrieval task. We propose the Selective
Convolutional Descriptor Aggregation (SCDA) method. SCDA firstly localizes the
main object in fine-grained images, a step that discards the noisy background
and keeps useful deep descriptors. The selected descriptors are then aggregated
and dimensionality reduced into a short feature vector using the best practices
we found. SCDA is unsupervised, using no image label or bounding box
annotation. Experiments on six fine-grained datasets confirm the effectiveness
of SCDA for fine-grained image retrieval. Besides, visualization of the SCDA
features shows that they correspond to visual attributes (even subtle ones),
which might explain SCDA's high mean average precision in fine-grained
retrieval. Moreover, on general image retrieval datasets, SCDA achieves
comparable retrieval results with state-of-the-art general image retrieval
approaches.
Oriented Response Networks
Authors: Yanzhao Zhou, Qixiang Ye, Qiang Qiu, Jianbin Jiao
Date: 2017-01-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, Image Classification

Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.