pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning Attention Selection for Person Re-Identification
Authors: Xu Lan, Hanxiao Wang, Shaogang Gong, Xiatian Zhu
Date: 2017-07-10 00:00:00
Tasks: Deep Reinforcement Learning, Person Re-Identification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Existing person re-identification (re-id) methods assume the provision of
accurately cropped person bounding boxes with minimum background noise, mostly
by manually cropping. This is significantly breached in practice when person
bounding boxes must be detected automatically given a very large number of
images and/or videos processed. Compared to carefully cropped manually,
auto-detected bounding boxes are far less accurate with random amount of
background clutter which can degrade notably person re-id matching accuracy. In
this work, we develop a joint learning deep model that optimises person re-id
attention selection within any auto-detected person bounding boxes by
reinforcement learning of background clutter minimisation subject to re-id
label pairwise constraints. Specifically, we formulate a novel unified re-id
architecture called Identity DiscriminativE Attention reinforcement Learning
(IDEAL) to accurately select re-id attention in auto-detected bounding boxes
for optimising re-id performance. Our model can improve re-id accuracy
comparable to that from exhaustive human manual cropping of bounding boxes with
additional advantages from identity discriminative attention selection that
specially benefits re-id tasks beyond human knowledge. Extensive comparative
evaluations demonstrate the re-id advantages of the proposed IDEAL model over a
wide range of state-of-the-art re-id methods on two auto-detected re-id
benchmarks CUHK03 and Market-1501.
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis
Authors: Matthäus Kleindessner, Ulrike Von Luxburg
Date: 2016-02-23 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

In recent years it has become popular to study machine learning problems in a
setting of ordinal distance information rather than numerical distance
measurements. By ordinal distance information we refer to binary answers to
distance comparisons such as $d(A,B)<d(C,D)$. For many problems in machine
learning and statistics it is unclear how to solve them in such a scenario. Up
to now, the main approach is to explicitly construct an ordinal embedding of
the data points in the Euclidean space, an approach that has a number of
drawbacks. In this paper, we propose algorithms for the problems of medoid
estimation, outlier identification, classification, and clustering when given
only ordinal data. They are based on estimating the lens depth function and the
$k$-relative neighborhood graph on a data set. Our algorithms are simple, are
much faster than an ordinal embedding approach and avoid some of its drawbacks,
and can easily be parallelized.
Analysing Errors of Open Information Extraction Systems
Authors: Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, Alexander Löser
Date: 2017-07-24 00:00:00
Proceeding: WS 2017 9
Tasks: Benchmarking, Open Information Extraction

We report results on benchmarking Open Information Extraction (OIE) systems
using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Our comprehensive benchmark contains three data sets from the news domain and
one data set from Wikipedia with overall 4522 labeled sentences and 11243
binary or n-ary OIE relations. In our analysis on these data sets we compared
the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford
OpenIE and PredPatt. In addition, we evaluated the impact of five common error
classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal
important research directions for a next generation of OIE systems.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
Authors: Rolf Jagerman, Julia Kiseleva, Maarten de Rijke
Date: 2017-07-24 00:00:00
Tasks: Learning-To-Rank

List-wise learning to rank methods are considered to be the state-of-the-art.
One of the major problems with these methods is that the ambiguous nature of
relevance labels in learning to rank data is ignored. Ambiguity of relevance
labels refers to the phenomenon that multiple documents may be assigned the
same relevance label for a given query, so that no preference order should be
learned for those documents. In this paper we propose a novel sampling
technique for computing a list-wise loss that can take into account this
ambiguity. We show the effectiveness of the proposed method by training a
3-layer deep neural network. We compare our new loss function to two strong
baselines: ListNet and ListMLE. We show that our method generalizes better and
significantly outperforms other methods on the validation and test sets.
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network
Authors: Vedran Vukotić, Silvia-Laura Pintea, Christian Raymond, Guillaume Gravier, Jan van Gemert
Date: 2017-02-14 00:00:00
Tasks: Decoder, Optical Flow Estimation

There is an inherent need for autonomous cars, drones, and other robots to
have a notion of how their environment behaves and to anticipate changes in the
near future. In this work, we focus on anticipating future appearance given the
current frame of a video. Existing work focuses on either predicting the future
appearance as the next frame of a video, or predicting future motion as optical
flow or motion trajectories starting from a single video frame. This work
stretches the ability of CNNs (Convolutional Neural Networks) to predict an
anticipation of appearance at an arbitrarily given future time, not necessarily
the next video frame. We condition our predicted future appearance on a
continuous time variable that allows us to anticipate future frames at a given
temporal distance, directly from the input video frame. We show that CNNs can
learn an intrinsic representation of typical appearance changes over time and
successfully generate realistic predictions at a deliberate time difference in
the near future.
Character-level Intra Attention Network for Natural Language Inference
Authors: Han Yang, Marta R. Costa-jussà, José A. R. Fonollosa
Date: 2017-07-24 00:00:00
Proceeding: WS 2017 9
Tasks: Natural Language Inference, Sentence

Natural language inference (NLI) is a central problem in language
understanding. End-to-end artificial neural networks have reached
state-of-the-art performance in NLI field recently.
  In this paper, we propose Character-level Intra Attention Network (CIAN) for
the NLI task. In our model, we use the character-level convolutional network to
replace the standard word embedding layer, and we use the intra attention to
capture the intra-sentence semantics. The proposed CIAN model provides improved
results based on a newly published MNLI corpus.
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Authors: Le Thi Khanh Hien, Cuong V. Nguyen, Huan Xu, Can-Yi Lu, Jiashi Feng
Date: 2016-05-23 00:00:00

We consider the problem of minimizing the sum of an average function of a
large number of smooth convex components and a general, possibly
non-differentiable, convex function. Although many methods have been proposed
to solve this problem with the assumption that the sum is strongly convex, few
methods support the non-strongly convex case. Adding a small quadratic
regularization is a common devise used to tackle non-strongly convex problems;
however, it may cause loss of sparsity of solutions or weaken the performance
of the algorithms. Avoiding this devise, we propose an accelerated randomized
mirror descent method for solving this problem without the strongly convex
assumption. Our method extends the deterministic accelerated proximal gradient
methods of Paul Tseng and can be applied even when proximal points are computed
inexactly. We also propose a scheme for solving the problem when the component
functions are non-smooth.
Asynchronous Temporal Fields for Action Recognition
Authors: Gunnar A. Sigurdsson, Santosh Divvala, Ali Farhadi, Abhinav Gupta
Date: 2016-12-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Classification, Action Recognition, Temporal Action Localization, Temporal Localization, Variational Inference

Actions are more than just movements and trajectories: we cook to eat and we
hold a cup to drink from it. A thorough understanding of videos requires going
beyond appearance modeling and necessitates reasoning about the sequence of
activities, as well as the higher-level constructs such as intentions. But how
do we model and reason about these? We propose a fully-connected temporal CRF
model for reasoning over various aspects of activities that includes objects,
actions, and intentions, where the potentials are predicted by a deep network.
End-to-end training of such structured models is a challenging endeavor: For
inference and learning we need to construct mini-batches consisting of whole
videos, leading to mini-batches with only a few videos. This causes
high-correlation between data points leading to breakdown of the backprop
algorithm. To address this challenge, we present an asynchronous variational
inference method that allows efficient end-to-end training. Our method achieves
a classification mAP of 22.4% on the Charades benchmark, outperforming the
state-of-the-art (17.2% mAP), and offers equal gains on the task of temporal
localization.
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret
Authors: A. Ömer Sarıtaç, Cem Tekin
Date: 2017-07-24 00:00:00
Tasks: Movie Recommendation, Thompson Sampling

In this paper, we study the combinatorial multi-armed bandit problem (CMAB)
with probabilistically triggered arms (PTAs). Under the assumption that the arm
triggering probabilities (ATPs) are positive for all arms, we prove that a
class of upper confidence bound (UCB) policies, named Combinatorial UCB with
exploration rate $\kappa$ (CUCB-$\kappa$), and Combinatorial Thompson Sampling
(CTS), which estimates the expected states of the arms via Thompson sampling,
achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur
$O(\sqrt{T})$ gap-independent regret. These results improve the results in
previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$
gap-independent regrets, respectively, under no assumptions on the ATPs. Then,
we numerically evaluate the performance of CUCB-$\kappa$ and CTS in a
real-world movie recommendation problem, where the actions correspond to
recommending a set of movies, the arms correspond to the edges between the
movies and the users, and the goal is to maximize the total number of users
that are attracted by at least one movie. Our numerical results complement our
theoretical findings on bounded regret. Apart from this problem, our results
also directly apply to the online influence maximization (OIM) problem studied
in numerous prior works.
Delineation of line patterns in images using B-COSFIRE filters
Authors: Nicola Strisciuglio, Nicolai Petkov
Date: 2017-07-24 00:00:00
Tasks: Representation Learning, Vessel Detection

Delineation of line patterns in images is a basic step required in various
applications such as blood vessel detection in medical images, segmentation of
rivers or roads in aerial images, detection of cracks in walls or pavements,
etc. In this paper we present trainable B-COSFIRE filters, which are a model of
some neurons in area V1 of the primary visual cortex, and apply it to the
delineation of line patterns in different kinds of images. B-COSFIRE filters
are trainable as their selectivity is determined in an automatic configuration
process given a prototype pattern of interest. They are configurable to detect
any preferred line structure (e.g. segments, corners, cross-overs, etc.), so
usable for automatic data representation learning. We carried out experiments
on two data sets, namely a line-network data set from INRIA and a data set of
retinal fundus images named IOSTAR. The results that we achieved confirm the
robustness of the proposed approach and its effectiveness in the delineation of
line structures in different kinds of images.
Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation
Authors: Shitao Tang, Yichen Pan
Date: 2017-07-24 00:00:00
Tasks: Diversity

This paper presents a novel ensemble framework to extract highly
discriminative feature representation of image and its application for
group-level happpiness intensity prediction in wild. In order to generate
enough diversity of decisions, n convolutional neural networks are trained by
bootstrapping the training set and extract n features for each image from them.
A recurrent neural network (RNN) is then used to remember which network
extracts better feature and generate the final feature representation for one
individual image. Several group emotion models (GEM) are used to aggregate face
fea- tures in a group and use parameter-optimized support vector regressor
(SVR) to get the final results. Through extensive experiments, the great
effectiveness of the proposed recurrent random deep ensembles (RRDE) is
demonstrated in both structural and decisional ways. The best result yields a
0.55 root-mean-square error (RMSE) on validation set of HAPPEI dataset,
significantly better than the baseline of 0.78.
Personalized Video Recommendation Using Rich Contents from Videos
Authors: Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou
Date: 2016-12-21 00:00:00
Tasks: Recommendation Systems

Video recommendation has become an essential way of helping people explore
the massive videos and discover the ones that may be of interest to them. In
the existing video recommender systems, the models make the recommendations
based on the user-video interactions and single specific content features. When
the specific content features are unavailable, the performance of the existing
models will seriously deteriorate. Inspired by the fact that rich contents
(e.g., text, audio, motion, and so on) exist in videos, in this paper, we
explore how to use these rich contents to overcome the limitations caused by
the unavailability of the specific ones. Specifically, we propose a novel
general framework that incorporates arbitrary single content feature with
user-video interactions, named as collaborative embedding regression (CER)
model, to make effective video recommendation in both in-matrix and
out-of-matrix scenarios. Our extensive experiments on two real-world
large-scale datasets show that CER beats the existing recommender models with
any single content feature and is more time efficient. In addition, we propose
a priority-based late fusion (PRI) method to gain the benefit brought by the
integrating the multiple content features. The corresponding experiment shows
that PRI brings real performance improvement to the baseline and outperforms
the existing fusion methods.
A Deep Multimodal Approach for Cold-start Music Recommendation
Authors: Sergio Oramas, Oriol Nieto, Mohamed Sordo, Xavier Serra
Date: 2017-06-29 00:00:00
Tasks: Music Recommendation

An increasing amount of digital music is being published daily. Music
streaming services often ingest all available music, but this poses a
challenge: how to recommend new artists for which prior knowledge is scarce? In
this work we aim to address this so-called cold-start problem by combining text
and audio information with user feedback data using deep network architectures.
Our method is divided into three steps. First, artist embeddings are learned
from biographies by combining semantics, text features, and aggregated usage
data. Second, track embeddings are learned from the audio signal and available
feedback data. Finally, artist and track embeddings are combined in a
multimodal network. Results suggest that both splitting the recommendation
problem between feature levels (i.e., artist metadata and audio track), and
merging feature embeddings in a multimodal approach improve the accuracy of the
recommendations.
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Authors: Bruno Stuner, Clément Chatelain, Thierry Paquet
Date: 2017-07-24 00:00:00
Tasks: Handwriting Recognition, Language Modeling, Language Modelling

Offline handwritten text line recognition is a hard task that requires both
an efficient optical character recognizer and language model. Handwriting
recognition state of the art methods are based on Long Short Term Memory (LSTM)
recurrent neural networks (RNN) coupled with the use of linguistic knowledge.
Most of the proposed approaches in the literature focus on improving one of the
two components and use constraint, dedicated to a database lexicon. However,
state of the art performance is achieved by combining multiple optical models,
and possibly multiple language models with the Recognizer Output Voting Error
Reduction (ROVER) framework. Though handwritten line recognition with ROVER has
been implemented by combining only few recognizers because training multiple
complete recognizers is hard. In this paper we propose a Lexicon Verified
ROVER: LV-ROVER, that has a reduce complexity compare to the original one and
that can combine hundreds of recognizers without language models. We achieve
state of the art for handwritten line text on the RIMES dataset.
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
Authors: Jang-Won Lee, Michael S. Ryoo
Date: 2017-03-03 00:00:00
Tasks: object-detection, Object Detection, regression

We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression network, and learn to transfer such model for its own motor
execution. We present a new deep learning model: We extend the state-of-the-art
convolutional object detection network for the representation/estimation of
human hands in training videos, and newly introduce the concept of using a
fully convolutional network to regress (i.e., predict) the intermediate scene
representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and
objects, which enables the robot to infer the motor control plan using our
manipulation network. We experimentally confirm that our approach makes
learning of robot activities from unlabeled human interaction videos possible,
and demonstrate that our robot is able to execute the learned collaborative
activities in real-time directly based on its camera input.
Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
Authors: Norman Hiob, Stefan Lessmann
Date: 2017-07-24 00:00:00
Tasks: BIG-bench Machine Learning

The paper presents a systematic review of state-of-the-art approaches to
identify patient cohorts using electronic health records. It gives a
comprehensive overview of the most commonly de-tected phenotypes and its
underlying data sets. Special attention is given to preprocessing of in-put
data and the different modeling approaches. The literature review confirms
natural language processing to be a promising approach for electronic
phenotyping. However, accessibility and lack of natural language process
standards for medical texts remain a challenge. Future research should develop
such standards and further investigate which machine learning approaches are
best suited to which type of medical data.
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
Authors: Vaclav Papez, Spiros Denaxas, Harry Hemingway
Date: 2017-07-24 00:00:00
Tasks: Descriptive, Translation

Electronic Health Records are electronic data generated during or as a
byproduct of routine patient care. Structured, semi-structured and unstructured
EHR offer researchers unprecedented phenotypic breadth and depth and have the
potential to accelerate the development of precision medicine approaches at
scale. A main EHR use-case is defining phenotyping algorithms that identify
disease status, onset and severity. Phenotyping algorithms utilize diagnoses,
prescriptions, laboratory tests, symptoms and other elements in order to
identify patients with or without a specific trait. No common standardized,
structured, computable format exists for storing phenotyping algorithms. The
majority of algorithms are stored as human-readable descriptive text documents
making their translation to code challenging due to their inherent complexity
and hinders their sharing and re-use across the community. In this paper, we
evaluate the two key Semantic Web Technologies, the Web Ontology Language and
the Resource Description Framework, for enabling computable representations of
EHR-driven phenotyping algorithms.
Generative OpenMax for Multi-Class Open Set Classification
Authors: ZongYuan Ge, Sergey Demyanov, Zetao Chen, Rahil Garnavi
Date: 2017-07-24 00:00:00
Tasks: Classification, General Classification, Image Generation, open-set classification

We present a conceptually new and flexible method for multi-class open set
classification. Unlike previous methods where unknown classes are inferred with
respect to the feature or decision distance to the known classes, our approach
is able to provide explicit modelling and decision score for unknown classes.
The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax
by employing generative adversarial networks (GANs) for novel category image
synthesis. We validate the proposed method on two datasets of handwritten
digits and characters, resulting in superior results over previous deep
learning based method OpenMax Moreover, G-OpenMax provides a way to visualize
samples representing the unknown classes from open space. Our simple and
effective approach could serve as a new direction to tackle the challenging
multi-class open set classification problem.
Exploring Neural Transducers for End-to-End Speech Recognition
Authors: Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur, Yi Li, Hairong Liu, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu
Date: 2017-07-24 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this work, we perform an empirical comparison among the CTC,
RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech
recognition. We show that, without any language model, Seq2Seq and
RNN-Transducer models both outperform the best reported CTC models with a
language model, on the popular Hub5'00 benchmark. On our internal diverse
dataset, these trends continue - RNNTransducer models rescored with a language
model after beam search outperform our best CTC models. These results simplify
the speech recognition pipeline so that decoding can now be expressed purely as
neural network operations. We also study how the choice of encoder architecture
affects the performance of the three models - when all encoder layers are
forward only, and when encoders downsample the input representation
aggressively.