pwc-archive/papers-with-abstracts huggingface dataset:
Improving Person Re-identification by Attribute and Identity Learning
Authors: Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhilan Hu, Chenggang Yan, Yi Yang
Date: 2017-03-21 00:00:00
Tasks: Attribute, Person Recognition, Person Re-Identification, Retrieval

Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.
FML-based Prediction Agent and Its Application to Game of Go
Authors: Chang-Shing Lee, Mei-Hui Wang, Chia-Hsiu Kao, Sheng-Chi Yang, Yusuke Nojima, Ryosuke Saga, Nan Shuo, Naoyuki Kubota
Date: 2017-04-16 00:00:00
Tasks: Game of Go, Prediction

In this paper, we present a robotic prediction agent including a darkforest
Go engine, a fuzzy markup language (FML) assessment engine, an FML-based
decision support engine, and a robot engine for game of Go application. The
knowledge base and rule base of FML assessment engine are constructed by
referring the information from the darkforest Go engine located in NUTN and
OPU, for example, the number of MCTS simulations and winning rate prediction.
The proposed robotic prediction agent first retrieves the database of Go
competition website, and then the FML assessment engine infers the winning
possibility based on the information generated by darkforest Go engine. The
FML-based decision support engine computes the winning possibility based on the
partial game situation inferred by FML assessment engine. Finally, the robot
engine combines with the human-friendly robot partner PALRO, produced by
Fujisoft incorporated, to report the game situation to human Go players.
Experimental results show that the FML-based prediction agent can work
effectively.
Trigger for the SoLid Reactor Antineutrino Experiment
Authors: Lukas On Arnold
Date: 2017-04-16 00:00:00

SoLid, located at SCK-CEN in Mol, Belgium, is a reactor antineutrino
experiment at a very short baseline of 5.5 -- 10m aiming at the search for
sterile neutrinos and for high precision measurement of the neutrino energy
spectrum of Uranium-235. It uses a novel approach using Lithium-6 sheets and
PVT cubes as scintillators for tagging the Inverse Beta-Decay products (neutron
and positron). Being located overground and close to the BR2 research reactor,
the experiment faces a large amount of backgrounds. Efficient real-time
background and noise rejection is essential in order to increase the
signal-background ratio for precise oscillation measurement and decrease data
production to a rate which can be handled by the online software. Therefore, a
reliable distinction between the neutrons and background signals is crucial.
This can be performed online with a dedicated firmware trigger. A peak counting
algorithm and an algorithm measuring time over threshold have been identified
as performing well both in terms of efficiency and fake rate, and have been
implemented onto FPGA.
Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions
Authors: Amir Mazaheri, Dong Zhang, Mubarak Shah
Date: 2017-04-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Sentence

Given a video and a description sentence with one missing word (we call it
the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the
missing word automatically. The contextual information of the sentence, as well
as visual cues from the video, are important to infer the missing word
accurately. Since the source sentence is broken into two fragments: the
sentence's left fragment (before the blank) and the sentence's right fragment
(after the blank), traditional Recurrent Neural Networks cannot encode this
structure accurately because of many possible variations of the missing word in
terms of the location and type of the word in the source sentence. For example,
a missing word can be the first word or be in the middle of the sentence and it
can be a verb or an adjective. In this paper, we propose a framework to tackle
the textual encoding: Two separate LSTMs (the LR and RL LSTMs) are employed to
encode the left and right sentence fragments and a novel structure is
introduced to combine each fragment with an "external memory" corresponding the
opposite fragments. For the visual encoding, end-to-end spatial and temporal
attention models are employed to select discriminative visual representations
to find the missing word. In the experiments, we demonstrate the superior
performance of the proposed method on challenging VFIB problem. Furthermore, we
introduce an extended and more generalized version of VFIB, which is not
limited to a single blank. Our experiments indicate the generalization
capability of our method in dealing with such more realistic scenarios.
Machine Learning and the Future of Realism
Authors: Giles Hooker, Cliff Hooker
Date: 2017-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Medical Diagnosis

The preceding three decades have seen the emergence, rise, and proliferation
of machine learning (ML). From half-recognised beginnings in perceptrons,
neural nets, and decision trees, algorithms that extract correlations (that is,
patterns) from a set of data points have broken free from their origin in
computational cognition to embrace all forms of problem solving, from voice
recognition to medical diagnosis to automated scientific research and
driverless cars, and it is now widely opined that the real industrial
revolution lies less in mobile phone and similar than in the maturation and
universal application of ML. Among the consequences just might be the triumph
of anti-realism over realism.
Generic LSH Families for the Angular Distance Based on Johnson-Lindenstrauss Projections and Feature Hashing LSH
Authors: Luis Argerich, Natalia Golmar
Date: 2017-04-15 00:00:00
Tasks: valid

In this paper we propose the creation of generic LSH families for the angular
distance based on Johnson-Lindenstrauss projections. We show that feature
hashing is a valid J-L projection and propose two new LSH families based on
feature hashing. These new LSH families are tested on both synthetic and real
datasets with very good results and a considerable performance improvement over
other LSH families. While the theoretical analysis is done for the angular
distance, these families can also be used in practice for the euclidean
distance with excellent results [2]. Our tests using real datasets show that
the proposed LSH functions work well for the euclidean distance.
#HashtagWars: Learning a Sense of Humor
Authors: Peter Potash, Alexey Romanov, Anna Rumshisky
Date: 2016-12-09 00:00:00
Tasks: Humor Detection

In this work, we present a new dataset for computational humor, specifically
comparative humor ranking, which attempts to eschew the ubiquitous binary
approach to humor detection. The dataset consists of tweets that are humorous
responses to a given hashtag. We describe the motivation for this new dataset,
as well as the collection process, which includes a description of our
semi-automated system for data collection. We also present initial experiments
for this dataset using both unsupervised and supervised approaches. Our best
supervised system achieved 63.7% accuracy, suggesting that this task is much
more difficult than comparable humor detection tasks. Initial experiments
indicate that a character-level model is more suitable for this task than a
token-level model, likely due to a large amount of puns that can be captured by
a character-level model.
Temporal Action Localization by Structured Maximal Sums
Authors: Zehuan Yuan, Jonathan C. Stroud, Tong Lu, Jia Deng
Date: 2017-04-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Detection, Action Localization, General Classification, Structured Prediction, Temporal Action Localization

We address the problem of temporal action localization in videos. We pose
action localization as a structured prediction over arbitrary-length temporal
windows, where each window is scored as the sum of frame-wise classification
scores. Additionally, our model classifies the start, middle, and end of each
action as separate components, allowing our system to explicitly model each
action's temporal evolution and take advantage of informative temporal
dependencies present in this structure. In this framework, we localize actions
by searching for the structured maximal sum, a problem for which we develop a
novel, provably-efficient algorithmic solution. The frame-wise classification
scores are computed using features from a deep Convolutional Neural Network
(CNN), which are trained end-to-end to directly optimize for a novel structured
objective. We evaluate our system on the THUMOS 14 action detection benchmark
and achieve competitive performance.
On Improving the Capacity of Solving Large-scale Wireless Network Design Problems by Genetic Algorithms
Authors: Fabio D'Andreagiovanni
Date: 2017-04-15 00:00:00

Over the last decade, wireless networks have experienced an impressive growth
and now play a main role in many telecommunications systems. As a consequence,
scarce radio resources, such as frequencies, became congested and the need for
effective and efficient assignment methods arose. In this work, we present a
Genetic Algorithm for solving large instances of the Power, Frequency and
Modulation Assignment Problem, arising in the design of wireless networks. To
our best knowledge, this is the first Genetic Algorithm that is proposed for
such problem. Compared to previous works, our approach allows a wider
exploration of the set of power solutions, while eliminating sources of
numerical problems. The performance of the algorithm is assessed by tests over
a set of large realistic instances of a Fixed WiMAX Network.
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Authors: Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc Bellemare, Remi Munos
Date: 2017-04-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Distributional Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we present a new agent architecture, called Reactor, which
combines multiple algorithmic and architectural contributions to produce an
agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al.,
2016) and Categorical DQN (Bellemare et al., 2017), while giving better
run-time performance than A3C (Mnih et al., 2016). Our first contribution is a
new policy evaluation algorithm called Distributional Retrace, which brings
multi-step off-policy updates to the distributional reinforcement learning
setting. The same approach can be used to convert several classes of multi-step
policy evaluation algorithms designed for expected value evaluation into
distributional ones. Next, we introduce the \b{eta}-leave-one-out policy
gradient algorithm which improves the trade-off between variance and bias by
using action values as a baseline. Our final algorithmic contribution is a new
prioritized replay algorithm for sequences, which exploits the temporal
locality of neighboring observations for more efficient replay prioritization.
Using the Atari 2600 benchmarks, we show that each of these innovations
contribute to both the sample efficiency and final agent performance. Finally,
we demonstrate that Reactor reaches state-of-the-art performance after 200
million frames and less than a day of training.
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Authors: Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel
Date: 2017-04-15 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning

Astrophysics and cosmology are rich with data. The advent of wide-area
digital cameras on large aperture telescopes has led to ever more ambitious
surveys of the sky. Data volumes of entire surveys a decade ago can now be
acquired in a single night and real-time analysis is often desired. Thus,
modern astronomy requires big data know-how, in particular it demands highly
efficient machine learning and image analysis algorithms. But scalability is
not the only challenge: Astronomy applications touch several current machine
learning research questions, such as learning from biased data and dealing with
label and measurement noise. We argue that this makes astronomy a great domain
for computer science research, as it pushes the boundaries of data analysis. In
the following, we will present this exciting application area for data
scientists. We will focus on exemplary results, discuss main challenges, and
highlight some recent methodological advancements in machine learning and image
analysis triggered by astronomical applications.
A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks
Authors: Fabio D'Andreagiovanni, Antonella Nardin, Enrico Natalizio
Date: 2017-04-15 00:00:00
Tasks: Robust Design

We consider the problem of optimally designing a body wireless sensor
network, while taking into account the uncertainty of data generation of
biosensors. Since the related min-max robustness Integer Linear Programming
(ILP) problem can be difficult to solve even for state-of-the-art commercial
optimization solvers, we propose an original heuristic for its solution. The
heuristic combines deterministic and probabilistic variable fixing strategies,
guided by the information coming from strengthened linear relaxations of the
ILP robust model, and includes a very large neighborhood search for reparation
and improvement of generated solutions, formulated as an ILP problem solved
exactly. Computational tests on realistic instances show that our heuristic
finds solutions of much higher quality than a state-of-the-art solver and than
an effective benchmark heuristic.
Temporal Attention-Gated Model for Robust Sequence Classification
Authors: Wenjie Pei, Tadas Baltrušaitis, David M. J. Tax, Louis-Philippe Morency
Date: 2016-12-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, model, Sentiment Analysis

Typical techniques for sequence classification are designed for
well-segmented sequences which have been edited to remove noisy or irrelevant
parts. Therefore, such methods cannot be easily applied on noisy sequences
expected in real-world applications. In this paper, we present the Temporal
Attention-Gated Model (TAGM) which integrates ideas from attention models and
gated recurrent networks to better deal with noisy or unsegmented sequences.
Specifically, we extend the concept of attention model to measure the relevance
of each observation (time step) of a sequence. We then use a novel gated
recurrent network to learn the hidden representation for the final prediction.
An important advantage of our approach is interpretability since the temporal
attention weights provide a meaningful value for the salience of each time step
in the sequence. We demonstrate the merits of our TAGM approach, both for
prediction accuracy and interpretability, on three different tasks: spoken
digit recognition, text-based sentiment analysis and visual event recognition.
Metropolis Sampling
Authors: Luca Martino, Victor Elvira
Date: 2017-04-15 00:00:00
Tasks: Bayesian Inference

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference,
system simulation and optimization problems. The Markov Chain Monte Carlo
(MCMC) algorithms are a well-known class of MC methods which generate a Markov
chain with the desired invariant distribution. In this document, we focus on
the Metropolis-Hastings (MH) sampler, which can be considered as the atom of
the MCMC techniques, introducing the basic notions and different properties. We
describe in details all the elements involved in the MH algorithm and the most
relevant variants. Several improvements and recent extensions proposed in the
literature are also briefly discussed, providing a quick but exhaustive
overview of the current Metropolis-based sampling's world.
A learning-based approach for automatic image and video colorization
Authors: Raj Kumar Gupta, Alex Yong-Sang Chia, Deepu Rajan, Huang Zhiyong
Date: 2017-04-15 00:00:00
Tasks: Colorization, Superpixels

In this paper, we present a color transfer algorithm to colorize a broad
range of gray images without any user intervention. The algorithm uses a
machine learning-based approach to automatically colorize grayscale images. The
algorithm uses the superpixel representation of the reference color images to
learn the relationship between different image features and their corresponding
color values. We use this learned information to predict the color value of
each grayscale image superpixel. As compared to processing individual image
pixels, our use of superpixels helps us to achieve a much higher degree of
spatial consistency as well as speeds up the colorization process. The
predicted color values of the gray-scale image superpixels are used to provide
a 'micro-scribble' at the centroid of the superpixels. These color scribbles
are refined by using a voting based approach. To generate the final
colorization result, we use an optimization-based approach to smoothly spread
the color scribble across all pixels within a superpixel. Experimental results
on a broad range of images and the comparison with existing state-of-the-art
colorization methods demonstrate the greater effectiveness of the proposed
algorithm.
MUSE: Modularizing Unsupervised Sense Embeddings
Authors: Guang-He Lee, Yun-Nung Chen
Date: 2017-04-15 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

This paper proposes to address the word sense ambiguity issue in an
unsupervised manner, where word sense representations are learned along a word
sense selection mechanism given contexts. Prior work focused on designing a
single model to deliver both mechanisms, and thus suffered from either
coarse-grained representation learning or inefficient sense selection. The
proposed modular approach, MUSE, implements flexible modules to optimize
distinct mechanisms, achieving the first purely sense-level representation
learning system with linear-time sense selection. We leverage reinforcement
learning to enable joint training on the proposed modules, and introduce
various exploration techniques on sense selection for better robustness. The
experiments on benchmark data show that the proposed approach achieves the
state-of-the-art performance on synonym selection as well as on contextual word
similarities in terms of MaxSimC.
Incremental Skip-gram Model with Negative Sampling
Authors: Nobuhiro Kaji, Hayato Kobayashi
Date: 2017-04-13 00:00:00
Proceeding: EMNLP 2017 9
Tasks: model, Word Embeddings

This paper explores an incremental training strategy for the skip-gram model
with negative sampling (SGNS) from both empirical and theoretical perspectives.
Existing methods of neural word embeddings, including SGNS, are multi-pass
algorithms and thus cannot perform incremental model update. To address this
problem, we present a simple incremental extension of SGNS and provide a
thorough theoretical analysis to demonstrate its validity. Empirical
experiments demonstrated the correctness of the theoretical analysis as well as
the practical usefulness of the incremental algorithm.
Finding Tiny Faces
Authors: Peiyun Hu, Deva Ramanan
Date: 2016-12-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Detection, Object Recognition

Though tremendous strides have been made in object recognition, one of the
remaining open challenges is detecting small objects. We explore three aspects
of the problem in the context of finding small faces: the role of scale
invariance, image resolution, and contextual reasoning. While most recognition
approaches aim to be scale-invariant, the cues for recognizing a 3px tall face
are fundamentally different than those for recognizing a 300px tall face. We
take a different approach and train separate detectors for different scales. To
maintain efficiency, detectors are trained in a multi-task fashion: they make
use of features extracted from multiple layers of single (deep) feature
hierarchy. While training detectors for large objects is straightforward, the
crucial challenge remains training detectors for small objects. We show that
context is crucial, and define templates that make use of massively-large
receptive fields (where 99% of the template extends beyond the object of
interest). Finally, we explore the role of scale in pre-trained deep networks,
providing ways to extrapolate networks tuned for limited scales to rather
extreme ranges. We demonstrate state-of-the-art results on
massively-benchmarked face datasets (FDDB and WIDER FACE). In particular, when
compared to prior art on WIDER FACE, our results reduce error by a factor of 2
(our models produce an AP of 82% while prior art ranges from 29-64%).
Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data
Authors: Yuncheng Li, Liangliang Cao, Jiang Zhu, Jiebo Luo
Date: 2016-08-10 00:00:00

Composing fashion outfits involves deep understanding of fashion standards
while incorporating creativity for choosing multiple fashion items (e.g.,
Jewelry, Bag, Pants, Dress). In fashion websites, popular or high-quality
fashion outfits are usually designed by fashion experts and followed by large
audiences. In this paper, we propose a machine learning system to compose
fashion outfits automatically. The core of the proposed automatic composition
system is to score fashion outfit candidates based on the appearances and
meta-data. We propose to leverage outfit popularity on fashion oriented
websites to supervise the scoring component. The scoring component is a
multi-modal multi-instance deep learning system that evaluates instance
aesthetics and set compatibility simultaneously. In order to train and evaluate
the proposed composition system, we have collected a large scale fashion outfit
dataset with 195K outfits and 368K fashion items from Polyvore. Although the
fashion outfit scoring and composition is rather challenging, we have achieved
an AUC of 85% for the scoring component, and an accuracy of 77% for a
constrained composition task.