pwc-archive/papers-with-abstracts huggingface dataset:
FaceNet: A Unified Embedding for Face Recognition and Clustering
Authors: Florian Schroff, Dmitry Kalenichenko, James Philbin
Date: 2015-03-12 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Disguised Face Verification, Face Identification, Face Recognition, Face Verification, Triplet

Despite significant recent advances in the field of face recognition,
implementing face verification and recognition efficiently at scale presents
serious challenges to current approaches. In this paper we present a system,
called FaceNet, that directly learns a mapping from face images to a compact
Euclidean space where distances directly correspond to a measure of face
similarity. Once this space has been produced, tasks such as face recognition,
verification and clustering can be easily implemented using standard techniques
with FaceNet embeddings as feature vectors.
  Our method uses a deep convolutional network trained to directly optimize the
embedding itself, rather than an intermediate bottleneck layer as in previous
deep learning approaches. To train, we use triplets of roughly aligned matching
/ non-matching face patches generated using a novel online triplet mining
method. The benefit of our approach is much greater representational
efficiency: we achieve state-of-the-art face recognition performance using only
128-bytes per face.
  On the widely used Labeled Faces in the Wild (LFW) dataset, our system
achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves
95.12%. Our system cuts the error rate in comparison to the best published
result by 30% on both datasets.
  We also introduce the concept of harmonic embeddings, and a harmonic triplet
loss, which describe different versions of face embeddings (produced by
different networks) that are compatible to each other and allow for direct
comparison between each other.
Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture
Authors: Ubai Sandouk, Ke Chen
Date: 2015-06-17 00:00:00
Tasks: Descriptive, Information Retrieval, Retrieval, Topic Models

One of the biggest challenges in Multimedia information retrieval and
understanding is to bridge the semantic gap by properly modeling concept
semantics in context. The presence of out of vocabulary (OOV) concepts
exacerbates this difficulty. To address the semantic gap issues, we formulate a
problem on learning contextualized semantics from descriptive terms and propose
a novel Siamese architecture to model the contextualized semantics from
descriptive terms. By means of pattern aggregation and probabilistic topic
models, our Siamese architecture captures contextualized semantics from the
co-occurring descriptive terms via unsupervised learning, which leads to a
concept embedding space of the terms in context. Furthermore, the co-occurring
OOV concepts can be easily represented in the learnt concept embedding space.
The main properties of the concept embedding space are demonstrated via
visualization. Using various settings in semantic priming, we have carried out
a thorough evaluation by comparing our approach to a number of state-of-the-art
methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500
and Million Song Dataset in the music domain as well as Corel5K, LabelMe and
SUNDatabase in the image domain. Experimental results on semantic priming
suggest that our approach outperforms those state-of-the-art methods
considerably in various aspects.
A New Fundamental Evidence of Non-Classical Structure in the Combination of Natural Concepts
Authors: Diederik Aerts, Sandro Sozzo, Tomas Veloz
Date: 2015-05-19 00:00:00
Tasks: Logical Reasoning

We recently performed cognitive experiments on conjunctions and negations of
two concepts with the aim of investigating the combination problem of concepts.
Our experiments confirmed the deviations (conceptual vagueness, underextension,
overextension, etc.) from the rules of classical (fuzzy) logic and probability
theory observed by several scholars in concept theory, while our data were
successfully modeled in a quantum-theoretic framework developed by ourselves.
In this paper, we isolate a new, very stable and systematic pattern of
violation of classicality that occurs in concept combinations. In addition, the
strength and regularity of this non-classical effect leads us to believe that
it occurs at a more fundamental level than the deviations observed up to now.
It is our opinion that we have identified a deep non-classical mechanism
determining not only how concepts are combined but, rather, how they are
formed. We show that this effect can be faithfully modeled in a two-sector Fock
space structure, and that it can be exactly explained by assuming that human
thought is the supersposition of two processes, a 'logical reasoning', guided
by 'logic', and a 'conceptual reasoning' guided by 'emergence', and that the
latter generally prevails over the former. All these findings provide a new
fundamental support to our quantum-theoretic approach to human cognition.
Pragmatic Side Effects
Authors: Jiri Marsik, Maxime Amblard
Date: 2015-06-17 00:00:00

In the quest to give a formal compositional semantics to natural languages,
semanticists have started turning their attention to phenomena that have been
also considered as parts of pragmatics (e.g., discourse anaphora and
presupposition projection). To account for these phenomena, the very kinds of
meanings assigned to words and phrases are often revisited. To be more
specific, in the prevalent paradigm of modeling natural language denotations
using the simply-typed lambda calculus (higher-order logic) this means
revisiting the types of denotations assigned to individual parts of speech.
However, the lambda calculus also serves as a fundamental theory of
computation, and in the study of computation, similar type shifts have been
employed to give a meaning to side effects. Side effects in programming
languages correspond to actions that go beyond the lexical scope of an
expression (a thrown exception might propagate throughout a program, a variable
modified at one point might later be read at an another) or even beyond the
scope of the program itself (a program might interact with the outside world by
e.g., printing documents, making sounds, operating robotic limbs...).
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Authors: Massimiliano Giulioni, Federico Corradi, Vittorio Dante, Paolo del Giudice
Date: 2015-06-17 00:00:00
Tasks: Retrieval

Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recurrent networks. Point attractors are equilibrium
states of the dynamics (up to fluctuations), determined by the synaptic
structure of the network; a `basin' of attraction comprises all initial states
leading to a given attractor upon relaxation, hence making attractor dynamics
suitable to implement robust associative memory. The initial network state is
dictated by the stimulus, and relaxation to the attractor state implements the
retrieval of the corresponding memorized prototypical pattern. In a previous
work we demonstrated that a neuromorphic recurrent network of spiking neurons
and suitably chosen, fixed synapses supports attractor dynamics. Here we focus
on learning: activating on-chip synaptic plasticity and using a theory-driven
strategy for choosing network parameters, we show that autonomous learning,
following repeated presentation of simple visual stimuli, shapes a synaptic
connectivity supporting stimulus-selective attractors. Associative memory
develops on chip as the result of the coupled stimulus-driven neural activity
and ensuing synaptic dynamics, with no artificial separation between learning
and retrieval phases.
Hybrid Algorithm for Multi-Objective Optimization by Greedy Hypervolume Maximization
Authors: Conrado Silva Miranda, Fernando José Von Zuben
Date: 2015-06-17 00:00:00

This paper introduces a high-performance hybrid algorithm, called Hybrid
Hypervolume Maximization Algorithm (H2MA), for multi-objective optimization
that alternates between exploring the decision space and exploiting the already
obtained non-dominated solutions. The proposal is centered on maximizing the
hypervolume indicator, thus converting the multi-objective problem into a
single-objective one. The exploitation employs gradient-based methods, but
considering a single candidate efficient solution at a time, to overcome
limitations associated with population-based approaches and also to allow an
easy control of the number of solutions provided. There is an interchange
between two steps. The first step is a deterministic local exploration, endowed
with an automatic procedure to detect stagnation. When stagnation is detected,
the search is switched to a second step characterized by a stochastic global
exploration using an evolutionary algorithm. Using five ZDT benchmarks with 30
variables, the performance of the new algorithm is compared to state-of-the-art
algorithms for multi-objective optimization, more specifically NSGA-II, SPEA2,
and SMS-EMOA. The solutions found by the H2MA guide to higher hypervolume and
smaller distance to the true Pareto frontier with significantly less function
evaluations, even when the gradient is estimated numerically. Furthermore,
although only continuous decision spaces have been considered here, discrete
decision spaces could also have been treated, replacing gradient-based search
by hill-climbing. Finally, a thorough explanation is provided to support the
expressive gain in performance that was achieved.
Editorial for the First Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics
Authors: Iana Atanassova, Marc Bertin, Philipp Mayr
Date: 2015-06-17 00:00:00

The workshop "Mining Scientific Papers: Computational Linguistics and
Bibliometrics" (CLBib 2015), co-located with the 15th International Society of
Scientometrics and Informetrics Conference (ISSI 2015), brought together
researchers in Bibliometrics and Computational Linguistics in order to study
the ways Bibliometrics can benefit from large-scale text analytics and sense
mining of scientific papers, thus exploring the interdisciplinarity of
Bibliometrics and Natural Language Processing (NLP). The goals of the workshop
were to answer questions like: How can we enhance author network analysis and
Bibliometrics using data obtained by text analytics? What insights can NLP
provide on the structure of scientific writing, on citation networks, and on
in-text citation analysis? This workshop is the first step to foster the
reflection on the interdisciplinarity and the benefits that the two disciplines
Bibliometrics and Natural Language Processing can drive from it.
MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting
Authors: Ze Wang
Date: 2015-06-17 00:00:00
Tasks: Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously
quantifying multiple MR parameters using one temporally resolved MR scan. But
its brute-force dictionary generating and searching (DGS) process causes a huge
disk space demand and computational burden, prohibiting it from a practical
multiple slice high-definition imaging. The purpose of this paper was to
provide a fast and space efficient DGS algorithm for MRF. Based on an empirical
analysis of properties of the distance function of the acquired MRF signal and
the pre-defined MRF dictionary entries, we proposed a parameter separable MRF
DGS method, which breaks the multiplicative computation complexity into an
additive one and enabling a resolution scalable multi-resolution DGS process,
which was dubbed as MRF ZOOM. The evaluation results showed that MRF ZOOM was
hundreds or thousands of times faster than the original brute-force DGS method.
The acceleration was even higher when considering the time difference for
generating the dictionary. Using a high precision quantification, MRF can find
the right parameter values for a 64x64 imaging slice in 117 secs. Our data also
showed that spatial constraints can be used to further speed up MRF ZOOM.
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Authors: Shweta Jain, Sujit Gujar, Satyanath Bhat, Onno Zoeter, Y. Narahari
Date: 2014-06-27 00:00:00

Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an optimal subset of workers so that the outcome obtained from the
selected workers guarantees a target accuracy level. The problem is a
challenging one even in a non strategic setting since the accuracy of
aggregated label depends on unknown qualities. We develop a novel multi-armed
bandit (MAB) mechanism for solving this problem. First, we propose a framework,
Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained
Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound
on the number of time steps the algorithm chooses a sub-optimal set that
depends on the target accuracy level and true qualities. A more challenging
situation arises when the requester not only has to learn the qualities of the
workers but also elicit their true costs. We modify the CCB-NS algorithm to
obtain an adaptive exploration separated algorithm which we call { \em
Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm
produces an ex-post monotone allocation rule and thus can be transformed into
an ex-post incentive compatible and ex-post individually rational mechanism
that learns the qualities of the workers and guarantees a given target accuracy
level in a cost optimal way. We provide a lower bound on the number of times
any algorithm should select a sub-optimal set and we see that the lower bound
matches our upper bound upto a constant factor. We provide insights on the
practical implementation of this framework through an illustrative example and
we show the efficacy of our algorithms through simulations.
MLitB: Machine Learning in the Browser
Authors: Edward Meeds, Remco Hendriks, Said Al Faraby, Magiel Bruntink, Max Welling
Date: 2014-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, GPU, Privacy Preserving

With few exceptions, the field of Machine Learning (ML) research has largely
ignored the browser as a computational engine. Beyond an educational resource
for ML, the browser has vast potential to not only improve the state-of-the-art
in ML research, but also, inexpensively and on a massive scale, to bring
sophisticated ML learning and prediction to the public at large. This paper
introduces MLitB, a prototype ML framework written entirely in JavaScript,
capable of performing large-scale distributed computing with heterogeneous
classes of devices. The development of MLitB has been driven by several
underlying objectives whose aim is to make ML learning and usage ubiquitous (by
using ubiquitous compute devices), cheap and effortlessly distributed, and
collaborative. This is achieved by allowing every internet capable device to
run training algorithms and predictive models with no software installation and
by saving models in universally readable formats. Our prototype library is
capable of training deep neural networks with synchronized, distributed
stochastic gradient descent. MLitB offers several important opportunities for
novel ML research, including: development of distributed learning algorithms,
advancement of web GPU algorithms, novel field and mobile applications, privacy
preserving computing, and green grid-computing. MLitB is available as open
source software.
Why Bother With Syntax?
Authors: Joseph Y. Halpern
Date: 2015-06-17 00:00:00
Tasks: Philosophy

This short note discusses the role of syntax vs. semantics and the interplay
between logic, philosophy, and language in computer science and game theory.
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
Authors: Zhiyun Lu, Avner May, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha
Date: 2014-11-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Model Selection, speech-recognition, Speech Recognition

The computational complexity of kernel methods has often been a major barrier
for applying them to large-scale learning problems. We argue that this barrier
can be effectively overcome. In particular, we develop methods to scale up
kernel models to successfully tackle large-scale learning problems that are so
far only approachable by deep learning architectures. Based on the seminal work
by Rahimi and Recht on approximating kernel functions with features derived
from random projections, we advance the state-of-the-art by proposing methods
that can efficiently train models with hundreds of millions of parameters, and
learn optimal representations from multiple kernels. We conduct extensive
empirical studies on problems from image recognition and automatic speech
recognition, and show that the performance of our kernel models matches that of
well-engineered deep neural nets (DNNs). To the best of our knowledge, this is
the first time that a direct comparison between these two methods on
large-scale problems is reported. Our kernel methods have several appealing
properties: training with convex optimization, cost for training a single model
comparable to DNNs, and significantly reduced total cost due to fewer
hyperparameters to tune for model selection. Our contrastive study between
these two very different but equally competitive models sheds light on
fundamental questions such as how to learn good representations.
Deep Denoising Auto-encoder for Statistical Speech Synthesis
Authors: Zhenzhou Wu, Shinji Takaki, Junichi Yamagishi
Date: 2015-06-17 00:00:00
Tasks: Denoising, Speech Synthesis, text-to-speech, Text to Speech

This paper proposes a deep denoising auto-encoder technique to extract better
acoustic features for speech synthesis. The technique allows us to
automatically extract low-dimensional features from high dimensional spectral
features in a non-linear, data-driven, unsupervised way. We compared the new
stochastic feature extractor with conventional mel-cepstral analysis in
analysis-by-synthesis and text-to-speech experiments. Our results confirm that
the proposed method increases the quality of synthetic speech in both
experiments.
Leveraging Textual Features for Best Answer Prediction in Community-based Question Answering
Authors: George Gkotsis, Maria Liakata, Carlos Pedrinaci, John Domingue
Date: 2015-06-09 00:00:00
Tasks: Question Answering

This paper addresses the problem of determining the best answer in
Community-based Question Answering (CQA) websites by focussing on the content.
In particular, we present a system, ACQUA [http://acqua.kmi.open.ac.uk], that
can be installed onto the majority of browsers as a plugin. The service offers
a seamless and accurate prediction of the answer to be accepted. Previous
research on this topic relies on the exploitation of community feedback on the
answers, which involves rating of either users (e.g., reputation) or answers
(e.g. scores manually assigned to answers). We propose a new technique that
leverages the content/textual features of answers in a novel way. Our approach
delivers better results than related linguistics-based solutions and manages to
match rating-based approaches. More specifically, the gain in performance is
achieved by rendering the values of these features into a discretised form. We
also show how our technique manages to deliver equally good results in
real-time settings, as opposed to having to rely on information not always
readily available, such as user ratings and answer scores. We ran an evaluation
on 21 StackExchange websites covering around 4 million questions and more than
8 million answers. We obtain 84% average precision and 70% recall, which shows
that our technique is robust, effective, and widely applicable.
CFORB: Circular FREAK-ORB Visual Odometry
Authors: Daniel J. Mankowitz, Ehud Rivlin
Date: 2015-06-17 00:00:00
Tasks: Visual Odometry

We present a novel Visual Odometry algorithm entitled Circular FREAK-ORB
(CFORB). This algorithm detects features using the well-known ORB algorithm
[12] and computes feature descriptors using the FREAK algorithm [14]. CFORB is
invariant to both rotation and scale changes, and is suitable for use in
environments with uneven terrain. Two visual geometric constraints have been
utilized in order to remove invalid feature descriptor matches. These
constraints have not previously been utilized in a Visual Odometry algorithm. A
variation to circular matching [16] has also been implemented. This allows
features to be matched between images without having to be dependent upon the
epipolar constraint. This algorithm has been run on the KITTI benchmark dataset
and achieves a competitive average translational error of $3.73 \%$ and average
rotational error of $0.0107 deg/m$. CFORB has also been run in an indoor
environment and achieved an average translational error of $3.70 \%$. After
running CFORB in a highly textured environment with an approximately uniform
feature spread across the images, the algorithm achieves an average
translational error of $2.4 \%$ and an average rotational error of $0.009
deg/m$.
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Authors: Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
Date: 2015-06-16 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Classification, General Classification, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

We propose a novel deep neural network architecture for semi-supervised
semantic segmentation using heterogeneous annotations. Contrary to existing
approaches posing semantic segmentation as a single task of region-based
classification, our algorithm decouples classification and segmentation, and
learns a separate network for each task. In this architecture, labels
associated with an image are identified by classification network, and binary
segmentation is subsequently performed for each identified label in
segmentation network. The decoupled architecture enables us to learn
classification and segmentation networks separately based on the training data
with image-level and pixel-wise class labels, respectively. It facilitates to
reduce search space for segmentation effectively by exploiting class-specific
activation maps obtained from bridging layers. Our algorithm shows outstanding
performance compared to other semi-supervised approaches even with much less
training images with strong annotations in PASCAL VOC dataset.
Novel Super-Resolution Method Based on High Order Nonlocal-Means
Authors: Kang Yong-Rim, Kim Yong-Jin
Date: 2015-03-14 00:00:00
Tasks: Image Reconstruction, Motion Estimation, regression, Super-Resolution, Vocal Bursts Intensity Prediction

Super-resolution without explicit sub-pixel motion estimation is a very
active subject of image reconstruction containing general motion. The Non-Local
Means (NLM) method is a simple image reconstruction method without explicit
motion estimation. In this paper we generalize NLM method to higher orders
using kernel regression can apply to super-resolution reconstruction. The
performance of the generalized method is compared with other methods.