pwc-archive/papers-with-abstracts huggingface dataset:
Listen, Interact and Talk: Learning to Speak via Interaction
Authors: Haichao Zhang, Haonan Yu, Wei Xu
Date: 2017-05-28 00:00:00
Tasks: Sentence

One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the statistics of the fixed external training data. As the
training data is essentially a static snapshot representation of the knowledge
from the annotator, the agent trained this way is limited in adaptiveness and
generalization of its behavior. Moreover, this is very different from the
language learning process of humans, where language is acquired during
communication by taking speaking action and learning from the consequences of
speaking action in an interactive manner. This paper presents an interactive
setting for grounded natural language learning, where an agent learns natural
language by interacting with a teacher and learning from feedback, thus
learning and improving language skills while taking part in the conversation.
To achieve this goal, we propose a model which incorporates both imitation and
reinforcement by leveraging jointly sentence and reward feedbacks from the
teacher. Experiments are conducted to validate the effectiveness of the
proposed approach.
Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network
Authors: F. Li, J. Du
Date: 2017-05-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Vision sensors lie in the heart of computer vision. In many computer vision
applications, such as AR/VR, non-contacting near-field communication (NFC) with
high throughput is required to transfer information to algorithms. In this
work, we proposed a novel NFC system which utilizes multiple frequency bands to
achieve high throughput.
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks
Authors: Brandon Victor, Zhen He, Stuart Morgan, Dino Miniutti
Date: 2017-05-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In many sports, it is useful to analyse video of an athlete in competition
for training purposes. In swimming, stroke rate is a common metric used by
coaches; requiring a laborious labelling of each individual stroke. We show
that using a Convolutional Neural Network (CNN) we can automatically detect
discrete events in continuous video (in this case, swimming strokes). We create
a CNN that learns a mapping from a window of frames to a point on a smooth 1D
target signal, with peaks denoting the location of a stroke, evaluated as a
sliding window. To our knowledge this process of training and utilizing a CNN
has not been investigated before; either in sports or fundamental computer
vision research. Most research has been focused on action recognition and using
it to classify many clips in continuous video for action localisation.
  In this paper we demonstrate our process works well on the task of detecting
swimming strokes in the wild. However, without modifying the model architecture
or training method, the process is also shown to work equally well on detecting
tennis strokes, implying that this is a general process.
  The outputs of our system are surprisingly smooth signals that predict an
arbitrary event at least as accurately as humans (manually evaluated from a
sample of negative results). A number of different architectures are evaluated,
pertaining to slightly different problem formulations and signal targets.
Care about you: towards large-scale human-centric visual relationship detection
Authors: Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton Van Den Hengel
Date: 2017-05-28 00:00:00
Tasks: Human-Object Interaction Detection, Relationship Detection, Visual Relationship Detection

Visual relationship detection aims to capture interactions between pairs of
objects in images. Relationships between objects and humans represent a
particularly important subset of this problem, with implications for challenges
such as understanding human behaviour, and identifying affordances, amongst
others. In addressing this problem we first construct a large-scale
human-centric visual relationship detection dataset (HCVRD), which provides
many more types of relationship annotation (nearly 10K categories) than the
previous released datasets.
  This large label space better reflects the reality of human-object
interactions, but gives rise to a long-tail distribution problem, which in turn
demands a zero-shot approach to labels appearing only in the test set. This is
the first time this issue has been addressed. We propose a webly-supervised
approach to these problems and demonstrate that the proposed model provides a
strong baseline on our HCVRD dataset.
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
Authors: Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma, Liang Wang, Tao Xiang, W. Bastiaan Kleijn, Jun Guo
Date: 2017-05-28 00:00:00
Tasks: Cross-Modal Retrieval, Image Retrieval, Image-text matching, Retrieval, Sketch-Based Image Retrieval, Text Matching

Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient, since a common low-level subspace that traverses semantically
across the two modalities is non-trivial to establish. Most existing SBIR
studies do not directly tackle this cross-modal problem. This naturally
motivates us to explore the effectiveness of cross-modal retrieval methods in
SBIR, which have been applied in the image-text matching successfully. In this
paper, we introduce and compare a series of state-of-the-art cross-modal
subspace learning methods and benchmark them on two recently released
fine-grained SBIR datasets. Through thorough examination of the experimental
results, we have demonstrated that the subspace learning can effectively model
the sketch-photo domain-gap. In addition we draw a few key insights to drive
future research.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Authors: Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth
Date: 2017-03-27 00:00:00
Tasks: Fairness

Objectives: Discussions of fairness in criminal justice risk assessments
typically lack conceptual precision. Rhetoric too often substitutes for careful
analysis. In this paper, we seek to clarify the tradeoffs between different
kinds of fairness and between fairness and accuracy.
  Methods: We draw on the existing literatures in criminology, computer science
and statistics to provide an integrated examination of fairness and accuracy in
criminal justice risk assessments. We also provide an empirical illustration
using data from arraignments.
  Results: We show that there are at least six kinds of fairness, some of which
are incompatible with one another and with accuracy.
  Conclusions: Except in trivial cases, it is impossible to maximize accuracy
and fairness at the same time, and impossible simultaneously to satisfy all
kinds of fairness. In practice, a major complication is different base rates
across different legally protected groups. There is a need to consider
challenging tradeoffs.
Approximation Vector Machines for Large-scale Online Learning
Authors: Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung
Date: 2016-04-22 00:00:00
Tasks: General Classification, regression

One of the most challenging problems in kernel online learning is to bound
the model size and to promote the model sparsity. Sparse models not only
improve computation and memory usage, but also enhance the generalization
capacity, a principle that concurs with the law of parsimony. However,
inappropriate sparsity modeling may also significantly degrade the performance.
In this paper, we propose Approximation Vector Machine (AVM), a model that can
simultaneously encourage the sparsity and safeguard its risk in compromising
the performance. When an incoming instance arrives, we approximate this
instance by one of its neighbors whose distance to it is less than a predefined
threshold. Our key intuition is that since the newly seen instance is expressed
by its nearby neighbor the optimal performance can be analytically formulated
and maintained. We develop theoretical foundations to support this intuition
and further establish an analysis to characterize the gap between the
approximation and optimal solutions. This gap crucially depends on the
frequency of approximation and the predefined threshold. We perform the
convergence analysis for a wide spectrum of loss functions including Hinge,
smooth Hinge, and Logistic for classification task, and $l_1$, $l_2$, and
$\epsilon$-insensitive for regression task. We conducted extensive experiments
for classification task in batch and online modes, and regression task in
online mode over several benchmark datasets. The results show that our proposed
AVM achieved a comparable predictive performance with current state-of-the-art
methods while simultaneously achieving significant computational speed-up due
to the ability of the proposed AVM in maintaining the model size.
Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification
Authors: Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen, Stefano Soatto
Date: 2017-05-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Person Re-Identification, Reinforcement Learning

We address the problem of person re-identification from commodity depth
sensors. One challenge for depth-based recognition is data scarcity. Our first
contribution addresses this problem by introducing split-rate RGB-to-Depth
transfer, which leverages large RGB datasets more effectively than popular
fine-tuning approaches. Our transfer scheme is based on the observation that
the model parameters at the bottom layers of a deep convolutional neural
network can be directly shared between RGB and depth data while the remaining
layers need to be fine-tuned rapidly. Our second contribution enhances
re-identification for video by implementing temporal attention as a
Bernoulli-Sigmoid unit acting upon frame-level features. Since this unit is
stochastic, the temporal attention parameters are trained using reinforcement
learning. Extensive experiments validate the accuracy of our method in person
re-identification from depth sequences. Finally, in a scenario where subjects
wear unseen clothes, we show large performance gains compared to a
state-of-the-art model which relies on RGB data.
STRIPS Planning in Infinite Domains
Authors: Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Date: 2017-01-01 00:00:00
Tasks: Motion Planning, Task and Motion Planning

Many robotic planning applications involve continuous actions with highly
non-linear constraints, which cannot be modeled using modern planners that
construct a propositional representation. We introduce STRIPStream: an
extension of the STRIPS language which can model these domains by supporting
the specification of blackbox generators to handle complex constraints. The
outputs of these generators interact with actions through possibly infinite
streams of objects and static predicates. We provide two algorithms which both
reduce STRIPStream problems to a sequence of finite-domain planning problems.
The representation and algorithms are entirely domain independent. We
demonstrate our framework on simple illustrative domains, and then on a
high-dimensional, continuous robotic task and motion planning domain.
Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
Authors: Patrick Rodler, Wolfgang Schmid, Konstantin Schekotihin
Date: 2017-05-28 00:00:00
Tasks: Sequential Diagnosis

In this work we present strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and guaranteeing query properties existing
methods cannot provide. Evaluation results using real-world problems indicate
that the new method computes (virtually) optimal queries instantly
independently of the size and complexity of the considered diagnosis problems.
Targeted Learning with Daily EHR Data
Authors: Oleg Sofrygin, Zheng Zhu, Julie A Schmittdiel, Alyce S. Adams, Richard W. Grant, Mark J. Van Der Laan, Romain Neugebauer
Date: 2017-05-27 00:00:00

Electronic health records (EHR) data provide a cost and time-effective
opportunity to conduct cohort studies of the effects of multiple time-point
interventions in the diverse patient population found in real-world clinical
settings. Because the computational cost of analyzing EHR data at daily (or
more granular) scale can be quite high, a pragmatic approach has been to
partition the follow-up into coarser intervals of pre-specified length. Current
guidelines suggest employing a 'small' interval, but the feasibility and
practical impact of this recommendation has not been evaluated and no formal
methodology to inform this choice has been developed. We start filling these
gaps by leveraging large-scale EHR data from a diabetes study to develop and
illustrate a fast and scalable targeted learning approach that allows to follow
the current recommendation and study its practical impact on inference. More
specifically, we map daily EHR data into four analytic datasets using 90, 30,
15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
treatment rules with each dataset, and compare the resulting inferences. To
overcome the computational challenges presented by the size of these data, we
propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
latest advances in scalable data-adaptive machine-learning software, xgboost
and h2o, for estimation of the TMLE nuisance parameters.
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
Authors: Haojin Yang, Martin Fritzsche, Christian Bartz, Christoph Meinel
Date: 2017-05-27 00:00:00
Tasks: CPU, GPU

Binary Neural Networks (BNNs) can drastically reduce memory size and accesses
by applying bit-wise operations instead of standard arithmetic operations.
Therefore it could significantly improve the efficiency and lower the energy
consumption at runtime, which enables the application of state-of-the-art deep
learning models on low power devices. BMXNet is an open-source BNN library
based on MXNet, which supports both XNOR-Networks and Quantized Neural
Networks. The developed BNN layers can be seamlessly applied with other
standard library components and work in both GPU and CPU mode. BMXNet is
maintained and developed by the multimedia research group at Hasso Plattner
Institute and released under Apache license. Extensive experiments validate the
efficiency and effectiveness of our implementation. The BMXNet library, several
sample projects, and a collection of pre-trained binary deep models are
available for download at https://github.com/hpi-xnor
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Authors: Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
Date: 2017-05-27 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Gaussian Processes, Variational Inference

Often in machine learning, data are collected as a combination of multiple
conditions, e.g., the voice recordings of multiple persons, each labeled with
an ID. How could we build a model that captures the latent information related
to these conditions and generalize to a new one with few data? We present a new
model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) and
that allows to jointly model multiple conditions for regression and generalize
to a new condition with a few data points at test time. LVMOGP infers the
posteriors of Gaussian processes together with a latent space representing the
information about different conditions. We derive an efficient variational
inference method for LVMOGP, of which the computational complexity is as low as
sparse Gaussian processes. We show that LVMOGP significantly outperforms
related Gaussian process methods on various tasks with both synthetic and real
data.
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
Authors: Edgar Sucar, Jean-Bernard Hayet
Date: 2017-05-27 00:00:00
Tasks: Object, object-detection, Object Detection

This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object detection. Each observation is produced on single frames,
so that we do not need a data association process along video frames. This is
because we associate the height priors with the image region sizes at image
places where map features projections fall within the object detection regions.
We present very promising results of this approach obtained on several
experiments with different object classes.
Improved Neural Relation Detection for Knowledge Base Question Answering
Authors: Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cicero dos Santos, Bing Xiang, Bo-Wen Zhou
Date: 2017-04-20 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Linking, Knowledge Base Question Answering, Question Answering, Relation

Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to compare questions and relation names via different
hierarchies of abstraction. Additionally, we propose a simple KBQA system that
integrates entity linking and our proposed relation detector to enable one
enhance another. Experimental results evidence that our approach achieves not
only outstanding relation detection performance, but more importantly, it helps
our KBQA system to achieve state-of-the-art accuracy for both single-relation
(SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.
Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis
Authors: Mark Lewis, Fred Glover
Date: 2017-05-27 00:00:00
Tasks: Combinatorial Optimization, Experimental Design

The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a
unifying model for representing a wide range of combinatorial optimization
problems, and for linking a variety of disciplines that face these problems. A
new class of quantum annealing computer that maps QUBO onto a physical qubit
network structure with specific size and edge density restrictions is
generating a growing interest in ways to transform the underlying QUBO
structure into an equivalent graph having fewer nodes and edges. In this paper
we present rules for reducing the size of the QUBO matrix by identifying
variables whose value at optimality can be predetermined. We verify that the
reductions improve both solution quality and time to solution and, in the case
of metaheuristic methods where optimal solutions cannot be guaranteed, the
quality of solutions obtained within reasonable time limits.
  We discuss the general QUBO structural characteristics that can take
advantage of these reduction techniques and perform careful experimental design
and analysis to identify and quantify the specific characteristics most
affecting reduction. The rules make it possible to dramatically improve
solution times on a new set of problems using both the exact Cplex solver and a
tabu search metaheuristic.
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Authors: Seyed Sajad Mousavi, Michael Schukat, Enda Howley
Date: 2017-04-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process.
Incorporating Prior Information in Compressive Online Robust Principal Component Analysis
Authors: Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup
Date: 2017-01-24 00:00:00

We consider an online version of the robust Principle Component Analysis
(PCA), which arises naturally in time-varying source separations such as video
foreground-background separation. This paper proposes a compressive online
robust PCA with prior information for recursively separating a sequences of
frames into sparse and low-rank components from a small set of measurements. In
contrast to conventional batch-based PCA, which processes all the frames
directly, the proposed method processes measurements taken from each frame.
Moreover, this method can efficiently incorporate multiple prior information,
namely previous reconstructed frames, to improve the separation and thereafter,
update the prior information for the next frame. We utilize multiple prior
information by solving $n\text{-}\ell_{1}$ minimization for incorporating the
previous sparse components and using incremental singular value decomposition
($\mathrm{SVD}$) for exploiting the previous low-rank components. We also
establish theoretical bounds on the number of measurements required to
guarantee successful separation under assumptions of static or slowly-changing
low-rank components. Using numerical experiments, we evaluate our bounds and
the performance of the proposed algorithm. In addition, we apply the proposed
algorithm to online video foreground and background separation from compressive
measurements. Experimental results show that the proposed method outperforms
the existing methods.