pwc-archive/papers-with-abstracts huggingface dataset:
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
Authors: Saumya Kumaar, Abhinandan Dogra, Abrar Majeedi, Hanan Gani, Ravi M. Vishwanath, S. N. Omkar
Date: 2018-09-08 00:00:00
Tasks: Face Identification, Face Recognition

Facial recognition has always been a challeng- ing task for computer vision
scientists and experts. Despite complexities arising due to variations in
camera parameters, illumination and face orientations, significant progress has
been made in the field with deep learning algorithms now competing with
human-level accuracy. But in contrast to the recent advances in face
recognition techniques, Disguised Facial Identification continues to be a
tougher challenge in the field of computer vision. The modern day scenario,
where security is of prime concern, regular face identification techniques do
not perform as required when the faces are disguised, which calls for a
different approach to handle situations where intruders have their faces
masked. Along the same lines, we propose a deep learning architecture for
disguised facial recognition (DFR). The algorithm put forward in this paper
detects 20 facial key-points in the first stage, using a 14-layered
convolutional neural network (CNN). These facial key-points are later utilized
by a support vector machine (SVM) for classifying the disguised faces based on
the euclidean distance ratios and angles between different facial key-points.
This overall architecture imparts a basic intelligence to our system. Our
key-point feature prediction accuracy is 65% while the classification rate is
72.4%. Moreover, the architecture works at 19 FPS, thereby performing in almost
real-time. The efficiency of our approach is also compared with the
state-of-the-art Disguised Facial Identification methods.
Unsupervised Person Re-identification by Deep Learning Tracklet Association
Authors: Minxian Li, Xiatian Zhu, Shaogang Gong
Date: 2018-09-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Benchmarking, Deep Learning, Domain Adaptation, Person Re-Identification, Unsupervised Person Re-Identification

Mostexistingpersonre-identification(re-id)methods relyon supervised model
learning on per-camera-pair manually labelled pairwise training data. This
leads to poor scalability in practical re-id deployment due to the lack of
exhaustive identity labelling of image positive and negative pairs for every
camera pair. In this work, we address this problem by proposing an unsupervised
re-id deep learning approach capable of incrementally discovering and
exploiting the underlying re-id discriminative information from automatically
generated person tracklet data from videos in an end-to-end model optimisation.
We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL)
framework characterised by jointly learning per-camera (within-camera) tracklet
association (labelling) and cross-camera tracklet correlation by maximising the
discovery of most likely tracklet relationships across camera views. Extensive
experiments demonstrate the superiority of the proposed TAUDL model over the
state-of-the-art unsupervised and domain adaptation re- id methods using six
person re-id benchmarking datasets.
Machine Teaching of Active Sequential Learners
Authors: Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
Date: 2018-09-08 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-Armed Bandits, Probabilistic Programming

Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution. However, for sequential learners which actively choose their queries, such as multi-armed bandits and active learners, the teacher can only provide responses to the learner's queries, not design the full data. In this setting, consistent teachers can be sub-optimal for finite horizons. We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses. Furthermore, we address the complementary problem of learning from a teacher that plans: to recognise the teaching intent of the responses, the learner is endowed with a model of the teacher. We test the formulation with multi-armed bandit learners in simulated experiments and a user study. The results show that learning is improved by (i) planning teaching and (ii) the learner having a model of the teacher. The approach gives tools to taking into account strategic (planning) behaviour of users of interactive intelligent systems, such as recommendation engines, by considering them as boundedly optimal teachers.
Online Adaptive Methods, Universality and Acceleration
Authors: Kfir. Y. Levy, Alp Yurtsever, Volkan Cevher
Date: 2018-09-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Stochastic Optimization

We present a novel method for convex unconstrained optimization that, without
any modifications, ensures: (i) accelerated convergence rate for smooth
objectives, (ii) standard convergence rate in the general (non-smooth) setting,
and (iii) standard convergence rate in the stochastic optimization setting. To
the best of our knowledge, this is the first method that simultaneously applies
to all of the above settings. At the heart of our method is an adaptive
learning rate rule that employs importance weights, in the spirit of adaptive
online learning algorithms (Duchi et al., 2011; Levy, 2017), combined with an
update that linearly couples two sequences, in the spirit of (Allen-Zhu and
Orecchia, 2017). An empirical examination of our method demonstrates its
applicability to the above mentioned scenarios and corroborates our theoretical
findings.
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Authors: Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli
Date: 2018-09-08 00:00:00

Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and training-time poisoning attacks. We provide a unifying optimization framework for evasion and poisoning attacks, and a formal definition of transferability of such attacks. We highlight two main factors contributing to attack transferability: the intrinsic adversarial vulnerability of the target model, and the complexity of the surrogate model used to optimize the attack. Based on these insights, we define three metrics that impact an attack's transferability. Interestingly, our results derived from theoretical analysis hold for both evasion and poisoning attacks, and are confirmed experimentally using a wide range of linear and non-linear classifiers and datasets.
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
Authors: Lixin Cui, Lu Bai, Zhihong Zhang, Yue Wang, Edwin R. Hancock
Date: 2018-09-08 00:00:00
Tasks: feature selection

Feature selection can efficiently identify the most informative features with
respect to the target feature used in training. However, state-of-the-art
vector-based methods are unable to encapsulate the relationships between
feature samples into the feature selection process, thus leading to significant
information loss. To address this problem, we propose a new graph-based
structurally interacting elastic net method for feature selection.
Specifically, we commence by constructing feature graphs that can incorporate
pairwise relationship between samples. With the feature graphs to hand, we
propose a new information theoretic criterion to measure the joint relevance of
different pairwise feature combinations with respect to the target feature
graph representation. This measure is used to obtain a structural interaction
matrix where the elements represent the proposed information theoretic measure
between feature pairs. We then formulate a new optimization model through the
combination of the structural interaction matrix and an elastic net regression
model for the feature subset selection problem. This allows us to a) preserve
the information of the original vectorial space, b) remedy the information loss
of the original feature space caused by using graph representation, and c)
promote a sparse solution and also encourage correlated features to be
selected. Because the proposed optimization problem is non-convex, we develop
an efficient alternating direction multiplier method (ADMM) to locate the
optimal solutions. Extensive experiments on various datasets demonstrate the
effectiveness of the proposed methods.
iDriveSense: Dynamic Route Planning Involving Roads Quality Information
Authors: Amr S. El-Wakeel, Aboelmagd Noureldin, Hossam S. Hassanein, Nizar Zorba
Date: 2018-09-08 00:00:00
Tasks: Management

Owing to the expeditious growth in the information and communication
technologies, smart cities have raised the expectations in terms of efficient
functioning and management. One key aspect of residents' daily comfort is
assured through affording reliable traffic management and route planning.
Comprehensively, the majority of the present trip planning applications and
service providers are enabling their trip planning recommendations relying on
shortest paths and/or fastest routes. However, such suggestions may discount
drivers' preferences with respect to safe and less disturbing trips. Road
anomalies such as cracks, potholes, and manholes induce risky driving scenarios
and can lead to vehicles damages and costly repairs. Accordingly, in this
paper, we propose a crowdsensing based dynamic route planning system.
Leveraging both the vehicle motion sensors and the inertial sensors within the
smart devices, road surface types and anomalies have been detected and
categorized. In addition, the monitored events are geo-referenced utilizing GPS
receivers on both vehicles and smart devices. Consequently, road segments
assessments are conducted using fuzzy system models based on aspects such as
the number of anomalies and their severity levels in each road segment.
Afterward, another fuzzy model is adopted to recommend the best trip routes
based on the road segments quality in each potential route. Extensive road
experiments are held to build and show the potential of the proposed system.
Learning Sports Camera Selection from Internet Videos
Authors: Jianhui Chen, Keyu Lu, Sijia Tian, James J. Little
Date: 2018-09-08 00:00:00

This work addresses camera selection, the task of predicting which camera
should be "on air" from multiple candidate cameras for soccer broadcast. The
task is challenging because of the scarcity of learning data with all candidate
views. Meanwhile, broadcast videos are freely available on the Internet (e.g.
Youtube). However, these videos only record the selected camera views, omitting
the other candidate views. To overcome this problem, we first introduce a
random survival forest (RSF) method to impute the incomplete data effectively.
Then, we propose a spatial-appearance heatmap to describe foreground objects
(e.g. players and balls) in an image. To evaluate the performance of our
system, we collect the largest-ever dataset for soccer broadcasting camera
selection. It has one main game which has all candidate views and twelve
auxiliary games which only have the broadcast view. Our method significantly
outperforms state-of-the-art methods on this challenging dataset. Further
analysis suggests that the improvement in performance is indeed from the extra
information from auxiliary games.
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
Authors: Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau, Robert Bergevin
Date: 2018-09-08 00:00:00
Tasks: Foreground Segmentation

The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.
Rate-Adaptive Neural Networks for Spatial Multiplexers
Authors: Suhas Lohit, Rajhans Singh, Kuldeep Kulkarni, Pavan Turaga
Date: 2018-09-08 00:00:00
Tasks: Object Tracking, valid

In resource-constrained environments, one can employ spatial multiplexing
cameras to acquire a small number of measurements of a scene, and perform
effective reconstruction or high-level inference using purely data-driven
neural networks. However, once trained, the measurement matrix and the network
are valid only for a single measurement rate (MR) chosen at training time. To
overcome this drawback, we answer the following question: How can we jointly
design the measurement operator and the reconstruction/inference network so
that the system can operate over a \textit{range} of MRs? To this end, we
present a novel training algorithm, for learning
\textbf{\textit{rate-adaptive}} networks. Using standard datasets, we
demonstrate that, when tested over a range of MRs, a rate-adaptive network can
provide high quality reconstruction over a the entire range, resulting in up to
about 15 dB improvement over previous methods, where the network is valid for
only one MR. We demonstrate the effectiveness of our approach for
sample-efficient object tracking where video frames are acquired at dynamically
varying MRs. We also extend this algorithm to learn the measurement operator in
conjunction with image recognition networks. Experiments on MNIST and CIFAR-10
confirm the applicability of our algorithm to different tasks.
Interpreting Neural Networks With Nearest Neighbors
Authors: Eric Wallace, Shi Feng, Jordan Boyd-Graber
Date: 2018-09-08 00:00:00
Proceeding: WS 2018 11
Tasks: Feature Importance, General Classification, text-classification, Text Classification

Local model interpretation methods explain individual predictions by
assigning an importance value to each input feature. This value is often
determined by measuring the change in confidence when a feature is removed.
However, the confidence of neural networks is not a robust measure of model
uncertainty. This issue makes reliably judging the importance of the input
features difficult. We address this by changing the test-time behavior of
neural networks using Deep k-Nearest Neighbors. Without harming text
classification accuracy, this algorithm provides a more robust uncertainty
metric which we use to generate feature importance values. The resulting
interpretations better align with human perception than baseline methods.
Finally, we use our interpretation method to analyze model predictions on
dataset annotation artifacts.
Neural Guided Constraint Logic Programming for Program Synthesis
Authors: Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Date: 2018-09-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Neural Network, Program Synthesis

Synthesizing programs using example input/outputs is a classic problem in
artificial intelligence. We present a method for solving Programming By Example
(PBE) problems by using a neural model to guide the search of a constraint
logic programming system called miniKanren. Crucially, the neural model uses
miniKanren's internal representation as input; miniKanren represents a PBE
problem as recursive constraints imposed by the provided examples. We explore
Recurrent Neural Network and Graph Neural Network models. We contribute a
modified miniKanren, drivable by an external agent, available at
https://github.com/xuexue/neuralkanren. We show that our neural-guided approach
using constraints can synthesize programs faster in many cases, and
importantly, can generalize to larger problems.
Efficient and Robust Parallel DNN Training through Model Parallelism on Multi-GPU Platform
Authors: Chi-Chung Chen, Chia-Lin Yang, Hsiang-Yun Cheng
Date: 2018-09-08 00:00:00
Tasks: GPU

The training process of Deep Neural Network (DNN) is compute-intensive, often taking days to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a widely adopted approach to speed up the process nowadays. Due to the implementation simplicity, data parallelism is currently the most commonly used parallelization method. Nonetheless, data parallelism suffers from excessive inter-GPU communication overhead due to frequent weight synchronization among GPUs. Another approach is pipelined model parallelism, which partitions a DNN model among GPUs, and processes multiple mini-batches concurrently. This approach can significantly reduce inter-GPU communication cost compared to data parallelism. However, pipelined model parallelism faces the weight staleness issue; that is, gradients are computed with stale weights, leading to training instability and accuracy loss. In this paper, we present a pipelined model parallel execution method that enables high GPU utilization while maintaining robust training accuracy via a novel weight prediction technique, SpecTrain. Experimental results show that our proposal achieves up to 8.91x speedup compared to data parallelism on a 4-GPU platform while maintaining comparable model accuracy.
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes
Authors: Linfeng Liu, Li-Ping Liu
Date: 2018-09-08 00:00:00
Tasks: Gaussian Processes, Stochastic Optimization, Variational Inference

Inference for GP models with non-Gaussian noises is computationally expensive
when dealing with large datasets. Many recent inference methods approximate the
posterior distribution with a simpler distribution defined on a small number of
inducing points. The inference is accurate only when data points have strong
correlation with these inducing points. In this paper, we consider the
inference problem in a different direction: GP function values in the posterior
are mostly correlated in short distance. We construct a variational
distribution such that the inference for a data point considers only its
neighborhood. With this construction, the variational lower bound is highly
decomposible, hence we can run stochastic optimization with very small batches.
We then train Graph Convolutional Networks as a reusable model to identify
variational parameters for each data point. Model reuse greatly reduces the
number of parameters and the number of iterations needed in optimization. The
proposed method significantly speeds up the inference and often gets more
accurate results than previous methods.
Context-Free Transductions with Neural Stacks
Authors: Yiding Hao, William Merrill, Dana Angluin, Robert Frank, Noah Amsel, Andrew Benz, Simon Mendelsohn
Date: 2018-09-08 00:00:00
Proceeding: WS 2018 11
Tasks: Language Modelling

This paper analyzes the behavior of stack-augmented recurrent neural network
(RNN) models. Due to the architectural similarity between stack RNNs and
pushdown transducers, we train stack RNN models on a number of tasks, including
string reversal, context-free language modelling, and cumulative XOR
evaluation. Examining the behavior of our networks, we show that
stack-augmented RNNs can discover intuitive stack-based strategies for solving
our tasks. However, stack RNNs are more difficult to train than classical
architectures such as LSTMs. Rather than employ stack-based strategies, more
complex networks often find approximate solutions by using the stack as
unstructured memory.
Extracting and Analyzing Semantic Relatedness between Cities Using News Articles
Authors: Yingjie Hu, Xinyue Ye, Shih-Lung Shaw
Date: 2018-09-08 00:00:00
Tasks: Articles

News articles capture a variety of topics about our society. They reflect not
only the socioeconomic activities that happened in our physical world, but also
some of the cultures, human interests, and public concerns that exist only in
the perceptions of people. Cities are frequently mentioned in news articles,
and two or more cities may co-occur in the same article. Such co-occurrence
often suggests certain relatedness between the mentioned cities, and the
relatedness may be under different topics depending on the contents of the news
articles. We consider the relatedness under different topics as semantic
relatedness. By reading news articles, one can grasp the general semantic
relatedness between cities, yet, given hundreds of thousands of news articles,
it is very difficult, if not impossible, for anyone to manually read them. This
paper proposes a computational framework which can "read" a large number of
news articles and extract the semantic relatedness between cities. This
framework is based on a natural language processing model and employs a machine
learning process to identify the main topics of news articles. We describe the
overall structure of this framework and its individual modules, and then apply
it to an experimental dataset with more than 500,000 news articles covering the
top 100 U.S. cities spanning a 10-year period. We perform exploratory
visualization of the extracted semantic relatedness under different topics and
over multiple years. We also analyze the impact of geographic distance on
semantic relatedness and find varied distance decay effects. The proposed
framework can be used to support large-scale content analysis in city network
research.
Multi-label Classification of User Reactions in Online News
Authors: Zacarias Curi, Alceu de Souza Britto Jr, Emerson Cabrera Paraiso
Date: 2018-09-08 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Opinion Mining

The increase in the number of Internet users and the strong interaction
brought by Web 2.0 made the Opinion Mining an important task in the area of
natural language processing. Although several methods are capable of performing
this task, few use multi-label classification, where there is a group of true
labels for each example. This type of classification is useful for situations
where the opinions are analyzed from the perspective of the reader, this
happens because each person can have different interpretations and opinions on
the same subject. This paper discuss the efficiency of problem transformation
methods combined with different classification algorithms for the task of
multi-label classification of reactions in news texts. To do that, extensive
tests were carried out on two news corpora written in Brazilian Portuguese
annotated with reactions. A new corpus called BFRC-PT is presented. In the
tests performed, the highest number of correct predictions was obtained with
the Classifier Chains method combined with the Random Forest algorithm. When
considering the class distribution, the best results were obtained with the
Binary Relevance method combined with the LSTM and Random Forest algorithms.