pwc-archive/papers-with-abstracts huggingface dataset:
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Authors: Taejun Kim, Jongpil Lee, Juhan Nam
Date: 2017-10-28 00:00:00
Tasks: General Classification, image-classification, Music Auto-Tagging

Recent work has shown that the end-to-end approach using convolutional neural
network (CNN) is effective in various types of machine learning tasks. For
audio signals, the approach takes raw waveforms as input using an 1-D
convolution layer. In this paper, we improve the 1-D CNN architecture for music
auto-tagging by adopting building blocks from state-of-the-art image
classification models, ResNets and SENets, and adding multi-level feature
aggregation to it. We compare different combinations of the modules in building
CNN architectures. The results show that they achieve significant improvements
over previous state-of-the-art models on the MagnaTagATune dataset and
comparable results on Million Song Dataset. Furthermore, we analyze and
visualize our model to show how the 1-D CNN operates.
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Authors: Aditya Devarakonda, Maxim Naumov, Michael Garland
Date: 2017-12-06 00:00:00
Tasks: Computational Efficiency

Training deep neural networks with Stochastic Gradient Descent, or its
variants, requires careful choice of both learning rate and batch size. While
smaller batch sizes generally converge in fewer training epochs, larger batch
sizes offer more parallelism and hence better computational efficiency. We have
developed a new training approach that, rather than statically choosing a
single batch size for all epochs, adaptively increases the batch size during
the training process. Our method delivers the convergence rate of small batch
sizes while achieving performance similar to large batch sizes. We analyse our
approach using the standard AlexNet, ResNet, and VGG networks operating on the
popular CIFAR-10, CIFAR-100, and ImageNet datasets. Our results demonstrate
that learning with adaptive batch sizes can improve performance by factors of
up to 6.25 on 4 NVIDIA Tesla P100 GPUs while changing accuracy by less than 1%
relative to training with fixed batch sizes.
An Improved Tabu Search Heuristic for Static Dial-A-Ride Problem
Authors: Songguang Ho, Sarat Chandra Nagavarapu, Ramesh Ramasamy Pandi, Justin Dauwels
Date: 2018-01-25 00:00:00
Tasks: Scheduling

Multi-vehicle routing has become increasingly important with the rapid
development of autonomous vehicle technology. Dial-a-ride problem, a variant of
vehicle routing problem (VRP), deals with the allocation of customer requests
to vehicles, scheduling the pick-up and drop-off times and the sequence of
serving those requests by ensuring high customer satisfaction with minimized
travel cost. In this paper, we propose an improved tabu search (ITS) heuristic
for static dial-a-ride problem (DARP) with the objective of obtaining
high-quality solutions in short time. Two new techniques, initialization
heuristic, and time window adjustment are proposed to achieve faster
convergence to the global optimum. Various numerical experiments are conducted
for the proposed solution methodology using DARP test instances from the
literature and the convergence speed up is validated.
Graph2Seq: Scalable Learning Dynamics for Graphs
Authors: Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath
Date: 2018-02-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: Combinatorial Optimization, Time Series, Time Series Analysis

Neural networks have been shown to be an effective tool for learning
algorithms over graph-structured data. However, graph representation
techniques---that convert graphs to real-valued vectors for use with neural
networks---are still in their infancy. Recent works have proposed several
approaches (e.g., graph convolutional networks), but these methods have
difficulty scaling and generalizing to graphs with different sizes and shapes.
We present Graph2Seq, a new technique that represents vertices of graphs as
infinite time-series. By not limiting the representation to a fixed dimension,
Graph2Seq scales naturally to graphs of arbitrary sizes and shapes. Graph2Seq
is also reversible, allowing full recovery of the graph structure from the
sequences. By analyzing a formal computational model for graph representation,
we show that an unbounded sequence is necessary for scalability. Our
experimental results with Graph2Seq show strong generalization and new
state-of-the-art performance on a variety of graph combinatorial optimization
problems.
Attack RMSE Leaderboard: An Introduction and Case Study
Authors: Cong Xie
Date: 2018-02-14 00:00:00

In this manuscript, we briefly introduce several tricks to climb the
leaderboards which use RMSE for evaluation without exploiting any training
data.
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
Authors: Zining Wang, Wei Zhan, Masayoshi Tomizuka
Date: 2017-11-17 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection

We propose a new method for fusing a LIDAR point cloud and camera-captured
images in the deep convolutional neural network (CNN). The proposed method
constructs a new layer called non-homogeneous pooling layer to transform
features between bird view map and front view map. The sparse LIDAR point cloud
is used to construct the mapping between the two maps. The pooling layer allows
efficient fusion of the bird view and front view features at any stage of the
network. This is favorable for the 3D-object detection using camera-LIDAR
fusion in autonomous driving scenarios. A corresponding deep CNN is designed
and tested on the KITTI bird view object detection dataset, which produces 3D
bounding boxes from the bird view map. The fusion method shows particular
benefit for detection of pedestrians in the bird view compared to other
fusion-based object detection networks.
MemeSequencer: Sparse Matching for Embedding Image Macros
Authors: Abhimanyu Dubey, Esteban Moro, Manuel Cebrian, Iyad Rahwan
Date: 2018-02-14 00:00:00
Tasks: Clustering, Image Clustering, Image Retrieval, Marketing, Retrieval

The analysis of the creation, mutation, and propagation of social media
content on the Internet is an essential problem in computational social
science, affecting areas ranging from marketing to political mobilization. A
first step towards understanding the evolution of images online is the analysis
of rapidly modifying and propagating memetic imagery or `memes'. However, a
pitfall in proceeding with such an investigation is the current incapability to
produce a robust semantic space for such imagery, capable of understanding
differences in Image Macros. In this study, we provide a first step in the
systematic study of image evolution on the Internet, by proposing an algorithm
based on sparse representations and deep learning to decouple various types of
content in such images and produce a rich semantic embedding. We demonstrate
the benefits of our approach on a variety of tasks pertaining to memes and
Image Macros, such as image clustering, image retrieval, topic prediction and
virality prediction, surpassing the existing methods on each. In addition to
its utility on quantitative tasks, our method opens up the possibility of
obtaining the first large-scale understanding of the evolution and propagation
of memetic imagery.
Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks
Authors: Hisashi Iwade, Kohei Nakajima, Takuma Tanaka, Toshio Aoyagi
Date: 2018-02-14 00:00:00

The inherent transient dynamics of recurrent neural networks (RNNs) have been
exploited as a computational resource in input-driven RNNs. However, the
information processing capability varies from RNN to RNN, depending on their
properties. Many authors have investigated the dynamics of RNNs and their
relevance to the information processing capability. In this study, we present a
detailed analysis of the information processing capability of an RNN optimized
by recurrent infomax (RI), which is an unsupervised learning scheme that
maximizes the mutual information of RNNs by adjusting the connection strengths
of the network. Thus, we observe that a delay-line structure emerges from the
RI and the network optimized by the RI possesses superior short-term memory,
which is the ability to store the temporal information of the input stream in
its transient dynamics.
Crime incidents embedding using restricted Boltzmann machines
Authors: Shixiang Zhu, Yao Xie
Date: 2017-10-28 00:00:00
Tasks: Clustering

We present a new approach for detecting related crime series, by unsupervised
learning of the latent feature embeddings from narratives of crime record via
the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
drastically different approach from prior work on crime analysis, which
typically considers only time and location and at most category information.
After the embedding, related cases are closer to each other in the Euclidean
feature space, and the unrelated cases are far apart, which is a good property
can enable subsequent analysis such as detection and clustering of related
cases. Experiments over several series of related crime incidents hand labeled
by the Atlanta Police Department reveal the promise of our embedding methods.
Energy Spatio-Temporal Pattern Prediction for Electric Vehicle Networks
Authors: Qinglong Wang
Date: 2018-02-14 00:00:00
Tasks: Prediction, Scheduling

Information about the spatio-temporal pattern of electricity energy carried
by EVs, instead of EVs themselves, is crucial for EVs to establish more
effective and intelligent interactions with the smart grid. In this paper, we
propose a framework for predicting the amount of the electricity energy stored
by a large number of EVs aggregated within different city-scale regions, based
on spatio-temporal pattern of the electricity energy. The spatial pattern is
modeled via using a neural network based spatial predictor, while the temporal
pattern is captured via using a linear-chain conditional random field (CRF)
based temporal predictor. Two predictors are fed with spatial and temporal
features respectively, which are extracted based on real trajectories data
recorded in Beijing. Furthermore, we combine both predictors to build the
spatio-temporal predictor, by using an optimal combination coefficient which
minimizes the normalized mean square error (NMSE) of the predictions. The
prediction performance is evaluated based on extensive experiments covering
both spatial and temporal predictions, and the improvement achieved by the
combined spatio-temporal predictor. The experiment results show that the NMSE
of the spatio-temporal predictor is maintained below 0.1 for all investigate
regions of Beijing. We further visualize the prediction and discuss the
potential benefits can be brought to smart grid scheduling and EV charging by
utilizing the proposed framework.
Open Loop Hyperparameter Optimization and Determinantal Point Processes
Authors: Jesse Dodge, Kevin Jamieson, Noah A. Smith
Date: 2017-06-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Diversity, Hyperparameter Optimization, Point Processes

Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions. In particular, we propose the use of $k$-determinantal point processes in hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a $k$-DPP promotes diversity. We describe an approach that transforms hyperparameter search spaces for efficient use with a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from $k$-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure. Our experiments show significant benefits in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Authors: Michael T. Lash, Qihang Lin, W. Nick Street
Date: 2018-02-14 00:00:00
Tasks: Classification, Gaussian Processes, General Classification

Inverse classification uses an induced classifier as a queryable oracle to
guide test instances towards a preferred posterior class label. The result
produced from the process is a set of instance-specific feature perturbations,
or recommendations, that optimally improve the probability of the class label.
In this work, we adopt a causal approach to inverse classification, eliciting
treatment policies (i.e., feature perturbations) for models induced with causal
properties. In so doing, we solve a long-standing problem of eliciting
multiple, continuously valued treatment policies, using an updated framework
and corresponding set of assumptions, which we term the inverse classification
potential outcomes framework (ICPOF), along with a new measure, referred to as
the individual future estimated effects ($i$FEE). We also develop the
approximate propensity score (APS), based on Gaussian processes, to weight
treatments, much like the inverse propensity score weighting used in past
works. We demonstrate the viability of our methods on student performance.
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
Authors: Mustansar Fiaz, Arif Mahmood, Soon Ki Jung
Date: 2018-02-09 00:00:00
Tasks: Autonomous Vehicles, Object Tracking, Visual Object Tracking

Visual object tracking is an important computer vision problem with numerous
real-world applications including human-computer interaction, autonomous
vehicles, robotics, motion-based recognition, video indexing, surveillance and
security. In this paper, we aim to extensively review the latest trends and
advances in the tracking algorithms and evaluate the robustness of trackers in
the presence of noise. The first part of this work comprises a comprehensive
survey of recently proposed tracking algorithms. We broadly categorize trackers
into correlation filter based trackers and the others as non-correlation filter
trackers. Each category is further classified into various types of trackers
based on the architecture of the tracking mechanism. In the second part of this
work, we experimentally evaluate tracking algorithms for robustness in the
presence of additive white Gaussian noise. Multiple levels of additive noise
are added to the Object Tracking Benchmark (OTB) 2015, and the precision and
success rates of the tracking algorithms are evaluated. Some algorithms
suffered more performance degradation than others, which brings to light a
previously unexplored aspect of the tracking algorithms. The relative rank of
the algorithms based on their performance on benchmark datasets may change in
the presence of noise. Our study concludes that no single tracker is able to
achieve the same efficiency in the presence of noise as under noise-free
conditions; thus, there is a need to include a parameter for robustness to
noise when evaluating newly proposed tracking algorithms.
Conditional Density Estimation with Bayesian Normalising Flows
Authors: Brian L. Trippe, Richard E. Turner
Date: 2018-02-14 00:00:00
Tasks: Density Estimation, Normalising Flows

Modeling complex conditional distributions is critical in a variety of
settings. Despite a long tradition of research into conditional density
estimation, current methods employ either simple parametric forms or are
difficult to learn in practice. This paper employs normalising flows as a
flexible likelihood model and presents an efficient method for fitting them to
complex densities. These estimators must trade-off between modeling
distributional complexity, functional complexity and heteroscedasticity without
overfitting. We recognize these trade-offs as modeling decisions and develop a
Bayesian framework for placing priors over these conditional density estimators
using variational Bayesian neural networks. We evaluate this method on several
small benchmark regression datasets, on some of which it obtains state of the
art performance. Finally, we apply the method to two spatial density modeling
tasks with over 1 million datapoints using the New York City yellow taxi
dataset and the Chicago crime dataset.
Matrix Completion via Factorizing Polynomials
Authors: Vatsal Shah, Nikhil Rao, Weicong Ding
Date: 2017-05-04 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Predicting unobserved entries of a partially observed matrix has found wide
applicability in several areas, such as recommender systems, computational
biology, and computer vision. Many scalable methods with rigorous theoretical
guarantees have been developed for algorithms where the matrix is factored into
low-rank components, and embeddings are learned for the row and column
entities. While there has been recent research on incorporating explicit side
information in the low-rank matrix factorization setting, often implicit
information can be gleaned from the data, via higher-order interactions among
entities. Such implicit information is especially useful in cases where the
data is very sparse, as is often the case in real-world datasets. In this
paper, we design a method to learn embeddings in the context of recommendation
systems, using the observation that higher powers of a graph transition
probability matrix encode the probability that a random walker will hit that
node in a given number of steps. We develop a coordinate descent algorithm to
solve the resulting optimization, that makes explicit computation of the higher
order powers of the matrix redundant, preserving sparsity and making
computations efficient. Experiments on several datasets show that our method,
that can use higher order information, outperforms methods that only use
explicitly available side information, those that use only second-order
implicit information and in some cases, methods based on deep neural networks
as well.
Molecular Structure Extraction From Documents Using Deep Learning
Authors: Joshua Staker, Kyle Marshall, Robert Abel, Carolyn McQuaw
Date: 2018-02-14 00:00:00
Tasks: Articles, Deep Learning, Diversity

Chemical structure extraction from documents remains a hard problem due to
both false positive identification of structures during segmentation and errors
in the predicted structures. Current approaches rely on handcrafted rules and
subroutines that perform reasonably well generally, but still routinely
encounter situations where recognition rates are not yet satisfactory and
systematic improvement is challenging. Complications impacting performance of
current approaches include the diversity in visual styles used by various
software to render structures, the frequent use of ad hoc annotations, and
other challenges related to image quality, including resolution and noise. We
here present end-to-end deep learning solutions for both segmenting molecular
structures from documents and for predicting chemical structures from these
segmented images. This deep learning-based approach does not require any
handcrafted features, is learned directly from data, and is robust against
variations in image quality and style. Using the deep-learning approach
described herein we show that it is possible to perform well on both
segmentation and prediction of low resolution images containing moderately
sized molecules found in journal articles and patents.
A Converse to Banach's Fixed Point Theorem and its CLS Completeness
Authors: Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis
Date: 2017-02-23 00:00:00

Banach's fixed point theorem for contraction maps has been widely used to
analyze the convergence of iterative methods in non-convex problems. It is a
common experience, however, that iterative maps fail to be globally contracting
under the natural metric in their domain, making the applicability of Banach's
theorem limited. We explore how generally we can apply Banach's fixed point
theorem to establish the convergence of iterative methods when pairing it with
carefully designed metrics.
  Our first result is a strong converse of Banach's theorem, showing that it is
a universal analysis tool for establishing global convergence of iterative
methods to unique fixed points, and for bounding their convergence rate. In
other words, we show that, whenever an iterative map globally converges to a
unique fixed point, there exists a metric under which the iterative map is
contracting and which can be used to bound the number of iterations until
convergence. We illustrate our approach in the widely used power method,
providing a new way of bounding its convergence rate through contraction
arguments.
  We next consider the computational complexity of Banach's fixed point
theorem. Making the proof of our converse theorem constructive, we show that
computing a fixed point whose existence is guaranteed by Banach's fixed point
theorem is CLS-complete. We thus provide the first natural complete problem for
the class CLS, which was defined in [Daskalakis, Papadimitriou 2011] to capture
the complexity of problems such as P-matrix LCP, computing KKT-points, and
finding mixed Nash equilibria in congestion and network coordination games.
Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey
Authors: Boyu Zhang, Yingtao Zhang, H. D. Cheng, Min Xian, Shan Gai, Olivia Cheng, Kuan Huang
Date: 2018-02-13 00:00:00
Tasks: Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation, Survey

Osteoarthritis (OA) is one of the major health issues among the elderly
population. MRI is the most popular technology to observe and evaluate the
progress of OA course. However, the extreme labor cost of MRI analysis makes
the process inefficient and expensive. Also, due to human error and subjective
nature, the inter- and intra-observer variability is rather high.
Computer-aided knee MRI segmentation is currently an active research field
because it can alleviate doctors and radiologists from the time consuming and
tedious job, and improve the diagnosis performance which has immense potential
for both clinic and scientific research. In the past decades, researchers have
investigated automatic/semi-automatic knee MRI segmentation methods
extensively. However, to the best of our knowledge, there is no comprehensive
survey paper in this field yet. In this survey paper, we classify the existing
methods by their principles and discuss the current research status and point
out the future research trend in-depth.