pwc-archive/papers-with-abstracts huggingface dataset:
Flow-free Video Object Segmentation
Authors: Aditya Vora, Shanmuganathan Raman
Date: 2017-06-29 00:00:00
Tasks: Clustering, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

Segmenting foreground object from a video is a challenging task because of
the large deformations of the objects, occlusions, and background clutter. In
this paper, we propose a frame-by-frame but computationally efficient approach
for video object segmentation by clustering visually similar generic object
segments throughout the video. Our algorithm segments various object instances
appearing in the video and then perform clustering in order to group visually
similar segments into one cluster. Since the object that needs to be segmented
appears in most part of the video, we can retrieve the foreground segments from
the cluster having maximum number of segments, thus filtering out noisy
segments that do not represent any object. We then apply a track and fill
approach in order to localize the objects in the frames where the object
segmentation framework fails to segment any object. Our algorithm performs
comparably to the recent automatic methods for video object segmentation when
benchmarked on DAVIS dataset while being computationally much faster.
Unsupervised Person Re-identification: Clustering and Fine-tuning
Authors: Hehe Fan, Liang Zheng, Yi Yang
Date: 2017-05-30 00:00:00
Tasks: Clustering, Person Re-Identification, Unsupervised Domain Adaptation, Unsupervised Person Re-Identification

The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transfer pretrained deep representations to unseen domains. Our
method is easy to implement and can be viewed as an effective baseline for
unsupervised re-ID feature learning. Specifically, PUL iterates between 1)
pedestrian clustering and 2) fine-tuning of the convolutional neural network
(CNN) to improve the original model trained on the irrelevant labeled dataset.
Since the clustering results can be very noisy, we add a selection operation
between the clustering and fine-tuning. At the beginning when the model is
weak, CNN is fine-tuned on a small amount of reliable examples which locate
near to cluster centroids in the feature space. As the model becomes stronger
in subsequent iterations, more images are being adaptively selected as CNN
training samples. Progressively, pedestrian clustering and the CNN model are
improved simultaneously until algorithm convergence. This process is naturally
formulated as self-paced learning. We then point out promising directions that
may lead to further improvement. Extensive experiments on three large-scale
re-ID datasets demonstrate that PUL outputs discriminative features that
improve the re-ID accuracy.
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Authors: Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, Yongxin Yang
Date: 2017-06-29 00:00:00
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

We propose a novel and flexible approach to meta-learning for
learning-to-learn from only a few examples. Our framework is motivated by
actor-critic reinforcement learning, but can be applied to both reinforcement
and supervised learning. The key idea is to learn a meta-critic: an
action-value function neural network that learns to criticise any actor trying
to solve any specified task. For supervised learning, this corresponds to the
novel idea of a trainable task-parametrised loss generator. This meta-critic
approach provides a route to knowledge transfer that can flexibly deal with
few-shot and semi-supervised conditions for both reinforcement and supervised
learning. Promising results are shown on both reinforcement and supervised
learning problems.
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
Authors: Swabha Swayamdipta, Sam Thomson, Chris Dyer, Noah A. Smith
Date: 2017-06-29 00:00:00
Tasks: Semantic Parsing

We present a new, efficient frame-semantic parser that labels semantic
arguments to FrameNet predicates. Built using an extension to the segmental RNN
that emphasizes recall, our basic system achieves competitive performance
without any calls to a syntactic parser. We then introduce a method that uses
phrase-syntactic annotations from the Penn Treebank during training only,
through a multitask objective; no parsing is required at training or test time.
This "syntactic scaffold" offers a cheaper alternative to traditional syntactic
pipelining, and achieves state-of-the-art performance.
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study
Authors: John P. Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu
Date: 2017-02-15 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Inference, Sentiment Analysis

Interpreting the performance of deep learning models beyond test set accuracy
is challenging. Characteristics of individual data points are often not
considered during evaluation, and each data point is treated equally. We
examine the impact of a test set question's difficulty to determine if there is
a relationship between difficulty and performance. We model difficulty using
well-studied psychometric methods on human response patterns. Experiments on
Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
likelihood of answering a question correctly is impacted by the question's
difficulty. As DNNs are trained with more data, easy examples are learned more
quickly than hard examples.
Soft Label Memorization-Generalization for Natural Language Inference
Authors: John P. Lalor, Hao Wu, Hong Yu
Date: 2017-02-27 00:00:00
Tasks: Memorization, Natural Language Inference

Often when multiple labels are obtained for a training example it is assumed
that there is an element of noise that must be accounted for. It has been shown
that this disagreement can be considered signal instead of noise. In this work
we investigate using soft labels for training data to improve generalization in
machine learning models. However, using soft labels for training Deep Neural
Networks (DNNs) is not practical due to the costs involved in obtaining
multiple labels for large data sets. We propose soft label
memorization-generalization (SLMG), a fine-tuning approach to using soft labels
for training DNNs. We assume that differences in labels provided by human
annotators represent ambiguity about the true label instead of noise.
Experiments with SLMG demonstrate improved generalization performance on the
Natural Language Inference (NLI) task. Our experiments show that by injecting a
small percentage of soft label training data (0.03% of training set size) we
can improve generalization performance over several baselines.
The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle
Authors: Ryan J. Cunningham, Peter J. Harding, Ian D. Loram
Date: 2017-06-28 00:00:00

This paper concerns the fully automatic direct in vivo measurement of active
and passive dynamic skeletal muscle states using ultrasound imaging. Despite
the long standing medical need (myopathies, neuropathies, pain, injury,
ageing), currently technology (electromyography, dynamometry, shear wave
imaging) provides no general, non-invasive method for online estimation of
skeletal intramuscular states. Ultrasound provides a technology in which static
and dynamic muscle states can be observed non-invasively, yet current
computational image understanding approaches are inadequate. We propose a new
approach in which deep learning methods are used for understanding the content
of ultrasound images of muscle in terms of its measured state. Ultrasound data
synchronized with electromyography of the calf muscles, with measures of joint
torque/angle were recorded from 19 healthy participants (6 female, ages: 30 +-
7.7). A segmentation algorithm previously developed by our group was applied to
extract a region of interest of the medial gastrocnemius. Then a deep
convolutional neural network was trained to predict the measured states (joint
angle/torque, electromyography) directly from the segmented images. Results
revealed for the first time that active and passive muscle states can be
measured directly from standard b-mode ultrasound images, accurately predicting
for a held out test participant changes in the joint angle, electromyography,
and torque with as little error as 0.022{\deg}, 0.0001V, 0.256Nm (root mean
square error) respectively.
Node Embedding via Word Embedding for Network Community Discovery
Authors: Weicong Ding, Christy Lin, Prakash Ishwar
Date: 2016-11-09 00:00:00
Tasks: Clustering, Graph Generation

Neural node embeddings have recently emerged as a powerful representation for
supervised learning tasks involving graph-structured data. We leverage this
recent advance to develop a novel algorithm for unsupervised community
discovery in graphs. Through extensive experimental studies on simulated and
real-world data, we demonstrate that the proposed approach consistently
improves over the current state-of-the-art. Specifically, our approach
empirically attains the information-theoretic limits for community recovery
under the benchmark Stochastic Block Models for graph generation and exhibits
better stability and accuracy over both Spectral Clustering and Acyclic Belief
Propagation in the community recovery limits.
Data-driven Natural Language Generation: Paving the Road to Success
Authors: Jekaterina Novikova, Ondřej Dušek, Verena Rieser
Date: 2017-06-28 00:00:00
Tasks: BIG-bench Machine Learning, Text Generation

We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughly analysing current evaluation metrics and motivating the need for a
new, more reliable metric. The second problem is addressed by presenting a
novel framework for developing and evaluating a high quality corpus for NLG
training.
Summarization of ICU Patient Motion from Multimodal Multiview Videos
Authors: Carlos Torres, Kenneth Rose, Jeffrey C. Fried, B. S. Manjunath
Date: 2017-06-28 00:00:00
Tasks: General Classification

Clinical observations indicate that during critical care at the hospitals,
patients sleep positioning and motion affect recovery. Unfortunately, there is
no formal medical protocol to record, quantify, and analyze patient motion.
There is a small number of clinical studies, which use manual analysis of sleep
poses and motion recordings to support medical benefits of patient positioning
and motion monitoring. Manual processes are not scalable, are prone to human
errors, and strain an already taxed healthcare workforce. This study introduces
DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses
these issues by autonomously monitoring healthcare environments and enabling
the recording and analysis of patient sleep poses and motion. DECU uses three
RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU).
The algorithms in DECU estimate pose direction at different temporal
resolutions and use keyframes to efficiently represent pose transition
dynamics. DECU combines deep features computed from the data with a modified
version of Hidden Markov Model to more flexibly model sleep pose duration,
analyze pose patterns, and summarize patient motion. Extensive experimental
results are presented. The performance of DECU is evaluated in ideal (BC:
Bright and Clear/occlusion-free) and natural (DO: Dark and Occluded) scenarios
at two motion resolutions in a mock-up and a real ICU. The results indicate
that deep features allow DECU to match the classification performance of
engineered features in BC scenes and increase the accuracy by up to 8% in DO
scenes. In addition, the overall pose history summarization tracing accuracy
shows an average detection rate of 85% in BC and of 76% in DO scenes. The
proposed keyframe estimation algorithm allows DECU to reach an average 78%
transition classification accuracy.
Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension
Authors: Andrew W. Long, Andrew L. Ferguson
Date: 2017-06-28 00:00:00

Diffusion maps are a nonlinear manifold learning technique based on harmonic
analysis of a diffusion process over the data. Out-of-sample extensions with
computational complexity $\mathcal{O}(N)$, where $N$ is the number of points
comprising the manifold, frustrate applications to online learning applications
requiring rapid embedding of high-dimensional data streams. We propose landmark
diffusion maps (L-dMaps) to reduce the complexity to $\mathcal{O}(M)$, where $M
\ll N$ is the number of landmark points selected using pruned spanning trees or
k-medoids. Offering $(N/M)$ speedups in out-of-sample extension, L-dMaps
enables the application of diffusion maps to high-volume and/or high-velocity
streaming data. We illustrate our approach on three datasets: the Swiss roll,
molecular simulations of a C$_{24}$H$_{50}$ polymer chain, and biomolecular
simulations of alanine dipeptide. We demonstrate up to 50-fold speedups in
out-of-sample extension for the molecular systems with less than 4% errors in
manifold reconstruction fidelity relative to calculations over the full
dataset.
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure
Authors: Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti
Date: 2017-06-28 00:00:00
Tasks: SSIM

In this paper, we study the missing sample recovery problem using methods
based on sparse approximation. In this regard, we investigate the algorithms
used for solving the inverse problem associated with the restoration of missed
samples of image signal. This problem is also known as inpainting in the
context of image processing and for this purpose, we suggest an iterative
sparse recovery algorithm based on constrained $l_1$-norm minimization with a
new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
is a simplified version of the Structural SIMilarity (SSIM) index, which is
convex and error-sensitive. The optimization problem incorporating this
criterion, is then solved via Alternating Direction Method of Multipliers
(ADMM). Simulation results show the efficiency of the proposed method for
missing sample recovery of 1D patch vectors and inpainting of 2D image signals.
DynASP2.5: Dynamic Programming on Tree Decompositions in Action
Authors: Johannes K. Fichte, Markus Hecher, Michael Morak, Stefan Woltran
Date: 2017-06-28 00:00:00
Tasks: Steiner Tree Problem

A vibrant theoretical research area are efficient exact parameterized
algorithms. Very recent solving competitions such as the PACE challenge show
that there is also increasing practical interest in the parameterized
algorithms community. An important research question is whether dedicated
parameterized exact algorithms exhibit certain practical relevance and one can
even beat well-established problem solvers. We consider the logic-based
declarative modeling language and problem solving framework Answer Set
Programming (ASP). State-of-the-art ASP solvers rely considerably on Sat-based
algorithms. An ASP solver (DynASP2), which is based on a classical dynamic
programming on tree decompositions, has been published very recently.
Unfortunately, DynASP2 can outperform modern ASP solvers on programs of small
treewidth only if the question of interest is to count the number of solutions.
In this paper, we describe underlying concepts of our new implementation
(DynASP2.5) that shows competitive behavior to state-of-the-art ASP solvers
even for finding just one solution when solving problems as the Steiner tree
problem that have been modeled in ASP on graphs with low treewidth. Our
implementation is based on a novel approach that we call multi-pass dynamic
programming (M-DPSINC).
Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization
Authors: Sejun Park, Jinwoo Shin
Date: 2014-12-16 00:00:00
Tasks: Combinatorial Optimization

The max-product {belief propagation} (BP) is a popular message-passing
heuristic for approximating a maximum-a-posteriori (MAP) assignment in a joint
distribution represented by a graphical model (GM). In the past years, it has
been shown that BP can solve a few classes of linear programming (LP)
formulations to combinatorial optimization problems including maximum weight
matching, shortest path and network flow, i.e., BP can be used as a
message-passing solver for certain combinatorial optimizations. However, those
LPs and corresponding BP analysis are very sensitive to underlying problem
setups, and it has been not clear what extent these results can be generalized
to. In this paper, we obtain a generic criteria that BP converges to the
optimal solution of given LP, and show that it is satisfied in LP formulations
associated to many classical combinatorial optimization problems including
maximum weight perfect matching, shortest path, traveling salesman, cycle
packing, vertex/edge cover and network flow.
autoBagging: Learning to Rank Bagging Workflows with Metalearning
Authors: Fábio Pinto, Vítor Cerqueira, Carlos Soares, João Mendes-Moreira
Date: 2017-06-28 00:00:00
Tasks: AutoML, Bayesian Optimization, BIG-bench Machine Learning, Ensemble Learning, Learning-To-Rank

Machine Learning (ML) has been successfully applied to a wide range of
domains and applications. One of the techniques behind most of these successful
applications is Ensemble Learning (EL), the field of ML that gave birth to
methods such as Random Forests or Boosting. The complexity of applying these
techniques together with the market scarcity on ML experts, has created the
need for systems that enable a fast and easy drop-in replacement for ML
libraries. Automated machine learning (autoML) is the field of ML that attempts
to answers these needs. Typically, these systems rely on optimization
techniques such as bayesian optimization to lead the search for the best model.
Our approach differs from these systems by making use of the most recent
advances on metalearning and a learning to rank approach to learn from
metadata. We propose autoBagging, an autoML system that automatically ranks 63
bagging workflows by exploiting past performance and dataset characterization.
Results on 140 classification datasets from the OpenML platform show that
autoBagging can yield better performance than the Average Rank method and
achieve results that are not statistically different from an ideal model that
systematically selects the best workflow for each dataset. For the purpose of
reproducibility and generalizability, autoBagging is publicly available as an R
package on CRAN.
Path planning for Robotic Mobile Fulfillment Systems
Authors: Marius Merschformann, Lin Xie, Daniel Erdmann
Date: 2017-06-28 00:00:00
Tasks: Retrieval, Robot Navigation

This paper presents a collection of path planning algorithms for real-time
movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS).
Robots are assigned to move storage units to pickers at working stations
instead of requiring pickers to go to the storage area. Path planning
algorithms aim to find paths for the robots to fulfill the requests without
collisions or deadlocks. The state-of-the-art path planning algorithms,
including WHCA*, FAR, BCP, OD&ID and CBS, were adapted to suit path planning in
RMFS and integrated within a simulation tool to guide the robots from their
starting points to their destinations during the storage and retrieval
processes. Ten different layouts with a variety of numbers of robots, floors,
pods, stations and the sizes of storage areas were considered in the simulation
study. Performance metrics of throughput, path length and search time were
monitored. Simulation results demonstrate the best algorithm based on each
performance metric.
Generating Appealing Brand Names
Authors: Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani
Date: 2017-06-28 00:00:00

Providing appealing brand names to newly launched products, newly formed
companies or for renaming existing companies is highly important as it can play
a crucial role in deciding its success or failure. In this work, we propose a
computational method to generate appealing brand names based on the description
of such entities. We use quantitative scores for readability, pronounceability,
memorability and uniqueness of the generated names to rank order them. A set of
diverse appealing names is recommended to the user for the brand naming task.
Experimental results show that the names generated by our approach are more
appealing than names which prior approaches and recruited humans could come up.
Learning to superoptimize programs
Authors: Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
Date: 2016-11-06 00:00:00

Code super-optimization is the task of transforming any given program to a
more efficient version while preserving its input-output behaviour. In some
sense, it is similar to the paraphrase problem from natural language processing
where the intention is to change the syntax of an utterance without changing
its semantics. Code-optimization has been the subject of years of research that
has resulted in the development of rule-based transformation strategies that
are used by compilers. More recently, however, a class of stochastic search
based methods have been shown to outperform these strategies. This approach
involves repeated sampling of modifications to the program from a proposal
distribution, which are accepted or rejected based on whether they preserve
correctness, and the improvement they achieve. These methods, however, neither
learn from past behaviour nor do they try to leverage the semantics of the
program under consideration. Motivated by this observation, we present a novel
learning based approach for code super-optimization. Intuitively, our method
works by learning the proposal distribution using unbiased estimators of the
gradient of the expected improvement. Experiments on benchmarks comprising of
automatically generated as well as existing ("Hacker's Delight") programs show
that the proposed method is able to significantly outperform state of the art
approaches for code super-optimization.
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Authors: Jaemin Son, Sang Jun Park, Kyu-Hwan Jung
Date: 2017-06-28 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation

Retinal vessel segmentation is an indispensable step for automatic detection
of retinal diseases with fundoscopic images. Though many approaches have been
proposed, existing methods tend to miss fine vessels or allow false positives
at terminal branches. Let alone under-segmentation, over-segmentation is also
problematic when quantitative studies need to measure the precise width of
vessels. In this paper, we present a method that generates the precise map of
retinal vessels using generative adversarial training. Our methods achieve dice
coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the
state-of-the-art performance on both datasets.