pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping
Authors: Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montufar, Syn Schmitt, Nihat Ay
Date: 2015-12-01 00:00:00

In the context of embodied artificial intelligence, morphological computation
refers to processes which are conducted by the body (and environment) that
otherwise would have to be performed by the brain. Exploiting environmental and
morphological properties is an important feature of embodied systems. The main
reason is that it allows to significantly reduce the controller complexity. An
important aspect of morphological computation is that it cannot be assigned to
an embodied system per se, but that it is, as we show, behavior- and
state-dependent. In this work, we evaluate two different measures of
morphological computation that can be applied in robotic systems and in
computer simulations of biological movement. As an example, these measures were
evaluated on muscle and DC-motor driven hopping models. We show that a
state-dependent analysis of the hopping behaviors provides additional insights
that cannot be gained from the averaged measures alone. This work includes
algorithms and computer code for the measures.
Words are not Equal: Graded Weighting Model for building Composite Document Vectors
Authors: Pranjal Singh, Amitabha Mukerjee
Date: 2015-12-11 00:00:00
Proceeding: WS 2015 12
Tasks: Sentiment Analysis, Sentiment Classification

Despite the success of distributional semantics, composing phrases from word
vectors remains an important challenge. Several methods have been tried for
benchmark tasks such as sentiment classification, including word vector
averaging, matrix-vector approaches based on parsing, and on-the-fly learning
of paragraph vectors. Most models usually omit stop words from the composition.
Instead of such an yes-no decision, we consider several graded schemes where
words are weighted according to their discriminatory relevance with respect to
its use in the document (e.g., idf). Some of these methods (particularly
tf-idf) are seen to result in a significant improvement in performance over
prior state of the art. Further, combining such approaches into an ensemble
based on alternate classifiers such as the RNN model, results in an 1.6%
performance improvement on the standard IMDB movie review dataset, and a 7.01%
improvement on Amazon product reviews. Since these are language free models and
can be obtained in an unsupervised manner, they are of interest also for
under-resourced languages such as Hindi as well and many more languages. We
demonstrate the language free aspects by showing a gain of 12% for two review
datasets over earlier results, and also release a new larger dataset for future
testing (Singh,2015).
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Authors: Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu
Date: 2015-12-11 00:00:00
Tasks: Computational Phenotyping, Decision Making, Deep Learning, Denoising, Knowledge Distillation, Time Series, Time Series Analysis

Exponential growth in Electronic Healthcare Records (EHR) has resulted in new
opportunities and urgent needs for discovery of meaningful data-driven
representations and patterns of diseases in Computational Phenotyping research.
Deep Learning models have shown superior performance for robust prediction in
computational phenotyping tasks, but suffer from the issue of model
interpretability which is crucial for clinicians involved in decision-making.
In this paper, we introduce a novel knowledge-distillation approach called
Interpretable Mimic Learning, to learn interpretable phenotype features for
making robust prediction while mimicking the performance of deep learning
models. Our framework uses Gradient Boosting Trees to learn interpretable
features from deep learning models such as Stacked Denoising Autoencoder and
Long Short-Term Memory. Exhaustive experiments on a real-world clinical
time-series dataset show that our method obtains similar or better performance
than the deep learning models, and it provides interpretable phenotypes for
clinical decision making.
Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection
Authors: N. Benjamin Erichson, Carl Donovan
Date: 2015-12-11 00:00:00
Tasks: Motion Detection

This paper introduces a fast algorithm for randomized computation of a
low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this
matrix to represent the development of a spatial grid through time e.g. data
from a static video source. DMD was originally introduced in the fluid
mechanics community, but is also suitable for motion detection in video streams
and its use for background subtraction has received little previous
investigation. In this study we present a comprehensive evaluation of
background subtraction, using the randomized DMD and compare the results with
leading robust principal component analysis algorithms. The results are
convincing and show the random DMD is an efficient and powerful approach for
background modeling, allowing processing of high resolution videos in
real-time. Supplementary materials include implementations of the algorithms in
Python.
A Unified Approach to Error Bounds for Structured Convex Optimization Problems
Authors: Zirui Zhou, Anthony Man-Cho So
Date: 2015-12-11 00:00:00

Error bounds, which refer to inequalities that bound the distance of vectors
in a test set to a given set by a residual function, have proven to be
extremely useful in analyzing the convergence rates of a host of iterative
methods for solving optimization problems. In this paper, we present a new
framework for establishing error bounds for a class of structured convex
optimization problems, in which the objective function is the sum of a smooth
convex function and a general closed proper convex function. Such a class
encapsulates not only fairly general constrained minimization problems but also
various regularized loss minimization formulations in machine learning, signal
processing, and statistics. Using our framework, we show that a number of
existing error bound results can be recovered in a unified and transparent
manner. To further demonstrate the power of our framework, we apply it to a
class of nuclear-norm regularized loss minimization problems and establish a
new error bound for this class under a strict complementarity-type regularity
condition. We then complement this result by constructing an example to show
that the said error bound could fail to hold without the regularity condition.
Consequently, we obtain a rather complete answer to a question raised by Tseng.
We believe that our approach will find further applications in the study of
error bounds for structured convex optimization problems.
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
Authors: A. M. Mohan Rao
Date: 2015-12-11 00:00:00
Tasks: Diagnostic, Negation

Description logic (DL) based biomedical terminology (SNOMED CT) is used
routinely in medical practice. However, diagnostic inference using such
terminology is precluded by its complexity. Here we propose a model that
simplifies these inferential components. We propose three concepts that
classify clinical features and examined their effect on inference using SNOMED
CT. We used PAIRS (Physician Assistant Artificial Intelligence Reference
System) database (1964 findings for 485 disorders, 18 397 disease feature
links) for our analysis. We also use a 50-million medical word corpus for
estimating the vectors of disease-feature links. Our major results are 10% of
finding-disorder links are concomitant in both assertion and negation where as
90% are either concomitant in assertion or negation. Logical implications of
PAIRS data on SNOMED CT include 70% of the links do not share any common system
while 18% share organ and 12% share both system and organ. Applications of
these principles for inference are discussed and suggestions are made for
deriving a diagnostic process using SNOMED CT. Limitations of these processes
and suggestions for improvements are also discussed.
Protein secondary structure prediction using deep convolutional neural fields
Authors: Sheng Wang, Jian Peng, Jianzhu Ma, Jinbo Xu
Date: 2015-12-02 00:00:00
Tasks: Prediction, Protein Secondary Structure Prediction

Protein secondary structure (SS) prediction is important for studying protein
structure and function. When only the sequence (profile) information is used as
input feature, currently the best predictors can obtain ~80% Q3 accuracy, which
has not been improved in the past decade. Here we present DeepCNF (Deep
Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep
Learning extension of Conditional Neural Fields (CNF), which is an integration
of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can
model not only complex sequence-structure relationship by a deep hierarchical
architecture, but also interdependency between adjacent SS labels, so it is
much more powerful than CNF. Experimental results show that DeepCNF can obtain
~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the
CASP and CAMEO test proteins, greatly outperforming currently popular
predictors. As a general framework, DeepCNF can be used to predict other
protein structure properties such as contact number, disorder regions, and
solvent accessibility.
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
Authors: Roberto Santana, Alexander Mendiburu, Jose A. Lozano
Date: 2015-12-10 00:00:00
Tasks: Evolutionary Algorithms

NM-landscapes have been recently introduced as a class of tunable rugged
models. They are a subset of the general interaction models where all the
interactions are of order less or equal $M$. The Boltzmann distribution has
been extensively applied in single-objective evolutionary algorithms to
implement selection and study the theoretical properties of model-building
algorithms. In this paper we propose the combination of the multi-objective
NM-landscape model and the Boltzmann distribution to obtain Pareto-front
approximations. We investigate the joint effect of the parameters of the
NM-landscapes and the probabilistic factorizations in the shape of the Pareto
front approximations.
Neural Self Talk: Image Understanding via Continuous Questioning and Answering
Authors: Yezhou Yang, Yi Li, Cornelia Fermuller, Yiannis Aloimonos
Date: 2015-12-10 00:00:00
Tasks: Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

In this paper we consider the problem of continuously discovering image
contents by actively asking image based questions and subsequently answering
the questions being asked. The key components include a Visual Question
Generation (VQG) module and a Visual Question Answering module, in which
Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN) are
used. Given a dataset that contains images, questions and their answers, both
modules are trained at the same time, with the difference being VQG uses the
images as input and the corresponding questions as output, while VQA uses
images and questions as input and the corresponding answers as output. We
evaluate the self talk process subjectively using Amazon Mechanical Turk, which
show effectiveness of the proposed method.
Fast Convergence of Regularized Learning in Games
Authors: Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Date: 2015-07-02 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Form

We show that natural classes of regularized learning algorithms with a form
of recency bias achieve faster convergence rates to approximate efficiency and
to coarse correlated equilibria in multiplayer normal form games. When each
player in a game uses an algorithm from our class, their individual regret
decays at $O(T^{-3/4})$, while the sum of utilities converges to an approximate
optimum at $O(T^{-1})$--an improvement upon the worst case $O(T^{-1/2})$ rates.
We show a black-box reduction for any algorithm in the class to achieve
$\tilde{O}(T^{-1/2})$ rates against an adversary, while maintaining the faster
rates against algorithms in the class. Our results extend those of [Rakhlin and
Shridharan 2013] and [Daskalakis et al. 2014], who only analyzed two-player
zero-sum games for specific algorithms.
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance
Authors: Amichai Painsky, Saharon Rosset
Date: 2015-12-10 00:00:00
Tasks: Variable Selection

Recursive partitioning approaches producing tree-like models are a long
standing staple of predictive modeling, in the last decade mostly as
``sub-learners'' within state of the art ensemble methods like Boosting and
Random Forest. However, a fundamental flaw in the partitioning (or splitting)
rule of commonly used tree building methods precludes them from treating
different types of variables equally. This most clearly manifests in these
methods' inability to properly utilize categorical variables with a large
number of categories, which are ubiquitous in the new age of big data. Such
variables can often be very informative, but current tree methods essentially
leave us a choice of either not using them, or exposing our models to severe
overfitting. We propose a conceptual framework to splitting using leave-one-out
(LOO) cross validation for selecting the splitting variable, then performing a
regular split (in our case, following CART's approach) for the selected
variable. The most important consequence of our approach is that categorical
variables with many categories can be safely used in tree building and are only
chosen if they contribute to predictive power. We demonstrate in extensive
simulation and real data analysis that our novel splitting approach
significantly improves the performance of both single tree models and ensemble
methods that utilize trees. Importantly, we design an algorithm for LOO
splitting variable selection which under reasonable assumptions does not
increase the overall computational complexity compared to CART for two-class
classification. For regression tasks, our approach carries an increased
computational burden, replacing a O(log(n)) factor in CART splitting rule
search with an O(n) term.
Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources
Authors: Yitan Zhu, Niya Wang, David J. Miller, Yue Wang
Date: 2014-06-28 00:00:00
Tasks: blind source separation, Clustering, Edge Detection

Blind Source Separation (BSS) has proven to be a powerful tool for the
analysis of composite patterns in engineering and science. We introduce Convex
Analysis of Mixtures (CAM) for separating non-negative well-grounded sources,
which learns the mixing matrix by identifying the lateral edges of the convex
data scatter plot. We prove a sufficient and necessary condition for
identifying the mixing matrix through edge detection, which also serves as the
foundation for CAM to be applied not only to the exact-determined and
over-determined cases, but also to the under-determined case. We show the
optimality of the edge detection strategy, even for cases where source
well-groundedness is not strictly satisfied. The CAM algorithm integrates
plug-in noise filtering using sector-based clustering, an efficient geometric
convex analysis scheme, and stability-based model order selection. We
demonstrate the principle of CAM on simulated data and numerically mixed
natural images. The superior performance of CAM against a panel of benchmark
BSS techniques is demonstrated on numerically mixed gene expression data. We
then apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging
data taken from breast tumors and time-course microarray gene expression data
derived from in-vivo muscle regeneration in mice, both producing biologically
plausible decomposition results.
Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums
Authors: Abhimanu Kumar, Shriphani Palakodety, Chong Wang, Carolyn P. Rose, Eric P. Xing, Miaomiao Wen
Date: 2015-12-10 00:00:00
Tasks: Topic Models, Variational Inference

Online discussion forums are complex webs of overlapping subcommunities
(macrolevel structure, across threads) in which users enact different roles
depending on which subcommunity they are participating in within a particular
time point (microlevel structure, within threads). This sub-network structure
is implicit in massive collections of threads. To uncover this structure, we
develop a scalable algorithm based on stochastic variational inference and
leverage topic models (LDA) along with mixed membership stochastic block (MMSB)
models. We evaluate our model on three large-scale datasets,
Cancer-ThreadStarter (22K users and 14.4K threads), Cancer-NameMention(15.1K
users and 12.4K threads) and StackOverFlow (1.19 million users and 4.55 million
threads). Qualitatively, we demonstrate that our model can provide useful
explanations of microlevel and macrolevel user presentation characteristics in
different communities using the topics discovered from posts. Quantitatively,
we show that our model does better than MMSB and LDA in predicting user reply
structure within threads. In addition, we demonstrate via synthetic data
experiments that the proposed active sub-network discovery model is stable and
recovers the original parameters of the experimental setup with high
probability.
Boosted Sparse Non-linear Distance Metric Learning
Authors: Yuting Ma, Tian Zheng
Date: 2015-12-10 00:00:00
Tasks: global-optimization, Metric Learning

This paper proposes a boosting-based solution addressing metric learning
problems for high-dimensional data. Distance measures have been used as natural
measures of (dis)similarity and served as the foundation of various learning
methods. The efficiency of distance-based learning methods heavily depends on
the chosen distance metric. With increasing dimensionality and complexity of
data, however, traditional metric learning methods suffer from poor scalability
and the limitation due to linearity as the true signals are usually embedded
within a low-dimensional nonlinear subspace. In this paper, we propose a
nonlinear sparse metric learning algorithm via boosting. We restructure a
global optimization problem into a forward stage-wise learning of weak learners
based on a rank-one decomposition of the weight matrix in the Mahalanobis
distance metric. A gradient boosting algorithm is devised to obtain a sparse
rank-one update of the weight matrix at each step. Nonlinear features are
learned by a hierarchical expansion of interactions incorporated within the
boosting algorithm. Meanwhile, an early stopping rule is imposed to control the
overall complexity of the learned metric. As a result, our approach guarantees
three desirable properties of the final metric: positive semi-definiteness, low
rank and element-wise sparsity. Numerical experiments show that our learning
model compares favorably with the state-of-the-art methods in the current
literature of metric learning.
Deep Residual Learning for Image Recognition
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Date: 2015-12-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, Domain Generalization, Dynamic Facial Expression Recognition, Face Anti-Spoofing, Image Classification, Image-to-Image Translation, Medical Image Classification, Multi-Label Image Classification, Object Detection, Out-of-Distribution Generalization, Pedestrian Trajectory Prediction, Person Re-Identification, Retinal OCT Disease Classification, Semantic Segmentation

Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced functions. We provide comprehensive empirical evidence
showing that these residual networks are easier to optimize, and can gain
accuracy from considerably increased depth. On the ImageNet dataset we evaluate
residual nets with a depth of up to 152 layers---8x deeper than VGG nets but
still having lower complexity. An ensemble of these residual nets achieves
3.57% error on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100
and 1000 layers.
  The depth of representations is of central importance for many visual
recognition tasks. Solely due to our extremely deep representations, we obtain
a 28% relative improvement on the COCO object detection dataset. Deep residual
nets are foundations of our submissions to ILSVRC & COCO 2015 competitions,
where we also won the 1st places on the tasks of ImageNet detection, ImageNet
localization, COCO detection, and COCO segmentation.
Convolutional Monte Carlo Rollouts in Go
Authors: Peter H. Jin, Kurt Keutzer
Date: 2015-12-10 00:00:00
Tasks: GPU, Thompson Sampling

In this work, we present a MCTS-based Go-playing program which uses
convolutional networks in all parts. Our method performs MCTS in batches,
explores the Monte Carlo search tree using Thompson sampling and a
convolutional network, and evaluates convnet-based rollouts on the GPU. We
achieve strong win rates against open source Go programs and attain competitive
results against state of the art convolutional net-based Go-playing programs.
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Authors: Feng Zhou, Yuanqing Lin
Date: 2015-12-08 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, Fine-Grained Image Classification, Fine-Grained Image Recognition, General Classification, image-classification, Image Classification, Object, Object Recognition

Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealing with the scarcity of training data. Fortunately, the ultra-fine
granularity naturally brings rich relationships among object classes. This
paper proposes a novel approach to exploit the rich relationships through
bipartite-graph labels (BGL). We show how to model BGL in an overall
convolutional neural networks and the resulting system can be optimized through
back-propagation. We also show that it is computationally efficient in
inference thanks to the bipartite structure. To facilitate the study, we
construct a new food benchmark dataset, which consists of 37,885 food images
collected from 6 restaurants and totally 975 menus. Experimental results on
this new food and three other datasets demonstrates BGL advances previous works
in fine-grained object recognition. An online demo is available at
http://www.f-zhou.com/fg_demo/.