pwc-archive/papers-with-abstracts huggingface dataset:
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification
Authors: Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Mohak Shah
Date: 2016-12-05 00:00:00
Tasks: Classification, General Classification, Representation Learning, Time Series, Time Series Analysis, Time Series Classification

In this paper, we consider the problem of event classification with
multi-variate time series data consisting of heterogeneous (continuous and
categorical) variables. The complex temporal dependencies between the variables
combined with sparsity of the data makes the event classification problem
particularly challenging. Most state-of-art approaches address this either by
designing hand-engineered features or breaking up the problem over homogeneous
variates. In this work, we propose and compare three representation learning
algorithms over symbolized sequences which enables classification of
heterogeneous time-series data using a deep architecture. The proposed
representations are trained jointly along with the rest of the network
architecture in an end-to-end fashion that makes the learned features
discriminative for the given task. Experiments on three real-world datasets
demonstrate the effectiveness of the proposed approaches.
Deep Image Category Discovery using a Transferred Similarity Function
Authors: Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Date: 2016-12-05 00:00:00
Tasks: Clustering, Transfer Learning

Automatically discovering image categories in unlabeled natural images is one
of the important goals of unsupervised learning. However, the task is
challenging and even human beings define visual categories based on a large
amount of prior knowledge. In this paper, we similarly utilize prior knowledge
to facilitate the discovery of image categories. We present a novel end-to-end
network to map unlabeled images to categories as a clustering network. We
propose that this network can be learned with contrastive loss which is only
based on weak binary pair-wise constraints. Such binary constraints can be
learned from datasets in other domains as transferred similarity functions,
which mimic a simple knowledge transfer. We first evaluate our experiments on
the MNIST dataset as a proof of concept, based on predicted similarities
trained on Omniglot, showing a 99\% accuracy which significantly outperforms
clustering based approaches. Then we evaluate the discovery performance on
Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy
and shows it can be scalable to various large natural images.
A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction
Authors: Turki Turki, Zhi Wei
Date: 2016-12-02 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Prediction, Retrieval, Sensitivity

Accurately predicting drug responses to cancer is an important problem
hindering oncologists' efforts to find the most effective drugs to treat
cancer, which is a core goal in precision medicine. The scientific community
has focused on improving this prediction based on genomic, epigenomic, and
proteomic datasets measured in human cancer cell lines. Real-world cancer cell
lines contain noise, which degrades the performance of machine learning
algorithms. This problem is rarely addressed in the existing approaches. In
this paper, we present a noise-filtering approach that integrates techniques
from numerical linear algebra and information retrieval targeted at filtering
out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can
train machine learning algorithms on better quality cancer cell lines. We
evaluate the performance of our approach and compare it with an existing
approach using the Area Under the ROC Curve (AUC) on clinical trial data. The
experimental results show that our proposed approach is stable and also yields
the highest AUC at a statistically significant level.
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets
Authors: Jaekoo Lee, Hyunjae Kim, Jongsun Lee, Sungroh Yoon
Date: 2016-11-15 00:00:00
Tasks: Deep Learning, Representation Learning, Transfer Learning

Graphs provide a powerful means for representing complex interactions between
entities. Recently, deep learning approaches are emerging for representing and
modeling graph-structured data, although the conventional deep learning methods
(such as convolutional neural networks and recurrent neural networks) have
mainly focused on grid-structured inputs (image and audio). Leveraged by the
capability of representation learning, deep learning based techniques are
reporting promising results for graph applications by detecting structural
characteristics of graphs in an automated fashion. In this paper, we attempt to
advance deep learning for graph-structured data by incorporating another
component, transfer learning. By transferring the intrinsic geometric
information learned in the source domain, our approach can help us to construct
a model for a new but related task in the target domain without collecting new
data and without training a new model from scratch. We thoroughly test our
approach with large-scale real corpora and confirm the effectiveness of the
proposed transfer learning framework for deep learning on graphs. According to
our experiments, transfer learning is most effective when the source and target
domains bear a high level of structural similarity in their graph
representations.
Scalable and Sustainable Deep Learning via Randomized Hashing
Authors: Ryan Spring, Anshumali Shrivastava
Date: 2016-02-26 00:00:00
Tasks: Deep Learning

Current deep learning architectures are growing larger in order to learn from
complex datasets. These architectures require giant matrix multiplication
operations to train millions of parameters. Conversely, there is another
growing trend to bring deep learning to low-power, embedded devices. The matrix
operations, associated with both training and testing of deep networks, are
very expensive from a computational and energy standpoint. We present a novel
hashing based technique to drastically reduce the amount of computation needed
to train and test deep networks. Our approach combines recent ideas from
adaptive dropouts and randomized hashing for maximum inner product search to
select the nodes with the highest activation efficiently. Our new algorithm for
deep learning reduces the overall computational cost of forward and
back-propagation by operating on significantly fewer (sparse) nodes. As a
consequence, our algorithm uses only 5% of the total multiplications, while
keeping on average within 1% of the accuracy of the original model. A unique
property of the proposed hashing based back-propagation is that the updates are
always sparse. Due to the sparse gradient updates, our algorithm is ideally
suited for asynchronous and parallel training leading to near linear speedup
with increasing number of cores. We demonstrate the scalability and
sustainability (energy efficiency) of our proposed algorithm via rigorous
experimental evaluations on several real datasets.
Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images
Authors: Pegah Faridi, Habibollah Danyali, Mohammad Sadegh Helfroush, Mojgan Akbarzadeh Jahromi
Date: 2016-12-05 00:00:00
Tasks: Prognosis

Early detection and prognosis of breast cancer are feasible by utilizing
histopathological grading of biopsy specimens. This research is focused on
detection and grading of nuclear pleomorphism in histopathological images of
breast cancer. The proposed method consists of three internal steps. First,
unmixing colors of H&E is used in the preprocessing step. Second, nuclei
boundaries are extracted incorporating the center of cancerous nuclei which are
detected by applying morphological operations and Difference of Gaussian filter
on the preprocessed image. Finally, segmented nuclei are scored to accomplish
one parameter of the Nottingham grading system for breast cancer. In this
approach, the nuclei area, chromatin density, contour regularity, and nucleoli
presence, are features for nuclear pleomorphism scoring. Experimental results
showed that the proposed algorithm, with an accuracy of 86.6%, made significant
advancement in detecting cancerous nuclei compared to existing methods in the
related literature.
Turning an Urban Scene Video into a Cinemagraph
Authors: Hang Yan, Yebin Liu, Yasutaka Furukawa
Date: 2016-12-05 00:00:00
Proceeding: CVPR 2017 7

This paper proposes an algorithm that turns a regular video capturing urban
scenes into a high-quality endless animation, known as a Cinemagraph. The
creation of a Cinemagraph usually requires a static camera in a carefully
configured scene. The task becomes challenging for a regular video with a
moving camera and objects. Our approach first warps an input video into the
viewpoint of a reference camera. Based on the warped video, we propose
effective temporal analysis algorithms to detect regions with static geometry
and dynamic appearance, where geometric modeling is reliable and visually
attractive animations can be created. Lastly, the algorithm applies a sequence
of video processing techniques to produce a Cinemagraph movie. We have tested
the proposed approach on numerous challenging real scenes. To our knowledge,
this work is the first to automatically generate Cinemagraph animations from
regular movies in the wild.
Multi-way Particle Swarm Fusion
Authors: Chen Liu, Hang Yan, Pushmeet Kohli, Yasutaka Furukawa
Date: 2016-12-05 00:00:00
Tasks: Optical Flow Estimation

This paper proposes a novel MAP inference framework for Markov Random Field
(MRF) in parallel computing environments. The inference framework, dubbed Swarm
Fusion, is a natural generalization of the Fusion Move method. Every thread (in
a case of multi-threading environments) maintains and updates a solution. At
each iteration, a thread can generate arbitrary number of solution proposals
and take arbitrary number of concurrent solutions from the other threads to
perform multi-way fusion in updating its solution. The framework is general,
making popular existing inference techniques such as alpha-expansion, fusion
move, parallel alpha-expansion, and hierarchical fusion, its special cases. We
have evaluated the effectiveness of our approach against competing methods on
three problems of varying difficulties, in particular, the stereo, the optical
flow, and the layered depthmap estimation problems.
Deep Pyramidal Residual Networks with Separated Stochastic Depth
Authors: Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise
Date: 2016-12-05 00:00:00
Tasks: Object Recognition

On general object recognition, Deep Convolutional Neural Networks (DCNNs)
achieve high accuracy. In particular, ResNet and its improvements have broken
the lowest error rate records. In this paper, we propose a method to
successfully combine two ResNet improvements, ResDrop and PyramidNet. We
confirmed that the proposed network outperformed the conventional methods; on
CIFAR-100, the proposed network achieved an error rate of 16.18% in contrast to
PiramidNet achieving that of 18.29% and ResNeXt 17.31%.
Deep Multi-Modal Image Correspondence Learning
Authors: Chen Liu, Jiajun Wu, Pushmeet Kohli, Yasutaka Furukawa
Date: 2016-12-05 00:00:00

Inference of correspondences between images from different modalities is an
extremely important perceptual ability that enables humans to understand and
recognize cross-modal concepts. In this paper, we consider an instance of this
problem that involves matching photographs of building interiors with their
corresponding floorplan. This is a particularly challenging problem because a
floorplan, as a stylized architectural drawing, is very different in appearance
from a color photograph. Furthermore, individual photographs by themselves
depict only a part of a floorplan (e.g., kitchen, bathroom, and living room).
We propose the use of a number of different neural network architectures for
this task, which are trained and evaluated on a novel large-scale dataset of 5
million floorplan images and 80 million associated photographs. Experimental
evaluation reveals that our neural network architectures are able to identify
visual cues that result in reliable matches across these two quite different
modalities. In fact, the trained networks are able to even outperform human
subjects in several challenging image matching problems. Our result implies
that neural networks are effective at perceptual tasks that require long
periods of reasoning even for humans to solve.
Using Fast Weights to Attend to the Recent Past
Authors: Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu
Date: 2016-10-20 00:00:00
Proceeding: NeurIPS 2016 12

Until recently, research on artificial neural networks was largely restricted
to systems with only two types of variable: Neural activities that represent
the current or recent input and weights that learn to capture regularities
among inputs, outputs and payoffs. There is no good reason for this
restriction. Synapses have dynamics at many different time-scales and this
suggests that artificial neural networks might benefit from variables that
change slower than activities but much faster than the standard weights. These
"fast weights" can be used to store temporary memories of the recent past and
they provide a neurally plausible way of implementing the type of attention to
the past that has recently proved very helpful in sequence-to-sequence models.
By using fast weights we can avoid the need to store copies of neural activity
patterns.
Intra-day Activity Better Predicts Chronic Conditions
Authors: Tom Quisel, David C. Kale, Luca Foschini
Date: 2016-12-04 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Time Series, Time Series Analysis

In this work we investigate intra-day patterns of activity on a population of
7,261 users of mobile health wearable devices and apps. We show that: (1) using
intra-day step and sleep data recorded from passive trackers significantly
improves classification performance on self-reported chronic conditions related
to mental health and nervous system disorders, (2) Convolutional Neural
Networks achieve top classification performance vs. baseline models when
trained directly on multivariate time series of activity data, and (3) jointly
predicting all condition classes via multi-task learning can be leveraged to
extract features that generalize across data sets and achieve the highest
classification performance.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Authors: Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao
Date: 2016-12-04 00:00:00
Tasks: Feature Engineering, Natural Language Understanding, Program induction, Semantic Parsing

Extending the success of deep neural networks to natural language
understanding and symbolic reasoning requires complex operations and external
memory. Recent neural program induction approaches have attempted to address
this problem, but are typically limited to differentiable memory, and
consequently cannot scale beyond small synthetic tasks. In this work, we
propose the Manager-Programmer-Computer framework, which integrates neural
networks with non-differentiable memory to support abstract, scalable and
precise operations through a friendly neural computer interface. Specifically,
we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence
neural "programmer", and a non-differentiable "computer" that is a Lisp
interpreter with code assist. To successfully apply REINFORCE for training, we
augment it with approximate gold programs found by an iterative maximum
likelihood training process. NSM is able to learn a semantic parser from weak
supervision over a large knowledge base. It achieves new state-of-the-art
performance on WebQuestionsSP, a challenging semantic parsing dataset, with
weak supervision. Compared to previous approaches, NSM is end-to-end, therefore
does not rely on feature engineering or domain specific knowledge.
Online Localization and Prediction of Actions and Interactions
Authors: Khurram Soomro, Haroon Idrees, Mubarak Shah
Date: 2016-12-04 00:00:00
Tasks: Pose Estimation, Prediction, Superpixels

This paper proposes a person-centric and online approach to the challenging
problem of localization and prediction of actions and interactions in videos.
Typically, localization or recognition is performed in an offline manner where
all the frames in the video are processed together. This prevents timely
localization and prediction of actions and interactions - an important
consideration for many tasks including surveillance and human-machine
interaction.
  In our approach, we estimate human poses at each frame and train
discriminative appearance models using the superpixels inside the pose bounding
boxes. Since the pose estimation per frame is inherently noisy, the conditional
probability of pose hypotheses at current time-step (frame) is computed using
pose estimations in the current frame and their consistency with poses in the
previous frames. Next, both the superpixel and pose-based foreground
likelihoods are used to infer the location of actors at each time through a
Conditional Random. The issue of visual drift is handled by updating the
appearance models, and refining poses using motion smoothness on joint
locations, in an online manner. For online prediction of action (interaction)
confidences, we propose an approach based on Structural SVM that operates on
short video segments, and is trained with the objective that confidence of an
action or interaction increases as time progresses. Lastly, we quantify the
performance of both detection and prediction together, and analyze how the
prediction accuracy varies as a time function of observed action (interaction)
at different levels of detection performance. Our experiments on several
datasets suggest that despite using only a few frames to localize actions
(interactions) at each time instant, we are able to obtain competitive results
to state-of-the-art offline methods.
Multiclass Classification of Cervical Cancer Tissues by Hidden Markov Model
Authors: Sabyasachi Mukhopadhyay, Sanket Nandan, Indrajit Kurmi
Date: 2015-12-18 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

In this paper, we report a hidden Markov model based multiclass
classification of cervical cancer tissues. This model has been validated
directly over time series generated by the medium refractive index fluctuations
extracted from differential interference contrast images of healthy and
different stages of cancer tissues. The method shows promising results for
multiclass classification with higher accuracy.
A method for the segmentation of images based on thresholding and applied to vesicular textures
Authors: Amelia Carolina Sparavigna
Date: 2016-12-04 00:00:00

In image processing, a segmentation is a process of partitioning an image
into multiple sets of pixels, that are defined as super-pixels. Each
super-pixel is characterized by a label or parameter. Here, we are proposing a
method for determining the super-pixels based on the thresholding of the image.
This approach is quite useful for studying the images showing vesicular
textures.
Differential Evolution for Efficient AUV Path Planning in Time Variant Uncertain Underwater Environment
Authors: S. Mahmoud Zadeh, D. M. W. Powers, A. Yazdani, K. Sammut, A. Atyabi
Date: 2016-04-09 00:00:00

The AUV three-dimension path planning in complex turbulent underwater
environment is investigated in this research, in which static current map data
and uncertain static-moving time variant obstacles are taken into account.
Robustness of AUVs path planning to this strong variability is known as a
complex NP-hard problem and is considered a critical issue to ensure vehicles
safe deployment. Efficient evolutionary techniques have substantial potential
of handling NP hard complexity of path planning problem as more powerful and
fast algorithms among other approaches for mentioned problem. For the purpose
of this research Differential Evolution (DE) technique is conducted to solve
the AUV path planning problem in a realistic underwater environment. The path
planners designed in this paper are capable of extracting feasible areas of a
real map to determine the allowed spaces for deployment, where coastal area,
islands, static/dynamic obstacles and ocean current is taken into account and
provides the efficient path with a small computation time. The results obtained
from analyze of experimental demonstrate the inherent robustness and drastic
efficiency of the proposed scheme in enhancement of the vehicles path planning
capability in coping undesired current, using useful current flow, and avoid
colliding collision boundaries in a real-time manner. The proposed approach is
also flexible and strictly respects to vehicle's kinematic constraints
resisting current instabilities.
Representing Independence Models with Elementary Triplets
Authors: Jose M. Peña
Date: 2016-12-04 00:00:00

In an independence model, the triplets that represent conditional
independences between singletons are called elementary. It is known that the
elementary triplets represent the independence model unambiguously under some
conditions. In this paper, we show how this representation helps performing
some operations with independence models, such as finding the dominant triplets
or a minimal independence map of an independence model, or computing the union
or intersection of a pair of independence models, or performing causal
reasoning. For the latter, we rephrase in terms of conditional independences
some of Pearl's results for computing causal effects.
Learning to superoptimize programs - Workshop Version
Authors: Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
Date: 2016-12-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Superoptimization requires the estimation of the best program for a given
computational task. In order to deal with large programs, superoptimization
techniques perform a stochastic search. This involves proposing a modification
of the current program, which is accepted or rejected based on the improvement
achieved. The state of the art method uses uniform proposal distributions,
which fails to exploit the problem structure to the fullest. To alleviate this
deficiency, we learn a proposal distribution over possible modifications using
Reinforcement Learning. We provide convincing results on the superoptimization
of "Hacker's Delight" programs.
Boost K-Means
Authors: Wan-Lei Zhao, Cheng-Hao Deng, Chong-Wah Ngo
Date: 2016-10-08 00:00:00
Tasks: Clustering, Image Clustering, Stochastic Optimization

Due to its simplicity and versatility, k-means remains popular since it was
proposed three decades ago. The performance of k-means has been enhanced from
different perspectives over the years. Unfortunately, a good trade-off between
quality and efficiency is hardly reached. In this paper, a novel k-means
variant is presented. Different from most of k-means variants, the clustering
procedure is driven by an explicit objective function, which is feasible for
the whole l2-space. The classic egg-chicken loop in k-means has been simplified
to a pure stochastic optimization procedure. The procedure of k-means becomes
simpler and converges to a considerably better local optima. The effectiveness
of this new variant has been studied extensively in different contexts, such as
document clustering, nearest neighbor search and image clustering. Superior
performance is observed across different scenarios.