pwc-archive/papers-with-abstracts huggingface dataset:
Using Deep Learning for Segmentation and Counting within Microscopy Data
Authors: Carlos X. Hernández, Mohammad M. Sultan, Vijay S. Pande
Date: 2018-02-28 00:00:00
Tasks: Deep Learning

Cell counting is a ubiquitous, yet tedious task that would greatly benefit
from automation. From basic biological questions to clinical trials, cell
counts provide key quantitative feedback that drive research. Unfortunately,
cell counting is most commonly a manual task and can be time-intensive. The
task is made even more difficult due to overlapping cells, existence of
multiple focal planes, and poor imaging quality, among other factors. Here, we
describe a convolutional neural network approach, using a recently described
feature pyramid network combined with a VGG-style neural network, for
segmenting and subsequent counting of cells in a given microscopy image.
Detecting and Correcting for Label Shift with Black Box Predictors
Authors: Zachary C. Lipton, Yu-Xiang Wang, Alex Smola
Date: 2018-02-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Medical Diagnosis

Faced with distribution shift between training and test set, we wish to
detect and quantify the shift, and to correct our classifiers without test set
labels. Motivated by medical diagnosis, where diseases (targets) cause symptoms
(observations), we focus on label shift, where the label marginal $p(y)$
changes but the conditional $p(x| y)$ does not. We propose Black Box Shift
Estimation (BBSE) to estimate the test distribution $p(y)$. BBSE exploits
arbitrary black box predictors to reduce dimensionality prior to shift
correction. While better predictors give tighter estimates, BBSE works even
when predictors are biased, inaccurate, or uncalibrated, so long as their
confusion matrices are invertible. We prove BBSE's consistency, bound its
error, and introduce a statistical test that uses BBSE to detect shift. We also
leverage BBSE to correct classifiers. Experiments demonstrate accurate
estimates and improved prediction, even on high-dimensional datasets of natural
images.
Memory-based Parameter Adaptation
Authors: Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae, Alexander Pritzel, Adrià Puigdomènech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell
Date: 2018-02-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: image-classification, Image Classification, Language Modelling

Deep neural networks have excelled on a wide range of problems, from vision
to language and game playing. Neural networks very gradually incorporate
information into weights as they process data, requiring very low learning
rates. If the training distribution shifts, the network is slow to adapt, and
when it does adapt, it typically performs badly on the training distribution
before the shift. Our method, Memory-based Parameter Adaptation, stores
examples in memory and then uses a context-based lookup to directly modify the
weights of a neural network. Much higher learning rates can be used for this
local adaptation, reneging the need for many iterations over similar data
before good predictions can be made. As our method is memory-based, it
alleviates several shortcomings of neural networks, such as catastrophic
forgetting, fast, stable acquisition of new knowledge, learning with an
imbalanced class labels, and fast learning during evaluation. We demonstrate
this on a range of supervised tasks: large-scale image classification and
language modelling.
When is a Convolutional Filter Easy To Learn?
Authors: Simon S. Du, Jason D. Lee, Yuandong Tian
Date: 2017-09-18 00:00:00
Proceeding: ICLR 2018 1

We analyze the convergence of (stochastic) gradient descent algorithm for
learning a convolutional filter with Rectified Linear Unit (ReLU) activation
function. Our analysis does not rely on any specific form of the input
distribution and our proofs only use the definition of ReLU, in contrast with
previous works that are restricted to standard Gaussian input. We show that
(stochastic) gradient descent with random initialization can learn the
convolutional filter in polynomial time and the convergence rate depends on the
smoothness of the input distribution and the closeness of patches. To the best
of our knowledge, this is the first recovery guarantee of gradient-based
algorithms for convolutional filter on non-Gaussian input distributions. Our
theory also justifies the two-stage learning rate strategy in deep neural
networks. While our focus is theoretical, we also present experiments that
illustrate our theoretical findings.
Learning High-level Representations from Demonstrations
Authors: Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar
Date: 2018-02-19 00:00:00
Tasks: Montezuma's Revenge, Open-Ended Question Answering, Vocal Bursts Intensity Prediction

Hierarchical learning (HL) is key to solving complex sequential decision
problems with long horizons and sparse rewards. It allows learning agents to
break-up large problems into smaller, more manageable subtasks. A common
approach to HL, is to provide the agent with a number of high-level skills that
solve small parts of the overall problem. A major open question, however, is
how to identify a suitable set of reusable skills. We propose a principled
approach that uses human demonstrations to infer a set of subgoals based on
changes in the demonstration dynamics. Using these subgoals, we decompose the
learning problem into an abstract high-level representation and a set of
low-level subtasks. The abstract description captures the overall problem
structure, while subtasks capture desired skills. We demonstrate that we can
jointly optimize over both levels of learning. We show that the resulting
method significantly outperforms previous baselines on two challenging
problems: the Atari 2600 game Montezuma's Revenge, and a simulated robotics
problem moving the ant robot through a maze.
Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream
Authors: Maurits Kaptein, Paul Ketelaar
Date: 2018-02-28 00:00:00
Tasks: Marketing, regression

In marketing we are often confronted with a continuous stream of responses to
marketing messages. Such streaming data provide invaluable information
regarding message effectiveness and segmentation. However, streaming data are
hard to analyze using conventional methods: their high volume and the fact that
they are continuously augmented means that it takes considerable time to
analyze them. We propose a method for estimating a finite mixture of logistic
regression models which can be used to cluster customers based on a continuous
stream of responses. This method, which we coin oFMLR, allows segments to be
identified in data streams or extremely large static datasets. Contrary to
black box algorithms, oFMLR provides model estimates that are directly
interpretable. We first introduce oFMLR, explaining in passing general topics
such as online estimation and the EM algorithm, making this paper a high level
overview of possible methods of dealing with large data streams in marketing
practice. Next, we discuss model convergence, identifiability, and relations to
alternative, Bayesian, methods; we also identify more general issues that arise
from dealing with continuously augmented data sets. Finally, we introduce the
oFMLR [R] package and evaluate the method by numerical simulation and by
analyzing a large customer clickstream dataset.
Application of Rényi and Tsallis Entropies to Topic Modeling Optimization
Authors: Koltcov Sergei
Date: 2018-02-28 00:00:00
Tasks: Topic Models

This is full length article (draft version) where problem number of topics in
Topic Modeling is discussed. We proposed idea that Renyi and Tsallis entropy
can be used for identification of optimal number in large textual collections.
We also report results of numerical experiments of Semantic stability for 4
topic models, which shows that semantic stability play very important role in
problem topic number. The calculation of Renyi and Tsallis entropy based on
thermodynamics approach.
Deep Private-Feature Extraction
Authors: Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee
Date: 2018-02-09 00:00:00

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model
which is trained and evaluated based on information theoretic constraints.
Using the selective exchange of information between a user's device and a
service provider, DPFE enables the user to prevent certain sensitive
information from being shared with a service provider, while allowing them to
extract approved information using their model. We introduce and utilize the
log-rank privacy, a novel measure to assess the effectiveness of DPFE in
removing sensitive information and compare different models based on their
accuracy-privacy tradeoff. We then implement and evaluate the performance of
DPFE on smartphones to understand its complexity, resource demands, and
efficiency tradeoffs. Our results on benchmark image datasets demonstrate that
under moderate resource utilization, DPFE can achieve high accuracy for primary
tasks while preserving the privacy of sensitive features.
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
Authors: Jonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour
Date: 2018-01-12 00:00:00
Tasks: Retrieval

We present a novel deep neural network architecture for representing robot
experiences in an episodic-like memory which facilitates encoding, recalling,
and predicting action experiences. Our proposed unsupervised deep episodic
memory model 1) encodes observed actions in a latent vector space and, based on
this latent encoding, 2) infers most similar episodes previously experienced,
3) reconstructs original episodes, and 4) predicts future frames in an
end-to-end fashion. Results show that conceptually similar actions are mapped
into the same region of the latent vector space. Based on these results, we
introduce an action matching and retrieval mechanism, benchmark its performance
on two large-scale action datasets, 20BN-something-something and ActivityNet
and evaluate its generalization capability in a real-world scenario on a
humanoid robot.
Improving Hypernymy Extraction with Distributional Semantic Classes
Authors: Alexander Panchenko, Dmitry Ustalov, Stefano Faralli, Simone P. Ponzetto, Chris Biemann
Date: 2017-11-08 00:00:00
Proceeding: LREC 2018 5
Tasks: Denoising

In this paper, we show how distributionally-induced semantic classes can be
helpful for extracting hypernyms. We present methods for inducing sense-aware
semantic classes using distributional semantics and using these induced
semantic classes for filtering noisy hypernymy relations. Denoising of
hypernyms is performed by labeling each semantic class with its hypernyms. On
the one hand, this allows us to filter out wrong extractions using the global
structure of distributionally similar senses. On the other hand, we infer
missing hypernyms via label propagation to cluster terms. We conduct a
large-scale crowdsourcing study showing that processing of automatically
extracted hypernyms using our approach improves the quality of the hypernymy
extraction in terms of both precision and recall. Furthermore, we show the
utility of our method in the domain taxonomy induction task, achieving the
state-of-the-art results on a SemEval'16 task on taxonomy induction.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Authors: Fabian Isensee, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, Klaus H. Maier-Hein
Date: 2018-02-28 00:00:00
Tasks: Brain Tumor Segmentation, Data Augmentation, Decision Making, Segmentation, Survival Prediction, Tumor Segmentation

Quantitative analysis of brain tumors is critical for clinical decision
making. While manual segmentation is tedious, time consuming and subjective,
this task is at the same time very challenging to solve for automatic
segmentation methods. In this paper we present our most recent effort on
developing a robust segmentation algorithm in the form of a convolutional
neural network. Our network architecture was inspired by the popular U-Net and
has been carefully modified to maximize brain tumor segmentation performance.
We use a dice loss function to cope with class imbalances and use extensive
data augmentation to successfully prevent overfitting. Our method beats the
current state of the art on BraTS 2015, is one of the leading methods on the
BraTS 2017 validation set (dice scores of 0.896, 0.797 and 0.732 for whole
tumor, tumor core and enhancing tumor, respectively) and achieves very good
Dice scores on the test set (0.858 for whole, 0.775 for core and 0.647 for
enhancing tumor). We furthermore take part in the survival prediction
subchallenge by training an ensemble of a random forest regressor and
multilayer perceptrons on shape features describing the tumor subregions. Our
approach achieves 52.6% accuracy, a Spearman correlation coefficient of 0.496
and a mean square error of 209607 on the test set.
LDOP: Local Directional Order Pattern for Robust Face Retrieval
Authors: Shiv Ram Dubey, Snehasis Mukherjee
Date: 2018-02-28 00:00:00
Tasks: Image Retrieval, Retrieval

The local descriptors have gained wide range of attention due to their enhanced discriminative abilities. It has been proved that the consideration of multi-scale local neighborhood improves the performance of the descriptor, though at the cost of increased dimension. This paper proposes a novel method to construct a local descriptor using multi-scale neighborhood by finding the local directional order among the intensity values at different scales in a particular direction. Local directional order is the multi-radius relationship factor in a particular direction. The proposed local directional order pattern (LDOP) for a particular pixel is computed by finding the relationship between the center pixel and local directional order indexes. It is required to transform the center value into the range of neighboring orders. Finally, the histogram of LDOP is computed over whole image to construct the descriptor. In contrast to the state-of-the-art descriptors, the dimension of the proposed descriptor does not depend upon the number of neighbors involved to compute the order; it only depends upon the number of directions. The introduced descriptor is evaluated over the image retrieval framework and compared with the state-of-the-art descriptors over challenging face databases such as PaSC, LFW, PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the superiority and robustness of the LDOP descriptor.
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Authors: Jeremy Aghaei Mazaheri, Elif Vural, Claude Labit, Christine Guillemot
Date: 2018-02-28 00:00:00
Tasks: Classification, Denoising, General Classification, image-classification, Image Classification, Super-Resolution

Sparse representations using overcomplete dictionaries have proved to be a
powerful tool in many signal processing applications such as denoising,
super-resolution, inpainting, compression or classification. The sparsity of
the representation very much depends on how well the dictionary is adapted to
the data at hand. In this paper, we propose a method for learning structured
multilevel dictionaries with discriminative constraints to make them well
suited for the supervised pixelwise classification of images. A multilevel
tree-structured discriminative dictionary is learnt for each class, with a
learning objective concerning the reconstruction errors of the image patches
around the pixels over each class-representative dictionary. After the initial
assignment of the class labels to image pixels based on their sparse
representations over the learnt dictionaries, the final classification is
achieved by smoothing the label image with a graph cut method and an erosion
method. Applied to a common set of texture images, our supervised
classification method shows competitive results with the state of the art.
Pop Music Highlighter: Marking the Emotion Keypoints
Authors: Yu-Siang Huang, Szu-Yu Chou, Yi-Hsuan Yang
Date: 2018-02-28 00:00:00
Tasks: Emotion Classification

The goal of music highlight extraction is to get a short consecutive segment
of a piece of music that provides an effective representation of the whole
piece. In a previous work, we introduced an attention-based convolutional
recurrent neural network that uses music emotion classification as a surrogate
task for music highlight extraction, for Pop songs. The rationale behind that
approach is that the highlight of a song is usually the most emotional part.
This paper extends our previous work in the following two aspects. First,
methodology-wise we experiment with a new architecture that does not need any
recurrent layers, making the training process faster. Moreover, we compare a
late-fusion variant and an early-fusion variant to study which one better
exploits the attention mechanism. Second, we conduct and report an extensive
set of experiments comparing the proposed attention-based methods against a
heuristic energy-based method, a structural repetition-based method, and a few
other simple feature-based methods for this task. Due to the lack of
public-domain labeled data for highlight extraction, following our previous
work we use the RWC POP 100-song data set to evaluate how the detected
highlights overlap with any chorus sections of the songs. The experiments
demonstrate the effectiveness of our methods over competing methods. For
reproducibility, we open source the code and pre-trained model at
https://github.com/remyhuang/pop-music-highlighter/.
Gradient Normalization & Depth Based Decay For Deep Learning
Authors: Robert Kwiatkowski, Oscar Chang
Date: 2017-12-10 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification

In this paper we introduce a novel method of gradient normalization and decay
with respect to depth. Our method leverages the simple concept of normalizing
all gradients in a deep neural network, and then decaying said gradients with
respect to their depth in the network. Our proposed normalization and decay
techniques can be used in conjunction with most current state of the art
optimizers and are a very simple addition to any network. This method, although
simple, showed improvements in convergence time on state of the art networks
such as DenseNet and ResNet on image classification tasks, as well as on an
LSTM for natural language processing tasks.
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image
Authors: Yanan Luo, Jie Zou, Chengfei Yao, Tao Li, Gang Bai
Date: 2018-02-28 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

With the development of deep learning, the performance of hyperspectral image
(HSI) classification has been greatly improved in recent years. The shortage of
training samples has become a bottleneck for further improvement of
performance. In this paper, we propose a novel convolutional neural network
framework for the characteristics of hyperspectral image data, called HSI-CNN.
Firstly, the spectral-spatial feature is extracted from a target pixel and its
neighbors. Then, a number of one-dimensional feature maps, obtained by
convolution operation on spectral-spatial features, are stacked into a
two-dimensional matrix. Finally, the two-dimensional matrix considered as an
image is fed into standard CNN. This is why we call it HSI-CNN. In addition, we
also implements two depth network classification models, called HSI-CNN+XGBoost
and HSI-CapsNet, in order to compare the performance of our framework.
Experiments show that the performance of hyperspectral image classification is
improved efficiently with HSI-CNN framework. We evaluate the model's
performance using four popular HSI datasets, which are the Kennedy Space Center
(KSC), Indian Pines (IP), Pavia University scene (PU) and Salinas scene (SA).
As far as we concerned, HSI-CNN has got the state-of-art accuracy among all
methods we have known on these datasets of 99.28%, 99.09%, 99.42%, 98.95%
separately.
Deterministic and Probabilistic Conditions for Finite Completability of Low-Tucker-Rank Tensor
Authors: Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang
Date: 2016-12-06 00:00:00

We investigate the fundamental conditions on the sampling pattern, i.e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank. In order to find the deterministic necessary and sufficient conditions, we propose an algebraic geometric analysis on the Tucker manifold, which allows us to incorporate multiple rank components in the proposed analysis in contrast with the conventional geometric approaches on the Grassmannian manifold. This analysis characterizes the algebraic independence of a set of polynomials defined based on the sampling pattern, which is closely related to finite completion. Probabilistic conditions are then studied and a lower bound on the sampling probability is given, which guarantees that the proposed deterministic conditions on the sampling patterns for finite completability hold with high probability. Furthermore, using the proposed geometric approach for finite completability, we propose a sufficient condition on the sampling pattern that ensures there exists exactly one completion for the sampled tensor.
Fine-grained wound tissue analysis using deep neural network
Authors: Hossein Nejati, Hamed Alizadeh Ghazijahani, Milad Abdollahzadeh, Tooba Malekzadeh, Ngai-Man Cheung, Kheng Hock Lee, Lian Leng Low
Date: 2018-02-28 00:00:00
Tasks: General Classification

Tissue assessment for chronic wounds is the basis of wound grading and
selection of treatment approaches. While several image processing approaches
have been proposed for automatic wound tissue analysis, there has been a
shortcoming in these approaches for clinical practices. In particular,
seemingly, all previous approaches have assumed only 3 tissue types in the
chronic wounds, while these wounds commonly exhibit 7 distinct tissue types
that presence of each one changes the treatment procedure. In this paper, for
the first time, we investigate the classification of 7 wound issue types. We
work with wound professionals to build a new database of 7 types of wound
tissue. We propose to use pre-trained deep neural networks for feature
extraction and classification at the patch-level. We perform experiments to
demonstrate that our approach outperforms other state-of-the-art. We will make
our database publicly available to facilitate research in wound assessment.
On the Sublinear Convergence of Randomly Perturbed Alternating Gradient Descent to Second Order Stationary Solutions
Authors: Songtao Lu, Mingyi Hong, Zhengdao Wang
Date: 2018-02-28 00:00:00

The alternating gradient descent (AGD) is a simple but popular algorithm
which has been applied to problems in optimization, machine learning, data
ming, and signal processing, etc. The algorithm updates two blocks of variables
in an alternating manner, in which a gradient step is taken on one block, while
keeping the remaining block fixed. When the objective function is nonconvex, it
is well-known the AGD converges to the first-order stationary solution with a
global sublinear rate.
  In this paper, we show that a variant of AGD-type algorithms will not be
trapped by "bad" stationary solutions such as saddle points and local maximum
points. In particular, we consider a smooth unconstrained optimization problem,
and propose a perturbed AGD (PA-GD) which converges (with high probability) to
the set of second-order stationary solutions (SS2) with a global sublinear
rate. To the best of our knowledge, this is the first alternating type
algorithm which takes $\mathcal{O}(\text{polylog}(d)/\epsilon^{7/3})$
iterations to achieve SS2 with high probability [where polylog$(d)$ is
polynomial of the logarithm of dimension $d$ of the problem].