pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Skin Lesion Classification
Authors: P. Mirunalini, Aravindan Chandrabose, Vignesh Gokul, S. M. Jaisakthi
Date: 2017-03-13 00:00:00
Tasks: Classification, Deep Learning, General Classification, Lesion Classification, Lesion Detection, Skin Lesion Classification

Melanoma, a malignant form of skin cancer is very threatening to life.
Diagnosis of melanoma at an earlier stage is highly needed as it has a very
high cure rate. Benign and malignant forms of skin cancer can be detected by
analyzing the lesions present on the surface of the skin using dermoscopic
images. In this work, an automated skin lesion detection system has been
developed which learns the representation of the image using Google's
pretrained CNN model known as Inception-v3 \cite{cnn}. After obtaining the
representation vector for our input dermoscopic images we have trained two
layer feed forward neural network to classify the images as malignant or
benign. The system also classifies the images based on the cause of the cancer
either due to melanocytic or non-melanocytic cells using a different neural
network. These classification tasks are part of the challenge organized by
International Skin Imaging Collaboration (ISIC) 2017. Our system learns to
classify the images based on the model built using the training images given in
the challenge and the experimental results were evaluated using validation and
test sets. Our system has achieved an overall accuracy of 65.8\% for the
validation set.
Toward a Formal Model of Cognitive Synergy
Authors: Ben Goertzel
Date: 2017-03-13 00:00:00
Tasks: model, Reinforcement Learning, Specificity

"Cognitive synergy" refers to a dynamic in which multiple cognitive
processes, cooperating to control the same cognitive system, assist each other
in overcoming bottlenecks encountered during their internal processing.
Cognitive synergy has been posited as a key feature of real-world general
intelligence, and has been used explicitly in the design of the OpenCog
cognitive architecture. Here category theory and related concepts are used to
give a formalization of the cognitive synergy concept.
  A series of formal models of intelligent agents is proposed, with increasing
specificity and complexity: simple reinforcement learning agents; "cognit"
agents with an abstract memory and processing model; hypergraph-based agents
(in which "cognit" operations are carried out via hypergraphs); hypergraph
agents with a rich language of nodes and hyperlinks (such as the OpenCog
framework provides); "PGMC" agents whose rich hypergraphs are endowed with
cognitive processes guided via Probabilistic Growth and Mining of Combinations;
and finally variations of the PrimeAGI design, which is currently being built
on top of OpenCog.
  A notion of cognitive synergy is developed for cognitive processes acting
within PGMC agents, based on developing a formal notion of "stuckness," and
defining synergy as a relationship between cognitive processes in which they
can help each other out when they get stuck. It is proposed that cognitive
processes relating to each other synergetically, associate in a certain way
with functors that map into each other via natural transformations. Cognitive
synergy is proposed to correspond to a certain inequality regarding the
relative costs of different paths through certain commutation diagrams.
  Applications of this notion of cognitive synergy to particular cognitive
phenomena, and specific cognitive processes in the PrimeAGI design, are
discussed.
Nematus: a Toolkit for Neural Machine Translation
Authors: Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, Maria Nădejde
Date: 2017-03-13 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Translation

We present Nematus, a toolkit for Neural Machine Translation. The toolkit
prioritizes high translation accuracy, usability, and extensibility. Nematus
has been used to build top-performing submissions to shared translation tasks
at WMT and IWSLT, and has been used to train systems for production
environments.
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
Authors: Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze
Date: 2017-03-13 00:00:00
Tasks: Data Augmentation

Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of the spine, segments the five lumbar vertebrae and simultaneously
labels them. The first stage employs a multi-layered perceptron performing
non-linear regression for locating the lumbar region using the global context.
The second stage, comprised of a fully-convolutional deep network, exploits the
local context in the localised lumbar region to segment and label the lumbar
vertebrae in one go. Aided with practical data augmentation for training, our
approach is highly generalisable, capable of successfully segmenting both
healthy and abnormal vertebrae (fractured and scoliotic spines). We
consistently achieve an average Dice coefficient of over 90 percent on a
publicly available dataset of the xVertSeg segmentation challenge of MICCAI
2016. This is particularly noteworthy because the xVertSeg dataset is beset
with severe deformities in the form of vertebral fractures and scoliosis.
A Visual Representation of Wittgenstein's Tractatus Logico-Philosophicus
Authors: Anca Bucur, Sergiu Nisioi
Date: 2017-03-13 00:00:00
Proceeding: WS 2016 12
Tasks: Data Visualization, Philosophy

In this paper we present a data visualization method together with its
potential usefulness in digital humanities and philosophy of language. We
compile a multilingual parallel corpus from different versions of
Wittgenstein's Tractatus Logico-Philosophicus, including the original in German
and translations into English, Spanish, French, and Russian. Using this corpus,
we compute a similarity measure between propositions and render a visual
network of relations for different languages.
Probabilistic Matching: Causal Inference under Measurement Errors
Authors: Fani Tsapeli, Peter Tino, Mirco Musolesi
Date: 2017-03-13 00:00:00
Tasks: Causal Inference

The abundance of data produced daily from large variety of sources has
boosted the need of novel approaches on causal inference analysis from
observational data. Observational data often contain noisy or missing entries.
Moreover, causal inference studies may require unobserved high-level
information which needs to be inferred from other observed attributes. In such
cases, inaccuracies of the applied inference methods will result in noisy
outputs. In this study, we propose a novel approach for causal inference when
one or more key variables are noisy. Our method utilizes the knowledge about
the uncertainty of the real values of key variables in order to reduce the bias
induced by noisy measurements. We evaluate our approach in comparison with
existing methods both on simulated and real scenarios and we demonstrate that
our method reduces the bias and avoids false causal inference conclusions in
most cases.
Active Learning and Proofreading for Delineation of Curvilinear Structures
Authors: Agata Mosinska, Jakub Tarnawski, Pascal Fua
Date: 2016-12-23 00:00:00
Tasks: Active Learning, General Classification

Many state-of-the-art delineation methods rely on supervised machine learning
algorithms. As a result, they require manually annotated training data, which
is tedious to obtain. Furthermore, even minor classification errors may
significantly affect the topology of the final result. In this paper we propose
a generic approach to addressing both of these problems by taking into account
the influence of a potential misclassification on the resulting delineation. In
an Active Learning context, we identify parts of linear structures that should
be annotated first in order to train a classifier effectively. In a
proofreading context, we similarly find regions of the resulting reconstruction
that should be verified in priority to obtain a nearly-perfect result. In both
cases, by focusing the attention of the human expert on potential
classification mistakes which are the most critical parts of the delineation,
we reduce the amount of required supervision. We demonstrate the effectiveness
of our approach on microscopy images depicting blood vessels and neurons.
Story Cloze Ending Selection Baselines and Data Examination
Authors: Todor Mihaylov, Anette Frank
Date: 2017-03-13 00:00:00
Proceeding: WS 2017 4
Tasks: Cloze Test, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model the relation between the story and the provided endings. Our
experiments show that a model using representation features based on average
word embedding vectors over the given story words and the candidate ending
sentences words, joint with similarity features between the story and candidate
ending representations performed better than the neural models. Our best model
achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Authors: Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran
Date: 2017-03-13 00:00:00
Tasks: Blocking, Medical Diagnosis

Advances in Machine Learning (ML) have led to its adoption as an integral
component in many applications, including banking, medical diagnosis, and
driverless cars. To further broaden the use of ML models, cloud-based services
offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
tools as black-box systems. However, ML classifiers are vulnerable to
adversarial examples: inputs that are maliciously modified can cause the
classifier to provide adversary-desired outputs. Moreover, it is known that
adversarial examples generated on one classifier are likely to cause another
classifier to make the same mistake, even if the classifiers have different
architectures or are trained on disjoint datasets. This property, which is
known as transferability, opens up the possibility of attacking black-box
systems by generating adversarial examples on a substitute classifier and
transferring the examples to the target classifier. Therefore, the key to
protect black-box learning systems against the adversarial examples is to block
their transferability. To this end, we propose a training method that, as the
input is more perturbed, the classifier smoothly outputs lower confidence on
the original label and instead predicts that the input is "invalid". In
essence, we augment the output class set with a NULL label and train the
classifier to reject the adversarial examples by classifying them as NULL. In
experiments, we apply a wide range of attacks based on adversarial examples on
the black-box systems. We show that a classifier trained with the proposed
method effectively resists against the adversarial examples, while maintaining
the accuracy on clean data.
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Authors: Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, Adam Bry
Date: 2017-03-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: regression, Stereo-LiDAR Fusion

We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.
Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique
Authors: S. M. Jaisakthi, Aravindan Chandrabose, P. Mirunalini
Date: 2017-03-13 00:00:00
Tasks: Clustering, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

Skin cancer is the most common of all cancers and each year million cases of
skin cancer are treated. Treating and curing skin cancer is easy, if it is
diagnosed and treated at an early stage. In this work we propose an automatic
technique for skin lesion segmentation in dermoscopic images which helps in
classifying the skin cancer types. The proposed method comprises of two major
phases (1) preprocessing and (2) segmentation using semi-supervised learning
algorithm. In the preprocessing phase noise are removed using filtering
technique and in the segmentation phase skin lesions are segmented based on
clustering technique. K-means clustering algorithm is used to cluster the
preprocessed images and skin lesions are filtered from these clusters based on
the color feature. Color of the skin lesions are learned from the training
images using histograms calculations in RGB color space. The training images
were downloaded from the ISIC 2017 challenge website and the experimental
results were evaluated using validation and test sets.
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Authors: Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei
Date: 2015-11-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Detection, Temporal Action Localization

In this work we introduce a fully end-to-end approach for action detection in
videos that learns to directly predict the temporal bounds of actions. Our
intuition is that the process of detecting actions is naturally one of
observation and refinement: observing moments in video, and refining hypotheses
about when an action is occurring. Based on this insight, we formulate our
model as a recurrent neural network-based agent that interacts with a video
over time. The agent observes video frames and decides both where to look next
and when to emit a prediction. Since backpropagation is not adequate in this
non-differentiable setting, we use REINFORCE to learn the agent's decision
policy. Our model achieves state-of-the-art results on the THUMOS'14 and
ActivityNet datasets while observing only a fraction (2% or less) of the video
frames.
Online Learning with Local Permutations and Delayed Feedback
Authors: Ohad Shamir, Liran Szlak
Date: 2017-03-13 00:00:00
Proceeding: ICML 2017 8

We propose an Online Learning with Local Permutations (OLLP) setting, in
which the learner is allowed to slightly permute the \emph{order} of the loss
functions generated by an adversary. On one hand, this models natural
situations where the exact order of the learner's responses is not crucial, and
on the other hand, might allow better learning and regret performance, by
mitigating highly adversarial loss sequences. Also, with random permutations,
this can be seen as a setting interpolating between adversarial and stochastic
losses. In this paper, we consider the applicability of this setting to convex
online learning with delayed feedback, in which the feedback on the prediction
made in round $t$ arrives with some delay $\tau$. With such delayed feedback,
the best possible regret bound is well-known to be $O(\sqrt{\tau T})$. We prove
that by being able to permute losses by a distance of at most $M$ (for $M\geq
\tau$), the regret can be improved to $O(\sqrt{T}(1+\sqrt{\tau^2/M}))$, using a
Mirror-Descent based algorithm which can be applied for both Euclidean and
non-Euclidean geometries. We also prove a lower bound, showing that for
$M<\tau/3$, it is impossible to improve the standard $O(\sqrt{\tau T})$ regret
bound by more than constant factors. Finally, we provide some experiments
validating the performance of our algorithm.
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
Authors: Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
Date: 2016-11-14 00:00:00

This paper proposes a learning strategy that extracts object-part concepts
from a pre-trained convolutional neural network (CNN), in an attempt to 1)
explore explicit semantics hidden in CNN units and 2) gradually grow a
semantically interpretable graphical model on the pre-trained CNN for
hierarchical object understanding. Given part annotations on very few (e.g.,
3-12) objects, our method mines certain latent patterns from the pre-trained
CNN and associates them with different semantic parts. We use a four-layer
And-Or graph to organize the mined latent patterns, so as to clarify their
internal semantic hierarchy. Our method is guided by a small number of part
annotations, and it achieves superior performance (about 13%-107% improvement)
in part center prediction on the PASCAL VOC and ImageNet datasets.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Authors: Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He
Date: 2017-03-13 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering, Recommendation Systems

Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods seem to have a strong bias towards low- or high-order interactions, or
require expertise feature engineering. In this paper, we show that it is
possible to derive an end-to-end learning model that emphasizes both low- and
high-order feature interactions. The proposed model, DeepFM, combines the power
of factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide \&
Deep model from Google, DeepFM has a shared input to its "wide" and "deep"
parts, with no need of feature engineering besides raw features. Comprehensive
experiments are conducted to demonstrate the effectiveness and efficiency of
DeepFM over the existing models for CTR prediction, on both benchmark data and
commercial data.
GUN: Gradual Upsampling Network for Single Image Super-Resolution
Authors: Yang Zhao, Guoqing Li, Wenjun Xie, Wei Jia, Hai Min, Xiaoping Liu
Date: 2017-03-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In this paper, an efficient super-resolution (SR) method based on deep
convolutional neural network (CNN) is proposed, namely Gradual Upsampling
Network (GUN). Recent CNN based SR methods often preliminarily magnify the low
resolution (LR) input to high resolution (HR) and then reconstruct the HR
input, or directly reconstruct the LR input and then recover the HR result at
the last layer. The proposed GUN utilizes a gradual process instead of these
two commonly used frameworks. The GUN consists of an input layer, multiple
upsampling and convolutional layers, and an output layer. By means of the
gradual process, the proposed network can simplify the direct SR problem to
multistep easier upsampling tasks with very small magnification factor in each
step. Furthermore, a gradual training strategy is presented for the GUN. In the
proposed training process, an initial network can be easily trained with
edge-like samples, and then the weights are gradually tuned with more complex
samples. The GUN can recover fine and vivid results, and is easy to be trained.
The experimental results on several image sets demonstrate the effectiveness of
the proposed network.
Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains
Authors: Miquel Ramirez, Enrico Scala, Patrik Haslum, Sylvie Thiebaux
Date: 2017-03-13 00:00:00
Tasks: Heuristic Search, Numerical Integration

In this paper we look into the problem of planning over hybrid domains, where
change can be both discrete and instantaneous, or continuous over time. In
addition, it is required that each state on the trajectory induced by the
execution of plans complies with a given set of global constraints. We approach
the computation of plans for such domains as the problem of searching over a
deterministic state model. In this model, some of the successor states are
obtained by solving numerically the so-called initial value problem over a set
of ordinary differential equations (ODE) given by the current plan prefix.
These equations hold over time intervals whose duration is determined
dynamically, according to whether zero crossing events take place for a set of
invariant conditions. The resulting planner, FS+, incorporates these features
together with effective heuristic guidance. FS+ does not impose any of the
syntactic restrictions on process effects often found on the existing
literature on Hybrid Planning. A key concept of our approach is that a clear
separation is struck between planning and simulation time steps. The former is
the time allowed to observe the evolution of a given dynamical system before
committing to a future course of action, whilst the later is part of the model
of the environment. FS+ is shown to be a robust planner over a diverse set of
hybrid domains, taken from the existing literature on hybrid planning and
systems.
Revisiting stochastic off-policy action-value gradients
Authors: Yemi Okesanjo, Victor Kofia
Date: 2017-03-06 00:00:00

Off-policy stochastic actor-critic methods rely on approximating the
stochastic policy gradient in order to derive an optimal policy. One may also
derive the optimal policy by approximating the action-value gradient. The use
of action-value gradients is desirable as policy improvement occurs along the
direction of steepest ascent. This has been studied extensively within the
context of natural gradient actor-critic algorithms and more recently within
the context of deterministic policy gradients. In this paper we briefly discuss
the off-policy stochastic counterpart to deterministic action-value gradients,
as well as an incremental approach for following the policy gradient in lieu of
the natural gradient.