pwc-archive/papers-with-abstracts huggingface dataset:
Peekaboo - Where are the Objects? Structure Adjusting Superpixels
Authors: Georg Maierhofer, Daniel Heydecker, Angelica I. Aviles-Rivero, Samar M. Alsaleh, Carola-Bibiane Schönlieb
Date: 2018-02-08 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation, Superpixels

This paper addresses the search for a fast and meaningful image segmentation
in the context of $k$-means clustering. The proposed method builds on a
widely-used local version of Lloyd's algorithm, called Simple Linear Iterative
Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically
adjust the local search, adopting superpixel resolution dynamically to
structure existent in the image, and thus provides for more meaningful
superpixels in the same linear runtime as standard SLIC. The proposed method is
evaluated against state-of-the-art techniques and improved boundary adherence
and undersegmentation error are observed, whilst still remaining among the
fastest algorithms which are tested.
Greedy Structure Learning of Hierarchical Compositional Models
Authors: Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter
Date: 2017-01-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Transfer Learning

In this work, we consider the problem of learning a hierarchical generative
model of an object from a set of images which show examples of the object in
the presence of variable background clutter. Existing approaches to this
problem are limited by making strong a-priori assumptions about the object's
geometric structure and require segmented training data for learning. In this
paper, we propose a novel framework for learning hierarchical compositional
models (HCMs) which do not suffer from the mentioned limitations. We present a
generalized formulation of HCMs and describe a greedy structure learning
framework that consists of two phases: Bottom-up part learning and top-down
model composition. Our framework integrates the foreground-background
segmentation problem into the structure learning task via a background model.
As a result, we can jointly optimize for the number of layers in the hierarchy,
the number of parts per layer and a foreground-background segmentation based on
class labels only. We show that the learned HCMs are semantically meaningful
and achieve competitive results when compared to other generative object models
at object classification on a standard transfer learning dataset.
Neural Inverse Rendering for General Reflectance Photometric Stereo
Authors: Tatsunori Taniai, Takanori Maehara
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Inverse Rendering

We present a novel convolutional neural network architecture for photometric
stereo (Woodham, 1980), a problem of recovering 3D object surface normals from
multiple images observed under varying illuminations. Despite its long history
in computer vision, the problem still shows fundamental challenges for surfaces
with unknown general reflectance properties (BRDFs). Leveraging deep neural
networks to learn complicated reflectance models is promising, but studies in
this direction are very limited due to difficulties in acquiring accurate
ground truth for training and also in designing networks invariant to
permutation of input images. In order to address these challenges, we propose a
physics based unsupervised learning framework where surface normals and BRDFs
are predicted by the network and fed into the rendering equation to synthesize
observed images. The network weights are optimized during testing by minimizing
reconstruction loss between observed and synthesized images. Thus, our learning
process does not require ground truth normals or even pre-training on external
images. Our method is shown to achieve the state-of-the-art performance on a
challenging real-world scene benchmark.
CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing
Authors: Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi
Date: 2018-05-29 00:00:00

Detection of contrast adjustments in the presence of JPEG postprocessing is
known to be a challenging task. JPEG post processing is often applied
innocently, as JPEG is the most common image format, or it may correspond to a
laundering attack, when it is purposely applied to erase the traces of
manipulation. In this paper, we propose a CNN-based detector for generic
contrast adjustment, which is robust to JPEG compression. The proposed system
relies on a patch-based Convolutional Neural Network (CNN), trained to
distinguish pristine images from contrast adjusted images, for some selected
adjustment operators of different nature. Robustness to JPEG compression is
achieved by training the CNN with JPEG examples, compressed over a range of
Quality Factors (QFs). Experimental results show that the detector works very
well and scales well with respect to the adjustment type, yielding very good
performance under a large variety of unseen tonal adjustments.
Neural networks for stock price prediction
Authors: Yue-Gang Song, Yu-Long Zhou, Ren-Jie Han
Date: 2018-05-29 00:00:00
Tasks: Prediction, regression, Stock Price Prediction

Due to the extremely volatile nature of financial markets, it is commonly
accepted that stock price prediction is a task full of challenge. However in
order to make profits or understand the essence of equity market, numerous
market participants or researchers try to forecast stock price using various
statistical, econometric or even neural network models. In this work, we survey
and compare the predictive power of five neural network models, namely, back
propagation (BP) neural network, radial basis function (RBF) neural network,
general regression neural network (GRNN), support vector machine regression
(SVMR), least squares support vector machine regresssion (LS-SVMR). We apply
the five models to make price prediction of three individual stocks, namely,
Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and
average absolute percentage error as criteria, we find BP neural network
consistently and robustly outperforms the other four models.
Intelligent Trainer for Model-Based Reinforcement Learning
Authors: Yuanlong Li, Linsen Dong, Xin Zhou, Yonggang Wen, Kyle Guan
Date: 2018-05-24 00:00:00
Tasks: model, Model-based Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical reinforcement learning (RL), by leveraging a learned model to generate synthesized data for policy training purpose. The MBRL framework, nevertheless, is inherently limited by the convoluted process of jointly learning control policy and configuring hyper-parameters (e.g., global/local models, real and synthesized data, etc). The training process could be tedious and prohibitively costly. In this research, we propose an "reinforcement on reinforcement" (RoR) architecture to decompose the convoluted tasks into two layers of reinforcement learning. The inner layer is the canonical model-based RL training process environment (TPE), which learns the control policy for the underlying system and exposes interfaces to access states, actions and rewards. The outer layer presents an RL agent, called as AI trainer, to learn an optimal hyper-parameter configuration for the inner TPE. This decomposition approach provides a desirable flexibility to implement different trainer designs, called as "train the trainer". In our research, we propose and optimize two alternative trainer designs: 1) a uni-head trainer and 2) a multi-head trainer. Our proposed RoR framework is evaluated for five tasks in the OpenAI gym (i.e., Pendulum, Mountain Car, Reacher, Half Cheetah and Swimmer). Compared to three other baseline algorithms, our proposed Train-the-Trainer algorithm has a competitive performance in auto-tuning capability, with upto 56% expected sampling cost saving without knowing the best parameter setting in advance. The proposed trainer framework can be easily extended to other cases in which the hyper-parameter tuning is costly.
Overcoming catastrophic forgetting with hard attention to the task
Authors: Joan Serrà, Dídac Surís, Marius Miron, Alexandros Karatzoglou
Date: 2018-01-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Continual Learning, Hard Attention

Catastrophic forgetting occurs when a neural network loses the information
learned in a previous task after training on subsequent tasks. This problem
remains a hurdle for artificial intelligence systems with sequential learning
capabilities. In this paper, we propose a task-based hard attention mechanism
that preserves previous tasks' information without affecting the current task's
learning. A hard attention mask is learned concurrently to every task, through
stochastic gradient descent, and previous masks are exploited to condition such
learning. We show that the proposed mechanism is effective for reducing
catastrophic forgetting, cutting current rates by 45 to 80%. We also show that
it is robust to different hyperparameter choices, and that it offers a number
of monitoring capabilities. The approach features the possibility to control
both the stability and compactness of the learned knowledge, which we believe
makes it also attractive for online learning or network compression
applications.
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients
Authors: Anil R. Yelundur, Srinivasan H. Sengamedu, Bamdev Mishra
Date: 2018-04-11 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Tensor Decomposition

Anomaly Detection has several important applications. In this paper, our
focus is on detecting anomalies in seller-reviewer data using tensor
decomposition. While tensor-decomposition is mostly unsupervised, we formulate
Bayesian semi-supervised tensor decomposition to take advantage of sparse
labeled data. In addition, we use Polya-Gamma data augmentation for the
semi-supervised Bayesian tensor decomposition. Finally, we show that the
P\'olya-Gamma formulation simplifies calculation of the Fisher information
matrix for partial natural gradient learning. Our experimental results show
that our semi-supervised approach outperforms state of the art unsupervised
baselines. And that the partial natural gradient learning outperforms
stochastic gradient learning and Online-EM with sufficient statistics.
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
Authors: Tony C. W. Mok, Albert C. S. Chung
Date: 2018-05-29 00:00:00
Tasks: Brain Tumor Segmentation, Data Augmentation, Tumor Segmentation

There is a common belief that the successful training of deep neural networks
requires many annotated training samples, which are often expensive and
difficult to obtain especially in the biomedical imaging field. While it is
often easy for researchers to use data augmentation to expand the size of
training sets, constructing and generating generic augmented data that is able
to teach the network the desired invariance and robustness properties using
traditional data augmentation techniques is challenging in practice. In this
paper, we propose a novel automatic data augmentation method that uses
generative adversarial networks to learn augmentations that enable machine
learning based method to learn the available annotated samples more
efficiently. The architecture consists of a coarse-to-fine generator to capture
the manifold of the training sets and generate generic augmented data. In our
experiments, we show the efficacy of our approach on a Magnetic Resonance
Imaging (MRI) image, achieving improvements of 3.5% Dice coefficient on the
BRATS15 Challenge dataset as compared to traditional augmentation approaches.
Also, our proposed method successfully boosts a common segmentation network to
reach the state-of-the-art performance on the BRATS15 Challenge.
KONG: Kernels for ordered-neighborhood graphs
Authors: Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12

We present novel graph kernels for graphs with node and edge labels that have
ordered neighborhoods, i.e. when neighbor nodes follow an order. Graphs with
ordered neighborhoods are a natural data representation for evolving graphs
where edges are created over time, which induces an order. Combining
convolutional subgraph kernels and string kernels, we design new scalable
algorithms for generation of explicit graph feature maps using sketching
techniques. We obtain precise bounds for the approximation accuracy and
computational complexity of the proposed approaches and demonstrate their
applicability on real datasets. In particular, our experiments demonstrate that
neighborhood ordering results in more informative features. For the special
case of general graphs, i.e. graphs without ordered neighborhoods, the new
graph kernels yield efficient and simple algorithms for the comparison of label
distributions between graphs.
Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?
Authors: Peter Jansen
Date: 2018-05-29 00:00:00
Proceeding: WS 2018 6
Tasks: graph construction, Knowledge Graphs, Question Answering, Science Question Answering, Sentence

Question Answering for complex questions is often modeled as a graph
construction or traversal task, where a solver must build or traverse a graph
of facts that answer and explain a given question. This "multi-hop" inference
has been shown to be extremely challenging, with few models able to aggregate
more than two facts before being overwhelmed by "semantic drift", or the
tendency for long chains of facts to quickly drift off topic. This is a major
barrier to current inference models, as even elementary science questions
require an average of 4 to 6 facts to answer and explain. In this work we
empirically characterize the difficulty of building or traversing a graph of
sentences connected by lexical overlap, by evaluating chance sentence
aggregation quality through 9,784 manually-annotated judgments across knowledge
graphs built from three free-text corpora (including study guides and Simple
Wikipedia). We demonstrate semantic drift tends to be high and aggregation
quality low, at between 0.04% and 3%, and highlight scenarios that maximize the
likelihood of meaningfully combining information.
Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data
Authors: Wei-Ning Hsu, James Glass
Date: 2018-05-29 00:00:00
Tasks: Representation Learning, Variational Inference

Multimodal sensory data resembles the form of information perceived by humans
for learning, and are easy to obtain in large quantities. Compared to unimodal
data, synchronization of concepts between modalities in such data provides
supervision for disentangling the underlying explanatory factors of each
modality. Previous work leveraging multimodal data has mainly focused on
retaining only the modality-invariant factors while discarding the rest. In
this paper, we present a partitioned variational autoencoder (PVAE) and several
training objectives to learn disentangled representations, which encode not
only the shared factors, but also modality-dependent ones, into separate latent
variables. Specifically, PVAE integrates a variational inference framework and
a multimodal generative model that partitions the explanatory factors and
conditions only on the relevant subset of them for generation. We evaluate our
model on two parallel speech/image datasets, and demonstrate its ability to
learn disentangled representations by qualitatively exploring within-modality
and cross-modality conditional generation with semantics and styles specified
by examples. For quantitative analysis, we evaluate the classification accuracy
of automatically discovered semantic units. Our PVAE can achieve over 99%
accuracy on both modalities.
Statistical mechanical analysis of sparse linear regression as a variable selection problem
Authors: Tomoyuki Obuchi, Yoshinori Nakanishi-Ohno, Masato Okada, Yoshiyuki Kabashima
Date: 2018-05-29 00:00:00
Tasks: compressed sensing, regression, Variable Selection

An algorithmic limit of compressed sensing or related variable-selection
problems is analytically evaluated when a design matrix is given by an
overcomplete random matrix. The replica method from statistical mechanics is
employed to derive the result. The analysis is conducted through evaluation of
the entropy, an exponential rate of the number of combinations of variables
giving a specific value of fit error to given data which is assumed to be
generated from a linear process using the design matrix. This yields the
typical achievable limit of the fit error when solving a representative
$\ell_0$ problem and includes the presence of unfavourable phase transitions
preventing local search algorithms from reaching the minimum-error
configuration. The associated phase diagrams are presented. A noteworthy
outcome of the phase diagrams is that there exists a wide parameter region
where any phase transition is absent from the high temperature to the lowest
temperature at which the minimum-error configuration or the ground state is
reached. This implies that certain local search algorithms can find the ground
state with moderate computational costs in that region. Another noteworthy
result is the presence of the random first-order transition in the strong noise
case. The theoretical evaluation of the entropy is confirmed by extensive
numerical methods using the exchange Monte Carlo and the multi-histogram
methods. Another numerical test based on a metaheuristic optimisation algorithm
called simulated annealing is conducted, which well supports the theoretical
predictions on the local search algorithms. In the successful region with no
phase transition, the computational cost of the simulated annealing to reach
the ground state is estimated as the third order polynomial of the model
dimensionality.
Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
Authors: Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
Date: 2018-05-28 00:00:00
Tasks: Drug Discovery

Molecular structure-property relationships are key to molecular engineering
for materials and drug discovery. The rise of deep learning offers a new viable
solution to elucidate the structure-property relationships directly from
chemical data. Here we show that the performance of graph convolutional
networks (GCNs) for the prediction of molecular properties can be improved by
incorporating attention and gate mechanisms. The attention mechanism enables a
GCN to identify atoms in different environments. The gated skip-connection
further improves the GCN by updating feature maps at an appropriate rate. We
demonstrate that the resulting attention- and gate-augmented GCN could extract
better structural features related to a target molecular property such as
solubility, polarity, synthetic accessibility and photovoltaic efficiency
compared to the vanilla GCN. More interestingly, it identified two distinct
parts of molecules as essential structural features for high photovoltaic
efficiency, and each of them coincided with the areas of donor and acceptor
orbitals for charge-transfer excitations, respectively. As a result, the new
model could accurately predict molecular properties and place molecules with
similar properties close to each other in a well-trained latent space, which is
critical for successful molecular engineering.
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy
Authors: Neema Davis, Gaurav Raina, Krishna Jagannathan
Date: 2018-05-17 00:00:00
Tasks: Demand Forecasting, Time Series Analysis

A key problem in location-based modeling and forecasting lies in identifying
suitable spatial and temporal resolutions. In particular, judicious spatial
partitioning can play a significant role in enhancing the performance of
location-based forecasting models. In this work, we investigate two widely used
tessellation strategies for partitioning city space, in the context of
real-time taxi demand forecasting. Our study compares (i) Geohash tessellation,
and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over
multiple time scales. For the purpose of comparison, we employ classical
time-series tools to model the spatio-temporal demand. Our study finds that the
performance of each tessellation strategy is highly dependent on the city
geography, spatial distribution of the data, and the time of the day, and that
neither strategy is found to perform optimally across the forecast horizon. We
propose a hybrid tessellation algorithm that picks the best tessellation
strategy at each instant, based on their performance in the recent past. Our
hybrid algorithm is a non-stationary variant of the well-known HEDGE algorithm
for choosing the best advice from multiple experts. We show that the hybrid
tessellation strategy performs consistently better than either of the two
strategies across the data sets considered, at multiple time scales, and with
different performance metrics. We achieve an average accuracy of above 80% per
km^2 for both data sets considered at 60 minute aggregation levels.
Microscopy Cell Segmentation via Convolutional LSTM Networks
Authors: Assaf Arbelle, Tammy Riklin Raviv
Date: 2018-05-29 00:00:00
Tasks: Cell Segmentation, Cell Tracking, Segmentation

Live cell microscopy sequences exhibit complex spatial structures and
complicated temporal behaviour, making their analysis a challenging task.
Considering cell segmentation problem, which plays a significant role in the
analysis, the spatial properties of the data can be captured using
Convolutional Neural Networks (CNNs). Recent approaches show promising
segmentation results using convolutional encoder-decoders such as the U-Net.
Nevertheless, these methods are limited by their inability to incorporate
temporal information, that can facilitate segmentation of individual touching
cells or of cells that are partially visible. In order to exploit cell dynamics
we propose a novel segmentation architecture which integrates Convolutional
Long Short Term Memory (C-LSTM) with the U-Net. The network's unique
architecture allows it to capture multi-scale, compact, spatio-temporal
encoding in the C-LSTMs memory units. The method was evaluated on the Cell
Tracking Challenge and achieved state-of-the-art results (1st on Fluo-N2DH-SIM+
and 2nd on DIC-C2DL-HeLa datasets) The code is freely available at:
https://github.com/arbellea/LSTM-UNet.git
On Robust Trimming of Bayesian Network Classifiers
Authors: YooJung Choi, Guy Van Den Broeck
Date: 2018-05-29 00:00:00
Tasks: Classification, General Classification

This paper considers the problem of removing costly features from a Bayesian
network classifier. We want the classifier to be robust to these changes, and
maintain its classification behavior. To this end, we propose a closeness
metric between Bayesian classifiers, called the expected classification
agreement (ECA). Our corresponding trimming algorithm finds an optimal subset
of features and a new classification threshold that maximize the expected
agreement, subject to a budgetary constraint. It utilizes new theoretical
insights to perform branch-and-bound search in the space of feature sets, while
computing bounds on the ECA. Our experiments investigate both the runtime cost
of trimming and its effect on the robustness and accuracy of the final
classifier.