pwc-archive/papers-with-abstracts huggingface dataset:
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Authors: Devinder Kumar, Graham W. Taylor, Alexander Wong
Date: 2017-09-05 00:00:00
Tasks: Decision Making, Deep Learning, Prediction, Stock Market Prediction

Deep learning has been shown to outperform traditional machine learning
algorithms across a wide range of problem domains. However, current deep
learning algorithms have been criticized as uninterpretable "black-boxes" which
cannot explain their decision making processes. This is a major shortcoming
that prevents the widespread application of deep learning to domains with
regulatory processes such as finance. As such, industries such as finance have
to rely on traditional models like decision trees that are much more
interpretable but less effective than deep learning for complex problems. In
this paper, we propose CLEAR-Trade, a novel financial AI visualization
framework for deep learning-driven stock market prediction that mitigates the
interpretability issue of deep learning methods. In particular, CLEAR-Trade
provides a effective way to visualize and explain decisions made by deep stock
market prediction models. We show the efficacy of CLEAR-Trade in enhancing the
interpretability of stock market prediction by conducting experiments based on
S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can
provide significant insight into the decision-making process of deep
learning-driven financial models, particularly for regulatory processes, thus
improving their potential uptake in the financial industry.
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Authors: Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool
Date: 2017-04-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: Translation

Despite a rapid rise in the quality of built-in smartphone cameras, their
physical limitations - small sensor size, compact lenses and the lack of
specific hardware, - impede them to achieve the quality results of DSLR
cameras. In this work we present an end-to-end deep learning approach that
bridges this gap by translating ordinary photos into DSLR-quality images. We
propose learning the translation function using a residual convolutional neural
network that improves both color rendition and image sharpness. Since the
standard mean squared loss is not well suited for measuring perceptual image
quality, we introduce a composite perceptual error function that combines
content, color and texture losses. The first two losses are defined
analytically, while the texture loss is learned in an adversarial fashion. We
also present DPED, a large-scale dataset that consists of real photos captured
from three different phones and one high-end reflex camera. Our quantitative
and qualitative assessments reveal that the enhanced image quality is
comparable to that of DSLR-taken photos, while the methodology is generalized
to any type of digital camera.
Linking Generative Adversarial Learning and Binary Classification
Authors: Akshay Balsubramani
Date: 2017-09-05 00:00:00
Tasks: Binary Classification, Classification, General Classification

In this note, we point out a basic link between generative adversarial (GA)
training and binary classification -- any powerful discriminator essentially
computes an (f-)divergence between real and generated samples. The result,
repeatedly re-derived in decision theory, has implications for GA Networks
(GANs), providing an alternative perspective on training f-GANs by designing
the discriminator loss function.
Fine-tuning deep CNN models on specific MS COCO categories
Authors: Daniel Sonntag, Michael Barz, Jan Zacharias, Sven Stauden, Vahid Rahmani, Áron Fóthi, András Lőrincz
Date: 2017-09-05 00:00:00

Fine-tuning of a deep convolutional neural network (CNN) is often desired.
This paper provides an overview of our publicly available py-faster-rcnn-ft
software library that can be used to fine-tune the VGG_CNN_M_1024 model on
custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.
For example, we improved the procedure so that the user does not have to look
for suitable image files in the dataset by hand which can then be used in the
demo program. Our implementation randomly selects images that contain at least
one object of the categories on which the model is fine-tuned.
Leveraging multiple datasets for deep leaf counting
Authors: Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A. Tsaftaris
Date: 2017-09-05 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

The number of leaves a plant has is one of the key traits (phenotypes)
describing its development and growth. Here, we propose an automated, deep
learning based approach for counting leaves in model rosette plants. While
state-of-the-art results on leaf counting with deep learning methods have
recently been reported, they obtain the count as a result of leaf segmentation
and thus require per-leaf (instance) segmentation to train the models (a rather
strong annotation). Instead, our method treats leaf counting as a direct
regression problem and thus only requires as annotation the total leaf count
per plant. We argue that combining different datasets when training a deep
neural network is beneficial and improves the results of the proposed approach.
We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which
contains images of Arabidopsis and tobacco plants. Experimental results show
that the proposed method significantly outperforms the winner of the previous
CVPPP challenge, improving the results by a minimum of ~50% on each of the test
datasets, and can achieve this performance without knowing the experimental
origin of the data (i.e. in the wild setting of the challenge). We also compare
the counting accuracy of our model with that of per leaf segmentation
algorithms, achieving a 20% decrease in mean absolute difference in count
(|DiC|).
Subspace Segmentation by Successive Approximations: A Method for Low-Rank and High-Rank Data with Missing Entries
Authors: João Carvalho, Manuel Marques, João P. Costeira
Date: 2017-09-05 00:00:00
Tasks: Clustering

We propose a method to reconstruct and cluster incomplete high-dimensional
data lying in a union of low-dimensional subspaces. Exploring the sparse
representation model, we jointly estimate the missing data while imposing the
intrinsic subspace structure. Since we have a non-convex problem, we propose an
iterative method to reconstruct the data and provide a sparse similarity
affinity matrix. This method is robust to initialization and achieves greater
reconstruction accuracy than current methods, which dramatically improves
clustering performance. Extensive experiments with synthetic and real data show
that our approach leads to significant improvements in the reconstruction and
segmentation, outperforming current state of the art for both low and high-rank
data.
6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality
Authors: David Joseph Tan, Nassir Navab, Federico Tombari
Date: 2017-09-05 00:00:00
Tasks: 6D Pose Estimation using RGB, CPU, Object, object-detection, Object Detection, Pose Estimation

To determine the 3D orientation and 3D location of objects in the
surroundings of a camera mounted on a robot or mobile device, we developed two
powerful algorithms in object detection and temporal tracking that are combined
seamlessly for robotic perception and interaction as well as Augmented Reality
(AR). A separate evaluation of, respectively, the object detection and the
temporal tracker demonstrates the important stride in research as well as the
impact on industrial robotic applications and AR. When evaluated on a standard
dataset, the detector produced the highest f1-score with a large margin while
the tracker generated the best accuracy at a very low latency of approximately
2 ms per frame with one CPU core: both algorithms outperforming the state of
the art. When combined, we achieve a powerful framework that is robust to
handle multiple instances of the same object under occlusion and clutter while
attaining real-time performance. Aiming at stepping beyond the simple scenarios
used by current systems, often constrained by having a single object in absence
of clutter, averting to touch the object to prevent close-range partial
occlusion, selecting brightly colored objects to easily segment them
individually or assuming that the object has simple geometric structure, we
demonstrate the capacity to handle challenging cases under clutter, partial
occlusion and varying lighting conditions with objects of different shapes and
sizes.
The Devil is in the Tails: Fine-grained Classification in the Wild
Authors: Grant Van Horn, Pietro Perona
Date: 2017-09-05 00:00:00
Tasks: Classification, General Classification, Transfer Learning

The world is long-tailed. What does this mean for computer vision and visual
recognition? The main two implications are (1) the number of categories we need
to consider in applications can be very large, and (2) the number of training
examples for most categories can be very small. Current visual recognition
algorithms have achieved excellent classification accuracy. However, they
require many training examples to reach peak performance, which suggests that
long-tailed distributions will not be dealt with well. We analyze this question
in the context of eBird, a large fine-grained classification dataset, and a
state-of-the-art deep network classification algorithm. We find that (a) peak
classification performance on well-represented categories is excellent, (b)
given enough data, classification performance suffers only minimally from an
increase in the number of classes, (c) classification performance decays
precipitously as the number of training examples decreases, (d) surprisingly,
transfer learning is virtually absent in current methods. Our findings suggest
that our community should come to grips with the question of long tails.
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Authors: Jakob Runge
Date: 2017-09-05 00:00:00
Tasks: Causal Discovery

Conditional independence testing is a fundamental problem underlying causal
discovery and a particularly challenging task in the presence of nonlinear and
high-dimensional dependencies. Here a fully non-parametric test for continuous
data based on conditional mutual information combined with a local permutation
scheme is presented. Through a nearest neighbor approach, the test efficiently
adapts also to non-smooth distributions due to strongly nonlinear dependencies.
Numerical experiments demonstrate that the test reliably simulates the null
distribution even for small sample sizes and with high-dimensional conditioning
sets. The test is better calibrated than kernel-based tests utilizing an
analytical approximation of the null distribution, especially for non-smooth
densities, and reaches the same or higher power levels. Combining the local
permutation scheme with the kernel tests leads to better calibration, but
suffers in power. For smaller sample sizes and lower dimensions, the test is
faster than random fourier feature-based kernel tests if the permutation scheme
is (embarrassingly) parallelized, but the runtime increases more sharply with
sample size and dimensionality. Thus, more theoretical research to analytically
approximate the null distribution and speed up the estimation for larger sample
sizes is desirable.
Dense Face Alignment
Authors: Yaojie Liu, Amin Jourabloo, William Ren, Xiaoming Liu
Date: 2017-09-05 00:00:00
Tasks: 3D Face Reconstruction, Face Alignment, Facial Landmark Detection

Face alignment is a classic problem in the computer vision field. Previous
works mostly focus on sparse alignment with a limited number of facial landmark
points, i.e., facial landmark detection. In this paper, for the first time, we
aim at providing a very dense 3D alignment for large-pose face images. To
achieve this, we train a CNN to estimate the 3D face shape, which not only
aligns limited facial landmarks but also fits face contours and SIFT feature
points. Moreover, we also address the bottleneck of training CNN with multiple
datasets, due to different landmark markups on different datasets, such as 5,
34, 68. Experimental results show our method not only provides high-quality,
dense 3D face fitting but also outperforms the state-of-the-art facial landmark
detection methods on the challenging datasets. Our model can run at real time
during testing.
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms
Authors: Gustavo A. Valencia-Zapata, Daniel Mejia, Gerhard Klimeck, Michael Zentner, Okan Ersoy
Date: 2017-09-05 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, Dimensionality Reduction, General Classification

Probabilistic mixture models have been widely used for different machine
learning and pattern recognition tasks such as clustering, dimensionality
reduction, and classification. In this paper, we focus on trying to solve the
most common challenges related to supervised learning algorithms by using
mixture probability distribution functions. With this modeling strategy, we
identify sub-labels and generate synthetic data in order to reach better
classification accuracy. It means we focus on increasing the training data
synthetically to increase the classification accuracy.
A Generic Approach for Escaping Saddle points
Authors: Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alexander J. Smola
Date: 2017-09-05 00:00:00
Tasks: Second-order methods

A central challenge to using first-order methods for optimizing nonconvex
problems is the presence of saddle points. First-order methods often get stuck
at saddle points, greatly deteriorating their performance. Typically, to escape
from saddles one has to use second-order methods. However, most works on
second-order methods rely extensively on expensive Hessian-based computations,
making them impractical in large-scale settings. To tackle this challenge, we
introduce a generic framework that minimizes Hessian based computations while
at the same time provably converging to second-order critical points. Our
framework carefully alternates between a first-order and a second-order
subroutine, using the latter only close to saddle points, and yields
convergence results competitive to the state-of-the-art. Empirical results
suggest that our strategy also enjoys a good practical performance.
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster
Authors: Alice Schoenauer-Sebag, Marc Schoenauer, Michèle Sebag
Date: 2017-09-05 00:00:00
Tasks: Change Point Detection

When applied to training deep neural networks, stochastic gradient descent
(SGD) often incurs steady progression phases, interrupted by catastrophic
episodes in which loss and gradient norm explode. A possible mitigation of such
events is to slow down the learning process. This paper presents a novel
approach to control the SGD learning rate, that uses two statistical tests. The
first one, aimed at fast learning, compares the momentum of the normalized
gradient vectors to that of random unit vectors and accordingly gracefully
increases or decreases the learning rate. The second one is a change point
detection test, aimed at the detection of catastrophic learning episodes; upon
its triggering the learning rate is instantly halved. Both abilities of
speeding up and slowing down the learning rate allows the proposed approach,
called SALeRA, to learn as fast as possible but not faster. Experiments on
standard benchmarks show that SALeRA performs well in practice, and compares
favorably to the state of the art.
Hierarchical Attentive Recurrent Tracking
Authors: Adam R. Kosiorek, Alex Bewley, Ingmar Posner
Date: 2017-06-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Activity Recognition, Object, Object Tracking

Class-agnostic object tracking is particularly difficult in cluttered
environments as target specific discriminative models cannot be learned a
priori. Inspired by how the human visual cortex employs spatial attention and
separate "where" and "what" processing pathways to actively suppress irrelevant
visual features, this work develops a hierarchical attentive recurrent model
for single object tracking in videos. The first layer of attention discards the
majority of background by selecting a region containing the object of interest,
while the subsequent layers tune in on visual features particular to the
tracked object. This framework is fully differentiable and can be trained in a
purely data driven fashion by gradient methods. To improve training
convergence, we augment the loss function with terms for a number of auxiliary
tasks relevant for tracking. Evaluation of the proposed model is performed on
two datasets: pedestrian tracking on the KTH activity recognition dataset and
the more difficult KITTI object tracking dataset.
The Advantage of Evidential Attributes in Social Networks
Authors: Salma Ben Dhaou, Kuang Zhou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane
Date: 2017-07-26 00:00:00
Tasks: Clustering

Nowadays, there are many approaches designed for the task of detecting
communities in social networks. Among them, some methods only consider the
topological graph structure, while others take use of both the graph structure
and the node attributes. In real-world networks, there are many uncertain and
noisy attributes in the graph. In this paper, we will present how we detect
communities in graphs with uncertain attributes in the first step. The
numerical, probabilistic as well as evidential attributes are generated
according to the graph structure. In the second step, some noise will be added
to the attributes. We perform experiments on graphs with different types of
attributes and compare the detection results in terms of the Normalized Mutual
Information (NMI) values. The experimental results show that the clustering
with evidential attributes gives better results comparing to those with
probabilistic and numerical attributes. This illustrates the advantages of
evidential attributes.
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
Authors: Diarmaid Conaty, Denis D. Mauá, Cassio P. de Campos
Date: 2017-03-17 00:00:00

We discuss the computational complexity of approximating maximum a posteriori
inference in sum-product networks. We first show NP-hardness in trees of height
two by a reduction from maximum independent set; this implies
non-approximability within a sublinear factor. We show that this is a tight
bound, as we can find an approximation within a linear factor in networks of
height two. We then show that, in trees of height three, it is NP-hard to
approximate the problem within a factor $2^{f(n)}$ for any sublinear function
$f$ of the size of the input $n$. Again, this bound is tight, as we prove that
the usual max-product algorithm finds (in any network) approximations within
factor $2^{c \cdot n}$ for some constant $c < 1$. Last, we present a simple
algorithm, and show that it provably produces solutions at least as good as,
and potentially much better than, the max-product algorithm. We empirically
analyze the proposed algorithm against max-product using synthetic and
realistic networks.
An embedded segmental K-means model for unsupervised segmentation and clustering of speech
Authors: Herman Kamper, Karen Livescu, Sharon Goldwater
Date: 2017-03-23 00:00:00
Tasks: Bayesian Inference, Clustering, Segmentation, Word Embeddings

Unsupervised segmentation and clustering of unlabelled speech are core
problems in zero-resource speech processing. Most approaches lie at
methodological extremes: some use probabilistic Bayesian models with
convergence guarantees, while others opt for more efficient heuristic
techniques. Despite competitive performance in previous work, the full Bayesian
approach is difficult to scale to large speech corpora. We introduce an
approximation to a recent Bayesian model that still has a clear objective
function but improves efficiency by using hard clustering and segmentation
rather than full Bayesian inference. Like its Bayesian counterpart, this
embedded segmental K-means model (ES-KMeans) represents arbitrary-length word
segments as fixed-dimensional acoustic word embeddings. We first compare
ES-KMeans to previous approaches on common English and Xitsonga data sets (5
and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in
word segmentation, giving similar scores to the Bayesian model while being 5
times faster with fewer hyperparameters. However, its clusters are less pure
than those of the other models. We then show that ES-KMeans scales to larger
corpora by applying it to the 5 languages of the Zero Resource Speech Challenge
2017 (up to 45 hours), where it performs competitively compared to the
challenge baseline.
Bandits with Knapsacks
Authors: Ashwinkumar Badanidiyuru, Robert Kleinberg, Aleksandrs Slivkins
Date: 2013-05-11 00:00:00
Tasks: Scheduling

Multi-armed bandit problems are the predominant theoretical model of
exploration-exploitation tradeoffs in learning, and they have countless
applications ranging from medical trials, to communication networks, to Web
search and advertising. In many of these application domains the learner may be
constrained by one or more supply (or budget) limits, in addition to the
customary limitation on the time horizon. The literature lacks a general model
encompassing these sorts of problems. We introduce such a model, called
"bandits with knapsacks", that combines aspects of stochastic integer
programming with online learning. A distinctive feature of our problem, in
comparison to the existing regret-minimization literature, is that the optimal
policy for a given latent distribution may significantly outperform the policy
that plays the optimal fixed arm. Consequently, achieving sublinear regret in
the bandits-with-knapsacks problem is significantly more challenging than in
conventional bandit problems.
  We present two algorithms whose reward is close to the information-theoretic
optimum: one is based on a novel "balanced exploration" paradigm, while the
other is a primal-dual algorithm that uses multiplicative updates. Further, we
prove that the regret achieved by both algorithms is optimal up to
polylogarithmic factors. We illustrate the generality of the problem by
presenting applications in a number of different domains including electronic
commerce, routing, and scheduling. As one example of a concrete application, we
consider the problem of dynamic posted pricing with limited supply and obtain
the first algorithm whose regret, with respect to the optimal dynamic policy,
is sublinear in the supply.
Rationally Biased Learning
Authors: Michel De Lara
Date: 2017-09-05 00:00:00

Humans display a tendency to pay more attention to bad outcomes, often in a disproportionate way relative to their statistical occurrence. They also display euphorism, as well as a preference for the current state of affairs (status quo bias). Based on the analysis of optimal solutions of infinite horizon stationary optimization problems under imperfect state observation, we show that such human perception and decision biases can be grounded in a form of rationality. We also provide conditions (boundaries) for their possible occurence and an analysis of their robustness.Thus, biases can be the product of rational behavior.