pwc-archive/papers-with-abstracts huggingface dataset:
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
Authors: L. A. Prashanth, Nathaniel Korda, Rémi Munos
Date: 2013-06-11 00:00:00
Tasks: Multi-Armed Bandits, News Recommendation, regression, Traffic Signal Control

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked uniformly from a given dataset. Our method results in an $O(d)$ improvement in complexity in comparison to LSTD, where $d$ is the dimension of the data. We provide non-asymptotic bounds for our proposed method, both in high probability and in expectation, under the assumption that the matrix underlying the LSTD solution is positive definite. The latter assumption can be easily satisfied for the pathwise LSTD variant proposed in [23]. Moreover, we also establish that using our method in place of LSTD does not impact the rate of convergence of the approximate value function to the true value function. These rate results coupled with the low computational complexity of our method make it attractive for implementation in big data settings, where $d$ is large. A similar low-complexity alternative for least squares regression is well-known as the stochastic gradient descent (SGD) algorithm. We provide finite-time bounds for SGD. We demonstrate the practicality of our method as an efficient alternative for pathwise LSTD empirically by combining it with the least squares policy iteration (LSPI) algorithm in a traffic signal control application. We also conduct another set of experiments that combines the SA based low-complexity variant for least squares regression with the LinUCB algorithm for contextual bandits, using the large scale news recommendation dataset from Yahoo.
Providing theoretical learning guarantees to Deep Learning Networks
Authors: Rodrigo Fernandes de Mello, Martha Dais Ferreira, Moacir Antonelli Ponti
Date: 2017-11-28 00:00:00
Tasks: Deep Learning, Learning Theory

Deep Learning (DL) is one of the most common subjects when Machine Learning
and Data Science approaches are considered. There are clearly two movements
related to DL: the first aggregates researchers in quest to outperform other
algorithms from literature, trying to win contests by considering often small
decreases in the empirical risk; and the second investigates overfitting
evidences, questioning the learning capabilities of DL classifiers. Motivated
by such opposed points of view, this paper employs the Statistical Learning
Theory (SLT) to study the convergence of Deep Neural Networks, with particular
interest in Convolutional Neural Networks. In order to draw theoretical
conclusions, we propose an approach to estimate the Shattering coefficient of
those classification algorithms, providing a lower bound for the complexity of
their space of admissible functions, a.k.a. algorithm bias. Based on such
estimator, we generalize the complexity of network biases, and, next, we study
AlexNet and VGG16 architectures in the point of view of their Shattering
coefficients, and number of training examples required to provide theoretical
learning guarantees. From our theoretical formulation, we show the conditions
which Deep Neural Networks learn as well as point out another issue: DL
benchmarks may be strictly driven by empirical risks, disregarding the
complexity of algorithms biases.
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines
Authors: Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Date: 2017-11-28 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

Despite the recent deep learning (DL) revolution, kernel machines still
remain powerful methods for action recognition. DL has brought the use of large
datasets and this is typically a problem for kernel approaches, which are not
scaling up efficiently due to kernel Gram matrices. Nevertheless, kernel
methods are still attractive and more generally applicable since they can
equally manage different sizes of the datasets, also in cases where DL
techniques show some limitations. This work investigates these issues by
proposing an explicit approximated representation that, together with a linear
model, is an equivalent, yet scalable, implementation of a kernel machine. Our
approximation is directly inspired by the exact feature map that is induced by
an RBF Gaussian kernel but, unlike the latter, it is finite dimensional and
very compact. We justify the soundness of our idea with a theoretical analysis
which proves the unbiasedness of the approximation, and provides a vanishing
bound for its variance, which is shown to decrease much rapidly than in
alternative methods in the literature. In a broad experimental validation, we
assess the superiority of our approximation in terms of 1) ease and speed of
training, 2) compactness of the model, and 3) improvements with respect to the
state-of-the-art performance.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Authors: Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo
Date: 2017-06-30 00:00:00
Tasks: Ad-Hoc Information Retrieval, Retrieval

Neural IR models, such as DRMM and PACRR, have achieved strong results by
successfully capturing relevance matching signals. We argue that the context of
these matching signals is also important. Intuitively, when extracting,
modeling, and combining matching signals, one would like to consider the
surrounding text (local context) as well as other signals from the same
document that can contribute to the overall relevance score. In this work, we
highlight three potential shortcomings caused by not considering context
information and propose three neural ingredients to address them: a
disambiguation component, cascade k-max pooling, and a shuffling combination
layer. Incorporating these components into the PACRR model yields Co-PACRR, a
novel context-aware neural IR model. Extensive comparisons with established
models on Trec Web Track data confirm that the proposed model can achieve
superior search results. In addition, an ablation analysis is conducted to gain
insights into the impact of and interactions between different components. We
release our code to enable future comparisons.
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Authors: Pietro Morerio, Jacopo Cavazza, Vittorio Murino
Date: 2017-11-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Domain Adaptation, General Classification, Unsupervised Domain Adaptation

In this work, we face the problem of unsupervised domain adaptation with a
novel deep learning approach which leverages on our finding that entropy
minimization is induced by the optimal alignment of second order statistics
between source and target domains. We formally demonstrate this hypothesis and,
aiming at achieving an optimal alignment in practical cases, we adopt a more
principled strategy which, differently from the current Euclidean approaches,
deploys alignment along geodesics. Our pipeline can be implemented by adding to
the standard classification loss (on the labeled source domain), a
source-to-target regularizer that is weighted in an unsupervised and
data-driven fashion. We provide extensive experiments to assess the superiority
of our framework on standard domain and modality adaptation benchmarks.
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Authors: Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Semantic Segmentation, LIDAR Semantic Segmentation, Semantic Segmentation

Submanifold sparse convolutional networks
Exploiting Nontrivial Connectivity for Automatic Speech Recognition
Authors: Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax, Marco Singh, Lars Maaløe
Date: 2017-11-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, image-classification, Image Classification, speech-recognition, Speech Recognition

Nontrivial connectivity has allowed the training of very deep networks by
addressing the problem of vanishing gradients and offering a more efficient
method of reusing parameters. In this paper we make a comparison between
residual networks, densely-connected networks and highway networks on an image
classification task. Next, we show that these methodologies can easily be
deployed into automatic speech recognition and provide significant improvements
to existing models.
Generative Partition Networks for Multi-Person Pose Estimation
Authors: Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan
Date: 2017-05-21 00:00:00
Tasks: Human Detection, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

This paper proposes a new Generative Partition Network (GPN) to address the
challenging multi-person pose estimation problem. Different from existing
models that are either completely top-down or bottom-up, the proposed GPN
introduces a novel strategy--it generates partitions for multiple persons from
their global joint candidates and infers instance-specific joint configurations
simultaneously. The GPN is favorably featured by low complexity and high
accuracy of joint detection and re-organization. In particular, GPN designs a
generative model that performs one feed-forward pass to efficiently generate
robust person detections with joint partitions, relying on dense regressions
from global joint candidates in an embedding space parameterized by centroids
of persons. In addition, GPN formulates the inference procedure for joint
configurations of human poses as a graph partition problem, and conducts local
optimization for each person detection with reliable global affinity cues,
leading to complexity reduction and performance improvement. GPN is implemented
with the Hourglass architecture as the backbone network to simultaneously learn
joint detector and dense regressor. Extensive experiments on benchmarks MPII
Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF, show the
efficiency of GPN with new state-of-the-art performance.
Mondrian Processes for Flow Cytometry Analysis
Authors: Disi Ji, Eric Nalisnick, Padhraic Smyth
Date: 2017-11-21 00:00:00
Tasks: Uncertainty Quantification

Analysis of flow cytometry data is an essential tool for clinical diagnosis
of hematological and immunological conditions. Current clinical workflows rely
on a manual process called gating to classify cells into their canonical types.
This dependence on human annotation limits the rate, reproducibility, and
complexity of flow cytometry analysis. In this paper, we propose using Mondrian
processes to perform automated gating by incorporating prior information of the
kind used by gating technicians. The method segments cells into types via
Bayesian nonparametric trees. Examining the posterior over trees allows for
interpretable visualizations and uncertainty quantification - two vital
qualities for implementation in clinical practice.
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Authors: Dat Thanh Tran, Martin Magris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Date: 2017-09-05 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.
Tracking for Half an Hour
Authors: Ran Tao, Efstratios Gavves, Arnold W. M. Smeulders
Date: 2017-11-28 00:00:00

Long-term tracking requires extreme stability to the multitude of model
updates and robustness to the disappearance and loss of the target as such will
inevitably happen. For motivation, we have taken 10 randomly selected
OTB-sequences, doubled each by attaching a reversed version and repeated each
double sequence 20 times. On most of these repetitive videos, the best current
tracker performs worse on each loop. This illustrates the difference between
optimization for short-term versus long-term tracking. In a long-term tracker a
combined global and local search strategy is beneficial, allowing for recovery
from failures and disappearance. Most importantly, the proposed tracker also
employs cautious updating, guided by self-quality assessment. The proposed
tracker is still among the best on the 20-sec OTB-videos while achieving
state-of-the-art on the 100-sec UAV20L benchmark. On 10 new half-an-hour videos
with city bicycling, sport games etc, the proposed tracker outperforms others
by a large margin where the 2010 TLD tracker comes second.
Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation
Authors: Guodong Zeng, Guoyan Zheng
Date: 2017-11-28 00:00:00
Tasks: Image Segmentation, Infant Brain Mri Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

We present a method to address the challenging problem of segmentation of
multi-modality isointense infant brain MR images into white matter (WM), gray
matter (GM), and cerebrospinal fluid (CSF). Our method is based on
context-guided, multi-stream fully convolutional networks (FCN), which after
training, can directly map a whole volumetric data to its volume-wise labels.
In order to alleviate the poten-tial gradient vanishing problem during
training, we designed multi-scale deep supervision. Furthermore, context
infor-mation was used to further improve the performance of our method.
Validated on the test data of the MICCAI 2017 Grand Challenge on 6-month infant
brain MRI segmentation (iSeg-2017), our method achieved an average Dice Overlap
Coefficient of 95.4%, 91.6% and 89.6% for CSF, GM and WM, respectively.
Warmstarting of Model-based Algorithm Configuration
Authors: Marius Lindauer, Frank Hutter
Date: 2017-09-14 00:00:00
Tasks: model

The performance of many hard combinatorial problem solvers depends strongly
on their parameter settings, and since manual parameter tuning is both tedious
and suboptimal the AI community has recently developed several algorithm
configuration (AC) methods to automatically address this problem. While all
existing AC methods start the configuration process of an algorithm A from
scratch for each new type of benchmark instances, here we propose to exploit
information about A's performance on previous benchmarks in order to warmstart
its configuration on new types of benchmarks. We introduce two complementary
ways in which we can exploit this information to warmstart AC methods based on
a predictive model. Experiments for optimizing a very flexible modern SAT
solver on twelve different instance sets show that our methods often yield
substantial speedups over existing AC methods (up to 165-fold) and can also
find substantially better configurations given the same compute budget.
Guaranteed Outlier Removal for Point Cloud Registration with Correspondences
Authors: Álvaro Parra Bustos, Tat-Jun Chin
Date: 2017-11-28 00:00:00
Tasks: Point Cloud Registration

An established approach for 3D point cloud registration is to estimate the
registration function from 3D keypoint correspondences. Typically, a robust
technique is required to conduct the estimation, since there are false
correspondences or outliers. Current 3D keypoint techniques are much less
accurate than their 2D counterparts, thus they tend to produce extremely high
outlier rates. A large number of putative correspondences must thus be
extracted to ensure that sufficient good correspondences are available. Both
factors (high outlier rates, large data sizes) however cause existing robust
techniques to require very high computational cost. In this paper, we present a
novel preprocessing method called \emph{guaranteed outlier removal} for point
cloud registration. Our method reduces the input to a smaller set, in a way
that any rejected correspondence is guaranteed to not exist in the globally
optimal solution. The reduction is performed using purely geometric operations
which are deterministic and fast. Our method significantly reduces the
population of outliers, such that further optimization can be performed
quickly. Further, since only true outliers are removed, the globally optimal
solution is preserved. On various synthetic and real data experiments, we
demonstrate the effectiveness of our preprocessing method. Demo code is
available as supplementary material.
Learning to Rank based on Analogical Reasoning
Authors: Mohsen Ahmadi Fahandar, Eyke Hüllermeier
Date: 2017-11-28 00:00:00
Tasks: Learning-To-Rank, Object, Transfer Learning

Object ranking or "learning to rank" is an important problem in the realm of
preference learning. On the basis of training data in the form of a set of
rankings of objects represented as feature vectors, the goal is to learn a
ranking function that predicts a linear order of any new set of objects. In
this paper, we propose a new approach to object ranking based on principles of
analogical reasoning. More specifically, our inference pattern is formalized in
terms of so-called analogical proportions and can be summarized as follows:
Given objects $A,B,C,D$, if object $A$ is known to be preferred to $B$, and $C$
relates to $D$ as $A$ relates to $B$, then $C$ is (supposedly) preferred to
$D$. Our method applies this pattern as a main building block and combines it
with ideas and techniques from instance-based learning and rank aggregation.
Based on first experimental results for data sets from various domains (sports,
education, tourism, etc.), we conclude that our approach is highly competitive.
It appears to be specifically interesting in situations in which the objects
are coming from different subdomains, and which hence require a kind of
knowledge transfer.
Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task
Authors: Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
Date: 2017-11-28 00:00:00

In the present work we propose a Deep Feed Forward network architecture which
can be trained according to a sequential learning paradigm, where tasks of
increasing difficulty are learned sequentially, yet avoiding catastrophic
forgetting. The proposed architecture can re-use the features learned on
previous tasks in a new task when the old tasks and the new one are related.
The architecture needs fewer computational resources (neurons and connections)
and less data for learning the new task than a network trained from scratch
Cost-Effective Active Learning for Melanoma Segmentation
Authors: Marc Gorriz, Axel Carlier, Emmanuel Faure, Xavier Giro-i-Nieto
Date: 2017-11-24 00:00:00
Tasks: Active Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

We propose a novel Active Learning framework capable to train effectively a
convolutional neural network for semantic segmentation of medical imaging, with
a limited amount of training labeled data. Our contribution is a practical
Cost-Effective Active Learning approach using dropout at test time as Monte
Carlo sampling to model the pixel-wise uncertainty and to analyze the image
information to improve the training performance. The source code of this
project is available at
https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .
Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end
Authors: Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
Date: 2017-11-28 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this work we propose a system for visual question answering. Our
architecture is composed of two parts, the first part creates the logical
knowledge base given the image. The second part evaluates questions against the
knowledge base. Differently from previous work, the knowledge base is
represented using hyper-dimensional computing. This choice has the advantage
that all the operations in the system, namely creating the knowledge base and
evaluating the questions against it, are differentiable, thereby making the
system easily trainable in an end-to-end fashion.
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network
Authors: Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov
Date: 2017-11-28 00:00:00
Tasks: Transfer Learning

In this paper we present an alternative strategy for fine-tuning the
parameters of a network. We named the technique Gradual Tuning. Once trained on
a first task, the network is fine-tuned on a second task by modifying a
progressively larger set of the network's parameters. We test Gradual Tuning on
different transfer learning tasks, using networks of different sizes trained
with different regularization techniques. The result shows that compared to the
usual fine tuning, our approach significantly reduces catastrophic forgetting
of the initial task, while still retaining comparable if not better performance
on the new task.
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation
Authors: Tomas Kliegr, Ebroul Izquierdo
Date: 2017-11-28 00:00:00
Tasks: Anomaly Detection, Classification, General Classification

A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their accuracy. The proposed QCBA method was initially developed to postprocess quantitative attributes in models generated by the Classification based on associations (CBA) algorithm, but it can also be applied to the results of other rule learning approaches. We demonstrate the effectiveness on the postprocessing of models generated by five association rule classification algorithms (CBA, CMAR, CPAR, IDS, SBRL) and two first-order logic rule learners (FOIL2 and PRM). Benchmarks on 22 datasets from the UCI repository show smaller size and the overall best predictive performance for FOIL2+QCBA compared to all seven baselines. Postoptimised CBA models have a better predictive performance compared to the state-of-the-art rule learner CORELS in this benchmark. The article contains an ablation study for the individual postprocessing steps and a scalability analysis on the KDD'99 Anomaly detection dataset.