pwc-archive/papers-with-abstracts huggingface dataset:
Reducing Crowdsourcing to Graphon Estimation, Statistically
Authors: Devavrat Shah, Christina Lee Yu
Date: 2017-03-23 00:00:00
Tasks: Graphon Estimation

Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions. In graphon estimation, one is interested in estimating edge intensities or probabilities between nodes using a single snapshot of a graph realization. In the recent literature, there has been exciting development within both of these topics. In the context of crowdsourcing, the key intellectual challenge is to understand whether a given task can be more accurately denoised by aggregating answers collected from other different tasks. In the context of graphon estimation, precise information limits and estimation algorithms remain of interest. In this paper, we utilize a statistical reduction from crowdsourcing to graphon estimation to advance the state-of-art for both of these challenges. We use concepts from graphon estimation to design an algorithm that achieves better performance than the {\em majority voting} scheme for a setup that goes beyond the {\em rank one} models considered in the literature. We use known explicit lower bounds for crowdsourcing to provide refined lower bounds for graphon estimation.
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
Authors: Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé
Date: 2018-02-21 00:00:00
Tasks: Clustering, Decision Making, Gaussian Processes

In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.
Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data
Authors: Félix G. Harvey, Julien Roy, David Kanaa, Christopher Pal
Date: 2015-11-20 00:00:00
Tasks: Clustering, Decoder, General Classification

We explore recurrent encoder multi-decoder neural network architectures for
semi-supervised sequence classification and reconstruction. We find that the
use of multiple reconstruction modules helps models generalize in a
classification task when only a small amount of labeled data is available,
which is often the case in practice. Such models provide useful high-level
representations of motions allowing clustering, searching and faster labeling
of new sequences. We also propose a new, realistic partitioning of a
well-known, high quality motion-capture dataset for better evaluations. We
further explore a novel formulation for future-predicting decoders based on
conditional recurrent generative adversarial networks, for which we propose
both soft and hard constraints for transition generation derived from desired
physical properties of synthesized future movements and desired animation
goals. We find that using such constraints allow to stabilize the training of
recurrent adversarial architectures for animation generation.
Smooth Loss Functions for Deep Top-k Classification
Authors: Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Date: 2018-02-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification

The top-k error is a common measure of performance in machine learning and
computer vision. In practice, top-k classification is typically performed with
deep neural networks trained with the cross-entropy loss. Theoretical results
indeed suggest that cross-entropy is an optimal learning objective for such a
task in the limit of infinite data. In the context of limited and noisy data
however, the use of a loss function that is specifically designed for top-k
classification can bring significant improvements. Our empirical evidence
suggests that the loss function must be smooth and have non-sparse gradients in
order to work well with deep neural networks. Consequently, we introduce a
family of smoothed loss functions that are suited to top-k optimization via
deep learning. The widely used cross-entropy is a special case of our family.
Evaluating our smooth loss functions is computationally challenging: a na\"ive
algorithm would require $\mathcal{O}(\binom{n}{k})$ operations, where n is the
number of classes. Thanks to a connection to polynomial algebra and a
divide-and-conquer approach, we provide an algorithm with a time complexity of
$\mathcal{O}(k n)$. Furthermore, we present a novel approximation to obtain
fast and stable algorithms on GPUs with single floating point precision. We
compare the performance of the cross-entropy loss and our margin-based losses
in various regimes of noise and data size, for the predominant use case of k=5.
Our investigation reveals that our loss is more robust to noise and overfitting
than cross-entropy.
Batch Normalization and the impact of batch structure on the behavior of deep convolution networks
Authors: Mohamed Hajaj, Duncan Gillies
Date: 2018-02-21 00:00:00
Tasks: Unity

Batch normalization was introduced in 2015 to speed up training of deep
convolution networks by normalizing the activations across the current batch to
have zero mean and unity variance. The results presented here show an
interesting aspect of batch normalization, where controlling the shape of the
training batches can influence what the network will learn. If training batches
are structured as balanced batches (one image per class), and inference is also
carried out on balanced test batches, using the batch's own means and
variances, then the conditional results will improve considerably. The network
uses the strong information about easy images in a balanced batch, and
propagates it through the shared means and variances to help decide the
identity of harder images on the same batch. Balancing the test batches
requires the labels of the test images, which are not available in practice,
however further investigation can be done using batch structures that are less
strict and might not require the test image labels. The conditional results
show the error rate almost reduced to zero for nontrivial datasets with small
number of classes such as the CIFAR10.
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification
Authors: Shaoning Zeng, Bob Zhang, Yanghao Zhang, Jianping Gou
Date: 2018-02-21 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, L2 Regularization, Object Recognition

Deep convolutional neural networks provide a powerful feature learning
capability for image classification. The deep image features can be utilized to
deal with many image understanding tasks like image classification and object
recognition. However, the robustness obtained in one dataset can be hardly
reproduced in the other domain, which leads to inefficient models far from
state-of-the-art. We propose a deep collaborative weight-based classification
(DeepCWC) method to resolve this problem, by providing a novel option to fully
take advantage of deep features in classic machine learning. It firstly
performs the L2-norm based collaborative representation on the original images,
as well as the deep features extracted by deep CNN models. Then, two distance
vectors, obtained based on the pair of linear representations, are fused
together via a novel collaborative weight. This collaborative weight enables
deep and classic representations to weigh each other. We observed the
complementarity between two representations in a series of experiments on 10
facial and object datasets. The proposed DeepCWC produces very promising
classification results, and outperforms many other benchmark methods,
especially the ones claimed for Fashion-MNIST. The code is going to be
published in our public repository.
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
Authors: Ruihao Li, Sen Wang, Zhiqiang Long, Dongbing Gu
Date: 2017-09-20 00:00:00
Tasks: Deep Learning, Monocular Visual Odometry, Visual Odometry

We propose a novel monocular visual odometry (VO) system called UnDeepVO in
this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera
and the depth of its view by using deep neural networks. There are two salient
features of the proposed UnDeepVO: one is the unsupervised deep learning
scheme, and the other is the absolute scale recovery. Specifically, we train
UnDeepVO by using stereo image pairs to recover the scale but test it by using
consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss
function defined for training the networks is based on spatial and temporal
dense information. A system overview is shown in Fig. 1. The experiments on
KITTI dataset show our UnDeepVO achieves good performance in terms of pose
accuracy.
A Unified Deep Learning Architecture for Abuse Detection
Authors: Antigoni-Maria Founta, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, Ilias Leontiadis
Date: 2018-02-01 00:00:00
Tasks: Abuse Detection, Blocking, Deep Learning

Hate speech, offensive language, sexism, racism and other types of abusive
behavior have become a common phenomenon in many online social media platforms.
In recent years, such diverse abusive behaviors have been manifesting with
increased frequency and levels of intensity. This is due to the openness and
willingness of popular media platforms, such as Twitter and Facebook, to host
content of sensitive or controversial topics. However, these platforms have not
adequately addressed the problem of online abusive behavior, and their
responsiveness to the effective detection and blocking of such inappropriate
behavior remains limited.
  In the present paper, we study this complex problem by following a more
holistic approach, which considers the various aspects of abusive behavior. To
make the approach tangible, we focus on Twitter data and analyze user and
textual properties from different angles of abusive posting behavior. We
propose a deep learning architecture, which utilizes a wide variety of
available metadata, and combines it with automatically-extracted hidden
patterns within the text of the tweets, to detect multiple abusive behavioral
norms which are highly inter-related. We apply this unified architecture in a
seamless, transparent fashion to detect different types of abusive behavior
(hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for
any tuning of the model architecture for each task. We test the proposed
approach with multiple datasets addressing different and multiple abusive
behaviors on Twitter. Our results demonstrate that it largely outperforms the
state-of-art methods (between 21 and 45\% improvement in AUC, depending on the
dataset).
VBALD - Variational Bayesian Approximation of Log Determinants
Authors: Diego Granziol, Edward Wagstaff, Bin Xin Ru, Michael Osborne, Stephen Roberts
Date: 2018-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Metric Learning, Point Processes

Evaluating the log determinant of a positive definite matrix is ubiquitous in
machine learning. Applications thereof range from Gaussian processes,
minimum-volume ellipsoids, metric learning, kernel learning, Bayesian neural
networks, Determinental Point Processes, Markov random fields to partition
functions of discrete graphical models. In order to avoid the canonical, yet
prohibitive, Cholesky $\mathcal{O}(n^{3})$ computational cost, we propose a
novel approach, with complexity $\mathcal{O}(n^{2})$, based on a constrained
variational Bayes algorithm. We compare our method to Taylor, Chebyshev and
Lanczos approaches and show state of the art performance on both synthetic and
real-world datasets.
(DE)^2 CO: Deep Depth Colorization
Authors: F. M. Carlucci, P. Russo, B. Caputo
Date: 2017-03-31 00:00:00
Tasks: Colorization, Transfer Learning

The ability to classify objects is fundamental for robots. Besides knowledge
about their visual appearance, captured by the RGB channel, robots heavily need
also depth information to make sense of the world. While the use of deep
networks on RGB robot images has benefited from the plethora of results
obtained on databases like ImageNet, using convnets on depth images requires
mapping them into three dimensional channels. This transfer learning procedure
makes them processable by pre-trained deep architectures. Current mappings are
based on heuristic assumptions over preprocessing steps and on what depth
properties should be most preserved, resulting often in cumbersome data
visualizations, and in sub-optimal performance in terms of generality and
recognition results. Here we take an alternative route and we attempt instead
to learn an optimal colorization mapping for any given pre-trained
architecture, using as training data a reference RGB-D database. We propose a
deep network architecture, exploiting the residual paradigm, that learns how to
map depth data to three channel images. A qualitative analysis of the images
obtained with this approach clearly indicates that learning the optimal mapping
preserves the richness of depth information better than current hand-crafted
approaches. Experiments on the Washington, JHUIT-50 and BigBIRD public
benchmark databases, using CaffeNet, VGG16, GoogleNet, and ResNet50 clearly
showcase the power of our approach, with gains in performance of up to 16%
compared to state of the art competitors on the depth channel only, leading to
top performances when dealing with RGB-D data
Information Theoretic Co-Training
Authors: David McAllester
Date: 2018-02-21 00:00:00

This paper introduces an information theoretic co-training objective for
unsupervised learning. We consider the problem of predicting the future. Rather
than predict future sensations (image pixels or sound waves) we predict
"hypotheses" to be confirmed by future sensations. More formally, we assume a
population distribution on pairs $(x,y)$ where we can think of $x$ as a past
sensation and $y$ as a future sensation. We train both a predictor model
$P_\Phi(z|x)$ and a confirmation model $P_\Psi(z|y)$ where we view $z$ as
hypotheses (when predicted) or facts (when confirmed). For a population
distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual
information between $x$ and $y$. By the data processing inequality this mutual
information is at least as large as the mutual information between $x$ and $z$
under the distribution on triples $(x,z,y)$ defined by the confirmation model
$P_\Psi(z|y)$. The information theoretic training objective for $P_\Phi(z|x)$
and $P_\Psi(z|y)$ can be viewed as a form of co-training where we want the
prediction from $x$ to match the confirmation from $y$.
Continual Lifelong Learning with Neural Networks: A Review
Authors: German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, Stefan Wermter
Date: 2018-02-21 00:00:00
Tasks: Lifelong learning, Retrieval, Transfer Learning

Humans and animals have the ability to continually acquire, fine-tune, and
transfer knowledge and skills throughout their lifespan. This ability, referred
to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
that together contribute to the development and specialization of our
sensorimotor skills as well as to long-term memory consolidation and retrieval.
Consequently, lifelong learning capabilities are crucial for autonomous agents
interacting in the real world and processing continuous streams of information.
However, lifelong learning remains a long-standing challenge for machine
learning and neural network models since the continual acquisition of
incrementally available information from non-stationary data distributions
generally leads to catastrophic forgetting or interference. This limitation
represents a major drawback for state-of-the-art deep neural network models
that typically learn representations from stationary batches of training data,
thus without accounting for situations in which information becomes
incrementally available over time. In this review, we critically summarize the
main challenges linked to lifelong learning for artificial learning systems and
compare existing neural network approaches that alleviate, to different
extents, catastrophic forgetting. We discuss well-established and emerging
research motivated by lifelong learning factors in biological systems such as
structural plasticity, memory replay, curriculum and transfer learning,
intrinsic motivation, and multisensory integration.
Clipped Action Policy Gradient
Authors: Yasuhiro Fujita, Shin-ichi Maeda
Date: 2018-02-21 00:00:00
Proceeding: ICML 2018 7
Tasks: continuous-control, Continuous Control, Policy Gradient Methods

Many continuous control tasks have bounded action spaces. When policy
gradient methods are applied to such tasks, out-of-bound actions need to be
clipped before execution, while policies are usually optimized as if the
actions are not clipped. We propose a policy gradient estimator that exploits
the knowledge of actions being clipped to reduce the variance in estimation. We
prove that our estimator, named clipped action policy gradient (CAPG), is
unbiased and achieves lower variance than the conventional estimator that
ignores action bounds. Experimental results demonstrate that CAPG generally
outperforms the conventional estimator, indicating that it is a better policy
gradient estimator for continuous control tasks. The source code is available
at https://github.com/pfnet-research/capg.
Discriminative Label Consistent Domain Adaptation
Authors: Lingkun Luo, Liming Chen, Ying Lu, Shiqiang Hu
Date: 2018-02-21 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Transfer Learning

Domain adaptation (DA) is transfer learning which aims to learn an effective
predictor on target data from source data despite data distribution mismatch
between source and target. We present in this paper a novel unsupervised DA
method for cross-domain visual recognition which simultaneously optimizes the
three terms of a theoretically established error bound. Specifically, the
proposed DA method iteratively searches a latent shared feature subspace where
not only the divergence of data distributions between the source domain and the
target domain is decreased as most state-of-the-art DA methods do, but also the
inter-class distances are increased to facilitate discriminative learning.
Moreover, the proposed DA method sparsely regresses class labels from the
features achieved in the shared subspace while minimizing the prediction errors
on the source data and ensuring label consistency between source and target.
Data outliers are also accounted for to further avoid negative knowledge
transfer. Comprehensive experiments and in-depth analysis verify the
effectiveness of the proposed DA method which consistently outperforms the
state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain
image classification tasks.
Pooling homogeneous ensembles to build heterogeneous ones
Authors: Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
Date: 2018-02-21 00:00:00

In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial. In this work, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. An empirical analysis of such combinations of bootstraped ensembles composed of neural networks, SVMs, and random trees (i.e. from a standard random forest) illustrates the gains that can be achieved by this heterogeneous ensemble creation method.
Vote-boosting ensembles
Authors: Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
Date: 2016-06-30 00:00:00
Tasks: Ensemble Learning

Vote-boosting is a sequential ensemble learning method in which the
individual classifiers are built on different weighted versions of the training
data. To build a new classifier, the weight of each training instance is
determined in terms of the degree of disagreement among the current ensemble
predictions for that instance. For low class-label noise levels, especially
when simple base learners are used, emphasis should be made on instances for
which the disagreement rate is high. When more flexible classifiers are used
and as the noise level increases, the emphasis on these uncertain instances
should be reduced. In fact, at sufficiently high levels of class-label noise,
the focus should be on instances on which the ensemble classifiers agree. The
optimal type of emphasis can be automatically determined using
cross-validation. An extensive empirical analysis using the beta distribution
as emphasis function illustrates that vote-boosting is an effective method to
generate ensembles that are both accurate and robust.