pwc-archive/papers-with-abstracts huggingface dataset:
POTs: Protective Optimization Technologies
Authors: Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, Seda Gürses
Date: 2018-06-07 00:00:00
Tasks: Decision Making, Fairness

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.
Surface Networks
Authors: Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna
Date: 2017-05-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D geometry

We study data-driven representations for three-dimensional triangle meshes,
which are one of the prevalent objects used to represent 3D geometry. Recent
works have developed models that exploit the intrinsic geometry of manifolds
and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants,
which learn from the local metric tensor via the Laplacian operator. Despite
offering excellent sample complexity and built-in invariances, intrinsic
geometry alone is invariant to isometric deformations, making it unsuitable for
many applications. To overcome this limitation, we propose several upgrades to
GNNs to leverage extrinsic differential geometry properties of
three-dimensional surfaces, increasing its modeling power.
  In particular, we propose to exploit the Dirac operator, whose spectrum
detects principal curvature directions --- this is in stark contrast with the
classical Laplace operator, which directly measures mean curvature. We coin the
resulting models \emph{Surface Networks (SN)}. We prove that these models
define shape representations that are stable to deformation and to
discretization, and we demonstrate the efficiency and versatility of SNs on two
challenging tasks: temporal prediction of mesh deformations under non-linear
dynamics and generative models using a variational autoencoder framework with
encoders/decoders given by SNs.
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Authors: Gail Weiss, Yoav Goldberg, Eran Yahav
Date: 2017-11-27 00:00:00
Proceeding: ICML 2018 7

We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.
Unsupervised Word Segmentation from Speech with Attention
Authors: Pierre Godard, Marcely Zanon-Boito, Lucas Ondel, Alexandre Berard, François Yvon, Aline Villavicencio, Laurent Besacier
Date: 2018-06-18 00:00:00
Tasks: Acoustic Unit Discovery, Machine Translation, Segmentation, Translation

We present a first attempt to perform attentional word segmentation directly
from the speech signal, with the final goal to automatically identify lexical
units in a low-resource, unwritten language (UL). Our methodology assumes a
pairing between recordings in the UL with translations in a well-resourced
language. It uses Acoustic Unit Discovery (AUD) to convert speech into a
sequence of pseudo-phones that is segmented using neural soft-alignments
produced by a neural machine translation model. Evaluation uses an actual Bantu
UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the
potential of attentional word segmentation for language documentation.
Semantically Selective Augmentation for Deep Compact Person Re-Identification
Authors: Víctor Ponce-López, Tilo Burghardt, Sion Hannunna, Dima Damen, Alessandro Masullo, Majid Mirmehdi
Date: 2018-06-11 00:00:00
Tasks: Clustering, Data Augmentation, Generative Adversarial Network, Person Re-Identification, Specificity

We present a deep person re-identification approach that combines
semantically selective, deep data augmentation with clustering-based network
compression to generate high performance, light and fast inference networks. In
particular, we propose to augment limited training data via sampling from a
deep convolutional generative adversarial network (DCGAN), whose discriminator
is constrained by a semantic classifier to explicitly control the domain
specificity of the generation process. Thereby, we encode information in the
classifier network which can be utilized to steer adversarial synthesis, and
which fuels our CondenseNet ID-network training. We provide a quantitative and
qualitative analysis of the approach and its variants on a number of datasets,
obtaining results that outperform the state-of-the-art on the LIMA dataset for
long-term monitoring in indoor living spaces.
Assessing robustness of radiomic features by image perturbation
Authors: Alex Zwanenburg, Stefan Leger, Linda Agolli, Karoline Pilz, Esther G. C. Troost, Christian Richter, Steffen Löck
Date: 2018-06-18 00:00:00
Tasks: Translation

Image features need to be robust against differences in positioning,
acquisition and segmentation to ensure reproducibility. Radiomic models that
only include robust features can be used to analyse new images, whereas models
with non-robust features may fail to predict the outcome of interest
accurately. Test-retest imaging is recommended to assess robustness, but may
not be available for the phenotype of interest. We therefore investigated 18
methods to determine feature robustness based on image perturbations.
Test-retest and perturbation robustness were compared for 4032 features that
were computed from the gross tumour volume in two cohorts with computed
tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19
head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was measured
using the intraclass correlation coefficient (1,1) (ICC). Features with
ICC$\geq0.90$ were considered robust. The NSCLC cohort contained more robust
features for test-retest imaging than the HNSCC cohort ($73.5\%$ vs. $34.0\%$).
A perturbation chain consisting of noise addition, affine translation, volume
growth/shrinkage and supervoxel-based contour randomisation identified the
fewest false positive robust features (NSCLC: $3.3\%$; HNSCC: $10.0\%$). Thus,
this perturbation chain may be used to assess feature robustness.
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation
Authors: Francesco Lattari, Marco Ciccone, Matteo Matteucci, Jonathan Masci, Francesco Visin
Date: 2018-06-14 00:00:00
Tasks: Object, Position, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We introduce ReConvNet, a recurrent convolutional architecture for
semi-supervised video object segmentation that is able to fast adapt its
features to focus on any specific object of interest at inference time.
Generalization to new objects never observed during training is known to be a
hard task for supervised approaches that would need to be retrained. To tackle
this problem, we propose a more efficient solution that learns spatio-temporal
features self-adapting to the object of interest via conditional affine
transformations. This approach is simple, can be trained end-to-end and does
not necessarily require extra training steps at inference time. Our method
shows competitive results on DAVIS2016 with respect to state-of-the art
approaches that use online fine-tuning, and outperforms them on DAVIS2017.
ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the
$10$-th position.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Authors: Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer
Date: 2018-06-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Metric Learning

Metric learning has the aim to improve classification accuracy by learning a
distance measure which brings data points from the same class closer together
and pushes data points from different classes further apart. Recent research
has demonstrated that metric learning approaches can also be applied to trees,
such as molecular structures, abstract syntax trees of computer programs, or
syntax trees of natural language, by learning the cost function of an edit
distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree.
However, learning such costs directly may yield an edit distance which violates
metric axioms, is challenging to interpret, and may not generalize well. In
this contribution, we propose a novel metric learning approach for trees which
we call embedding edit distance learning (BEDL) and which learns an edit
distance indirectly by embedding the tree nodes as vectors, such that the
Euclidean distance between those vectors supports class discrimination. We
learn such embeddings by reducing the distance to prototypical trees from the
same class and increasing the distance to prototypical trees from different
classes. In our experiments, we show that BEDL improves upon the
state-of-the-art in metric learning for trees on six benchmark data sets,
ranging from computer science over biomedical data to a natural-language
processing data set containing over 300,000 nodes.
Towards multi-instrument drum transcription
Authors: Richard Vogl, Gerhard Widmer, Peter Knees
Date: 2018-06-18 00:00:00
Tasks: Drum Transcription, Music Transcription

Automatic drum transcription, a subtask of the more general automatic music
transcription, deals with extracting drum instrument note onsets from an audio
source. Recently, progress in transcription performance has been made using
non-negative matrix factorization as well as deep learning methods. However,
these works primarily focus on transcribing three drum instruments only: snare
drum, bass drum, and hi-hat. Yet, for many applications, the ability to
transcribe more drum instruments which make up standard drum kits used in
western popular music would be desirable. In this work, convolutional and
convolutional recurrent neural networks are trained to transcribe a wider range
of drum instruments. First, the shortcomings of publicly available datasets in
this context are discussed. To overcome these limitations, a larger synthetic
dataset is introduced. Then, methods to train models using the new dataset
focusing on generalization to real world data are investigated. Finally, the
trained models are evaluated on publicly available datasets and results are
discussed. The contributions of this work comprise: (i.) a large-scale
synthetic dataset for drum transcription, (ii.) first steps towards an
automatic drum transcription system that supports a larger range of instruments
by evaluating and discussing training setups and the impact of datasets in this
context, and (iii.) a publicly available set of trained models for drum
transcription. Additional materials are available at
http://ifs.tuwien.ac.at/~vogl/dafx2018
Subword and Crossword Units for CTC Acoustic Models
Authors: Thomas Zenkel, Ramon Sanabria, Florian Metze, Alex Waibel
Date: 2017-12-19 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

This paper proposes a novel approach to create an unit set for CTC based
speech recognition systems. By using Byte Pair Encoding we learn an unit set of
an arbitrary size on a given training text. In contrast to using characters or
words as units this allows us to find a good trade-off between the size of our
unit set and the available training data. We evaluate both Crossword units,
that may span multiple word, and Subword units. By combining this approach with
decoding methods using a separate language model we are able to achieve state
of the art results for grapheme based CTC systems.
Cardinality Leap for Open-Ended Evolution: Theoretical Consideration and Demonstration by "Hash Chemistry"
Authors: Hiroki Sayama
Date: 2018-06-18 00:00:00

Open-ended evolution requires unbounded possibilities that evolving entities
can explore. The cardinality of a set of those possibilities thus has a
significant implication for the open-endedness of evolution. We propose that
facilitating formation of higher-order entities is a generalizable, effective
way to cause a "cardinality leap" in the set of possibilities that promotes
open-endedness. We demonstrate this idea with a simple, proof-of-concept toy
model called "Hash Chemistry" that uses a hash function as a fitness evaluator
of evolving entities of any size/order. Simulation results showed that the
cumulative number of unique replicating entities that appeared in evolution
increased almost linearly along time without an apparent bound, demonstrating
the effectiveness of the proposed cardinality leap. It was also observed that
the number of individual entities involved in a single replication event
gradually increased over time, indicating evolutionary appearance of
higher-order entities. Moreover, these behaviors were not observed in control
experiments in which fitness evaluators were replaced by random number
generators. This strongly suggests that the dynamics observed in Hash Chemistry
were indeed evolutionary behaviors driven by selection and adaptation taking
place at multiple scales.
Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive Partitioning
Authors: Meng Li, Li Ma
Date: 2017-11-02 00:00:00
Tasks: Bayesian Inference, Image Reconstruction

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods---linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
Authors: Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson
Date: 2018-06-18 00:00:00
Tasks: Cross-corpus, Emotion Recognition, Speech Emotion Recognition

Generative Adversarial Networks (GANs) have gained a lot of attention from
machine learning community due to their ability to learn and mimic an input
data distribution. GANs consist of a discriminator and a generator working in
tandem playing a min-max game to learn a target underlying data distribution;
when fed with data-points sampled from a simpler distribution (like uniform or
Gaussian distribution). Once trained, they allow synthetic generation of
examples sampled from the target distribution. We investigate the application
of GANs to generate synthetic feature vectors used for speech emotion
recognition. Specifically, we investigate two set ups: (i) a vanilla GAN that
learns the distribution of a lower dimensional representation of the actual
higher dimensional feature vector and, (ii) a conditional GAN that learns the
distribution of the higher dimensional feature vectors conditioned on the
labels or the emotional class to which it belongs. As a potential practical
application of these synthetically generated samples, we measure any
improvement in a classifier's performance when the synthetic data is used along
with real data for training. We perform cross-validation analyses followed by a
cross-corpus study.
Banach Wasserstein GAN
Authors: Jonas Adler, Sebastian Lunz
Date: 2018-06-18 00:00:00
Proceeding: NeurIPS 2018 12

Wasserstein Generative Adversarial Networks (WGANs) can be used to generate
realistic samples from complicated image distributions. The Wasserstein metric
used in WGANs is based on a notion of distance between individual images, which
induces a notion of distance between probability distributions of images. So
far the community has considered $\ell^2$ as the underlying distance. We
generalize the theory of WGAN with gradient penalty to Banach spaces, allowing
practitioners to select the features to emphasize in the generator. We further
discuss the effect of some particular choices of underlying norms, focusing on
Sobolev norms. Finally, we demonstrate a boost in performance for an
appropriate choice of norm on CIFAR-10 and CelebA.
Comparison-Based Random Forests
Authors: Siavash Haghiri, Damien Garreau, Ulrike Von Luxburg
Date: 2018-06-18 00:00:00
Proceeding: ICML 2018 7
Tasks: General Classification, regression, Triplet

Assume we are given a set of items from a general metric space, but we
neither have access to the representation of the data nor to the distances
between data points. Instead, suppose that we can actively choose a triplet of
items (A,B,C) and ask an oracle whether item A is closer to item B or to item
C. In this paper, we propose a novel random forest algorithm for regression and
classification that relies only on such triplet comparisons. In the theory part
of this paper, we establish sufficient conditions for the consistency of such a
forest. In a set of comprehensive experiments, we then demonstrate that the
proposed random forest is efficient both for classification and regression. In
particular, it is even competitive with other methods that have direct access
to the metric representation of the data.
On Multi-resident Activity Recognition in Ambient Smart-Homes
Authors: Son N. Tran, Qing Zhang, Mohan Karunanithi
Date: 2018-06-18 00:00:00
Tasks: Activity Recognition

Increasing attention to the research on activity monitoring in smart homes
has motivated the employment of ambient intelligence to reduce the deployment
cost and solve the privacy issue. Several approaches have been proposed for
multi-resident activity recognition, however, there still lacks a comprehensive
benchmark for future research and practical selection of models. In this paper
we study different methods for multi-resident activity recognition and evaluate
them on same sets of data. The experimental results show that recurrent neural
network with gated recurrent units is better than other models and also
considerably efficient, and that using combined activities as single labels is
more effective than represent them as separate labels.
Evaluating and Characterizing Incremental Learning from Non-Stationary Data
Authors: Alejandro Cervantes, Christian Gagné, Pedro Isasi, Marc Parizeau
Date: 2018-06-18 00:00:00
Tasks: Incremental Learning

Incremental learning from non-stationary data poses special challenges to the
field of machine learning. Although new algorithms have been developed for
this, assessment of results and comparison of behaviors are still open
problems, mainly because evaluation metrics, adapted from more traditional
tasks, can be ineffective in this context. Overall, there is a lack of common
testing practices. This paper thus presents a testbed for incremental
non-stationary learning algorithms, based on specially designed synthetic
datasets. Also, test results are reported for some well-known algorithms to
show that the proposed methodology is effective at characterizing their
strengths and weaknesses. It is expected that this methodology will provide a
common basis for evaluating future contributions in the field.
Quantized Compressive K-Means
Authors: Vincent Schellekens, Laurent Jacques
Date: 2018-04-26 00:00:00
Tasks: Clustering, Quantization

The recent framework of compressive statistical learning aims at designing
tractable learning algorithms that use only a heavily compressed
representation-or sketch-of massive datasets. Compressive K-Means (CKM) is such
a method: it estimates the centroids of data clusters from pooled, non-linear,
random signatures of the learning examples. While this approach significantly
reduces computational time on very large datasets, its digital implementation
wastes acquisition resources because the learning examples are compressed only
after the sensing stage. The present work generalizes the sketching procedure
initially defined in Compressive K-Means to a large class of periodic
nonlinearities including hardware-friendly implementations that compressively
acquire entire datasets. This idea is exemplified in a Quantized Compressive
K-Means procedure, a variant of CKM that leverages 1-bit universal quantization
(i.e. retaining the least significant bit of a standard uniform quantizer) as
the periodic sketch nonlinearity. Trading for this resource-efficient signature
(standard in most acquisition schemes) has almost no impact on the clustering
performances, as illustrated by numerical experiments.
Self-Attentional Acoustic Models
Authors: Matthias Sperber, Jan Niehues, Graham Neubig, Sebastian Stüker, Alex Waibel
Date: 2018-03-26 00:00:00

Self-attention is a method of encoding sequences of vectors by relating these
vectors to each-other based on pairwise similarities. These models have
recently shown promising results for modeling discrete sequences, but they are
non-trivial to apply to acoustic modeling due to computational and modeling
issues. In this paper, we apply self-attention to acoustic modeling, proposing
several improvements to mitigate these issues: First, self-attention memory
grows quadratically in the sequence length, which we address through a
downsampling technique. Second, we find that previous approaches to incorporate
position information into the model are unsuitable and explore other
representations and hybrid models to this end. Third, to stress the importance
of local context in the acoustic signal, we propose a Gaussian biasing approach
that allows explicit control over the context range. Experiments find that our
model approaches a strong baseline based on LSTMs with network-in-network
connections while being much faster to compute. Besides speed, we find that
interpretability is a strength of self-attentional acoustic models, and
demonstrate that self-attention heads learn a linguistically plausible division
of labor.