pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Authors: Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation, Time Series Analysis

Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable
option to restore voluntary movements after paralysis. These devices are based
on the ability to extract information about movement intent from neural signals
recorded using multi-electrode arrays chronically implanted in the motor
cortices of the brain. However, the inherent loss and turnover of recorded
neurons requires repeated recalibrations of the interface, which can
potentially alter the day-to-day user experience. The resulting need for
continued user adaptation interferes with the natural, subconscious use of the
BMI. Here, we introduce a new computational approach that decodes movement
intent from a low-dimensional latent representation of the neural data. We
implement various domain adaptation methods to stabilize the interface over
significantly long times. This includes Canonical Correlation Analysis used to
align the latent variables across days; this method requires prior
point-to-point correspondence of the time series across domains. Alternatively,
we match the empirical probability distributions of the latent variables across
days through the minimization of their Kullback-Leibler divergence. These two
methods provide a significant and comparable improvement in the performance of
the interface. However, implementation of an Adversarial Domain Adaptation
Network trained to match the empirical probability distribution of the
residuals of the reconstructed neural signals outperforms the two methods based
on latent variables, while requiring remarkably few data points to solve the
domain adaptation problem.
Active Fairness in Algorithmic Decision Making
Authors: Alejandro Noriega-Campero, Michiel A. Bakker, Bernardo Garcia-Bulle, Alex Pentland
Date: 2018-09-28 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

Society increasingly relies on machine learning models for automated decision
making. Yet, efficiency gains from automation have come paired with concern for
algorithmic discrimination that can systematize inequality. Recent work has
proposed optimal post-processing methods that randomize classification
decisions for a fraction of individuals, in order to achieve fairness measures
related to parity in errors and calibration. These methods, however, have
raised concern due to the information inefficiency, intra-group unfairness, and
Pareto sub-optimality they entail. The present work proposes an alternative
active framework for fair classification, where, in deployment, a
decision-maker adaptively acquires information according to the needs of
different groups or individuals, towards balancing disparities in
classification performance. We propose two such methods, where information
collection is adapted to group- and individual-level needs respectively. We
show on real-world datasets that these can achieve: 1) calibration and single
error parity (e.g., equal opportunity); and 2) parity in both false positive
and false negative rates (i.e., equal odds). Moreover, we show that by
leveraging their additional degree of freedom, active approaches can
substantially outperform randomization-based classifiers previously considered
optimal, while avoiding limitations such as intra-group unfairness.
Minimization of Gini impurity via connections with the k-means problem
Authors: Eduardo Sany Laber, Lucas Murtinho
Date: 2018-09-28 00:00:00
Tasks: Attribute, Clustering

The Gini impurity is one of the measures used to select attribute in Decision
Trees/Random Forest construction. In this note we discuss connections between
the problem of computing the partition with minimum Weighted Gini impurity and
the $k$-means clustering problem. Based on these connections we show that the
computation of the partition with minimum Weighted Gini is a NP-Complete
problem and we also discuss how to obtain new algorithms with provable
approximation for the Gini Minimization problem.
Explainable Black-Box Attacks Against Model-based Authentication
Authors: Washington Garcia, Joseph I. Choi, Suman K. Adari, Somesh Jha, Kevin R. B. Butler
Date: 2018-09-28 00:00:00
Tasks: BIG-bench Machine Learning, Explainable Artificial Intelligence (XAI), model

Establishing unique identities for both humans and end systems has been an
active research problem in the security community, giving rise to innovative
machine learning-based authentication techniques. Although such techniques
offer an automated method to establish identity, they have not been vetted
against sophisticated attacks that target their core machine learning
technique. This paper demonstrates that mimicking the unique signatures
generated by host fingerprinting and biometric authentication systems is
possible. We expose the ineffectiveness of underlying machine learning
classification models by constructing a blind attack based around the query
synthesis framework and utilizing Explainable-AI (XAI) techniques. We launch an
attack in under 130 queries on a state-of-the-art face authentication system,
and under 100 queries on a host authentication system. We examine how these
attacks can be defended against and explore their limitations. XAI provides an
effective means for adversaries to infer decision boundaries and provides a new
way forward in constructing attacks against systems using machine learning
models for authentication.
Fluctuation-dissipation relations for stochastic gradient descent
Authors: Sho Yaida
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning

The notion of the stationary equilibrium ensemble has played a central role
in statistical mechanics. In machine learning as well, training serves as
generalized equilibration that drives the probability distribution of model
parameters toward stationarity. Here, we derive stationary
fluctuation-dissipation relations that link measurable quantities and
hyperparameters in the stochastic gradient descent algorithm. These relations
hold exactly for any stationary state and can in particular be used to
adaptively set training schedule. We can further use the relations to
efficiently extract information pertaining to a loss-function landscape such as
the magnitudes of its Hessian and anharmonicity. Our claims are empirically
verified.
Real Time Monitoring of Social Media and Digital Press
Authors: Iñaki San Vicente, Xabier Saralegi, Rodrigo Agerri
Date: 2018-09-28 00:00:00
Tasks: Data Visualization, Domain Adaptation, Sentiment Analysis

Talaia is a platform for monitoring social media and digital press. A
configurable crawler gathers content with respect to user defined domains or
topics. Crawled data is processed by means of the EliXa Sentiment Analysis
system. A Django powered interface provides data visualization for a user-based
analysis of the data. This paper presents the architecture of the system and
describes in detail its different components. To prove the validity of the
approach, two real use cases are accounted for: one in the cultural domain and
one in the political domain. Evaluation for the sentiment analysis task in both
scenarios is also provided, showing the capacity for domain adaptation.
Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractography
Authors: Cesar F. Caiafa, Franco Pestilli
Date: 2015-05-27 00:00:00

The number of neuroimaging data sets publicly available is growing at fast
rate. The increase in availability and resolution of neuroimaging data requires
modern approaches to signal processing for data analysis and results
validation. We introduce the application of sparse multiway decomposition
methods (Caiafa and Cichocki, 2012) to linearized neuroimaging models. We show
that decomposed models are more compact but as accurate as full models and can
be successfully used for fast data analysis. We focus as example on a recent
model for the evaluation of white matter connectomes (Pestilli et al, 2014). We
show that the multiway decomposed model achieves accuracy comparable to the
full model, while requiring only a small fraction of the memory and compute
time. The approach has implications for a majority of neuroimaging methods
using linear approximations to measured signals.
Neural Collaborative Filtering
Authors: Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Date: 2017-08-16 00:00:00
Proceeding: WWW 2017 4
Tasks: Collaborative Filtering, Recommendation Systems

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Authors: Sudeep Pillai, Rares Ambrus, Adrien Gaidon
Date: 2018-10-03 00:00:00
Tasks: Depth Estimation, Depth Prediction, Image Super-Resolution, Monocular Depth Estimation, Pose Estimation, Super-Resolution

Recent techniques in self-supervised monocular depth estimation are
approaching the performance of supervised methods, but operate in low
resolution only. We show that high resolution is key towards high-fidelity
self-supervised monocular depth prediction. Inspired by recent deep learning
methods for Single-Image Super-Resolution, we propose a sub-pixel convolutional
layer extension for depth super-resolution that accurately synthesizes
high-resolution disparities from their corresponding low-resolution
convolutional features. In addition, we introduce a differentiable
flip-augmentation layer that accurately fuses predictions from the image and
its horizontally flipped version, reducing the effect of left and right shadow
regions generated in the disparity map due to occlusions. Both contributions
provide significant performance gains over the state-of-the-art in
self-supervised depth and pose estimation on the public KITTI benchmark. A
video of our approach can be found at https://youtu.be/jKNgBeBMx0I.
Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation
Authors: Dafni Antotsiou, Guillermo Garcia-Hernando, Tae-Kyun Kim
Date: 2018-10-03 00:00:00
Tasks: Imitation Learning, motion retargeting

Human hand actions are quite complex, especially when they involve object
manipulation, mainly due to the high dimensionality of the hand and the vast
action space that entails. Imitating those actions with dexterous hand models
involves different important and challenging steps: acquiring human hand
information, retargeting it to a hand model, and learning a policy from
acquired data. In this work, we capture the hand information by using a
state-of-the-art hand pose estimator. We tackle the retargeting problem from
the hand pose to a 29 DoF hand model by combining inverse kinematics and PSO
with a task objective optimisation. This objective encourages the virtual hand
to accomplish the manipulation task, relieving the effect of the estimator's
noise and the domain gap. Our approach leads to a better success rate in the
grasping task compared to our inverse kinematics baseline, allowing us to
record successful human demonstrations. Furthermore, we used these
demonstrations to learn a policy network using generative adversarial imitation
learning (GAIL) that is able to autonomously grasp an object in the virtual
space.
Mining Contrasting Quasi-Clique Patterns
Authors: Roberto Alonso, Stephan Günnemann
Date: 2018-10-03 00:00:00
Tasks: Clustering

Mining dense quasi-cliques is a well-known clustering task with applications
ranging from social networks over collaboration graphs to document analysis.
Recent work has extended this task to multiple graphs; i.e. the goal is to find
groups of vertices highly dense among multiple graphs. In this paper, we argue
that in a multi-graph scenario the sparsity is valuable for knowledge
extraction as well. We introduce the concept of contrasting quasi-clique
patterns: a collection of vertices highly dense in one graph but highly sparse
(i.e. less connected) in a second graph. Thus, these patterns specifically
highlight the difference/contrast between the considered graphs. Based on our
novel model, we propose an algorithm that enables fast computation of
contrasting patterns by exploiting intelligent traversal and pruning
techniques. We showcase the potential of contrasting patterns on a variety of
synthetic and real-world datasets.
Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy
Authors: Lex Fridman
Date: 2018-10-03 00:00:00
Tasks: Autonomous Vehicles

Building effective, enjoyable, and safe autonomous vehicles is a lot harder
than has historically been considered. The reason is that, simply put, an
autonomous vehicle must interact with human beings. This interaction is not a
robotics problem nor a machine learning problem nor a psychology problem nor an
economics problem nor a policy problem. It is all of these problems put into
one. It challenges our assumptions about the limitations of human beings at
their worst and the capabilities of artificial intelligence systems at their
best. This work proposes a set of principles for designing and building
autonomous vehicles in a human-centered way that does not run away from the
complexity of human nature but instead embraces it. We describe our development
of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study
of implementing these principles in practice.
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
Authors: Kangfu Mei, Aiwen Jiang, Juncheng Li, Jihua Ye, Mingwen Wang
Date: 2018-10-03 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent works on single-image super-resolution are concentrated on improving
performance through enhancing spatial encoding between convolutional layers. In
this paper, we focus on modeling the correlations between channels of
convolutional features. We present an effective deep residual network based on
squeeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR)
image from low-resolution (LR) image. SEBlock is used to adaptively recalibrate
channel-wise feature mappings. Further, short connections between each SEBlock
are used to remedy information loss. Extensive experiments show that our model
can achieve the state-of-the-art performance and get finer texture details.
Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network
Authors: Masayuki Tanaka
Date: 2018-10-03 00:00:00
Tasks: Image Restoration, Object, Object Recognition

An activation function has crucial role in a deep neural network.
  A simple rectified linear unit (ReLU) are widely used for the activation
function.
  In this paper, a weighted sigmoid gate unit (WiG) is proposed as the
activation function.
  The proposed WiG consists of a multiplication of inputs and the weighted
sigmoid gate.
  It is shown that the WiG includes the ReLU and same activation functions as a
special case.
  Many activation functions have been proposed to overcome the performance of
the ReLU.
  In the literature, the performance is mainly evaluated with an object
recognition task.
  The proposed WiG is evaluated with the object recognition task and the image
restoration task.
  Then, the expeirmental comparisons demonstrate the proposed WiG overcomes the
existing activation functions including the ReLU.
Neural Segmental Hypergraphs for Overlapping Mention Recognition
Authors: Bailin Wang, Wei Lu
Date: 2018-10-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Named Entity Recognition (NER), Nested Mention Recognition, Nested Named Entity Recognition, Overlapping Mention Recognition

In this work, we propose a novel segmental hypergraph representation to model
overlapping entity mentions that are prevalent in many practical datasets. We
show that our model built on top of such a new representation is able to
capture features and interactions that cannot be captured by previous models
while maintaining a low time complexity for inference. We also present a
theoretical analysis to formally assess how our representation is better than
alternative representations reported in the literature in terms of
representational power. Coupled with neural networks for feature learning, our
model achieves the state-of-the-art performance in three benchmark datasets
annotated with overlapping mentions.
McTorch, a manifold optimization library for deep learning
Authors: Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Mishra
Date: 2018-10-03 00:00:00
Tasks: Deep Learning

In this paper, we introduce McTorch, a manifold optimization library for deep
learning that extends PyTorch. It aims to lower the barrier for users wishing
to use manifold constraints in deep learning applications, i.e., when the
parameters are constrained to lie on a manifold. Such constraints include the
popular orthogonality and rank constraints, and have been recently used in a
number of applications in deep learning. McTorch follows PyTorch's architecture
and decouples manifold definitions and optimizers, i.e., once a new manifold is
added it can be used with any existing optimizer and vice-versa. McTorch is
available at https://github.com/mctorch .
A Neural Transition-based Model for Nested Mention Recognition
Authors: Bailin Wang, Wei Lu, Yu Wang, Hongxia Jin
Date: 2018-10-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Named Entity Recognition (NER), Nested Mention Recognition, Nested Named Entity Recognition, Sentence

It is common that entity mentions can contain other mentions recursively.
This paper introduces a scalable transition-based method to model the nested
structure of mentions. We first map a sentence with nested mentions to a
designated forest where each mention corresponds to a constituent of the
forest. Our shift-reduce based system then learns to construct the forest
structure in a bottom-up manner through an action sequence whose maximal length
is guaranteed to be three times of the sentence length. Based on Stack-LSTM
which is employed to efficiently and effectively represent the states of the
system in a continuous space, our system is further incorporated with a
character-based component to capture letter-level patterns. Our model achieves
the state-of-the-art results on ACE datasets, showing its effectiveness in
detecting nested mentions.
Disambiguating Music Artists at Scale with Audio Metric Learning
Authors: Jimena Royo-Letelier, Romain Hennequin, Viet-Anh Tran, Manuel Moussallam
Date: 2018-10-03 00:00:00
Tasks: Clustering, Metric Learning

We address the problem of disambiguating large scale catalogs through the
definition of an unknown artist clustering task. We explore the use of metric
learning techniques to learn artist embeddings directly from audio, and using a
dedicated homonym artists dataset, we compare our method with a recent approach
that learn similar embeddings using artist classifiers. While both systems have
the ability to disambiguate unknown artists relying exclusively on audio, we
show that our system is more suitable in the case when enough audio data is
available for each artist in the train dataset. We also propose a new negative
sampling method for metric learning that takes advantage of side information
such as music genre during the learning phase and shows promising results for
the artist clustering task.
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Authors: Juho Lee, Lancelot F. James, Seungjin Choi, François Caron
Date: 2018-10-03 00:00:00

We consider a non-projective class of inhomogeneous random graph models with
interpretable parameters and a number of interesting asymptotic properties.
Using the results of Bollob\'as et al. [2007], we show that i) the class of
models is sparse and ii) depending on the choice of the parameters, the model
is either scale-free, with power-law exponent greater than 2, or with an
asymptotic degree distribution which is power-law with exponential cut-off. We
propose an extension of the model that can accommodate an overlapping community
structure. Scalable posterior inference can be performed due to the specific
choice of the link probability. We present experiments on five different
real-world networks with up to 100,000 nodes and edges, showing that the model
can provide a good fit to the degree distribution and recovers well the latent
community structure.
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
Authors: Elham Saraee, Mona Jalal, Margrit Betke
Date: 2018-10-03 00:00:00
Tasks: Matrix Completion

Visual complexity identifies the level of intricacy and details in an image
or the level of difficulty to describe the image. It is an important concept in
a variety of areas such as cognitive psychology, computer vision and
visualization, and advertisement. Yet, efforts to create large, downloadable
image datasets with diverse content and unbiased groundtruthing are lacking. In
this work, we introduce Savoias, a visual complexity dataset that compromises
of more than 1,400 images from seven image categories relevant to the above
research areas, namely Scenes, Advertisements, Visualization and infographics,
Objects, Interior design, Art, and Suprematism. The images in each category
portray diverse characteristics including various low-level and high-level
features, objects, backgrounds, textures and patterns, text, and graphics. The
ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise
comparisons of images using the forced-choice methodology and with more than
1,600 contributors. The resulting relative scores are then converted to
absolute visual complexity scores using the Bradley-Terry method and matrix
completion. When applying five state-of-the-art algorithms to analyze the
visual complexity of the images in the Savoias dataset, we found that the
scores obtained from these baseline tools only correlate well with crowdsourced
labels for abstract patterns in the Suprematism category (Pearson correlation
r=0.84). For the other categories, in particular, the objects and advertisement
categories, low correlation coefficients were revealed (r=0.3 and 0.56,
respectively). These findings suggest that (1) state-of-the-art approaches are
mostly insufficient and (2) Savoias enables category-specific method
development, which is likely to improve the impact of visual complexity
analysis on specific application areas, including computer vision.