pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
Authors: Pingfan Song, Xin Deng, João F. C. Mota, Nikos Deligiannis, Pier Luigi Dragotti, Miguel R. D. Rodrigues
Date: 2017-09-25 00:00:00
Tasks: Dictionary Learning, Image Super-Resolution, Super-Resolution

Real-world data processing problems often involve various image modalities
associated with a certain scene, including RGB images, infrared images or
multi-spectral images. The fact that different image modalities often share
certain attributes, such as certain edges, textures and other structure
primitives, represents an opportunity to enhance various image processing
tasks. This paper proposes a new approach to construct a high-resolution (HR)
version of a low-resolution (LR) image given another HR image modality as
reference, based on joint sparse representations induced by coupled
dictionaries. Our approach, which captures the similarities and disparities
between different image modalities in a learned sparse feature domain in
\emph{lieu} of the original image domain, consists of two phases. The coupled
dictionary learning phase is used to learn a set of dictionaries that couple
different image modalities in the sparse feature domain given a set of training
data. In turn, the coupled super-resolution phase leverages such coupled
dictionaries to construct a HR version of the LR target image given another
related image modality. One of the merits of our sparsity-driven approach
relates to the fact that it overcomes drawbacks such as the texture copying
artifacts commonly resulting from inconsistency between the guidance and target
images. Experiments on real multimodal images demonstrate that incorporating
appropriate guidance information via joint sparse representation induced by
coupled dictionary learning brings notable benefits in the super-resolution
task with respect to the state-of-the-art. Of particular relevance, the
proposed approach also demonstrates better robustness than competing
deep-learning-based methods in the presence of noise.
GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation
Authors: Noriaki Hirose, Amir Sadeghian, Marynel Vázquez, Patrick Goebel, Silvio Savarese
Date: 2018-03-08 00:00:00
Tasks: Deep Learning

We present semi-supervised deep learning approaches for traversability
estimation from fisheye images. Our method, GONet, and the proposed extensions
leverage Generative Adversarial Networks (GANs) to effectively predict whether
the area seen in the input image(s) is safe for a robot to traverse. These
methods are trained with many positive images of traversable places, but just a
small set of negative images depicting blocked and unsafe areas. This makes the
proposed methods practical. Positive examples can be collected easily by simply
operating a robot through traversable spaces, while obtaining negative examples
is time consuming, costly, and potentially dangerous. Through extensive
experiments and several demonstrations, we show that the proposed
traversability estimation approaches are robust and can generalize to unseen
scenarios. Further, we demonstrate that our methods are memory efficient and
fast, allowing for real-time operation on a mobile robot with single or stereo
fisheye cameras. As part of our contributions, we open-source two new datasets
for traversability estimation. These datasets are composed of approximately 24h
of videos from more than 25 indoor environments. Our methods outperform
baseline approaches for traversability estimation on these new datasets.
Learning to SMILE(S)
Authors: Stanisław Jastrzębski, Damian Leśniak, Wojciech Marian Czarnecki
Date: 2016-02-19 00:00:00
Tasks: Activity Prediction, Drug Design, General Classification

This paper shows how one can directly apply natural language processing (NLP)
methods to classification problems in cheminformatics. Connection between these
seemingly separate fields is shown by considering standard textual
representation of compound, SMILES. The problem of activity prediction against
a target protein is considered, which is a crucial part of computer aided drug
design process. Conducted experiments show that this way one can not only
outrank state of the art results of hand crafted representations but also gets
direct structural insights into the way decisions are made.
Residual Connections Encourage Iterative Inference
Authors: Stanisław Jastrzębski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio
Date: 2017-10-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Representation Learning

Residual networks (Resnets) have become a prominent architecture in deep
learning. However, a comprehensive understanding of Resnets is still a topic of
ongoing research.
  A recent view argues that Resnets perform iterative refinement of features.
We attempt to further expose properties of this aspect. To this end, we study
Resnets both analytically and empirically. We formalize the notion of iterative
refinement in Resnets by showing that residual connections naturally encourage
features of residual blocks to move along the negative gradient of loss as we
go from one block to the next. In addition, our empirical analysis suggests
that Resnets are able to perform both representation learning and iterative
refinement. In general, a Resnet block tends to concentrate representation
learning behavior in the first few layers while higher layers perform iterative
refinement of features. Finally we observe that sharing residual layers naively
leads to representation explosion and counterintuitively, overfitting, and we
show that simple existing strategies can help alleviating this problem.
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Authors: Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, Luc van Gool
Date: 2018-03-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Object, object-detection, Object Detection, Region Proposal, Robust Object Detection, Unsupervised Domain Adaptation

Object detection typically assumes that training and test data are drawn from
an identical distribution, which, however, does not always hold in practice.
Such a distribution mismatch will lead to a significant performance drop. In
this work, we aim to improve the cross-domain robustness of object detection.
We tackle the domain shift on two levels: 1) the image-level shift, such as
image style, illumination, etc, and 2) the instance-level shift, such as object
appearance, size, etc. We build our approach based on the recent
state-of-the-art Faster R-CNN model, and design two domain adaptation
components, on image level and instance level, to reduce the domain
discrepancy. The two domain adaptation components are based on H-divergence
theory, and are implemented by learning a domain classifier in adversarial
training manner. The domain classifiers on different levels are further
reinforced with a consistency regularization to learn a domain-invariant region
proposal network (RPN) in the Faster R-CNN model. We evaluate our newly
proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K,
etc. The results demonstrate the effectiveness of our proposed approach for
robust object detection in various domain shift scenarios.
Probably Approximately Metric-Fair Learning
Authors: Guy N. Rothblum, Gal Yona
Date: 2018-03-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Fairness

The seminal work of Dwork {\em et al.} [ITCS 2012] introduced a metric-based
notion of individual fairness. Given a task-specific similarity metric, their
notion required that every pair of similar individuals should be treated
similarly. In the context of machine learning, however, individual fairness
does not generalize from a training set to the underlying population. We show
that this can lead to computational intractability even for simple
fair-learning tasks.
  With this motivation in mind, we introduce and study a relaxed notion of {\em
approximate metric-fairness}: for a random pair of individuals sampled from the
population, with all but a small probability of error, if they are similar then
they should be treated similarly. We formalize the goal of achieving
approximate metric-fairness simultaneously with best-possible accuracy as
Probably Approximately Correct and Fair (PACF) Learning. We show that
approximate metric-fairness {\em does} generalize, and leverage these
generalization guarantees to construct polynomial-time PACF learning algorithms
for the classes of linear and logistic predictors.
Fairness Through Computationally-Bounded Awareness
Authors: Michael P. Kim, Omer Reingold, Guy N. Rothblum
Date: 2018-03-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Fairness

We study the problem of fair classification within the versatile framework of
Dwork et al. [ITCS '12], which assumes the existence of a metric that measures
similarity between pairs of individuals. Unlike earlier work, we do not assume
that the entire metric is known to the learning algorithm; instead, the learner
can query this arbitrary metric a bounded number of times. We propose a new
notion of fairness called metric multifairness and show how to achieve this
notion in our setting. Metric multifairness is parameterized by a similarity
metric $d$ on pairs of individuals to classify and a rich collection ${\cal C}$
of (possibly overlapping) "comparison sets" over pairs of individuals. At a
high level, metric multifairness guarantees that similar subpopulations are
treated similarly, as long as these subpopulations are identified within the
class ${\cal C}$.
Stochastic Hyperparameter Optimization through Hypernetworks
Authors: Jonathan Lorraine, David Duvenaud
Date: 2018-02-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization, Stochastic Optimization

Machine learning models are often tuned by nesting optimization of model
weights inside the optimization of hyperparameters. We give a method to
collapse this nested optimization into joint stochastic optimization of weights
and hyperparameters. Our process trains a neural network to output
approximately optimal weights as a function of hyperparameters. We show that
our technique converges to locally optimal weights and hyperparameters for
sufficiently large hypernetworks. We compare this method to standard
hyperparameter optimization strategies and demonstrate its effectiveness for
tuning thousands of hyperparameters.
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Authors: Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Stefan Ultes, Lina Rojas-Barahona, Bo-Hsiang Tseng, Milica Gašić
Date: 2018-03-08 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a promising approach to solve dialogue policy
optimisation. Traditional RL algorithms, however, fail to scale to large
domains due to the curse of dimensionality. We propose a novel Dialogue
Management architecture, based on Feudal RL, which decomposes the decision into
two steps; a first step where a master policy selects a subset of primitive
actions, and a second step where a primitive action is chosen from the selected
subset. The structural information included in the domain ontology is used to
abstract the dialogue state space, taking the decisions at each step using
different parts of the abstracted state. This, combined with an information
sharing mechanism between slots, increases the scalability to large domains. We
show that an implementation of this approach, based on Deep-Q Networks,
significantly outperforms previous state of the art in several dialogue domains
and environments, without the need of any additional reward signal.
Penalizing Unfairness in Binary Classification
Authors: Yahav Bechavod, Katrina Ligett
Date: 2017-06-30 00:00:00
Tasks: Binary Classification, Classification, Fairness, General Classification

We present a new approach for mitigating unfairness in learned classifiers.
In particular, we focus on binary classification tasks over individuals from
two populations, where, as our criterion for fairness, we wish to achieve
similar false positive rates in both populations, and similar false negative
rates in both populations. As a proof of concept, we implement our approach and
empirically evaluate its ability to achieve both fairness and accuracy, using
datasets from the fields of criminal risk assessment, credit, lending, and
college admissions.
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Authors: Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Value prediction

Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need to be learned from data, the deficiencies of learned models have
limited their utility for planning. To address these challenges, we propose
TreeQN, a differentiable, recursive, tree-structured model that serves as a
drop-in replacement for any value function network in deep RL with discrete
actions. TreeQN dynamically constructs a tree by recursively applying a
transition model in a learned abstract state space and then aggregating
predicted rewards and state-values using a tree backup to estimate Q-values. We
also propose ATreeC, an actor-critic variant that augments TreeQN with a
softmax layer to form a stochastic policy network. Both approaches are trained
end-to-end, such that the learned model is optimised for its actual use in the
tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a
box-pushing task, as well as n-step DQN and value prediction networks (Oh et
al. 2017) on multiple Atari games. Furthermore, we present ablation studies
that demonstrate the effect of different auxiliary losses on learning
transition models.
Excitation Backprop for RNNs
Authors: Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, Stan Sclaroff
Date: 2017-11-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Temporal Action Localization, Video Captioning

Deep models are state-of-the-art for many vision tasks including video action
recognition and video captioning. Models are trained to caption or classify
activity in videos, but little is known about the evidence used to make such
decisions. Grounding decisions made by deep networks has been studied in
spatial visual content, giving more insight into model predictions for images.
However, such studies are relatively lacking for models of spatiotemporal
visual content - videos. In this work, we devise a formulation that
simultaneously grounds evidence in space and time, in a single pass, using
top-down saliency. We visualize the spatiotemporal cues that contribute to a
deep model's classification/captioning output using the model's internal
representation. Based on these spatiotemporal cues, we are able to localize
segments within a video that correspond with a specific action, or phrase from
a caption, without explicitly optimizing/training for these tasks.
A Mixture of Matrix Variate Bilinear Factor Analyzers
Authors: Michael P. B. Gallaugher, Paul D. McNicholas
Date: 2017-12-22 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification, parameter estimation

Over the years data has become increasingly higher dimensional, which has
prompted an increased need for dimension reduction techniques. This is perhaps
especially true for clustering (unsupervised classification) as well as
semi-supervised and supervised classification. Although dimension reduction in
the area of clustering for multivariate data has been quite thoroughly
discussed within the literature, there is relatively little work in the area of
three-way, or matrix variate, data. Herein, we develop a mixture of matrix
variate bilinear factor analyzers (MMVBFA) model for use in clustering
high-dimensional matrix variate data. This work can be considered both the
first matrix variate bilinear factor analysis model as well as the first MMVBFA
model. Parameter estimation is discussed, and the MMVBFA model is illustrated
using simulated and real data.
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Authors: Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone
Date: 2017-07-09 00:00:00
Tasks: Causal Inference

Statisticians have made great progress in creating methods that reduce our
reliance on parametric assumptions. However this explosion in research has
resulted in a breadth of inferential strategies that both create opportunities
for more reliable inference as well as complicate the choices that an applied
researcher has to make and defend. Relatedly, researchers advocating for new
methods typically compare their method to at best 2 or 3 other causal inference
strategies and test using simulations that may or may not be designed to
equally tease out flaws in all the competing methods. The causal inference data
analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016
Atlantic Causal Inference Conference, sought to make progress with respect to
both of these issues. The researchers creating the data testing grounds were
distinct from the researchers submitting methods whose efficacy would be
evaluated. Results from 30 competitors across the two versions of the
competition (black box algorithms and do-it-yourself analyses) are presented
along with post-hoc analyses that reveal information about the characteristics
of causal inference strategies and settings that affect performance. The most
consistent conclusion was that methods that flexibly model the response surface
perform better overall than methods that fail to do so. Finally new methods are
proposed that combine features of several of the top-performing submitted
methods.
A Bayesian and Machine Learning approach to estimating Influence Model parameters for IM-RO
Authors: Trisha Lawrence
Date: 2018-03-08 00:00:00
Tasks: BIG-bench Machine Learning

The rise of Online Social Networks (OSNs) has caused an insurmountable amount
of interest from advertisers and researchers seeking to monopolize on its
features. Researchers aim to develop strategies for determining how information
is propagated among users within an OSN that is captured by diffusion or
influence models. We consider the influence models for the IM-RO problem, a
novel formulation to the Influence Maximization (IM) problem based on
implementing Stochastic Dynamic Programming (SDP). In contrast to existing
approaches involving influence spread and the theory of submodular functions,
the SDP method focuses on optimizing clicks and ultimately revenue to
advertisers in OSNs. Existing approaches to influence maximization have been
actively researched over the past decade, with applications to multiple fields,
however, our approach is a more practical variant to the original IM problem.
In this paper, we provide an analysis on the influence models of the IM-RO
problem by conducting experiments on synthetic and real-world datasets. We
propose a Bayesian and Machine Learning approach for estimating the parameters
of the influence models for the (Influence Maximization- Revenue Optimization)
IM-RO problem. We present a Bayesian hierarchical model and implement the
well-known Naive Bayes classifier (NBC), Decision Trees classifier (DTC) and
Random Forest classifier (RFC) on three real-world datasets. Compared to
previous approaches to estimating influence model parameters, our strategy has
the great advantage of being directly implementable in standard software
packages such as WinBUGS/OpenBUGS/JAGS and Apache Spark. We demonstrate the
efficiency and usability of our methods in terms of spreading information and
generating revenue for advertisers in the context of OSNs.
Drug Recommendation toward Safe Polypharmacy
Authors: Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning
Date: 2018-03-08 00:00:00

Adverse drug reactions (ADRs) induced from high-order drug-drug interactions
(DDIs) due to polypharmacy represent a significant public health problem. In
this paper, we formally formulate the to-avoid and safe (with respect to ADRs)
drug recommendation problems when multiple drugs have been taken
simultaneously. We develop a joint model with a recommendation component and an
ADR label prediction component to recommend for a prescription a set of
to-avoid drugs that will induce ADRs if taken together with the prescription.
We also develop real drug-drug interaction datasets and corresponding
evaluation protocols. Our experimental results on real datasets demonstrate the
strong performance of the joint model compared to other baseline methods.
Fact Checking in Community Forums
Authors: Tsvetomila Mihaylova, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Mitra Mohtarami, Georgi Karadzhov, James Glass
Date: 2018-03-08 00:00:00
Tasks: Community Question Answering, Fact Checking, Question Answering

Community Question Answering (cQA) forums are very popular nowadays, as they
represent effective means for communities around particular topics to share
information. Unfortunately, this information is not always factual. Thus, here
we explore a new dimension in the context of cQA, which has been ignored so
far: checking the veracity of answers to particular questions in cQA forums. As
this is a new problem, we create a specialized dataset for it. We further
propose a novel multi-faceted model, which captures information from the answer
content (what is said and how), from the author profile (who says it), from the
rest of the community forum (where it is said), and from external authoritative
sources of information (external support). Evaluation results show a MAP value
of 86.54, which is 21 points absolute above the baseline.
Learning and Free Energies for Vector Approximate Message Passing
Authors: Alyson K. Fletcher, Philip Schniter
Date: 2016-02-26 00:00:00

Vector approximate message passing (VAMP) is a computationally simple
approach to the recovery of a signal $\mathbf{x}$ from noisy linear
measurements $\mathbf{y}=\mathbf{Ax}+\mathbf{w}$. Like the AMP proposed by
Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous
state evolution (SE) that holds under certain large random matrices and that
matches the replica prediction of optimality. But while AMP's SE holds only for
large i.i.d. sub-Gaussian $\mathbf{A}$, VAMP's SE holds under the much larger
class: right-rotationally invariant $\mathbf{A}$. To run VAMP, however, one
must specify the statistical parameters of the signal and noise. This work
combines VAMP with Expectation-Maximization to yield an algorithm, EM-VAMP,
that can jointly recover $\mathbf{x}$ while learning those statistical
parameters. The fixed points of the proposed EM-VAMP algorithm are shown to be
stationary points of a certain constrained free-energy, providing a variational
interpretation of the algorithm. Numerical simulations show that EM-VAMP is
robust to highly ill-conditioned $\mathbf{A}$ with performance nearly matching
oracle-parameter VAMP.