pwc-archive/papers-with-abstracts huggingface dataset:
Searching for modified growth patterns with tomographic surveys
Authors: Gong-Bo Zhao, Levon Pogosian, Alessandra Silvestri, Joel Zylberberg
Date: 2008-09-23 00:00:00
Tasks: Clustering

In alternative theories of gravity, designed to produce cosmic acceleration
at the current epoch, the growth of large scale structure can be modified. We
study the potential of upcoming and future tomographic surveys such as DES and
LSST, with the aid of CMB and supernovae data, to detect departures from the
growth of cosmic structure expected within General Relativity. We employ
parametric forms to quantify the potential time- and scale-dependent variation
of the effective gravitational constant, and the differences between the two
Newtonian potentials. We then apply the Fisher matrix technique to forecast the
errors on the modified growth parameters from galaxy clustering, weak lensing,
CMB, and their cross-correlations across multiple photometric redshift bins. We
find that even with conservative assumptions about the data, DES will produce
non-trivial constraints on modified growth, and that LSST will do significantly
better.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Authors: Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
Date: 2018-07-18 00:00:00
Tasks: Camouflaged Object Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation, Video Polyp Segmentation

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Soft Constraints for Inference with Declarative Knowledge
Authors: Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar Lezama, Rajesh Ranganath
Date: 2019-01-16 00:00:00

We develop a likelihood free inference procedure for conditioning a
probabilistic model on a predicate. A predicate is a Boolean valued function
which expresses a yes/no question about a domain. Our contribution, which we
call predicate exchange, constructs a softened predicate which takes value in
the unit interval [0, 1] as opposed to a simply true or false. Intuitively, 1
corresponds to true, and a high value (such as 0.999) corresponds to "nearly
true" as determined by a distance metric. We define Boolean algebra for soft
predicates, such that they can be negated, conjoined and disjoined arbitrarily.
A softened predicate can serve as a tractable proxy to a likelihood function
for approximate posterior inference. However, to target exact inference, we
temper the relaxation by a temperature parameter, and add a accept/reject phase
use to replica exchange Markov Chain Mont Carlo, which exchanges states between
a sequence of models conditioned on predicates at varying temperatures. We
describe a lightweight implementation of predicate exchange that it provides a
language independent layer that can be implemented on top of existingn modeling
formalisms.
Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents
Authors: Michael Cerny Green, Benjamin Sergent, Pushyami Shandilya, Vibhor Kumar
Date: 2019-01-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we propose a new training loop for deep reinforcement learning
agents with an evolutionary generator. Evolutionary procedural content
generation has been used in the creation of maps and levels for games before.
Our system incorporates an evolutionary map generator to construct a training
curriculum that is evolved to maximize loss within the state-of-the-art Double
Dueling Deep Q Network architecture with prioritized replay. We present a
case-study in which we prove the efficacy of our new method on a game with a
discrete, large action space we made called Attackers and Defenders. Our
results demonstrate that training on an evolutionarily-curated curriculum
(directed sampling) of maps both expedites training and improves generalization
when compared to a network trained on an undirected sampling of maps.
Domain Adaptation for Structured Output via Discriminative Patch Representations
Authors: Yi-Hsuan Tsai, Kihyuk Sohn, Samuel Schulter, Manmohan Chandraker
Date: 2019-01-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Synthetic-to-Real Translation

Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks. However, models trained on one data domain may not generalize well to other domains without annotations for model finetuning. To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain. We propose to learn discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space. With such representations as guidance, we use an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches. In addition, we show that our framework is complementary to existing domain adaptation techniques and achieves consistent improvements on semantic segmentation. Extensive ablations and results are demonstrated on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
Authors: Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston
Date: 2019-01-16 00:00:00
Proceeding: ACL 2019 7
Tasks: Chatbot

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.
Location, Occupation, and Semantics based Socioeconomic Status Inference on Twitter
Authors: Jacobo Levy Abitbol, Márton Karsai, Eric Fleury
Date: 2019-01-16 00:00:00

The socioeconomic status of people depends on a combination of individual
characteristics and environmental variables, thus its inference from online
behavioral data is a difficult task. Attributes like user semantics in
communication, habitat, occupation, or social network are all known to be
determinant predictors of this feature. In this paper we propose three
different data collection and combination methods to first estimate and, in
turn, infer the socioeconomic status of French Twitter users from their online
semantics. Our methods are based on open census data, crawled professional
profiles, and remotely sensed, expert annotated information on living
environment. Our inference models reach similar performance of earlier results
with the advantage of relying on broadly available datasets and of providing a
generalizable framework to estimate socioeconomic status of large numbers of
Twitter users. These results may contribute to the scientific discussion on
social stratification and inequalities, and may fuel several applications.
Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
Authors: Rene Lacher, Francisco Vasconcelos, Norman Williams, Gerrit Rindermann, John Hipwell, David Hawkes, Danail Stoyanov
Date: 2019-01-16 00:00:00
Tasks: Prognosis

Accounting for 26% of all new cancer cases worldwide, breast cancer remains
the most common form of cancer in women. Although early breast cancer has a
favourable long-term prognosis, roughly a third of patients suffer from a
suboptimal aesthetic outcome despite breast conserving cancer treatment.
Clinical-quality 3D modelling of the breast surface therefore assumes an
increasingly important role in advancing treatment planning, prediction and
evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive
and either infrastructure-heavy or subject to motion artefacts. In this paper
we employ a single consumer-grade RGBD camera with an ICP-based registration
approach to jointly align all points from a sequence of depth images
non-rigidly. Subtle body deformation due to postural sway and respiration is
successfully mitigated leading to a higher geometric accuracy through
regularised locally affine transformations. We present results from 6 clinical
cases where our method compares well with the gold standard and outperforms a
previous approach. We show that our method produces better reconstructions
qualitatively by visual assessment and quantitatively by consistently obtaining
lower landmark error scores and yielding more accurate breast volume estimates.
Joint Spatial and Layer Attention for Convolutional Networks
Authors: Tony Joseph, Konstantinos G. Derpanis, Faisal Z. Qureshi
Date: 2019-01-16 00:00:00
Tasks: Camera Localization, Classification, General Classification, Scene Classification

In this paper, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., ``what'' feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., ``where'') to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the ``what'' and ``where'' aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8\% for position and 8.2\% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4\% over previous methods.
DAFE-FD: Density Aware Feature Enrichment for Face Detection
Authors: Vishwanath A. Sindagi, Vishal M. Patel
Date: 2019-01-16 00:00:00
Tasks: Crowd Counting, Density Estimation, Face Detection

Recent research on face detection, which is focused primarily on improving
accuracy of detecting smaller faces, attempt to develop new anchor design
strategies to facilitate increased overlap between anchor boxes and ground
truth faces of smaller sizes. In this work, we approach the problem of small
face detection with the motivation of enriching the feature maps using a
density map estimation module. This module, inspired by recent crowd
counting/density estimation techniques, performs the task of estimating the per
pixel density of people/faces present in the image. Output of this module is
employed to accentuate the feature maps from the backbone network using a
feature enrichment module before being used for detecting smaller faces. The
proposed approach can be used to complement recent anchor-design based novel
methods to further improve their results. Experiments conducted on different
datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of
the proposed approach.
Technical Report on Visual Quality Assessment for Frame Interpolation
Authors: Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andres Bruhn, Dietmar Saupe
Date: 2019-01-16 00:00:00
Tasks: Optical Flow Estimation, Re-Ranking

Current benchmarks for optical flow algorithms evaluate the estimation
quality by comparing their predicted flow field with the ground truth, and
additionally may compare interpolated frames, based on these predictions, with
the correct frames from the actual image sequences. For the latter comparisons,
objective measures such as mean square errors are applied. However, for
applications like image interpolation, the expected user's quality of
experience cannot be fully deduced from such simple quality measures.
Therefore, we conducted a subjective quality assessment study by crowdsourcing
for the interpolated images provided in one of the optical flow benchmarks, the
Middlebury benchmark. We used paired comparisons with forced choice and
reconstructed absolute quality scale values according to Thurstone's model
using the classical least squares method. The results give rise to a re-ranking
of 141 participating algorithms w.r.t. visual quality of interpolated frames
mostly based on optical flow estimation. Our re-ranking result shows the
necessity of visual quality assessment as another evaluation metric for optical
flow and frame interpolation benchmarks.
Lightweight Markerless Monocular Face Capture with 3D Spatial Priors
Authors: Shridhar Ravikumar
Date: 2019-01-16 00:00:00

We present a simple lightweight markerless facial performance capture
framework using just a monocular video input that combines Active Appearance
Models for feature tracking and prior constraints on 3D shapes into an
integrated objective function. 2D monocular inputs inherently lack information
along the depth axis and can lead to physically implausible solutions. In order
to address this loss of information, we enforce a constraint on our objective
function within a probabilistic framework that uses preexisting animations
obtained from accurate 3D tracking systems, thus achieving more plausible
results. Our system fits a Blendshape model to tracked 2D features while also
handling noise in estimation of features and camera parameters. We learn
separate constraints for the upper and lower regions of the face thus
maintaining flexibility. We show that using this approach, we can obtain
significant improvement in tracking especially along the depth dimension. Our
method uses easily obtainable prior animation data. We show that our method can
generate convincing animations using only a monocular video input. We
quantitatively evaluate our results comparing it with an approach using a
monocular input without our spatial constraints and show that our results are
closer to the ground-truth geometry. Finally, we also evaluate the effect that
the choice of the Blendshape set has on the results of the solver by solving
for a different set of Blendshapes and quantitatively comparing it with our
previous results and to the ground truth. We show that while the choice of
Blendshapes does make a difference, the use of our spatial constraints
generates results that are closer to the ground truth.
A Primer on PAC-Bayesian Learning
Authors: Benjamin Guedj
Date: 2019-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.
Representation Learning on Graphs: A Reinforcement Learning Application
Authors: Sephora Madjiheurem, Laura Toni
Date: 2019-01-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

In this work, we study value function approximation in reinforcement learning
(RL) problems with high dimensional state or action spaces via a generalized
version of representation policy iteration (RPI). We consider the limitations
of proto-value functions (PVFs) at accurately approximating the value function
in low dimensions and we highlight the importance of features learning for an
improved low-dimensional value function approximation. Then, we adopt different
representation learning algorithm on graphs to learn the basis functions that
best represent the value function. We empirically show that node2vec, an
algorithm for scalable feature learning in networks, and the Variational Graph
Auto-Encoder constantly outperform the commonly used smooth proto-value
functions in low-dimensional feature space.
TensorFlow.js: Machine Learning for the Web and Beyond
Authors: Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viégas, Martin Wattenberg
Date: 2019-01-16 00:00:00
Tasks: BIG-bench Machine Learning

TensorFlow.js is a library for building and executing machine learning
algorithms in JavaScript. TensorFlow.js models run in a web browser and in the
Node.js environment. The library is part of the TensorFlow ecosystem, providing
a set of APIs that are compatible with those in Python, allowing models to be
ported between the Python and JavaScript ecosystems. TensorFlow.js has
empowered a new set of developers from the extensive JavaScript community to
build and deploy machine learning models and enabled new classes of on-device
computation. This paper describes the design, API, and implementation of
TensorFlow.js, and highlights some of the impactful use cases.
A review of domain adaptation without target labels
Authors: Wouter M. Kouw, Marco Loog
Date: 2019-01-16 00:00:00
Tasks: Domain Adaptation, Domain Generalization, parameter estimation, Unsupervised Domain Adaptation

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.
Optimization Problems for Machine Learning: A Survey
Authors: Claudio Gambella, Bissan Ghaddar, Joe Naoum-Sawaya
Date: 2019-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, General Classification, regression, Survey

This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Authors: Saeid Amiri, Mohammad Shokrolah Shirazi, Shiqi Zhang
Date: 2019-01-16 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making, State Estimation

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.
Assessing BERT's Syntactic Abilities
Authors: Yoav Goldberg
Date: 2019-01-16 00:00:00

I assess the extent to which the recently introduced BERT model captures
English syntactic phenomena, using (1) naturally-occurring subject-verb
agreement stimuli; (2) "coloreless green ideas" subject-verb agreement stimuli,
in which content words in natural sentences are randomly replaced with words
sharing the same part-of-speech and inflection; and (3) manually crafted
stimuli for subject-verb agreement and reflexive anaphora phenomena. The BERT
model performs remarkably well on all cases.
LHC analysis-specific datasets with Generative Adversarial Networks
Authors: Bobak Hashemi, Nick Amin, Kaustuv Datta, Dominick Olivito, Maurizio Pierini
Date: 2019-01-16 00:00:00

Using generative adversarial networks (GANs), we investigate the possibility
of creating large amounts of analysis-specific simulated LHC events at limited
computing cost. This kind of generative model is analysis specific in the sense
that it directly generates the high-level features used in the last stage of a
given physics analyses, learning the N-dimensional distribution of relevant
features in the context of a specific analysis selection. We apply this idea to
the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We
highlight how use-case specific issues emerge when the distributions of the
considered quantities exhibit particular features. We show how substantial
performance improvements and convergence speed-up can be obtained by including
regression terms in the loss function of the generator. We develop an objective
criterion to assess the geenrator performance in a quantitative way. With
further development, a generalization of this approach could substantially
reduce the needed amount of centrally produced fully simulated events in large
particle physics experiments.
Dependency or Span, End-to-End Uniform Semantic Role Labeling
Authors: Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Date: 2019-01-16 00:00:00
Tasks: Model Optimization, Semantic Role Labeling, Sentence

Semantic role labeling (SRL) aims to discover the predicateargument structure
of a sentence. End-to-end SRL without syntactic input has received great
attention. However, most of them focus on either span-based or dependency-based
semantic representation form and only show specific model optimization
respectively. Meanwhile, handling these two SRL tasks uniformly was less
successful. This paper presents an end-to-end model for both dependency and
span SRL with a unified argument representation to deal with two different
types of argument annotations in a uniform fashion. Furthermore, we jointly
predict all predicates and arguments, especially including long-term ignored
predicate identification subtask. Our single model achieves new
state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL
2008, 2009) SRL benchmarks.