pwc-archive/papers-with-abstracts huggingface dataset:
Frank-Wolfe Splitting via Augmented Lagrangian Method
Authors: Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien
Date: 2018-04-09 00:00:00

Minimizing a function over an intersection of convex sets is an important
task in optimization that is often much more challenging than minimizing it
over each individual constraint set. While traditional methods such as
Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or
quadratic oracle on the intersection, splitting techniques take advantage of
the structure of each sets, and only require access to the oracle on the
individual constraints. In this work, we develop and analyze the Frank-Wolfe
Augmented Lagrangian (FW-AL) algorithm, a method for minimizing a smooth
function over convex compact sets related by a "linear consistency" constraint
that only requires access to a linear minimization oracle over the individual
constraints. It is based on the Augmented Lagrangian Method (ALM), also known
as Method of Multipliers, but unlike most existing splitting methods, it only
requires access to linear (instead of quadratic) minimization oracles. We use
recent advances in the analysis of Frank-Wolfe and the alternating direction
method of multipliers algorithms to prove a sublinear convergence rate for
FW-AL over general convex compact sets and a linear convergence rate for
polytopes.
Boundary-sensitive Network for Portrait Segmentation
Authors: Xianzhi Du, Xiaolong Wang, Dawei Li, Jingwen Zhu, Serafettin Tasci, Cameron Upright, Stephen Walsh, Larry Davis
Date: 2017-12-22 00:00:00
Tasks: Attribute, Image Segmentation, Portrait Segmentation, Segmentation, Semantic Segmentation

Compared to the general semantic segmentation problem, portrait segmentation
has higher precision requirement on boundary area. However, this problem has
not been well studied in previous works. In this paper, we propose a
boundary-sensitive deep neural network (BSN) for portrait segmentation. BSN
introduces three novel techniques. First, an individual boundary-sensitive
kernel is proposed by dilating the contour line and assigning the boundary
pixels with multi-class labels. Second, a global boundary-sensitive kernel is
employed as a position sensitive prior to further constrain the overall shape
of the segmentation map. Third, we train a boundary-sensitive attribute
classifier jointly with the segmentation network to reinforce the network with
semantic boundary shape information. We have evaluated BSN on the current
largest public portrait segmentation dataset, i.e, the PFCN dataset, as well as
the portrait images collected from other three popular image segmentation
datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior
quantitative and qualitative performance over state-of-the-arts on all the
datasets, especially on the boundary area.
Improving Confidence Estimates for Unfamiliar Examples
Authors: Zhizhong Li, Derek Hoiem
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2020 6
Tasks: Attribute, Domain Adaptation

Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with relevant but unfamiliar examples. A classifier we trained to recognize gender is 12 times more likely to be wrong with a 99% confident prediction if presented with a subject from a different age group than those seen during training. In this paper, we compare and evaluate several methods to improve confidence estimates for unfamiliar and familiar samples. We propose a testing methodology of splitting unfamiliar and familiar samples by attribute (age, breed, subcategory) or sampling (similar datasets collected by different people at different times). We evaluate methods including confidence calibration, ensembles, distillation, and a Bayesian model and use several metrics to analyze label, likelihood, and calibration error. While all methods reduce over-confident errors, the ensemble of calibrated models performs best overall, and T-scaling performs best among the approaches with fastest inference. Our code is available at https://github.com/lizhitwo/ConfidenceEstimates . $\color{red}{\text{Please see UPDATED ERRATA.}}$
The Sound of Pixels
Authors: Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh Mcdermott, Antonio Torralba
Date: 2018-04-09 00:00:00
Proceeding: ECCV 2018 9

We introduce PixelPlayer, a system that, by leveraging large amounts of
unlabeled videos, learns to locate image regions which produce sounds and
separate the input sounds into a set of components that represents the sound
from each pixel. Our approach capitalizes on the natural synchronization of the
visual and audio modalities to learn models that jointly parse sounds and
images, without requiring additional manual supervision. Experimental results
on a newly collected MUSIC dataset show that our proposed Mix-and-Separate
framework outperforms several baselines on source separation. Qualitative
results suggest our model learns to ground sounds in vision, enabling
applications such as independently adjusting the volume of sound sources.
Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents
Authors: Tomohiro Hayase
Date: 2018-04-09 00:00:00
Tasks: parameter estimation, Stochastic Optimization

For random matrix models, the parameter estimation based on the traditional likelihood functions is not straightforward in particular when we have only one sample matrix. We introduce a new parameter optimization method for random matrix models which works even in such a case. The method is based on the spectral distribution instead of the traditional likelihood. In the method, the Cauchy noise has an essential role because the free deterministic equivalent, which is a tool in free probability theory, allows us to approximate the spectral distribution perturbed by Cauchy noises by a smooth and accessible density function. Moreover, we study an asymptotic property of determination gap, which has a similar role as generalization gap. Besides, we propose a new dimensionality recovery method for the signal-plus-noise model, and experimentally demonstrate that it recovers the rank of the signal part even if the true rank is not small. It is a simultaneous rank selection and parameter estimation procedure.
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Authors: Yuhua Chen, Jordi Pont-Tuset, Alberto Montes, Luc van Gool
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Metric Learning, Object, Retrieval, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Triplet, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

This paper tackles the problem of video object segmentation, given some user
annotation which indicates the object of interest. The problem is formulated as
pixel-wise retrieval in a learned embedding space: we embed pixels of the same
object instance into the vicinity of each other, using a fully convolutional
network trained by a modified triplet loss as the embedding model. Then the
annotated pixels are set as reference and the rest of the pixels are classified
using a nearest-neighbor approach. The proposed method supports different kinds
of user input such as segmentation mask in the first frame (semi-supervised
scenario), or a sparse set of clicked points (interactive scenario). In the
semi-supervised scenario, we achieve results competitive with the state of the
art but at a fraction of computation cost (275 milliseconds per frame). In the
interactive scenario where the user is able to refine their input iteratively,
the proposed method provides instant response to each input, and reaches
comparable quality to competing methods with much less interaction.
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
Authors: Jing Qian, Mai ElSherief, Elizabeth M. Belding, William Yang Wang
Date: 2018-04-09 00:00:00
Proceeding: NAACL 2018 6
Tasks: Hate Speech Detection, Representation Learning

Hate speech detection is a critical, yet challenging problem in Natural
Language Processing (NLP). Despite the existence of numerous studies dedicated
to the development of NLP hate speech detection approaches, the accuracy is
still poor. The central problem is that social media posts are short and noisy,
and most existing hate speech detection solutions take each post as an isolated
input instance, which is likely to yield high false positive and negative
rates. In this paper, we radically improve automated hate speech detection by
presenting a novel model that leverages intra-user and inter-user
representation learning for robust hate speech detection on Twitter. In
addition to the target Tweet, we collect and analyze the user's historical
posts to model intra-user Tweet representations. To suppress the noise in a
single Tweet, we also model the similar Tweets posted by all other users with
reinforced inter-user representation learning techniques. Experimentally, we
show that leveraging these two representations can significantly improve the
f-score of a strong bidirectional LSTM baseline model by 10.1%.
Bayesian stochastic blockmodeling
Authors: Tiago P. Peixoto
Date: 2017-05-29 00:00:00
Tasks: Bayesian Inference, Model Selection

This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects of the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.
AMNet: Memorability Estimation with Attention
Authors: Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, image-classification, Image Classification, Transfer Learning

In this paper we present the design and evaluation of an end-to-end
trainable, deep neural network with a visual attention mechanism for
memorability estimation in still images. We analyze the suitability of transfer
learning of deep models from image classification to the memorability task.
Further on we study the impact of the attention mechanism on the memorability
estimation and evaluate our network on the SUN Memorability and the LaMem
datasets. Our network outperforms the existing state of the art models on both
datasets in terms of the Spearman's rank correlation as well as the mean
squared error, closely matching human consistency.
Markerless tracking of user-defined features with deep learning
Authors: Alexander Mathis, Pranav Mamidanna, Taiga Abe, Kevin M. Cury, Venkatesh N. Murthy, Mackenzie W. Mathis, Matthias Bethge
Date: 2018-04-09 00:00:00
Tasks: Animal Pose Estimation, Deep Learning, Transfer Learning

Quantifying behavior is crucial for many applications in neuroscience.
Videography provides easy methods for the observation and recording of animal
behavior in diverse settings, yet extracting particular aspects of a behavior
for further analysis can be highly time consuming. In motor control studies,
humans or other animals are often marked with reflective markers to assist with
computer-based tracking, yet markers are intrusive (especially for smaller
animals), and the number and location of the markers must be determined a
priori. Here, we present a highly efficient method for markerless tracking
based on transfer learning with deep neural networks that achieves excellent
results with minimal training data. We demonstrate the versatility of this
framework by tracking various body parts in a broad collection of experimental
settings: mice odor trail-tracking, egg-laying behavior in drosophila, and
mouse hand articulation in a skilled forelimb task. For example, during the
skilled reaching behavior, individual joints can be automatically tracked (and
a confidence score is reported). Remarkably, even when a small number of frames
are labeled ($\approx 200$), the algorithm achieves excellent tracking
performance on test frames that is comparable to human accuracy.
Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo Recognition
Authors: Hadi Kazemi, Sobhan Soleymani, Ali Dabouei, Mehdi Iranmanesh, Nasser M. Nasrabadi
Date: 2018-04-09 00:00:00
Tasks: Attribute

Face sketches are able to capture the spatial topology of a face while
lacking some facial attributes such as race, skin, or hair color. Existing
sketch-photo recognition approaches have mostly ignored the importance of
facial attributes. In this paper, we propose a new loss function, called
attribute-centered loss, to train a Deep Coupled Convolutional Neural Network
(DCCNN) for the facial attribute guided sketch to photo matching. Specifically,
an attribute-centered loss is proposed which learns several distinct centers,
in a shared embedding space, for photos and sketches with different
combinations of attributes. The DCCNN simultaneously is trained to map photos
and pairs of testified attributes and corresponding forensic sketches around
their associated centers, while preserving the spatial topology information.
Importantly, the centers learn to keep a relative distance from each other,
related to their number of contradictory attributes. Extensive experiments are
performed on composite (E-PRIP) and semi-forensic (IIIT-D Semi-forensic)
databases. The proposed method significantly outperforms the state-of-the-art.
Binge Watching: Scaling Affordance Learning from Sitcoms
Authors: Xiaolong Wang, Rohit Girdhar, Abhinav Gupta
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2017 7

In recent years, there has been a renewed interest in jointly modeling
perception and action. At the core of this investigation is the idea of
modeling affordances(Affordances are opportunities of interaction in the scene.
In other words, it represents what actions can the object be used for).
However, when it comes to predicting affordances, even the state of the art
approaches still do not use any ConvNets. Why is that? Unlike semantic or 3D
tasks, there still does not exist any large-scale dataset for affordances. In
this paper, we tackle the challenge of creating one of the biggest dataset for
learning affordances. We use seven sitcoms to extract a diverse set of scenes
and how actors interact with different objects in the scenes. Our dataset
consists of more than 10K scenes and 28K ways humans can interact with these
10K images. We also propose a two-step approach to predict affordances in a new
scene. In the first step, given a location in the scene we classify which of
the 30 pose classes is the likely affordance pose. Given the pose class and the
scene, we then use a Variational Autoencoder (VAE) to extract the scale and
deformation of the pose. The VAE allows us to sample the distribution of
possible poses at test time. Finally, we show the importance of large-scale
data in learning a generalizable and robust model of affordances.
A Tamper-Free Semi-Universal Communication System for Deletion Channels
Authors: Shahab Asoodeh, Yi Huang, Ishanu Chattopadhyay
Date: 2018-04-09 00:00:00

We investigate the problem of reliable communication between two legitimate
parties over deletion channels under an active eavesdropping (aka jamming)
adversarial model. To this goal, we develop a theoretical framework based on
probabilistic finite-state automata to define novel encoding and decoding
schemes that ensure small error probability in both message decoding as well as
tamper detecting. We then experimentally verify the reliability and
tamper-detection property of our scheme.
A plug-in approach to maximising precision at the top and recall at the top
Authors: Dirk Tasche
Date: 2018-04-09 00:00:00
Tasks: Binary Classification, Classification, General Classification, Information Retrieval, Retrieval

For information retrieval and binary classification, we show that precision
at the top (or precision at k) and recall at the top (or recall at k) are
maximised by thresholding the posterior probability of the positive class. This
finding is a consequence of a result on constrained minimisation of the
cost-sensitive expected classification error which generalises an earlier
related result from the literature.
Robust fusion algorithms for unsupervised change detection between multi-band optical images - A comprehensive case study
Authors: Vinicius Ferraris, Nicolas Dobigeon, Marie Chabert
Date: 2018-04-09 00:00:00
Tasks: Change Detection

Unsupervised change detection techniques are generally constrained to two
multi-band optical images acquired at different times through sensors sharing
the same spatial and spectral resolution. This scenario is suitable for a
straight comparison of homologous pixels such as pixel-wise differencing.
However, in some specific cases such as emergency situations, the only
available images may be those acquired through different kinds of sensors with
different resolutions. Recently some change detection techniques dealing with
images with different spatial and spectral resolutions, have been proposed.
Nevertheless, they are focused on a specific scenario where one image has a
high spatial and low spectral resolution while the other has a low spatial and
high spectral resolution. This paper addresses the problem of detecting changes
between any two multi-band optical images disregarding their spatial and
spectral resolution disparities. We propose a method that effectively uses the
available information by modeling the two observed images as spatially and
spectrally degraded versions of two (unobserved) latent images characterized by
the same high spatial and high spectral resolutions. Covering the same scene,
the latent images are expected to be globally similar except for possible
changes in spatially sparse locations. Thus, the change detection task is
envisioned through a robust fusion task which enforces the differences between
the estimated latent images to be spatially sparse. We show that this robust
fusion can be formulated as an inverse problem which is iteratively solved
using an alternate minimization strategy. The proposed framework is implemented
for an exhaustive list of applicative scenarios and applied to real multi-band
optical images. A comparison with state-of-the-art change detection methods
evidences the accuracy of the proposed robust fusion-based strategy.
Less is more: sampling chemical space with active learning
Authors: Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg
Date: 2018-01-28 00:00:00
Tasks: Active Learning

The development of accurate and transferable machine learning (ML) potentials
for predicting molecular energetics is a challenging task. The process of data
generation to train such ML potentials is a task neither well understood nor
researched in detail. In this work, we present a fully automated approach for
the generation of datasets with the intent of training universal ML potentials.
It is based on the concept of active learning (AL) via Query by Committee
(QBC), which uses the disagreement between an ensemble of ML potentials to
infer the reliability of the ensemble's prediction. QBC allows the presented AL
algorithm to automatically sample regions of chemical space where the ML
potential fails to accurately predict the potential energy. AL improves the
overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test
cases by mitigating human biases in deciding what new training data to use. AL
also reduces the training set size to a fraction of the data required when
using naive random sampling techniques. To provide validation of our AL
approach we develop the COMP6 benchmark (publicly available on GitHub), which
contains a diverse set of organic molecules. Through the AL process, it is
shown that the AL-based potentials perform as well as the ANI-1 potential on
COMP6 with only 10% of the data, and vastly outperforms ANI-1 with 25% the
amount of data. Finally, we show that our proposed AL technique develops a
universal ANI potential (ANI-1x) that provides accurate energy and force
predictions on the entire COMP6 benchmark. This universal ML potential achieves
a level of accuracy on par with the best ML potentials for single molecule or
materials, while remaining applicable to the general class of organic molecules
comprised of the elements CHNO.
Efficient Anomaly Detection via Matrix Sketching
Authors: Vatsal Sharan, Parikshit Gopalan, Udi Wieder
Date: 2018-04-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Anomaly Detection

We consider the problem of finding anomalies in high-dimensional data using
popular PCA based anomaly scores. The naive algorithms for computing these
scores explicitly compute the PCA of the covariance matrix which uses space
quadratic in the dimensionality of the data. We give the first streaming
algorithms that use space that is linear or sublinear in the dimension. We
prove general results showing that \emph{any} sketch of a matrix that satisfies
a certain operator norm guarantee can be used to approximate these scores. We
instantiate these results with powerful matrix sketching techniques such as
Frequent Directions and random projections to derive efficient and practical
algorithms for these problems, which we validate over real-world data sets. Our
main technical contribution is to prove matrix perturbation inequalities for
operators arising in the computation of these measures.
Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes
Authors: Igor Adamski, Robert Adamski, Tomasz Grel, Adam Jędrych, Kamil Kaczmarek, Henryk Michalewski
Date: 2018-01-09 00:00:00
Tasks: Atari Games, CPU, Deep Reinforcement Learning, Playing the Game of 2048, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a study in Distributed Deep Reinforcement Learning (DDRL) focused
on scalability of a state-of-the-art Deep Reinforcement Learning algorithm
known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using
the Adam optimization algorithm with a batch size of up to 2048 is a viable
choice for carrying out large scale machine learning computations. This,
combined with careful reexamination of the optimizer's hyperparameters, using
synchronous training on the node level (while keeping the local, single node
part of the algorithm asynchronous) and minimizing the memory footprint of the
model, allowed us to achieve linear scaling for up to 64 CPU nodes. This
corresponds to a training time of 21 minutes on 768 CPU cores, as opposed to 10
hours when using a single node with 24 cores achieved by a baseline single-node
implementation.