pwc-archive/papers-with-abstracts huggingface dataset:
Using Syntax to Ground Referring Expressions in Natural Images
Authors: Volkan Cirik, Taylor Berg-Kirkpatrick, Louis-Philippe Morency
Date: 2018-05-26 00:00:00
Tasks: Object, Referring Expression

We introduce GroundNet, a neural network for referring expression recognition
-- the task of localizing (or grounding) in an image the object referred to by
a natural language expression. Our approach to this task is the first to rely
on a syntactic analysis of the input referring expression in order to inform
the structure of the computation graph. Given a parse tree for an input
expression, we explicitly map the syntactic constituents and relationships
present in the tree to a composed graph of neural modules that defines our
architecture for performing localization. This syntax-based approach aids
localization of \textit{both} the target object and auxiliary supporting
objects mentioned in the expression. As a result, GroundNet is more
interpretable than previous methods: we can (1) determine which phrase of the
referring expression points to which object in the image and (2) track how the
localization of the target object is determined by the network. We study this
property empirically by introducing a new set of annotations on the GoogleRef
dataset to evaluate localization of supporting objects. Our experiments show
that GroundNet achieves state-of-the-art accuracy in identifying supporting
objects, while maintaining comparable performance in the localization of target
objects.
Transductive Label Augmentation for Improved Deep Network Learning
Authors: Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo
Date: 2018-05-26 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification, Transfer Learning

A major impediment to the application of deep learning to real-world problems
is the scarcity of labeled data. Small training sets are in fact of no use to
deep networks as, due to the large number of trainable parameters, they will
very likely be subject to overfitting phenomena. On the other hand, the
increment of the training set size through further manual or semi-automatic
labellings can be costly, if not possible at times. Thus, the standard
techniques to address this issue are transfer learning and data augmentation,
which consists of applying some sort of "transformation" to existing labeled
instances to let the training set grow in size. Although this approach works
well in applications such as image classification, where it is relatively
simple to design suitable transformation operators, it is not obvious how to
apply it in more structured scenarios. Motivated by the observation that in
virtually all application domains it is easy to obtain unlabeled data, in this
paper we take a different perspective and propose a \emph{label augmentation}
approach. We start from a small, curated labeled dataset and let the labels
propagate through a larger set of unlabeled data using graph transduction
techniques. This allows us to naturally use (second-order) similarity
information which resides in the data, a source of information which is
typically neglected by standard augmentation techniques. In particular, we show
that by using known game theoretic transductive processes we can create larger
and accurate enough labeled datasets which use results in better trained neural
networks. Preliminary experiments are reported which demonstrate a consistent
improvement over standard image classification datasets.
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
Authors: Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan
Date: 2017-08-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Denoising, Image Denoising

This paper studies the Tensor Robust Principal Component (TRPCA) problem
which extends the known Robust PCA (Candes et al. 2011) to the tensor case. Our
model is based on a new tensor Singular Value Decomposition (t-SVD) (Kilmer and
Martin 2011) and its induced tensor tubal rank and tensor nuclear norm.
Consider that we have a 3-way tensor ${\mathcal{X}}\in\mathbb{R}^{n_1\times
n_2\times n_3}$ such that ${\mathcal{X}}={\mathcal{L}}_0+{\mathcal{E}}_0$,
where ${\mathcal{L}}_0$ has low tubal rank and ${\mathcal{E}}_0$ is sparse. Is
that possible to recover both components? In this work, we prove that under
certain suitable assumptions, we can recover both the low-rank and the sparse
components exactly by simply solving a convex program whose objective is a
weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i.e.,
$\min_{{\mathcal{L}},\ {\mathcal{E}}} \
\|{{\mathcal{L}}}\|_*+\lambda\|{{\mathcal{E}}}\|_1, \ \text{s.t.} \
{\mathcal{X}}={\mathcal{L}}+{\mathcal{E}}$, where $\lambda=
{1}/{\sqrt{\max(n_1,n_2)n_3}}$. Interestingly, TRPCA involves RPCA as a special
case when $n_3=1$ and thus it is a simple and elegant tensor extension of RPCA.
Also numerical experiments verify our theory and the application for the image
denoising demonstrates the effectiveness of our method.
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
Authors: Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann
Date: 2018-03-27 00:00:00
Tasks: General Classification, Object, Object Recognition, Scene Understanding, Semantic Segmentation

We present the DeepScores dataset with the goal of advancing the
state-of-the-art in small objects recognition, and by placing the question of
object recognition in the context of scene understanding. DeepScores contains
high quality images of musical scores, partitioned into 300,000 sheets of
written music that contain symbols of different shapes and sizes. With close to
a hundred millions of small objects, this makes our dataset not only unique,
but also the largest public dataset. DeepScores comes with ground truth for
object classification, detection and semantic segmentation. DeepScores thus
poses a relevant challenge for computer vision in general, beyond the scope of
optical music recognition (OMR) research. We present a detailed statistical
analysis of the dataset, comparing it with other computer vision datasets like
Caltech101/256, PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, smaller computer
vision datasets, as well as with other OMR datasets. Finally, we provide
baseline performances for object classification and give pointers to future
research based on this dataset.
Video Summarization Using Fully Convolutional Sequence Networks
Authors: Mrigank Rochan, Linwei Ye, Yang Wang
Date: 2018-05-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Retrieval, Segmentation, Semantic Segmentation, Video Summarization

This paper addresses the problem of video summarization. Given an input
video, the goal is to select a subset of the frames to create a summary video
that optimally captures the important information of the input video. With the
large amount of videos available online, video summarization provides a useful
tool that assists video search, retrieval, browsing, etc. In this paper, we
formulate video summarization as a sequence labeling problem. Unlike existing
approaches that use recurrent models, we propose fully convolutional sequence
models to solve video summarization. We firstly establish a novel connection
between semantic segmentation and video summarization, and then adapt popular
semantic segmentation networks for video summarization. Extensive experiments
and analysis on two benchmark datasets demonstrate the effectiveness of our
models.
Natural Language Inference over Interaction Space
Authors: Yichen Gong, Heng Luo, Jian Zhang
Date: 2017-09-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Natural Language Inference, Paraphrase Identification, Sentence

Natural Language Inference (NLI) task requires an agent to determine the
logical relationship between a natural language premise and a natural language
hypothesis. We introduce Interactive Inference Network (IIN), a novel class of
neural network architectures that is able to achieve high-level understanding
of the sentence pair by hierarchically extracting semantic features from
interaction space. We show that an interaction tensor (attention weight)
contains semantic information to solve natural language inference, and a denser
interaction tensor contains richer semantic information. One instance of such
architecture, Densely Interactive Inference Network (DIIN), demonstrates the
state-of-the-art performance on large scale NLI copora and large-scale NLI
alike corpus. It's noteworthy that DIIN achieve a greater than 20% error
reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to
the strongest published system.
Contextualized Word Representations for Reading Comprehension
Authors: Shimi Salant, Jonathan Berant
Date: 2017-12-10 00:00:00
Proceeding: NAACL 2018 6
Tasks: Language Modeling, Language Modelling, Question Answering, Reading Comprehension

Reading a document and extracting an answer to a question about its content
has attracted substantial attention recently. While most work has focused on
the interaction between the question and the document, in this work we evaluate
the importance of context when the question and document are processed
independently. We take a standard neural architecture for this task, and show
that by providing rich contextualized word representations from a large
pre-trained language model as well as allowing the model to choose between
context-dependent and context-independent word representations, we can obtain
dramatic improvements and reach performance comparable to state-of-the-art on
the competitive SQuAD dataset.
Unsupervised Learning with Stein's Unbiased Risk Estimator
Authors: Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk
Date: 2018-05-26 00:00:00
Tasks: Astronomy, compressed sensing, Denoising, Image Denoising

Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data. Specifically, our goal is to reconstruct an image $x$ from a noisy linear transformation (measurement) of the image. We consider two scenarios: one where no additional data is available and one where we have measurements of other images that are drawn from the same noisy distribution as $x$, but have no access to the clean images. Such is the case, for instance, in the context of medical imaging, microscopy, and astronomy, where noise-less ground truth data is rarely available. We show that in this situation, SURE can be used to estimate the mean-squared-error loss associated with an estimate of $x$. Using this estimate of the loss, we train networks to perform denoising and compressed sensing recovery. In addition, we also use the SURE framework to partially explain and improve upon an intriguing results presented by Ulyanov et al. in "Deep Image Prior": that a network initialized with random weights and fit to a single noisy image can effectively denoise that image. Public implementations of the networks and methods described in this paper can be found at https://github.com/ricedsp/D-AMP_Toolbox.
Training Classifiers with Natural Language Explanations
Authors: Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Binary Classification, General Classification, Relation Extraction

Training accurate classifiers requires many labels, but each label provides
only limited information (one bit for binary classification). In this work, we
propose BabbleLabble, a framework for training classifiers in which an
annotator provides a natural language explanation for each labeling decision. A
semantic parser converts these explanations into programmatic labeling
functions that generate noisy labels for an arbitrary amount of unlabeled data,
which is used to train a classifier. On three relation extraction tasks, we
find that users are able to train classifiers with comparable F1 scores from
5-100$\times$ faster by providing explanations instead of just labels.
Furthermore, given the inherent imperfection of labeling functions, we find
that a simple rule-based semantic parser suffices.
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
Authors: Guiliang Liu, Oliver Schulte
Date: 2018-05-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A variety of machine learning models have been proposed to assess the
performance of players in professional sports. However, they have only a
limited ability to model how player performance depends on the game context.
This paper proposes a new approach to capturing game context: we apply Deep
Reinforcement Learning (DRL) to learn an action-value Q function from 3M
play-by-play events in the National Hockey League (NHL). The neural network
representation integrates both continuous context signals and game history,
using a possession-based LSTM. The learned Q-function is used to value players'
actions under different game contexts. To assess a player's overall
performance, we introduce a novel Game Impact Metric (GIM) that aggregates the
values of the player's actions. Empirical Evaluation shows GIM is consistent
throughout a play season, and correlates highly with standard success measures
and future salary.
Calibrating Deep Convolutional Gaussian Processes
Authors: Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
Date: 2018-05-26 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Gaussian Processes, General Classification, image-classification, Image Classification

The wide adoption of Convolutional Neural Networks (CNNs) in applications
where decision-making under uncertainty is fundamental, has brought a great
deal of attention to the ability of these models to accurately quantify the
uncertainty in their predictions. Previous work on combining CNNs with Gaussian
processes (GPs) has been developed under the assumption that the predictive
probabilities of these models are well-calibrated. In this paper we show that,
in fact, current combinations of CNNs and GPs are miscalibrated. We proposes a
novel combination that considerably outperforms previous approaches on this
aspect, while achieving state-of-the-art performance on image classification
tasks.
Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning
Authors: Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
Date: 2018-05-22 00:00:00
Tasks: Few-Shot Learning, Metric Learning, Transfer Learning

The focus in machine learning has branched beyond training classifiers on a
single task to investigating how previously acquired knowledge in a source
domain can be leveraged to facilitate learning in a related target domain,
known as inductive transfer learning. Three active lines of research have
independently explored transfer learning using neural networks. In weight
transfer, a model trained on the source domain is used as an initialization
point for a network to be trained on the target domain. In deep metric
learning, the source domain is used to construct an embedding that captures
class structure in both the source and target domains. In few-shot learning,
the focus is on generalizing well in the target domain based on a limited
number of labeled examples. We compare state-of-the-art methods from these
three paradigms and also explore hybrid adapted-embedding methods that use
limited target-domain data to fine tune embeddings constructed from
source-domain data. We conduct a systematic comparison of methods in a variety
of domains, varying the number of labeled instances available in the target
domain ($k$), as well as the number of target-domain classes. We reach three
principal conclusions: (1) Deep embeddings are far superior, compared to weight
transfer, as a starting point for inter-domain transfer or model re-use (2) Our
hybrid methods robustly outperform every few-shot learning and every deep
metric learning method previously proposed, with a mean error reduction of 34%
over state-of-the-art. (3) Among loss functions for discovering embeddings, the
histogram loss (Ustinova & Lempitsky, 2016) is most robust. We hope our results
will motivate a unification of research in weight transfer, deep metric
learning, and few-shot learning.
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Authors: Tom Dupré La Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Frequency-specific patterns of neural activity are traditionally interpreted
as sustained rhythmic oscillations, and related to cognitive mechanisms such as
attention, high level visual processing or motor control. While alpha waves
(8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed
by Fourier analysis or wavelet transforms, there is an evolving debate that
electromagnetic neural signals are composed of more complex waveforms that
cannot be analyzed by linear filters and traditional signal representations. In
this paper, we propose to learn dedicated representations of such recordings
using a multivariate convolutional sparse coding (CSC) algorithm. Applied to
electroencephalography (EEG) or magnetoencephalography (MEG) data, this method
is able to learn not only prototypical temporal waveforms, but also associated
spatial patterns so their origin can be localized in the brain. Our algorithm
is based on alternated minimization and a greedy coordinate descent solver that
leads to state-of-the-art running time on long time series. To demonstrate the
implications of this method, we apply it to MEG data and show that it is able
to recover biological artifacts. More remarkably, our approach also reveals the
presence of non-sinusoidal mu-shaped patterns, along with their topographic
maps related to the somatosensory cortex.
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Authors: Boris Muzellec, Marco Cuturi
Date: 2018-05-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: valid

Embedding complex objects as vectors in low dimensional spaces is a
longstanding problem in machine learning. We propose in this work an extension
of that approach, which consists in embedding objects as elliptical probability
distributions, namely distributions whose densities have elliptical level sets.
We endow these measures with the 2-Wasserstein metric, with two important
benefits: (i) For such measures, the squared 2-Wasserstein metric has a closed
form, equal to a weighted sum of the squared Euclidean distance between means
and the squared Bures metric between covariance matrices. The latter is a
Riemannian metric between positive semi-definite matrices, which turns out to
be Euclidean on a suitable factor representation of such matrices, which is
valid on the entire geodesic between these matrices. (ii) The 2-Wasserstein
distance boils down to the usual Euclidean metric when comparing Diracs, and
therefore provides a natural framework to extend point embeddings. We show that
for these reasons Wasserstein elliptical embeddings are more intuitive and
yield tools that are better behaved numerically than the alternative choice of
Gaussian embeddings with the Kullback-Leibler divergence. In particular, and
unlike previous work based on the KL geometry, we learn elliptical
distributions that are not necessarily diagonal. We demonstrate the advantages
of elliptical embeddings by using them for visualization, to compute embeddings
of words, and to reflect entailment or hypernymy.
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Authors: Wentao Zhu, Yeeleng S. Vang, Yufang Huang, Xiaohui Xie
Date: 2018-05-14 00:00:00
Tasks: Computed Tomography (CT), Lung Nodule Detection

Recently deep learning has been witnessing widespread adoption in various
medical image applications. However, training complex deep neural nets requires
large-scale datasets labeled with ground truth, which are often unavailable in
many medical image domains. For instance, to train a deep neural net to detect
pulmonary nodules in lung computed tomography (CT) images, current practice is
to manually label nodule locations and sizes in many CT images to construct a
sufficiently large training dataset, which is costly and difficult to scale. On
the other hand, electronic medical records (EMR) contain plenty of partial
information on the content of each medical image. In this work, we explore how
to tap this vast, but currently unexplored data source to improve pulmonary
nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework
augmented with expectation-maximization (EM), to mine weakly supervised labels
in EMRs for pulmonary nodule detection. Experimental results show that DeepEM
can lead to 1.5\% and 3.9\% average improvement in free-response receiver
operating characteristic (FROC) scores on LUNA16 and Tianchi datasets,
respectively, demonstrating the utility of incomplete information in EMRs for
improving deep learning
algorithms.\footnote{https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection.git}
Automatic context window composition for distant speech recognition
Authors: Mirco Ravanelli, Maurizio Omologo
Date: 2018-05-26 00:00:00
Tasks: Distant Speech Recognition, speech-recognition, Speech Recognition

Distant speech recognition is being revolutionized by deep learning, that has
contributed to significantly outperform previous HMM-GMM systems. A key aspect
behind the rapid rise and success of DNNs is their ability to better manage
large time contexts. With this regard, asymmetric context windows that embed
more past than future frames have been recently used with feed-forward neural
networks. This context configuration turns out to be useful not only to address
low-latency speech recognition, but also to boost the recognition performance
under reverberant conditions. This paper investigates on the mechanisms
occurring inside DNNs, which lead to an effective application of asymmetric
contexts.In particular, we propose a novel method for automatic context window
composition based on a gradient analysis. The experiments, performed with
different acoustic environments, features, DNN architectures, microphone
settings, and recognition tasks show that our simple and efficient strategy
leads to a less redundant frame configuration, which makes DNN training more
effective in reverberant scenarios.
Biologically Motivated Algorithms for Propagating Local Target Representations
Authors: Alexander G. Ororbia, Ankur Mali
Date: 2018-05-26 00:00:00

Finding biologically plausible alternatives to back-propagation of errors is
a fundamentally important challenge in artificial neural network research. In
this paper, we propose a learning algorithm called error-driven Local
Representation Alignment (LRA-E), which has strong connections to predictive
coding, a theory that offers a mechanistic way of describing neurocomputational
machinery. In addition, we propose an improved variant of Difference Target
Propagation, another procedure that comes from the same family of algorithms as
LRA-E. We compare our procedures to several other biologically-motivated
algorithms, including two feedback alignment algorithms and Equilibrium
Propagation. In two benchmarks, we find that both of our proposed algorithms
yield stable performance and strong generalization compared to other competing
back-propagation alternatives when training deeper, highly nonlinear networks,
with LRA-E performing the best overall.