pwc-archive/papers-with-abstracts huggingface dataset:
On $w$-mixtures: Finite convex combinations of prescribed component distributions
Authors: Frank Nielsen, Richard Nock
Date: 2017-08-02 00:00:00

We consider the space of $w$-mixtures which is defined as the set of finite statistical mixtures sharing the same prescribed component distributions closed under convex combinations. The information geometry induced by the Bregman generator set to the Shannon negentropy on this space yields a dually flat space called the mixture family manifold. We show how the Kullback-Leibler (KL) divergence can be recovered from the corresponding Bregman divergence for the negentropy generator: That is, the KL divergence between two $w$-mixtures amounts to a Bregman Divergence (BD) induced by the Shannon negentropy generator. Thus the KL divergence between two Gaussian Mixture Models (GMMs) sharing the same Gaussian components is equivalent to a Bregman divergence. This KL-BD equivalence on a mixture family manifold implies that we can perform optimal KL-averaging aggregation of $w$-mixtures without information loss. More generally, we prove that the statistical skew Jensen-Shannon divergence between $w$-mixtures is equivalent to a skew Jensen divergence between their corresponding parameters. Finally, we state several properties, divergence identities, and inequalities relating to $w$-mixtures.
Analyzing Neural MT Search and Model Performance
Authors: Jan Niehues, Eunah Cho, Thanh-Le Ha, Alex Waibel
Date: 2017-08-02 00:00:00
Proceeding: WS 2017 8
Tasks: model, NMT, Reranking, Translation

In this paper, we offer an in-depth analysis about the modeling and search
performance. We address the question if a more complex search algorithm is
necessary. Furthermore, we investigate the question if more complex models
which might only be applicable during rescoring are promising.
  By separating the search space and the modeling using $n$-best list
reranking, we analyze the influence of both parts of an NMT system
independently. By comparing differently performing NMT systems, we show that
the better translation is already in the search space of the translation
systems with less performance. This results indicate that the current search
algorithms are sufficient for the NMT systems. Furthermore, we could show that
even a relatively small $n$-best list of $50$ hypotheses already contain
notably better translations.
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
Authors: Matias Quiroz, Mattias Villani, Robert Kohn
Date: 2015-07-10 00:00:00
Tasks: Survey Sampling

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up
computations for datasets with many observations. A key feature of our approach
is the use of the highly efficient difference estimator from the survey
sampling literature to estimate the log-likelihood accurately using only a
small fraction of the data. Our algorithm improves on the $O(n)$ complexity of
regular MCMC by operating over local data clusters instead of the full sample
when computing the likelihood. The likelihood estimate is used in a
Pseudo-marginal framework to sample from a perturbed posterior which is within
$O(m^{-1/2})$ of the true posterior, where $m$ is the subsample size. The
method is applied to a logistic regression model to predict firm bankruptcy for
a large data set. We document a significant speed up in comparison to the
standard MCMC on the full dataset.
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
Authors: Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, Andrew McCallum
Date: 2017-08-02 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In textual information extraction and other sequence labeling tasks it is now
common to use recurrent neural networks (such as LSTM) to form rich embedded
representations of long-term input co-occurrence patterns. Representation of
output co-occurrence patterns is typically limited to a hand-designed graphical
model, such as a linear-chain CRF representing short-term Markov dependencies
among successive labels. This paper presents a method that learns embedded
representations of latent output structure in sequence data. Our model takes
the form of a finite-state machine with a large number of latent states per
label (a latent variable CRF), where the state-transition matrix is
factorized---effectively forming an embedded representation of
state-transitions capable of enforcing long-term label dependencies, while
supporting exact Viterbi inference over output labels. We demonstrate accuracy
improvements and interpretable latent structure in a synthetic but complex task
based on CoNLL named entity recognition.
Learning ReLUs via Gradient Descent
Authors: Mahdi Soltanolkotabi
Date: 2017-05-10 00:00:00
Proceeding: NeurIPS 2017 12

In this paper we study the problem of learning Rectified Linear Units (ReLUs)
which are functions of the form $max(0,<w,x>)$ with $w$ denoting the weight
vector. We study this problem in the high-dimensional regime where the number
of observations are fewer than the dimension of the weight vector. We assume
that the weight vector belongs to some closed set (convex or nonconvex) which
captures known side-information about its structure. We focus on the realizable
model where the inputs are chosen i.i.d.~from a Gaussian distribution and the
labels are generated according to a planted weight vector. We show that
projected gradient descent, when initialization at 0, converges at a linear
rate to the planted model with a number of samples that is optimal up to
numerical constants. Our results on the dynamics of convergence of these very
shallow neural nets may provide some insights towards understanding the
dynamics of deeper architectures.
Improved Representation Learning for Predicting Commonsense Ontologies
Authors: Xiang Li, Luke Vilnis, Andrew McCallum
Date: 2017-08-01 00:00:00
Tasks: Representation Learning, Triplet

Recent work in learning ontologies (hierarchical and partially-ordered
structures) has leveraged the intrinsic geometry of spaces of learned
representations to make predictions that automatically obey complex structural
constraints. We explore two extensions of one such model, the order-embedding
model for hierarchical relation learning, with an aim towards improved
performance on text data for commonsense knowledge representation. Our first
model jointly learns ordering relations and non-hierarchical knowledge in the
form of raw text. Our second extension exploits the partial order structure of
the training data to find long-distance triplet constraints among embeddings
which are poorly enforced by the pairwise training procedure. We find that both
incorporating free text and augmented training constraints improve over the
original order-embedding model and other strong baselines.
Visual Dialog
Authors: Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José M. F. Moura, Devi Parikh, Dhruv Batra
Date: 2016-11-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: AI Agent, Chatbot, Retrieval, Visual Dialog

We introduce the task of Visual Dialog, which requires an AI agent to hold a
meaningful dialog with humans in natural, conversational language about visual
content. Specifically, given an image, a dialog history, and a question about
the image, the agent has to ground the question in image, infer context from
history, and answer the question accurately. Visual Dialog is disentangled
enough from a specific downstream task so as to serve as a general test of
machine intelligence, while being grounded in vision enough to allow objective
evaluation of individual responses and benchmark progress. We develop a novel
two-person chat data-collection protocol to curate a large-scale Visual Dialog
dataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10
question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialog
question-answer pairs.
  We introduce a family of neural encoder-decoder models for Visual Dialog with
3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network --
and 2 decoders (generative and discriminative), which outperform a number of
sophisticated baselines. We propose a retrieval-based evaluation protocol for
Visual Dialog where the AI agent is asked to sort a set of candidate answers
and evaluated on metrics such as mean-reciprocal-rank of human response. We
quantify gap between machine and human performance on the Visual Dialog task
via human studies. Putting it all together, we demonstrate the first 'visual
chatbot'! Our dataset, code, trained models and visual chatbot are available on
https://visualdialog.org
Dense Piecewise Planar RGB-D SLAM for Indoor Environments
Authors: Phi-Hung Le, Jana Kosecka
Date: 2017-08-01 00:00:00
Tasks: Visual Odometry

The paper exploits weak Manhattan constraints to parse the structure of
indoor environments from RGB-D video sequences in an online setting. We extend
the previous approach for single view parsing of indoor scenes to video
sequences and formulate the problem of recovering the floor plan of the
environment as an optimal labeling problem solved using dynamic programming.
The temporal continuity is enforced in a recursive setting, where labeling from
previous frames is used as a prior term in the objective function. In addition
to recovery of piecewise planar weak Manhattan structure of the extended
environment, the orthogonality constraints are also exploited by visual
odometry and pose graph optimization. This yields reliable estimates in the
presence of large motions and absence of distinctive features to track. We
evaluate our method on several challenging indoors sequences demonstrating
accurate SLAM and dense mapping of low texture environments. On existing TUM
benchmark we achieve competitive results with the alternative approaches which
fail in our environments.
Speeding-up ProbLog's Parameter Learning
Authors: Francisco H. O. V. de Faria, Arthur C. Gusmão, Fabio G. Cozman, Denis D. Mauá
Date: 2017-07-25 00:00:00

ProbLog is a state-of-art combination of logic programming and probabilities;
in particular ProbLog offers parameter learning through a variant of the EM
algorithm. However, the resulting learning algorithm is rather slow, even when
the data are complete. In this short paper we offer some insights that lead to
orders of magnitude improvements in ProbLog's parameter learning speed with
complete data.
No Fuss Distance Metric Learning using Proxies
Authors: Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, Saurabh Singh
Date: 2017-03-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Metric Learning, Semantic Similarity, Semantic Textual Similarity, Triplet, Zero-Shot Learning

We address the problem of distance metric learning (DML), defined as learning
a distance consistent with a notion of semantic similarity. Traditionally, for
this problem supervision is expressed in the form of sets of points that follow
an ordinal relationship -- an anchor point $x$ is similar to a set of positive
points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined
over these distances is minimized. While the specifics of the optimization
differ, in this work we collectively call this type of supervision Triplets and
all methods that follow this pattern Triplet-Based methods. These methods are
challenging to optimize. A main issue is the need for finding informative
triplets, which is usually achieved by a variety of tricks such as increasing
the batch size, hard or semi-hard triplet mining, etc. Even with these tricks,
the convergence rate of such methods is slow. In this paper we propose to
optimize the triplet loss on a different space of triplets, consisting of an
anchor data point and similar and dissimilar proxy points which are learned as
well. These proxies approximate the original data points, so that a triplet
loss over the proxies is a tight upper bound of the original loss. This
proxy-based loss is empirically better behaved. As a result, the proxy-loss
improves on state-of-art results for three standard zero-shot learning
datasets, by up to 15% points, while converging three times as fast as other
triplet-based losses.
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Authors: Sanjeev Arora, Rong Ge, YIngyu Liang, Tengyu Ma, Yi Zhang
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Generative Adversarial Network

We show that training of generative adversarial network (GAN) may not have
good generalization properties; e.g., training may appear successful but the
trained distribution may be far from target distribution in standard metrics.
However, generalization does occur for a weaker metric called neural net
distance. It is also shown that an approximate pure equilibrium exists in the
discriminator/generator game for a special class of generators with natural
training objectives when generator capacity and training set sizes are
moderate.
  This existence of equilibrium inspires MIX+GAN protocol, which can be
combined with any existing GAN training, and empirically shown to improve some
of them.
Using Transfer Learning for Image-Based Cassava Disease Detection
Authors: Amanda Ramcharan, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed, James Legg, David Hughes
Date: 2017-06-19 00:00:00
Tasks: Transfer Learning

Cassava is the third largest source of carbohydrates for human food in the
world but is vulnerable to virus diseases, which threaten to destabilize food
security in sub-Saharan Africa. Novel methods of cassava disease detection are
needed to support improved control which will prevent this crisis. Image
recognition offers both a cost effective and scalable technology for disease
detection. New transfer learning methods offer an avenue for this technology to
be easily deployed on mobile devices. Using a dataset of cassava disease images
taken in the field in Tanzania, we applied transfer learning to train a deep
convolutional neural network to identify three diseases and two types of pest
damage (or lack thereof). The best trained model accuracies were 98% for brown
leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage
(GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic
disease (CMD). The best model achieved an overall accuracy of 93% for data not
used in the training process. Our results show that the transfer learning
approach for image recognition of field images offers a fast, affordable, and
easily deployable strategy for digital plant disease detection.
A Lightweight Front-end Tool for Interactive Entity Population
Authors: Hidekazu Oiwa, Yoshihiko Suhara, Jiyu Komiya, Andrei Lopatenko
Date: 2017-08-01 00:00:00

Entity population, a task of collecting entities that belong to a particular
category, has attracted attention from vertical domains. There is still a high
demand for creating entity dictionaries in vertical domains, which are not
covered by existing knowledge bases. We develop a lightweight front-end tool
for facilitating interactive entity population. We implement key components
necessary for effective interactive entity population: 1) GUI-based dashboards
to quickly modify an entity dictionary, and 2) entity highlighting on documents
for quickly viewing the current progress. We aim to reduce user cost from
beginning to end, including package installation and maintenance. The
implementation enables users to use this tool on their web browsers without any
additional packages --- users can focus on their missions to create entity
dictionaries. Moreover, an entity expansion module is implemented as external
APIs. This design makes it easy to continuously improve interactive entity
population pipelines. We are making our demo publicly available
(http://bit.ly/luwak-demo).
SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set
Authors: William Havard, Laurent Besacier, Olivier Rosec
Date: 2017-07-26 00:00:00
Tasks: text-to-speech, Text to Speech

This paper presents an augmentation of MSCOCO dataset where speech is added to image and text. Speech captions are generated using text-to-speech (TTS) synthesis resulting in 616,767 spoken captions (more than 600h) paired with images. Disfluencies and speed perturbation are added to the signal in order to sound more natural. Each speech signal (WAV) is paired with a JSON file containing exact timecode for each word/syllable/phoneme in the spoken caption. Such a corpus could be used for Language and Vision (LaVi) tasks including speech input or output instead of text. Investigating multimodal learning schemes for unsupervised speech pattern discovery is also possible with this corpus, as demonstrated by a preliminary study conducted on a subset of the corpus (10h, 10k spoken captions). The dataset is available on Zenodo: https://zenodo.org/record/4282267
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
Authors: Liyuan Liu, Xiang Ren, Qi Zhu, Shi Zhi, Huan Gui, Heng Ji, Jiawei Han
Date: 2017-07-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Relation, Relation Extraction, Representation Learning

Relation extraction is a fundamental task in information extraction. Most
existing methods have heavy reliance on annotations labeled by human experts,
which are costly and time-consuming. To overcome this drawback, we propose a
novel framework, REHession, to conduct relation extractor learning using
annotations from heterogeneous information source, e.g., knowledge base and
domain heuristics. These annotations, referred as heterogeneous supervision,
often conflict with each other, which brings a new challenge to the original
relation extraction task: how to infer the true label from noisy labels for a
given instance. Identifying context information as the backbone of both
relation extraction and true label discovery, we adopt embedding techniques to
learn the distributed representations of context, which bridges all components
with mutual enhancement in an iterative fashion. Extensive experimental results
demonstrate the superiority of REHession over the state-of-the-art.
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
Authors: Adam C. Earle, Andrew M. Saxe, Benjamin Rosman
Date: 2017-08-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Hierarchical Reinforcement Learning, Reinforcement Learning

Hierarchical reinforcement learning methods offer a powerful means of
planning flexible behavior in complicated domains. However, learning an
appropriate hierarchical decomposition of a domain into subtasks remains a
substantial challenge. We present a novel algorithm for subtask discovery,
based on the recently introduced multitask linearly-solvable Markov decision
process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by
representing them as a linear combination of a previously learned basis set of
tasks. In this setting, the subtask discovery problem can naturally be posed as
finding an optimal low-rank approximation of the set of tasks the agent will
face in a domain. We use non-negative matrix factorization to discover this
minimal basis set of tasks, and show that the technique learns intuitive
decompositions in a variety of domains. Our method has several qualitatively
desirable features: it is not limited to learning subtasks with single goal
states, instead learning distributed patterns of preferred states; it learns
qualitatively different hierarchical decompositions in the same domain
depending on the ensemble of tasks the agent will face; and it may be
straightforwardly iterated to obtain deeper hierarchical decompositions.
Reconfiguring the Imaging Pipeline for Computer Vision
Authors: Mark Buckler, Suren Jayasuriya, Adrian Sampson
Date: 2017-05-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Demosaicking

Advancements in deep learning have ignited an explosion of research on
efficient hardware for embedded computer vision. Hardware vision acceleration,
however, does not address the cost of capturing and processing the image data
that feeds these algorithms. We examine the role of the image signal processing
(ISP) pipeline in computer vision to identify opportunities to reduce
computation and save energy. The key insight is that imaging pipelines should
be designed to be configurable: to switch between a traditional photography
mode and a low-power vision mode that produces lower-quality image data
suitable only for computer vision. We use eight computer vision algorithms and
a reversible pipeline simulation tool to study the imaging system's impact on
vision performance. For both CNN-based and classical vision algorithms, we
observe that only two ISP stages, demosaicing and gamma compression, are
critical for task performance. We propose a new image sensor design that can
compensate for skipping these stages. The sensor design features an adjustable
resolution and tunable analog-to-digital converters (ADCs). Our proposed
imaging system's vision mode disables the ISP entirely and configures the
sensor to produce subsampled, lower-precision image data. This vision mode can
save ~75% of the average energy of a baseline photography mode while having
only a small impact on vision task accuracy.
Multimodal Machine Learning: A Survey and Taxonomy
Authors: Tadas Baltrušaitis, Chaitanya Ahuja, Louis-Philippe Morency
Date: 2017-05-26 00:00:00
Tasks: BIG-bench Machine Learning, Survey, Translation

Our experience of the world is multimodal - we see objects, hear sounds, feel
texture, smell odors, and taste flavors. Modality refers to the way in which
something happens or is experienced and a research problem is characterized as
multimodal when it includes multiple such modalities. In order for Artificial
Intelligence to make progress in understanding the world around us, it needs to
be able to interpret such multimodal signals together. Multimodal machine
learning aims to build models that can process and relate information from
multiple modalities. It is a vibrant multi-disciplinary field of increasing
importance and with extraordinary potential. Instead of focusing on specific
multimodal applications, this paper surveys the recent advances in multimodal
machine learning itself and presents them in a common taxonomy. We go beyond
the typical early and late fusion categorization and identify broader
challenges that are faced by multimodal machine learning, namely:
representation, translation, alignment, fusion, and co-learning. This new
taxonomy will enable researchers to better understand the state of the field
and identify directions for future research.
Breaking the curse of dimensionality in regression
Authors: Yinchu Zhu, Jelena Bradic
Date: 2017-08-01 00:00:00
Tasks: regression, valid

Models with many signals, high-dimensional models, often impose structures on
the signal strengths. The common assumption is that only a few signals are
strong and most of the signals are zero or close (collectively) to zero.
However, such a requirement might not be valid in many real-life applications.
In this article, we are interested in conducting large-scale inference in
models that might have signals of mixed strengths. The key challenge is that
the signals that are not under testing might be collectively non-negligible
(although individually small) and cannot be accurately learned. This article
develops a new class of tests that arise from a moment matching formulation. A
virtue of these moment-matching statistics is their ability to borrow strength
across features, adapt to the sparsity size and exert adjustment for testing
growing number of hypothesis. GRoup-level Inference of Parameter, GRIP, test
harvests effective sparsity structures with hypothesis formulation for an
efficient multiple testing procedure. Simulated data showcase that GRIPs error
control is far better than the alternative methods. We develop a minimax
theory, demonstrating optimality of GRIP for a broad range of models, including
those where the model is a mixture of a sparse and high-dimensional dense
signals.