pwc-archive/papers-with-abstracts huggingface dataset:
IGLOO: Slicing the Features Space to Represent Sequences
Authors: Vsevolod Sourkov
Date: 2018-07-09 00:00:00
Tasks: Permuted-MNIST

Historically, Recurrent neural networks (RNNs) and its variants such as LSTM and GRU and more recently Transformers have been the standard go-to components when processing sequential data with neural networks. One notable issue is the relative difficulty to deal with long sequences (i.e. more than 20,000 steps). We introduce IGLOO, a new neural network architecture which aims at being efficient for short sequences but also at being able to deal with long sequences. IGLOOs core idea is to use the relationships between non-local patches sliced out of the features maps of successively applied convolutions to build a representation for the sequence. We show that the model can deal with dependencies of more than 20,000 steps in a reasonable time frame. We stress test IGLOO on the copy-memory and addition tasks, as well as permuted MNIST (98.4%). For a larger task we apply this new structure to the Wikitext-2 dataset Merity et al. (2017b) and achieve a perplexity in line with baseline Transformers but lower than baseline AWD-LSTM. We also present how IGLOO is already used today in production for bioinformatics tasks.
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks
Authors: Dimitrios Korkinof, Tobias Rijken, Michael O'Neill, Joseph Yearsley, Hugh Harvey, Ben Glocker
Date: 2018-07-09 00:00:00
Tasks: Data Augmentation, Image Generation, Out-of-Distribution Detection, Vocal Bursts Intensity Prediction

The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.
Jointly Embedding Entities and Text with Distant Supervision
Authors: Denis Newman-Griffis, Albert M. Lai, Eric Fosler-Lussier
Date: 2018-07-09 00:00:00
Proceeding: WS 2018 7

Learning representations for knowledge base entities and concepts is becoming
increasingly important for NLP applications. However, recent entity embedding
methods have relied on structured resources that are expensive to create for
new domains and corpora. We present a distantly-supervised method for jointly
learning embeddings of entities and text from an unnanotated corpus, using only
a list of mappings between entities and surface forms. We learn embeddings from
open-domain and biomedical corpora, and compare against prior methods that rely
on human-annotated text or large knowledge graph structure. Our embeddings
capture entity similarity and relatedness better than prior work, both in
existing biomedical datasets and a new Wikipedia-based dataset that we release
to the community. Results on analogy completion and entity sense disambiguation
indicate that entities and words capture complementary information that can be
effectively combined for downstream use.
Detecting Levels of Depression in Text Based on Metrics
Authors: Ashwath Kumar Salimath, Robin K Thomas, Sethuram Ramalinga Reddy, Yuhao Qiao
Date: 2018-07-09 00:00:00
Tasks: Depression Detection

Depression is one of the most common and a major concern for society. Proper
monitoring using devices that can aid in its detection could be helpful to
prevent it all together. The Distress Analysis Interview Corpus (DAIC) is used
to build a metric-based depression detection. We have designed a metric to
describe the level of depression using negative sentences and classify the
participant accordingly. The score generated from the algorithm is then
levelled up to denote the intensity of depression. The results show that
measuring depression is very complex to using text alone as other factors are
not taken into consideration. Further, In the paper, the limitations of
measuring depression using text are described, and future suggestions are made.
On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition
Authors: Hao Tang, James Glass
Date: 2018-07-09 00:00:00
Tasks: speech-recognition, Speech Recognition

Recurrent neural networks have been the dominant models for many speech and
language processing tasks. However, we understand little about the behavior and
the class of functions recurrent networks can realize. Moreover, the heuristics
used during training complicate the analyses. In this paper, we study recurrent
networks' ability to learn long-term dependency in the context of speech
recognition. We consider two decoding approaches, online and batch decoding,
and show the classes of functions to which the decoding approaches correspond.
We then draw a connection between batch decoding and a popular training
approach for recurrent networks, truncated backpropagation through time.
Changing the decoding approach restricts the amount of past history recurrent
networks can use for prediction, allowing us to analyze their ability to
remember. Empirically, we utilize long-term dependency in subphonetic states,
phonemes, and words, and show how the design decisions, such as the decoding
approach, lookahead, context frames, and consecutive prediction, characterize
the behavior of recurrent networks. Finally, we draw a connection between
Markov processes and vanishing gradients. These results have implications for
studying the long-term dependency in speech data and how these properties are
learned by recurrent networks.
Towards Non-Parametric Learning to Rank
Authors: Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia
Date: 2018-07-09 00:00:00
Tasks: Feature Engineering, Learning-To-Rank

This paper studies a stylized, yet natural, learning-to-rank problem and
points out the critical incorrectness of a widely used nearest neighbor
algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$
and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated
with a latent feature vector. Agents rank items nondeterministically according
to the Plackett-Luce model, where the higher the utility of an item to the
agent, the more likely this item will be ranked high by the agent. Our goal is
to find neighbors of an arbitrary agent or alternative in the latent space.
  We first show that the Kendall-tau distance based kNN produces incorrect
results in our model. Next, we fix the problem by introducing a new algorithm
with features constructed from "global information" of the data matrix. Our
approach is in sharp contrast to most existing feature engineering methods.
Finally, we design another new algorithm identifying similar alternatives. The
construction of alternative features can be done using "local information,"
highlighting the algorithmic difference between finding similar agents and
similar alternatives.
Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm
Authors: Joost Huizinga, Jeff Clune
Date: 2018-07-09 00:00:00
Tasks: Reinforcement Learning

An important challenge in reinforcement learning, including evolutionary robotics, is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is helpful to take advantage of staging, where a difficult task is divided into simpler subtasks that can serve as stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the speed and performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm (CMOEA), which avoids ordering subtasks by allowing all combinations of subtasks to be explored simultaneously. We compare CMOEA against two algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II and Lexicase Selection. The algorithms are tested on a multimodal robotics problem with six subtasks as well as a maze navigation problem with a hundred subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. Separately, we show that adding a linear combination over all objectives can improve the ability of NSGA-II to solve these multimodal problems. Lastly, we show that, in contrast to NSGA-II and Lexicase Selection, CMOEA can effectively leverage secondary objectives to achieve state-of-the-art results on the robotics task. In general, our experiments suggest that CMOEA is a promising, state-of-the-art algorithm for solving multimodal problems.
Process Monitoring Using Maximum Sequence Divergence
Authors: Yihuang Kang, Vladimir Zadorozhny
Date: 2018-07-09 00:00:00
Tasks: Anomaly Detection, Temporal Sequences, Time Series, Time Series Analysis

Process Monitoring involves tracking a system's behaviors, evaluating the
current state of the system, and discovering interesting events that require
immediate actions. In this paper, we consider monitoring temporal system state
sequences to help detect the changes of dynamic systems, check the divergence
of the system development, and evaluate the significance of the deviation. We
begin with discussions of data reduction, symbolic data representation, and the
anomaly detection in temporal discrete sequences. Time-series representation
methods are also discussed and used in this paper to discretize raw data into
sequences of system states. Markov Chains and stationary state distributions
are continuously generated from temporal sequences to represent snapshots of
the system dynamics in different time frames. We use generalized Jensen-Shannon
Divergence as the measure to monitor changes of the stationary symbol
probability distributions and evaluate the significance of system deviations.
We prove that the proposed approach is able to detect deviations of the systems
we monitor and assess the deviation significance in probabilistic manner.
An Attention Model for group-level emotion recognition
Authors: Aarush Gupta, Dakshit Agrawal, Hardik Chauhan, Jose Dolz, Marco Pedersoli
Date: 2018-07-09 00:00:00
Tasks: Emotion Recognition, model

In this paper we propose a new approach for classifying the global emotion of
images containing groups of people. To achieve this task, we consider two
different and complementary sources of information: i) a global representation
of the entire image (ii) a local representation where only faces are
considered. While the global representation of the image is learned with a
convolutional neural network (CNN), the local representation is obtained by
merging face features through an attention mechanism. The two representations
are first learned independently with two separate CNN branches and then fused
through concatenation in order to obtain the final group-emotion classifier.
For our submission to the EmotiW 2018 group-level emotion recognition
challenge, we combine several variations of the proposed model into an
ensemble, obtaining a final accuracy of 64.83% on the test set and ranking 4th
among all challenge participants.
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
Authors: Avishek Ghosh, Kannan Ramchandran
Date: 2018-07-09 00:00:00

We consider a system where agents enter in an online fashion and are
evaluated based on their attributes or context vectors. There can be practical
situations where this context is partially observed, and the unobserved part
comes after some delay. We assume that an agent, once left, cannot re-enter the
system. Therefore, the job of the system is to provide an estimated score for
the agent based on her instantaneous score and possibly some inference of the
instantaneous score over the delayed score. In this paper, we estimate the
delayed context via an online convex game between the agent and the system. We
argue that the error in the score estimate accumulated over $T$ iterations is
small if the regret of the online convex game is small. Further, we leverage
side information about the delayed context in the form of a correlation
function with the known context. We consider the settings where the delay is
fixed or arbitrarily chosen by an adversary. Furthermore, we extend the
formulation to the setting where the contexts are drawn from some Banach space.
Overall, we show that the average penalty for not knowing the delayed context
while making a decision scales with $\mathcal{O}(\frac{1}{\sqrt{T}})$, where
this can be improved to $\mathcal{O}(\frac{\log T}{T})$ under special setting.
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Authors: Aparna Bharati, Daniel Moreira, Joel Brogan, Patricia Hale, Kevin W. Bowyer, Patrick J. Flynn, Anderson Rocha, Walter J. Scheirer
Date: 2018-07-09 00:00:00
Tasks: graph construction, Image Forensics

Creative works, whether paintings or memes, follow unique journeys that
result in their final form. Understanding these journeys, a process known as
"provenance analysis", provides rich insights into the use, motivation, and
authenticity underlying any given work. The application of this type of study
to the expanse of unregulated content on the Internet is what we consider in
this paper. Provenance analysis provides a snapshot of the chronology and
validity of content as it is uploaded, re-uploaded, and modified over time.
Although still in its infancy, automated provenance analysis for online
multimedia is already being applied to different types of content. Most current
works seek to build provenance graphs based on the shared content between
images or videos. This can be a computationally expensive task, especially when
considering the vast influx of content that the Internet sees every day.
Utilizing non-content-based information, such as timestamps, geotags, and
camera IDs can help provide important insights into the path a particular image
or video has traveled during its time on the Internet without large
computational overhead. This paper tests the scope and applicability of
metadata-based inferences for provenance graph construction in two different
scenarios: digital image forensics and cultural analytics.
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Authors: Danil Kuzin, Le Yang, Olga Isupova, Lyudmila Mihaylova
Date: 2018-07-09 00:00:00
Tasks: Gaussian Processes, regression

Gaussian process regression is a machine learning approach which has been
shown its power for estimation of unknown functions. However, Gaussian
processes suffer from high computational complexity, as in a basic form they
scale cubically with the number of observations. Several approaches based on
inducing points were proposed to handle this problem in a static context. These
methods though face challenges with real-time tasks and when the data is
received sequentially over time. In this paper, a novel online algorithm for
training sparse Gaussian process models is presented. It treats the mean and
hyperparameters of the Gaussian process as the state and parameters of the
ensemble Kalman filter, respectively. The online evaluation of the parameters
and the state is performed on new upcoming samples of data. This procedure
iteratively improves the accuracy of parameter estimates. The ensemble Kalman
filter reduces the computational complexity required to obtain predictions with
Gaussian processes preserving the accuracy level of these predictions. The
performance of the proposed method is demonstrated on the synthetic dataset and
real large dataset of UK house prices.
Tractable $n$-Metrics for Multiple Graphs
Authors: Sam Safavi, José Bento
Date: 2018-07-09 00:00:00
Tasks: Clustering

Graphs are used in almost every scientific discipline to express relations among a set of objects. Algorithms that compare graphs, and output a closeness score, or a correspondence among their nodes, are thus extremely important. Despite the large amount of work done, many of the scalable algorithms to compare graphs do not produce closeness scores that satisfy the intuitive properties of metrics. This is problematic since non-metrics are known to degrade the performance of algorithms such as distance-based clustering of graphs (Stratis and Bento 2018). On the other hand, the use of metrics increases the performance of several machine learning tasks (Indyk et al. 1999, Clarkson et al. 1999, Angiulli et al. 2002, Ackermann et al. 2010). In this paper, we introduce a new family of multi-distances (a distance between more than two elements) that satisfies a generalization of the properties of metrics to multiple elements. In the context of comparing graphs, we are the first to show the existence of multi-distances that simultaneously incorporate the useful property of alignment consistency (Nguyen et al. 2011), and a generalized metric property. Furthermore, we show that these multi-distances can be relaxed to convex optimization problems, without losing the generalized metric property.
Talk the Walk: Navigating New York City through Grounded Dialogue
Authors: Harm de Vries, Kurt Shuster, Dhruv Batra, Devi Parikh, Jason Weston, Douwe Kiela
Date: 2018-07-09 00:00:00
Tasks: Navigate

We introduce "Talk The Walk", the first large-scale dialogue dataset grounded
in action and perception. The task involves two agents (a "guide" and a
"tourist") that communicate via natural language in order to achieve a common
goal: having the tourist navigate to a given target location. The task and
dataset, which are described in detail, are challenging and their full solution
is an open problem that we pose to the community. We (i) focus on the task of
tourist localization and develop the novel Masked Attention for Spatial
Convolutions (MASC) mechanism that allows for grounding tourist utterances into
the guide's map, (ii) show it yields significant improvements for both emergent
and natural language communication, and (iii) using this method, we establish
non-trivial baselines on the full task.
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
Authors: Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren
Date: 2018-07-09 00:00:00
Tasks: Image Registration

One of the fundamental challenges in supervised learning for multimodal image
registration is the lack of ground-truth for voxel-level spatial
correspondence. This work describes a method to infer voxel-level
transformation from higher-level correspondence information contained in
anatomical labels. We argue that such labels are more reliable and practical to
obtain for reference sets of image pairs than voxel-level correspondence.
Typical anatomical labels of interest may include solid organs, vessels, ducts,
structure boundaries and other subject-specific ad hoc landmarks. The proposed
end-to-end convolutional neural network approach aims to predict displacement
fields to align multiple labelled corresponding structures for individual image
pairs during the training, while only unlabelled image pairs are used as the
network input for inference. We highlight the versatility of the proposed
strategy, for training, utilising diverse types of anatomical labels, which
need not to be identifiable over all training image pairs. At inference, the
resulting 3D deformable image registration algorithm runs in real-time and is
fully-automated without requiring any anatomical labels or initialisation.
Several network architecture variants are compared for registering T2-weighted
magnetic resonance images and 3D transrectal ultrasound images from prostate
cancer patients. A median target registration error of 3.6 mm on landmark
centroids and a median Dice of 0.87 on prostate glands are achieved from
cross-validation experiments, in which 108 pairs of multimodal images from 76
patients were tested with high-quality anatomical labels.
HDFD --- A High Deformation Facial Dynamics Benchmark for Evaluation of Non-Rigid Surface Registration and Classification
Authors: Gareth Andrews, Sam Endean, Roberto Dyke, Yu-Kun Lai, Gwenno Ffrancon, Gary KL Tam
Date: 2018-07-09 00:00:00
Tasks: General Classification

Objects that undergo non-rigid deformation are common in the real world. A
typical and challenging example is the human faces. While various techniques
have been developed for deformable shape registration and classification,
benchmarks with detailed labels and landmarks suitable for evaluating such
techniques are still limited. In this paper, we present a novel facial dynamic
dataset HDFD which addresses the gap of existing datasets, including 4D funny
faces with substantial non-isometric deformation, and 4D visual-audio faces of
spoken phrases in a minority language (Welsh). Both datasets are captured from
21 participants. The sequences are manually landmarked, with the spoken phrases
further rated by a Welsh expert for level of fluency. These are useful for
quantitative evaluation of both registration and classification tasks. We
further develop a methodology to evaluate several recent non-rigid surface
registration techniques, using our dynamic sequences as test cases. The study
demonstrates the significance and usefulness of our new dataset --- a
challenging benchmark dataset for future techniques.
Using Swarm Optimization To Enhance Autoencoders Images
Authors: Maisa Doaud, Michael Mayo
Date: 2018-07-09 00:00:00
Tasks: Decoder

Autoencoders learn data representations through reconstruction. Robust
training is the key factor affecting the quality of the learned representations
and, consequently, the accuracy of the application that use them. Previous
works suggested methods for deciding the optimal autoencoder configuration
which allows for robust training. Nevertheless, improving the accuracy of a
trained autoencoder has got limited, if no, attention. We propose a new
approach that improves the accuracy of a trained autoencoders results and
answers the following question, Given a trained autoencoder, a test image, and
using a real-parameter optimizer, can we generate better quality reconstructed
image version than the one generated by the autoencoder?. Our proposed approach
combines both the decoder part of a trained Resitricted Boltman Machine-based
autoencoder with the Competitive Swarm Optimization algorithm. Experiments show
that it is possible to reconstruct images using trained decoder from randomly
initialized representations. Results also show that our approach reconstructed
better quality images than the autoencoder in most of the test cases.
Indicating that, we can use the approach for improving the performance of a
pre-trained autoencoder if it does not give satisfactory results.