pwc-archive/papers-with-abstracts huggingface dataset:
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Authors: Runtao Liu, Chenxi Liu, Yutong Bai, Alan Yuille
Date: 2019-01-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diagnostic, Image Segmentation, object-detection, Object Detection, Question Answering, Referring Expression, Referring Expression Comprehension, Referring Expression Segmentation, Semantic Segmentation, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Referring object detection and referring image segmentation are important
tasks that require joint understanding of visual information and natural
language. Yet there has been evidence that current benchmark datasets suffer
from bias, and current state-of-the-art models cannot be easily evaluated on
their intermediate reasoning process. To address these issues and complement
similar efforts in visual question answering, we build CLEVR-Ref+, a synthetic
diagnostic dataset for referring expression comprehension. The precise
locations and attributes of the objects are readily available, and the
referring expressions are automatically associated with functional programs.
The synthetic nature allows control over dataset bias (through sampling
strategy), and the modular programs enable intermediate reasoning ground truth
without human annotators.
  In addition to evaluating several state-of-the-art models on CLEVR-Ref+, we
also propose IEP-Ref, a module network approach that significantly outperforms
other models on our dataset. In particular, we present two interesting and
important findings using IEP-Ref: (1) the module trained to transform feature
maps into segmentation masks can be attached to any intermediate module to
reveal the entire reasoning process step-by-step; (2) even if all training data
has at least one object referred, IEP-Ref can correctly predict no-foreground
when presented with false-premise referring expressions. To the best of our
knowledge, this is the first direct and quantitative proof that neural modules
behave in the way they are intended.
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
Authors: Mohammad Mohammadi Amiri, Deniz Gunduz
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning, Quantization

We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a remote parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and transmitted to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC). We then introduce a novel analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation, and provide convergence analysis for this approach. In A-DSGD, the devices first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are sent directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also illustrate for a classification problem that, A-DSGD is more robust to bias in data distribution across devices, while D-DSGD significantly outperforms other digital schemes in the literature. We also observe that both D-DSGD and A-DSGD perform better by increasing the number of devices (while keeping the total dataset size constant), showing their ability in harnessing the computation power of edge devices.
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
Authors: Eric V. Mazumdar, Michael. I. Jordan, S. Shankar Sastry
Date: 2019-01-03 00:00:00
Tasks: Generative Adversarial Network

We propose local symplectic surgery, a two-timescale procedure for finding
local Nash equilibria in two-player zero-sum games. We first show that previous
gradient-based algorithms cannot guarantee convergence to local Nash equilibria
due to the existence of non-Nash stationary points. By taking advantage of the
differential structure of the game, we construct an algorithm for which the
local Nash equilibria are the only attracting fixed points. We also show that
the algorithm exhibits no oscillatory behaviors in neighborhoods of equilibria
and show that it has the same per-iteration complexity as other recently
proposed algorithms. We conclude by validating the algorithm on two numerical
examples: a toy example with multiple Nash equilibria and a non-Nash
equilibrium, and the training of a small generative adversarial network (GAN).
From exploration to control: learning object manipulation skills through novelty search and local adaptation
Authors: Seungsu Kim, Alexandre Coninx, Stephane Doncieux
Date: 2019-01-03 00:00:00

Programming a robot to deal with open-ended tasks remains a challenge, in particular if the robot has to manipulate objects. Launching, grasping, pushing or any other object interaction can be simulated but the corresponding models are not reversible and the robot behavior thus cannot be directly deduced. These behaviors are hard to learn without a demonstration as the search space is large and the reward sparse. We propose a method to autonomously generate a diverse repertoire of simple object interaction behaviors in simulation. Our goal is to bootstrap a robot learning and development process with limited information about what the robot has to achieve and how. This repertoire can be exploited to solve different tasks in reality thanks to a proposed adaptation method or could be used as a training set for data-hungry algorithms. The proposed approach relies on the definition of a goal space and generates a repertoire of trajectories to reach attainable goals, thus allowing the robot to control this goal space. The repertoire is built with an off-the-shelf simulation thanks to a quality diversity algorithm. The result is a set of solutions tested in simulation only. It may result in two different problems: (1) as the repertoire is discrete and finite, it may not contain the trajectory to deal with a given situation or (2) some trajectories may lead to a behavior in reality that differs from simulation because of a reality gap. We propose an approach to deal with both issues by using a local linearization of the mapping between the motion parameters and the observed effects. Furthermore, we present an approach to update the existing solutions repertoire with the tests done on the real robot. The approach has been validated on two different experiments on the Baxter robot: a ball launching and a joystick manipulation tasks.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Authors: Yotam Gigi, Gal Elidan, Avinatan Hassidim, Yossi Matias, Zach Moshe, Sella Nevo, Guy Shalev, Ami Wiesel
Date: 2019-01-03 00:00:00

Learning hydrologic models for accurate riverine flood prediction at scale is
a challenge of great importance. One of the key difficulties is the need to
rely on in-situ river discharge measurements, which can be quite scarce and
unreliable, particularly in regions where floods cause the most damage every
year. Accordingly, in this work we tackle the problem of river discharge
estimation at different river locations. A core characteristic of the data at
hand (e.g. satellite measurements) is that we have few measurements for many
locations, all sharing the same physics that underlie the water discharge. We
capture this scenario in a simple but powerful common mechanism regression
(CMR) model with a local component as well as a shared one which captures the
global discharge mechanism. The resulting learning objective is non-convex, but
we show that we can find its global optimum by leveraging the power of joining
local measurements across sites. In particular, using a spectral initialization
with provable near-optimal accuracy, we can find the optimum using standard
descent methods. We demonstrate the efficacy of our approach for the problem of
discharge estimation using simulations.
A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Authors: Kui Xu, Zhe Wang, Jiangping Shi, Hongsheng Li, Qiangfeng Cliff Zhang
Date: 2019-01-03 00:00:00
Tasks: Pose Estimation, Translation

Constructing of molecular structural models from Cryo-Electron Microscopy
(Cryo-EM) density volumes is the critical last step of structure determination
by Cryo-EM technologies. Methods have evolved from manual construction by
structural biologists to perform 6D translation-rotation searching, which is
extremely compute-intensive. In this paper, we propose a learning-based method
and formulate this problem as a vision-inspired 3D detection and pose
estimation task. We develop a deep learning framework for amino acid
determination in a 3D Cryo-EM density volume. We also design a sequence-guided
Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form
the molecular structure. This framework achieves 91% coverage on our newly
proposed dataset and takes only a few minutes for a typical structure with a
thousand amino acids. Our method is hundreds of times faster and several times
more accurate than existing automated solutions without any human intervention.
Learning a Generator Model from Terminal Bus Data
Authors: Nikolay Stulov, Dejan J Sobajic, Yury Maximov, Deepjyoti Deka, Michael Chertkov
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning, model

In this work we investigate approaches to reconstruct generator models from
measurements available at the generator terminal bus using machine learning
(ML) techniques. The goal is to develop an emulator which is trained online and
is capable of fast predictive computations. The training is illustrated on
synthetic data generated based on available open-source dynamical generator
model. Two ML techniques were developed and tested: (a) standard vector
auto-regressive (VAR) model; and (b) novel customized long short-term memory
(LSTM) deep learning model. Trade-offs in reconstruction ability between
computationally light but linear AR model and powerful but computationally
demanding LSTM model are established and analyzed.
Personalized explanation in machine learning: A conceptualization
Authors: Johanes Schneider, Joshua Handali
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning

Explanation in machine learning and related fields such as artificial
intelligence aims at making machine learning models and their decisions
understandable to humans. Existing work suggests that personalizing
explanations might help to improve understandability. In this work, we derive a
conceptualization of personalized explanation by defining and structuring the
problem based on prior work on machine learning explanation, personalization
(in machine learning) and concepts and techniques from other domains such as
privacy and knowledge elicitation. We perform a categorization of explainee
data used in the process of personalization as well as describing means to
collect this data. We also identify three key explanation properties that are
amendable to personalization: complexity, decision information and
presentation. We also enhance existing work on explanation by introducing
additional desiderata and measures to quantify the quality of personalized
explanations.
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
Authors: Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz, Diego Perez-Liebana
Date: 2019-01-03 00:00:00
Tasks: SMAC, SMAC+

This paper introduces a simple and fast variant of Planet Wars as a test-bed
for statistical planning based Game AI agents, and for noisy hyper-parameter
optimisation. Planet Wars is a real-time strategy game with simple rules but
complex game-play. The variant introduced in this paper is designed for speed
to enable efficient experimentation, and also for a fixed action space to
enable practical inter-operability with General Video Game AI agents. If we
treat the game as a win-loss game (which is standard), then this leads to
challenging noisy optimisation problems both in tuning agents to play the game,
and in tuning game parameters. Here we focus on the problem of tuning an agent,
and report results using the recently developed N-Tuple Bandit Evolutionary
Algorithm and a number of other optimisers, including Sequential Model-based
Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit
Evolutionary offers competitive performance as well as insight into the effects
of combinations of parameter choices.
Face Recognition: A Novel Multi-Level Taxonomy based Survey
Authors: Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia
Date: 2019-01-03 00:00:00
Tasks: Face Recognition, Survey

In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.
A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
Authors: Marcus Klasson, Cheng Zhang, Hedvig Kjellström
Date: 2019-01-03 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Image classification models built into visual support systems and other
assistive devices need to provide accurate predictions about their environment.
We focus on an application of assistive technology for people with visual
impairments, for daily activities such as shopping or cooking. In this paper,
we provide a new benchmark dataset for a challenging task in this application -
classification of fruits, vegetables, and refrigerated products, e.g. milk
packages and juice cartons, in grocery stores. To enable the learning process
to utilize multiple sources of structured information, this dataset not only
contains a large volume of natural images but also includes the corresponding
information of the product from an online shopping website. Such information
encompasses the hierarchical structure of the object classes, as well as an
iconic image of each type of object. This dataset can be used to train and
evaluate image classification models for helping visually impaired people in
natural environments. Additionally, we provide benchmark results evaluated on
pretrained convolutional neural networks often used for image understanding
purposes, and also a multi-view variational autoencoder, which is capable of
utilizing the rich product information in the dataset.
Feature reinforcement with word embedding and parsing information in neural TTS
Authors: Huaiping Ming, Lei He, Haohan Guo, Frank K. Soong
Date: 2019-01-03 00:00:00
Tasks: Sentence, text-to-speech, Text to Speech

In this paper, we propose a feature reinforcement method under the
sequence-to-sequence neural text-to-speech (TTS) synthesis framework. The
proposed method utilizes the multiple input encoder to take three levels of
text information, i.e., phoneme sequence, pre-trained word embedding, and
grammatical structure of sentences from parser as the input feature for the
neural TTS system. The added word and sentence level information can be viewed
as the feature based pre-training strategy, which clearly enhances the model
generalization ability. The proposed method not only improves the system
robustness significantly but also improves the synthesized speech to near
recording quality in our experiments for out-of-domain text.
The Extended Kalman Filter is a Natural Gradient Descent in Trajectory Space
Authors: Yann Ollivier
Date: 2019-01-03 00:00:00

The extended Kalman filter is perhaps the most standard tool to estimate in
real time the state of a dynamical system from noisy measurements of some
function of the system, with extensive practical applications (such as position
tracking via GPS). While the plain Kalman filter for linear systems is
well-understood, the extended Kalman filter relies on linearizations which have
been debated.
  We recover the exact extended Kalman filter equations from first principles
in statistical learning: the extended Kalman filter is equal to Amari's online
natural gradient, applied in the space of trajectories of the system. Namely,
each possible trajectory of the dynamical system defines a probability law over
possible observations. In principle this makes it possible to treat the
underlying trajectory as the parameter of a statistical model of the
observations. Then the parameter can be learned by gradient ascent on the
log-likelihood of observations, as they become available. Using Amari's natural
gradient from information geometry (a gradient descent preconditioned with the
Fisher matrix, which provides parameterization-invariance) exactly recovers the
extended Kalman filter.
  This applies only to a particular choice of process noise in the Kalman
filter, namely, taking noise proportional to the posterior covariance - a
canonical choice in the absence of specific model information.
Generating Multiple Objects at Spatially Distinct Locations
Authors: Tobias Hinz, Stefan Heinrich, Stefan Wermter
Date: 2019-01-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Conditional Image Generation, Image Generation, Object, Text-to-Image Generation

Recent improvements to Generative Adversarial Networks (GANs) have made it
possible to generate realistic images in high resolution based on natural
language descriptions such as image captions. Furthermore, conditional GANs
allow us to control the image generation process through labels or even natural
language descriptions. However, fine-grained control of the image layout, i.e.
where in the image specific objects should be located, is still difficult to
achieve. This is especially true for images that should contain multiple
distinct objects at different spatial locations. We introduce a new approach
which allows us to control the location of arbitrarily many objects within an
image by adding an object pathway to both the generator and the discriminator.
Our approach does not need a detailed semantic layout but only bounding boxes
and the respective labels of the desired objects are needed. The object pathway
focuses solely on the individual objects and is iteratively applied at the
locations specified by the bounding boxes. The global pathway focuses on the
image background and the general image layout. We perform experiments on the
Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show
that through the use of the object pathway we can control object locations
within images and can model complex scenes with multiple objects at various
locations. We further show that the object pathway focuses on the individual
objects and learns features relevant for these, while the global pathway
focuses on global image characteristics and the image background.
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Authors: Tong He, Haibin Huang, Li Yi, Yuqian Zhou, Chi-Hao Wu, Jue Wang, Stefano Soatto
Date: 2019-01-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification

Surface-based geodesic topology provides strong cues for object semantic
analysis and geometric modeling. However, such connectivity information is lost
in point clouds. Thus we introduce GeoNet, the first deep learning architecture
trained to model the intrinsic structure of surfaces represented as point
clouds. To demonstrate the applicability of learned geodesic-aware
representations, we propose fusion schemes which use GeoNet in conjunction with
other baseline or backbone networks, such as PU-Net and PointNet++, for
down-stream point cloud analysis. Our method improves the state-of-the-art on
multiple representative tasks that can benefit from understandings of the
underlying surface topology, including point upsampling, normal estimation,
mesh reconstruction and non-rigid shape classification.
Active Learning with TensorBoard Projector
Authors: Francois Luus, Naweed Khan, Ismail Akhalwaya
Date: 2019-01-03 00:00:00
Tasks: Active Learning, Dimensionality Reduction

An ML-based system for interactive labeling of image datasets is contributed
in TensorBoard Projector to speed up image annotation performed by humans. The
tool visualizes feature spaces and makes it directly editable by online
integration of applied labels, and it is a system for verifying and managing
machine learning data pertaining to labels. We propose realistic annotation
emulation to evaluate the system design of interactive active learning, based
on our improved semi-supervised extension of t-SNE dimensionality reduction.
Our active learning tool can significantly increase labeling efficiency
compared to uncertainty sampling, and we show that less than 100 labeling
actions are typically sufficient for good classification on a variety of
specialized image datasets. Our contribution is unique given that it needs to
perform dimensionality reduction, feature space visualization and editing,
interactive label propagation, low-complexity active learning, human perceptual
modeling, annotation emulation and unsupervised feature extraction for
specialized datasets in a production-quality implementation.
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
Authors: Dayana Ribas, Jorge Llombart, Antonio Miguel, Luis Vicente
Date: 2019-01-03 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

This paper proposes a deep speech enhancement method which exploits the high
potential of residual connections in a wide neural network architecture, a
topology known as Wide Residual Network. This is supported on single
dimensional convolutions computed alongside the time domain, which is a
powerful approach to process contextually correlated representations through
the temporal domain, such as speech feature sequences. We find the residual
mechanism extremely useful for the enhancement task since the signal always has
a linear shortcut and the non-linear path enhances it in several steps by
adding or subtracting corrections. The enhancement capacity of the proposal is
assessed by objective quality metrics and the performance of a speech
recognition system. This was evaluated in the framework of the REVERB Challenge
dataset, including simulated and real samples of reverberated and noisy speech
signals. Results showed that enhanced speech from the proposed method succeeded
for both, the enhancement task with intelligibility purposes and the speech
recognition system. The DNN model, trained with artificial synthesized
reverberation data, was able to deal with far-field reverberated speech from
real scenarios. Furthermore, the method was able to take advantage of the
residual connection achieving to enhance signals with low noise level, which is
usually a strong handicap of traditional enhancement methods.