pwc-archive/papers-with-abstracts huggingface dataset:
A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Authors: Diego Ulisse Pizzagalli, Santiago Fernandez Gonzalez, Rolf Krause
Date: 2018-12-31 00:00:00
Tasks: Clustering

Clustering is a technique for the analysis of datasets obtained by empirical
studies in several disciplines with a major application for biomedical
research. Essentially, clustering algorithms are executed by machines aiming at
finding groups of related points in a dataset. However, the result of grouping
depends on both metrics for point-to-point similarity and rules for
point-to-group association. Indeed, non-appropriate metrics and rules can lead
to undesirable clustering artifacts. This is especially relevant for datasets,
where groups with heterogeneous structures co-exist. In this work, we propose
an algorithm that achieves clustering by exploring the paths between points.
This allows both, to evaluate the properties of the path (such as gaps, density
variations, etc.), and expressing the preference for certain paths. Moreover,
our algorithm supports the integration of existing knowledge about admissible
and non-admissible clusters by training a path classifier. We demonstrate the
accuracy of the proposed method on challenging datasets including points from
synthetic shapes in publicly available benchmarks and microscopy data.
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks
Authors: Hsiao-Yu Fish Tung, Ricson Cheng, Katerina Fragkiadaki
Date: 2018-12-31 00:00:00
Proceeding: CVPR 2019 6
Tasks: Common Sense Reasoning, Representation Learning, Scene Understanding

We integrate two powerful ideas, geometry and deep visual representation
learning, into recurrent network architectures for mobile visual scene
understanding. The proposed networks learn to "lift" and integrate 2D visual
features over time into latent 3D feature maps of the scene. They are equipped
with differentiable geometric operations, such as projection, unprojection,
egomotion estimation and stabilization, in order to compute a
geometrically-consistent mapping between the world scene and their 3D latent
feature state. We train the proposed architectures to predict novel camera
views given short frame sequences as input. Their predictions strongly
generalize to scenes with a novel number of objects, appearances and
configurations; they greatly outperform previous works that do not consider
egomotion stabilization or a space-aware latent feature state. We train the
proposed architectures to detect and segment objects in 3D using the latent 3D
feature map as input--as opposed to per frame features. The resulting object
detections persist over time: they continue to exist even when an object gets
occluded or leaves the field of view. Our experiments suggest the proposed
space-aware latent feature memory and egomotion-stabilized convolutions are
essential architectural choices for spatial common sense to emerge in
artificial embodied visual agents.
Do GANs leave artificial fingerprints?
Authors: Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, Giovanni Poggi
Date: 2018-12-31 00:00:00

In the last few years, generative adversarial networks (GAN) have shown
tremendous potential for a number of applications in computer vision and
related fields. With the current pace of progress, it is a sure bet they will
soon be able to generate high-quality images and videos, virtually
indistinguishable from real ones. Unfortunately, realistic GAN-generated images
pose serious threats to security, to begin with a possible flood of fake
multimedia, and multimedia forensic countermeasures are in urgent need. In this
work, we show that each GAN leaves its specific fingerprint in the images it
generates, just like real-world cameras mark acquired images with traces of
their photo-response non-uniformity pattern. Source identification experiments
with several popular GANs show such fingerprints to represent a precious asset
for forensic analyses.
Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning
Authors: Mattia G. Bergomi, Patrizio Frosini, Daniela Giorgi, Nicola Quercioli
Date: 2018-12-31 00:00:00
Tasks: BIG-bench Machine Learning, Metric Learning

The aim of this paper is to provide a general mathematical framework for
group equivariance in the machine learning context. The framework builds on a
synergy between persistent homology and the theory of group actions. We define
group-equivariant non-expansive operators (GENEOs), which are maps between
function spaces associated with groups of transformations. We study the
topological and metric properties of the space of GENEOs to evaluate their
approximating power and set the basis for general strategies to initialise and
compose operators. We begin by defining suitable pseudo-metrics for the
function spaces, the equivariance groups, and the set of non-expansive
operators. Basing on these pseudo-metrics, we prove that the space of GENEOs is
compact and convex, under the assumption that the function spaces are compact
and convex. These results provide fundamental guarantees in a machine learning
perspective. We show examples on the MNIST and fashion-MNIST datasets. By
considering isometry-equivariant non-expansive operators, we describe a simple
strategy to select and sample operators, and show how the selected and sampled
operators can be used to perform both classical metric learning and an
effective initialisation of the kernels of a convolutional neural network.
Types, Tokens, and Hapaxes: A New Heap's Law
Authors: Victor Davis
Date: 2018-12-31 00:00:00

Heap's Law states that in a large enough text corpus, the number of types as
a function of tokens grows as $N=KM^\beta$ for some free parameters $K,\beta$.
Much has been written about how this result and various generalizations can be
derived from Zipf's Law. Here we derive from first principles a completely
novel expression of the type-token curve and prove its superior accuracy on
real text. This expression naturally generalizes to equally accurate estimates
for counting hapaxes and higher $n$-legomena.
An introduction to domain adaptation and transfer learning
Authors: Wouter M. Kouw, Marco Loog
Date: 2018-12-31 00:00:00
Tasks: BIG-bench Machine Learning, Domain Adaptation, Transfer Learning

In machine learning, if the training data is an unbiased sample of an
underlying distribution, then the learned classification function will make
accurate predictions for new samples. However, if the training data is not an
unbiased sample, then there will be differences between how the training data
is distributed and how the test data is distributed. Standard classifiers
cannot cope with changes in data distributions between training and test
phases, and will not perform well. Domain adaptation and transfer learning are
sub-fields within machine learning that are concerned with accounting for these
types of changes. Here, we present an introduction to these fields, guided by
the question: when and how can a classifier generalize from a source to a
target domain? We will start with a brief introduction into risk minimization,
and how transfer learning and domain adaptation expand upon this framework.
Following that, we discuss three special cases of data set shift, namely prior,
covariate and concept shift. For more complex domain shifts, there are a wide
variety of approaches. These are categorized into: importance-weighting,
subspace mapping, domain-invariant spaces, feature augmentation, minimax
estimators and robust algorithms. A number of points will arise, which we will
discuss in the last section. We conclude with the remark that many open
questions will have to be addressed before transfer learners and
domain-adaptive classifiers become practical.
Regularized Binary Network Training
Authors: Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux, Vahid Partovi Nia
Date: 2018-12-31 00:00:00
Proceeding: ICLR 2019 5

There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs). We propose to improve the binary training method, by introducing a new regularization function that encourages training weights around binary values. In addition, we add trainable scaling factors to our regularization functions. Additionally, an improved approximation of the derivative of the sign activation function in the backward computation. These modifications are based on linear operations that are easily implementable into the binary training framework. Experimental results on ImageNet shows our method outperforms the traditional BNN method and XNOR-net.
Pixel personality for dense object tracking in a 2D honeybee hive
Authors: Katarzyna Bozek, Laetitia Hebert, Alexander S Mikheyev, Greg J. Stephens
Date: 2018-12-31 00:00:00
Tasks: Attribute, Multi-Object Tracking, Object, object-detection, Object Detection, Object Recognition, Object Tracking

Tracking large numbers of densely-arranged, interacting objects is
challenging due to occlusions and the resulting complexity of possible
trajectory combinations, as well as the sparsity of relevant, labeled datasets.
Here we describe a novel technique of collective tracking in the model
environment of a 2D honeybee hive in which sample colonies consist of
$N\sim10^3$ highly similar individuals, tightly packed, and in rapid, irregular
motion. Such a system offers universal challenges for multi-object tracking,
while being conveniently accessible for image recording. We first apply an
accurate, segmentation-based object detection method to build initial short
trajectory segments by matching object configurations based on class, position
and orientation. We then join these tracks into full single object trajectories
by creating an object recognition model which is adaptively trained to
recognize honeybee individuals through their visual appearance across multiple
frames, an attribute we denote as pixel personality. Overall, we reconstruct
~46% of the trajectories in 5 min recordings from two different hives and over
71% of the tracks for at least 2 min. We provide validated trajectories
spanning 3000 video frames of 876 unmarked moving bees in two distinct colonies
in different locations and filmed with different pixel resolutions, which we
expect to be useful in the further development of general-purpose tracking
solutions.
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
Authors: Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
Date: 2018-12-31 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Transfer Learning

Reinforcement learning (RL) algorithms have been around for decades and
employed to solve various sequential decision-making problems. These algorithms
however have faced great challenges when dealing with high-dimensional
environments. The recent development of deep learning has enabled RL methods to
drive optimal policies for sophisticated and capable agents, which can perform
efficiently in these challenging environments. This paper addresses an
important aspect of deep RL related to situations that require multiple agents
to communicate and cooperate to solve complex tasks. A survey of different
approaches to problems related to multi-agent deep RL (MADRL) is presented,
including non-stationarity, partial observability, continuous state and action
spaces, multi-agent training schemes, multi-agent transfer learning. The merits
and demerits of the reviewed methods will be analyzed and discussed, with their
corresponding applications explored. It is envisaged that this review provides
insights about various MADRL methods and can lead to future development of more
robust and highly useful multi-agent learning methods for solving real-world
problems.
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
Authors: Sida Peng, Yu-An Liu, Qi-Xing Huang, Hujun Bao, Xiaowei Zhou
Date: 2018-12-31 00:00:00
Proceeding: CVPR 2019 6
Tasks: 6D Pose Estimation using RGB, Pose Estimation

This paper addresses the challenge of 6DoF pose estimation from a single RGB
image under severe occlusion or truncation. Many recent works have shown that a
two-stage approach, which first detects keypoints and then solves a
Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable
performance. However, most of these methods only localize a set of sparse
keypoints by regressing their image coordinates or heatmaps, which are
sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise
Voting Network (PVNet) to regress pixel-wise unit vectors pointing to the
keypoints and use these vectors to vote for keypoint locations using RANSAC.
This creates a flexible representation for localizing occluded or truncated
keypoints. Another important feature of this representation is that it provides
uncertainties of keypoint locations that can be further leveraged by the PnP
solver. Experiments show that the proposed approach outperforms the state of
the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large
margin, while being efficient for real-time pose estimation. We further create
a Truncation LINEMOD dataset to validate the robustness of our approach against
truncation. The code will be avaliable at https://zju-3dv.github.io/pvnet/.
Weakly Supervised Active Learning with Cluster Annotation
Authors: Fábio Perez, Rémi Lebret, Karl Aberer
Date: 2018-12-31 00:00:00
Tasks: Active Learning

In this work, we introduce a novel framework that employs cluster annotation
to boost active learning by reducing the number of human interactions required
to train deep neural networks. Instead of annotating single samples
individually, humans can also label clusters, producing a higher number of
annotated samples with the cost of a small label error. Our experiments show
that the proposed framework requires 82% and 87% less human interactions for
CIFAR-10 and EuroSAT datasets respectively when compared with the
fully-supervised training while maintaining similar performance on the test
set.
Predicting Group Cohesiveness in Images
Authors: Shreya Ghosh, Abhinav Dhall, Nicu Sebe, Tom Gedeon
Date: 2018-12-31 00:00:00
Tasks: Attribute

The cohesiveness of a group is an essential indicator of the emotional state,
structure and success of a group of people. We study the factors that influence
the perception of group-level cohesion and propose methods for estimating the
human-perceived cohesion on the group cohesiveness scale. In order to identify
the visual cues (attributes) for cohesion, we conducted a user survey. Image
analysis is performed at a group-level via a multi-task convolutional neural
network. For analyzing the contribution of facial expressions of the group
members for predicting the Group Cohesion Score (GCS), a capsule network is
explored. We add GCS to the Group Affect database and propose the `GAF-Cohesion
database'. The proposed model performs well on the database and is able to
achieve near human-level performance in predicting a group's cohesion score. It
is interesting to note that group cohesion as an attribute, when jointly
trained for group-level emotion prediction, helps in increasing the performance
for the later task. This suggests that group-level emotion and cohesion are
correlated.
Multilingual Constituency Parsing with Self-Attention and Pre-Training
Authors: Nikita Kitaev, Steven Cao, Dan Klein
Date: 2018-12-31 00:00:00
Proceeding: ACL 2019 7
Tasks: Constituency Parsing, Unsupervised Pre-training

We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).
Approximate Inference for Multiplicative Latent Force Models
Authors: Daniel J. Tait, Bruce J. Worton
Date: 2018-12-31 00:00:00

Latent force models are a class of hybrid models for dynamic systems,
combining simple mechanistic models with flexible Gaussian process (GP)
perturbations. An extension of this framework to include multiplicative
interactions between the state and GP terms allows strong a priori control of
the model geometry at the expense of tractable inference. In this paper we
consider two methods of carrying out inference within this broader class of
models. The first is based on an adaptive gradient matching approximation, and
the second is constructed around mixtures of local approximations to the
solution. We compare the performance of both methods on simulated data, and
also demonstrate an application of the multiplicative latent force model on
motion capture data.
Federated Learning via Over-the-Air Computation
Authors: Kai Yang, Tao Jiang, Yuanming Shi, Zhi Ding
Date: 2018-12-31 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing, Federated Learning

The stringent requirements for low-latency and privacy of the emerging
high-stake applications with intelligent devices such as drones and smart
vehicles make the cloud computing inapplicable in these scenarios. Instead,
edge machine learning becomes increasingly attractive for performing training
and inference directly at network edges without sending data to a centralized
data center. This stimulates a nascent field termed as federated learning for
training a machine learning model on computation, storage, energy and bandwidth
limited mobile devices in a distributed manner. To preserve data privacy and
address the issues of unbalanced and non-IID data points across different
devices, the federated averaging algorithm has been proposed for global model
aggregation by computing the weighted average of locally updated model at each
selected device. However, the limited communication bandwidth becomes the main
bottleneck for aggregating the locally computed updates. We thus propose a
novel over-the-air computation based approach for fast global model aggregation
via exploring the superposition property of a wireless multiple-access channel.
This is achieved by joint device selection and beamforming design, which is
modeled as a sparse and low-rank optimization problem to support efficient
algorithms design. To achieve this goal, we provide a
difference-of-convex-functions (DC) representation for the sparse and low-rank
function to enhance sparsity and accurately detect the fixed-rank constraint in
the procedure of device selection. A DC algorithm is further developed to solve
the resulting DC program with global convergence guarantees. The algorithmic
advantages and admirable performance of the proposed methodologies are
demonstrated through extensive numerical results.
The meaning of "most" for visual question answering models
Authors: Alexander Kuhnle, Ann Copestake
Date: 2018-12-31 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms. For the example of "most", we discuss two strategies which rely on fundamentally different cognitive concepts. Our aim is to identify what strategy deep learning models for visual question answering learn when trained on such questions. To this end, we carefully design data to replicate experiments from psycholinguistics where the same question was investigated for humans. Focusing on the FiLM visual question answering model, our experiments indicate that a form of approximate number system emerges whose performance declines with more difficult scenes as predicted by Weber's law. Moreover, we identify confounding factors, like spatial arrangement of the scene, which impede the effectiveness of this system.
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
Authors: Charbel Sakr, Naresh Shanbhag
Date: 2018-12-31 00:00:00
Proceeding: ICLR 2019 5
Tasks: Quantization

The high computational and parameter complexity of neural networks makes
their training very slow and difficult to deploy on energy and
storage-constrained computing systems. Many network complexity reduction
techniques have been proposed including fixed-point implementation. However, a
systematic approach for designing full fixed-point training and inference of
deep neural networks remains elusive. We describe a precision assignment
methodology for neural network training in which all network parameters, i.e.,
activations and weights in the feedforward path, gradients and weight
accumulators in the feedback path, are assigned close to minimal precision. The
precision assignment is derived analytically and enables tracking the
convergence behavior of the full precision training, known to converge a
priori. Thus, our work leads to a systematic methodology of determining
suitable precision for fixed-point training. The near optimality (minimality)
of the resulting precision assignment is validated empirically for four
networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The complexity
reduction arising from our approach is compared with other fixed-point neural
network designs.
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
Authors: Yuriy Kochura, Yuri Gordienko, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko
Date: 2018-12-31 00:00:00
Tasks: GPU

The impact of the maximally possible batch size (for the better runtime) on
performance of graphic processing units (GPU) and tensor processing units (TPU)
during training and inference phases is investigated. The numerous runs of the
selected deep neural network (DNN) were performed on the standard MNIST and
Fashion-MNIST datasets. The significant speedup was obtained even for extremely
low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite
powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training
stage (without taking into account the overheads) and speedup up to 2x for
prediction stage (with and without taking into account overheads). The precise
speedup values depend on the utilization level of TPUv2 units and increase with
the increase of the data volume under processing, but for the datasets used in
this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was
observed for batch sizes >512 images for training phase and >40 000 images for
prediction phase. It should be noted that these results were obtained without
detriment to the prediction accuracy and loss that were equal for both GPU and
TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd
significant digit for Fashion-MNIST dataset.