pwc-archive/papers-with-abstracts huggingface dataset:
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Authors: Andrew Brock, Jeff Donahue, Karen Simonyan
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Conditional Image Generation, Image Generation, Vocal Bursts Intensity Prediction

Despite recent progress in generative image modeling, successfully generating
high-resolution, diverse samples from complex datasets such as ImageNet remains
an elusive goal. To this end, we train Generative Adversarial Networks at the
largest scale yet attempted, and study the instabilities specific to such
scale. We find that applying orthogonal regularization to the generator renders
it amenable to a simple "truncation trick," allowing fine control over the
trade-off between sample fidelity and variety by reducing the variance of the
Generator's input. Our modifications lead to models which set the new state of
the art in class-conditional image synthesis. When trained on ImageNet at
128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of
166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous
best IS of 52.52 and FID of 18.6.
A kernel-based approach to molecular conformation analysis
Authors: Stefan Klus, Andreas Bittracher, Ingmar Schuster, Christof Schütte
Date: 2018-09-28 00:00:00
Tasks: BIG-bench Machine Learning

We present a novel machine learning approach to understanding conformation
dynamics of biomolecules. The approach combines kernel-based techniques that
are popular in the machine learning community with transfer operator theory for
analyzing dynamical systems in order to identify conformation dynamics based on
molecular dynamics simulation data. We show that many of the prominent methods
like Markov State Models, EDMD, and TICA can be regarded as special cases of
this approach and that new efficient algorithms can be constructed based on
this derivation. The results of these new powerful methods will be illustrated
with several examples, in particular the alanine dipeptide and the protein
NTL9.
A Systems Approach to Achieving the Benefits of Artificial Intelligence in UK Defence
Authors: Gavin Pearson, Phil Jolley, Geraint Evans
Date: 2018-09-28 00:00:00

The ability to exploit the opportunities offered by AI within UK Defence
calls for an understanding of systemic issues required to achieve an effective
operational capability. This paper provides the authors' views of issues which
currently block UK Defence from fully benefitting from AI technology. These are
situated within a reference model for the AI Value Train, so enabling the
community to address the exploitation of such data and software intensive
systems in a systematic, end to end manner. The paper sets out the conditions
for success including: Researching future solutions to known problems and
clearly defined use cases; Addressing achievable use cases to show benefit;
Enhancing the availability of Defence-relevant data; Enhancing Defence 'know
how' in AI; Operating Software Intensive supply chain eco-systems at required
breadth and pace; Governance and, the integration of software and platform
supply chains and operating models.
Learning to Remember, Forget and Ignore using Attention Control in Memory
Authors: T. S. Jayram, Younes Bouhadjar, Ryan L. McAvoy, Tomasz Kornuta, Alexis Asseman, Kamil Rocki, Ahmet S. Ozcan
Date: 2018-09-28 00:00:00

Typical neural networks with external memory do not effectively separate
capacity for episodic and working memory as is required for reasoning in
humans. Applying knowledge gained from psychological studies, we designed a new
model called Differentiable Working Memory (DWM) in order to specifically
emulate human working memory. As it shows the same functional characteristics
as working memory, it robustly learns psychology inspired tasks and converges
faster than comparable state-of-the-art models. Moreover, the DWM model
successfully generalizes to sequences two orders of magnitude longer than the
ones used in training. Our in-depth analysis shows that the behavior of DWM is
interpretable and that it learns to have fine control over memory, allowing it
to retain, ignore or forget information based on its relevance.
Learning Recurrent Binary/Ternary Weights
Authors: Arash Ardakani, Zhengyun Ji, Sean C. Smithson, Brett H. Meyer, Warren J. Gross
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modeling, Language Modelling

Recurrent neural networks (RNNs) have shown excellent performance in
processing sequence data. However, they are both complex and memory intensive
due to their recursive nature. These limitations make RNNs difficult to embed
on mobile devices requiring real-time processes with limited hardware
resources. To address the above issues, we introduce a method that can learn
binary and ternary weights during the training phase to facilitate hardware
implementations of RNNs. As a result, using this approach replaces all
multiply-accumulate operations by simple accumulations, bringing significant
benefits to custom hardware in terms of silicon area and power consumption. On
the software side, we evaluate the performance (in terms of accuracy) of our
method using long short-term memories (LSTMs) on various sequential models
including sequence classification and language modeling. We demonstrate that
our method achieves competitive results on the aforementioned tasks while using
binary/ternary weights during the runtime. On the hardware side, we present
custom hardware for accelerating the recurrent computations of LSTMs with
binary/ternary weights. Ultimately, we show that LSTMs with binary/ternary
weights can achieve up to 12x memory saving and 10x inference speedup compared
to the full-precision implementation on an ASIC platform.
Reuse and Adaptation for Entity Resolution through Transfer Learning
Authors: Saravanan Thirumuruganathan, Shameem A Puthiya Parambath, Mourad Ouzzani, Nan Tang, Shafiq Joty
Date: 2018-09-28 00:00:00
Tasks: Data Integration, Entity Resolution, Feature Engineering, Transfer Learning

Entity resolution (ER) is one of the fundamental problems in data
integration, where machine learning (ML) based classifiers often provide the
state-of-the-art results. Considerable human effort goes into feature
engineering and training data creation. In this paper, we investigate a new
problem: Given a dataset D_T for ER with limited or no training data, is it
possible to train a good ML classifier on D_T by reusing and adapting the
training data of dataset D_S from same or related domain? Our major
contributions include (1) a distributed representation based approach to encode
each tuple from diverse datasets into a standard feature space; (2)
identification of common scenarios where the reuse of training data can be
beneficial; and (3) five algorithms for handling each of the aforementioned
scenarios. We have performed comprehensive experiments on 12 datasets from 5
different domains (publications, movies, songs, restaurants, and books). Our
experiments show that our algorithms provide significant benefits such as
providing superior performance for a fixed training data size.
Robot Representation and Reasoning with Knowledge from Reinforcement Learning
Authors: Keting Lu, Shiqi Zhang, Peter Stone, Xiaoping Chen
Date: 2018-09-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) agents aim at learning by interacting with an
environment, and are not designed for representing or reasoning with
declarative knowledge. Knowledge representation and reasoning (KRR) paradigms
are strong in declarative KRR tasks, but are ill-equipped to learn from such
experiences. In this work, we integrate logical-probabilistic KRR with
model-based RL, enabling agents to simultaneously reason with declarative
knowledge and learn from interaction experiences. The knowledge from humans and
RL is unified and used for dynamically computing task-specific planning models
under potentially new environments. Experiments were conducted using a mobile
robot working on dialog, navigation, and delivery tasks. Results show
significant improvements, in comparison to existing model-based RL methods.
Extrinsic camera calibration method and its performance evaluation
Authors: Jacek Komorowski, Przemyslaw Rokita
Date: 2018-09-28 00:00:00
Tasks: Camera Calibration, Translation

This paper presents a method for extrinsic camera calibration (estimation of
camera rotation and translation matrices) from a sequence of images. It is
assumed camera intrinsic matrix and distortion coefficients are known and fixed
during the entire sequence. %This allows to decrease a number of pairs of
corresponding keypoints between images needed to estimate epipolar geometry
compared to uncalibrated case. Performance of the presented method is evaluated
on a number of multi-view stereo test datasets. Presented algorithm can be used
as a first stage in a dense stereo reconstruction system.
Face Recognition Based on Sequence of Images
Authors: Jacek Komorowski, Przemyslaw Rokita
Date: 2018-09-28 00:00:00
Tasks: Face Model, Face Recognition

This paper presents a face recognition method based on a sequence of images.
Face shape is reconstructed from images using a combination of
structure-from-motion and multi-view stereo methods. The reconstructed 3D face
model is compared against models held in a gallery. The novel element in the
presented approach is the fact, that the reconstruction is based only on input
images and doesn't require a generic, deformable face model. Experimental
verification of the proposed method is also included.
Spoken Pass-Phrase Verification in the i-vector Space
Authors: Hossein Zeinali, Lukas Burget, Hossein Sameti, Jan Cernocky
Date: 2018-09-28 00:00:00
Tasks: Speaker Verification, Text-Dependent Speaker Verification

The task of spoken pass-phrase verification is to decide whether a test
utterance contains the same phrase as given enrollment utterances. Beside other
applications, pass-phrase verification can complement an independent speaker
verification subsystem in text-dependent speaker verification. It can also be
used for liveness detection by verifying that the user is able to correctly
respond to a randomly prompted phrase. In this paper, we build on our previous
work on i-vector based text-dependent speaker verification, where we have shown
that i-vectors extracted using phrase specific Hidden Markov Models (HMMs) or
using Deep Neural Network (DNN) based bottle-neck (BN) features help to reject
utterances with wrong pass-phrases. We apply the same i-vector extraction
techniques to the stand-alone task of speaker-independent spoken pass-phrase
classification and verification. The experiments on RSR2015 and RedDots
databases show that very simple scoring techniques (e.g. cosine distance
scoring) applied to such i-vectors can provide results superior to those
previously published on the same data.
Camera Pose Estimation from Sequence of Calibrated Images
Authors: Jacek Komorowski, Przemyslaw Rokita
Date: 2018-09-28 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

In this paper a method for camera pose estimation from a sequence of images
is presented. The method assumes camera is calibrated (intrinsic parameters are
known) which allows to decrease a number of required pairs of corresponding
points compared to uncalibrated case. Our algorithm can be used as a first
stage in a structure from motion stereo reconstruction system.
Aggregation of binary feature descriptors for compact scene model representation in large scale structure-from-motion applications
Authors: Jacek Komorowski, Tomasz Trzcinski
Date: 2018-09-28 00:00:00

In this paper we present an efficient method for aggregating binary feature
descriptors to allow compact representation of 3D scene model in incremental
structure-from-motion and SLAM applications. All feature descriptors linked
with one 3D scene point or landmark are represented by a single low-dimensional
real-valued vector called a \emph{prototype}. The method allows significant
reduction of memory required to store and process feature descriptors in
large-scale structure-from-motion applications. An efficient approximate
nearest neighbours search methods suited for real-valued descriptors, such as
FLANN, can be used on the resulting prototypes to speed up matching processed
frames.
SConE: Siamese Constellation Embedding Descriptor for Image Matching
Authors: Tomasz Trzcinski, Jacek Komorowski, Lukasz Dabala, Konrad Czarnota, Grzegorz Kurzejamski, Simon Lynen
Date: 2018-09-28 00:00:00

Numerous computer vision applications rely on local feature descriptors, such
as SIFT, SURF or FREAK, for image matching. Although their local character
makes image matching processes more robust to occlusions, it often leads to
geometrically inconsistent keypoint matches that need to be filtered out, e.g.
using RANSAC. In this paper we propose a novel, more discriminative, descriptor
that includes not only local feature representation, but also information about
the geometric layout of neighbouring keypoints. To that end, we use a Siamese
architecture that learns a low-dimensional feature embedding of keypoint
constellation by maximizing the distances between non-corresponding pairs of
matched image patches, while minimizing it for correct matches. The
48-dimensional oating point descriptor that we train is built on top of the
state-of-the-art FREAK descriptor achieves significant performance improvement
over the competitors on a challenging TUM dataset.
Cross-situational learning of large lexicons with finite memory
Authors: James Holehouse, Richard A. Blythe
Date: 2018-09-28 00:00:00
Tasks: Two-sample testing

Cross-situational word learning, wherein a learner combines information about
possible meanings of a word across multiple exposures, has previously been
shown to be a very powerful strategy to acquire a large lexicon in a short
time. However, this success may derive from idealizations that are made when
modeling the word-learning process. In particular, an earlier model assumed
that a learner could perfectly recall all previous instances of a word's use
and the inferences that were drawn about its meaning. In this work, we relax
this assumption and determine the performance of a model cross-situational
learner who forgets word-meaning associations over time. Our main finding is
that it is possible for this learner to acquire a human-scale lexicon by
adulthood with word-exposure and memory-decay rates that are consistent with
empirical research on childhood word learning, as long as the degree of
referential uncertainty is not too high or the learner employs a mutual
exclusivity constraint. Our findings therefore suggest that successful word
learning does not necessarily demand either highly accurate long-term tracking
of word and meaning statistics or hypothesis-testing strategies.
A Symmetric Keyring Encryption Scheme for Biometric Cryptosystems
Authors: Yen-Lung Lai, Jung-Yeon Hwang, Zhe Jin, Soohyong Kim, Sangrae Cho, Andrew Beng Jin Teoh
Date: 2018-09-28 00:00:00

In this paper, we propose a novel biometric cryptosystem for vectorial
biometrics named symmetric keyring encryption (SKE) inspired by Rivest's
keyring model (2016). Unlike conventional biometric secret-binding primitives,
such as fuzzy commitment and fuzzy vault, the proposed scheme reframes the
biometric secret-binding problem as a fuzzy symmetric encryption problem with a
notion called resilient vector pair. In this study, the pair resembles the
encryption-decryption key pair in symmetric key cryptosystems. This notion is
realized using the index of maximum hashed vectors - a special instance of the
ranking-based locality-sensitive hashing function. With a simple filtering
mechanism and [m,k] Shamir's secret-sharing scheme, we show that SKE, both in
theoretical and empirical evaluation, can retrieve the exact secret with
overwhelming probability for a genuine input yet negligible probability for an
imposter input. Though SKE can be applied to any vectorial biometrics, we adopt
the fingerprint vector as a case of study in this work. The experiments have
been performed under several subsets of FVC 2002, 2004, and 2006 datasets. We
formalize and analyze the threat model of SKE that encloses several major
security attacks.
Relational Forward Models for Multi-Agent Learning
Authors: Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5

The behavioral dynamics of multi-agent systems have a rich and orderly
structure, which can be leveraged to understand these systems, and to improve
how artificial agents learn to operate in them. Here we introduce Relational
Forward Models (RFM) for multi-agent learning, networks that can learn to make
accurate predictions of agents' future behavior in multi-agent environments.
Because these models operate on the discrete entities and relations present in
the environment, they produce interpretable intermediate representations which
offer insights into what drives agents' behavior, and what events mediate the
intensity and valence of social interactions. Furthermore, we show that
embedding RFM modules inside agents results in faster learning systems compared
to non-augmented baselines. As more and more of the autonomous systems we
develop and interact with become multi-agent in nature, developing richer
analysis tools for characterizing how and why agents make decisions is
increasingly necessary. Moreover, developing artificial agents that quickly and
safely learn to coordinate with one another, and with humans in shared
environments, is crucial.
Interest point detectors stability evaluation on ApolloScape dataset
Authors: Jacek Komorowski, Konrad Czarnota, Tomasz Trzcinski, Lukasz Dabala, Simon Lynen
Date: 2018-09-28 00:00:00
Tasks: Autonomous Driving, Deep Learning

In the recent years, a number of novel, deep-learning based, interest point
detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However
there's a lack of a standard benchmark to evaluate suitability of these novel
keypoint detectors for real-live applications such as autonomous driving.
Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of
relatively few images of mostly planar scenes taken in favourable conditions.
In this paper we verify if the recent, deep-learning based interest point
detectors have the advantage over the traditional, hand-crafted keypoint
detectors. To this end, we evaluate stability of a number of hand crafted and
recent, learning-based interest point detectors on the street-level view
ApolloScape dataset.
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
Authors: B Ravi Kiran, Luis Roldão, Benat Irastorza, Renzo Verastegui, Sebastian Suss, Senthil Yogamani, Victor Talpaert, Alexandre Lepoutre, Guillaume Trehard
Date: 2018-09-28 00:00:00
Tasks: Autonomous Driving, Clustering, Depth Estimation, object-detection, Object Detection

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.
Efficient Linear Bandits through Matrix Sketching
Authors: Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi
Date: 2018-09-28 00:00:00
Tasks: Thompson Sampling

We prove that two popular linear contextual bandit algorithms, OFUL and Thompson Sampling, can be made efficient using Frequent Directions, a deterministic online sketching technique. More precisely, we show that a sketch of size $m$ allows a $\mathcal{O}(md)$ update time for both algorithms, as opposed to $\Omega(d^2)$ required by their non-sketched versions in general (where $d$ is the dimension of context vectors). This computational speedup is accompanied by regret bounds of order $(1+\varepsilon_m)^{3/2}d\sqrt{T}$ for OFUL and of order $\big((1+\varepsilon_m)d\big)^{3/2}\sqrt{T}$ for Thompson Sampling, where $\varepsilon_m$ is bounded by the sum of the tail eigenvalues not covered by the sketch. In particular, when the selected contexts span a subspace of dimension at most $m$, our algorithms have a regret bound matching that of their slower, non-sketched counterparts. Experiments on real-world datasets corroborate our theoretical results.
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Authors: Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama
Date: 2018-09-28 00:00:00
Proceeding: ICML 2020 1
Tasks: Learning with noisy labels, Memorization

Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels, many learning methods in this area still suffer overfitting due to undesired memorization. In this paper, to relieve this issue, we propose stochastic integrated gradient underweighted ascent (SIGUA): in a mini-batch, we adopt gradient descent on good data as usual, and learning-rate-reduced gradient ascent on bad data; the proposal is a versatile approach where data goodness or badness is w.r.t. desired or undesired memorization given a base learning method. Technically, SIGUA pulls optimization back for generalization when their goals conflict with each other; philosophically, SIGUA shows forgetting undesired memorization can reinforce desired memorization. Experiments demonstrate that SIGUA successfully robustifies two typical base learning methods, so that their performance is often significantly improved.
Cost-Sensitive Learning for Predictive Maintenance
Authors: Stephan Spiegel, Fabian Mueller, Dorothea Weismann, John Bird
Date: 2018-09-28 00:00:00
Tasks: Model Selection

In predictive maintenance, model performance is usually assessed by means of
precision, recall, and F1-score. However, employing the model with best
performance, e.g. highest F1-score, does not necessarily result in minimum
maintenance cost, but can instead lead to additional expenses. Thus, we propose
to perform model selection based on the economic costs associated with the
particular maintenance application. We show that cost-sensitive learning for
predictive maintenance can result in significant cost reduction and fault
tolerant policies, since it allows to incorporate various business constraints
and requirements.
CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge
Authors: Rémi Delassus, Romain Giot
Date: 2018-09-28 00:00:00
Tasks: Segmentation

This paper presents our contribution to the DeepGlobe Building Detection
Challenge. We enhanced the SpaceNet Challenge winning solution by proposing a
new fusion strategy based on a deep combiner using segmentation both results of
different CNN and input data to segment. Segmentation results for all cities
have been significantly improved (between 1% improvement over the baseline for
the smallest one to more than 7% for the largest one). The separation of
adjacent buildings should be the next enhancement made to the solution.