pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
Authors: Onur Tasar, Yuliya Tarabalka, Pierre Alliez
Date: 2018-10-29 00:00:00
Tasks: Incremental Learning, Semantic Segmentation

In spite of remarkable success of the convolutional neural networks on
semantic segmentation, they suffer from catastrophic forgetting: a significant
performance drop for the already learned classes when new classes are added on
the data, having no annotations for the old classes. We propose an incremental
learning methodology, enabling to learn segmenting new classes without
hindering dense labeling abilities for the previous classes, although the
entire previous data are not accessible. The key points of the proposed
approach are adapting the network to learn new as well as old classes on the
new training data, and allowing it to remember the previously learned
information for the old classes. For adaptation, we keep a frozen copy of the
previously trained network, which is used as a memory for the updated network
in absence of annotations for the former classes. The updated network minimizes
a loss function, which balances the discrepancy between outputs for the
previous classes from the memory and updated networks, and the
mis-classification rate between outputs for the new classes from the updated
network and the new ground-truth. For remembering, we either regularly feed
samples from the stored, little fraction of the previous data or use the memory
network, depending on whether the new data are collected from completely
different geographic areas or from the same city. Our experimental results
prove that it is possible to add new classes to the network, while maintaining
its performance for the previous classes, despite the whole previous training
data are not available.
Geometric Median Shapes
Authors: Alexandre Cunha
Date: 2018-10-29 00:00:00

We present an algorithm to compute the geometric median of shapes which is
based on the extension of median to high dimensions. The median finding problem
is formulated as an optimization over distances and it is solved directly using
the watershed method as an optimizer. We show that computing the geometric
median of shapes is robust in the presence of outliers and it is superior to
the mean shape which can easily be affected by the presence of outliers. The
geometric median shape thus faithfully represents the true central tendency of
the data, contaminated or not. Our approach can be applied to manifold and non
manifold shapes, with connected or disconnected shapes. The application of
distance transforms and watershed algorithm, two well established constructs of
image processing, lead to an algorithm that can be quickly implemented to
generate fast solutions with linear storage requirements. We demonstrate our
methods in synthetic and natural shapes and compare median and mean results
under increasing contamination by strong outliers.
Learning Better Internal Structure of Words for Sequence Labeling
Authors: Yingwei Xin, Ethan Hart, Vibhuti Mahajan, Jean-David Ruvini
Date: 2018-10-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentence

Character-based neural models have recently proven very useful for many NLP
tasks. However, there is a gap of sophistication between methods for learning
representations of sentences and words. While most character models for
learning representations of sentences are deep and complex, models for learning
representations of words are shallow and simple. Also, in spite of considerable
research on learning character embeddings, it is still not clear which kind of
architecture is the best for capturing character-to-word representations. To
address these questions, we first investigate the gaps between methods for
learning word and sentence representations. We conduct detailed experiments and
comparisons of different state-of-the-art convolutional models, and also
investigate the advantages and disadvantages of their constituents.
Furthermore, we propose IntNet, a funnel-shaped wide convolutional neural
architecture with no down-sampling for learning representations of the internal
structure of words by composing their characters from limited, supervised
training corpora. We evaluate our proposed model on six sequence labeling
datasets, including named entity recognition, part-of-speech tagging, and
syntactic chunking. Our in-depth analysis shows that IntNet significantly
outperforms other character embedding models and obtains new state-of-the-art
performance without relying on any external knowledge or resources.
TallyQA: Answering Complex Counting Questions
Authors: Manoj Acharya, Kushal Kafle, Christopher Kanan
Date: 2018-10-29 00:00:00
Tasks: Attribute, Object Counting, object-detection, Object Detection, Question Answering, Relation, Visual Question Answering, Visual Question Answering (VQA)

Most counting questions in visual question answering (VQA) datasets are
simple and require no more than object detection. Here, we study algorithms for
complex counting questions that involve relationships between objects,
attribute identification, reasoning, and more. To do this, we created TallyQA,
the world's largest dataset for open-ended counting. We propose a new algorithm
for counting that uses relation networks with region proposals. Our method lets
relation networks be efficiently used with high-resolution imagery. It yields
state-of-the-art results compared to baseline and recent systems on both
TallyQA and the HowMany-QA benchmark.
Prior-preconditioned conjugate gradient method for accelerated Gibbs sampling in "large $n$ & large $p$" Bayesian sparse regression
Authors: Akihiko Nishimura, Marc A. Suchard
Date: 2018-10-29 00:00:00

In a modern observational study based on healthcare databases, the number of observations and of predictors typically range in the order of $10^5$ ~ $10^6$ and of $10^4$ ~ $10^5$. Despite the large sample size, data rarely provide sufficient information to reliably estimate such a large number of parameters. Sparse regression techniques provide potential solutions, one notable approach being the Bayesian methods based on shrinkage priors. In the "large n & large p" setting, however, posterior computation encounters a major bottleneck at repeated sampling from a high-dimensional Gaussian distribution, whose precision matrix $\Phi$ is expensive to compute and factorize. In this article, we present a novel algorithm to speed up this bottleneck based on the following observation: we can cheaply generate a random vector $b$ such that the solution to the linear system $\Phi \beta = b$ has the desired Gaussian distribution. We can then solve the linear system by the conjugate gradient (CG) algorithm through matrix-vector multiplications by $\Phi$; this involves no explicit factorization or calculation of $\Phi$ itself. Rapid convergence of CG in this context is guaranteed by the theory of prior-preconditioning we develop. We apply our algorithm to a clinically relevant large-scale observational study with n = 72,489 patients and p = 22,175 clinical covariates, designed to assess the relative risk of adverse events from two alternative blood anti-coagulants. Our algorithm demonstrates an order of magnitude speed-up in posterior inference, in our case cutting the computation time from two weeks to less than a day.
Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networks
Authors: Shibo Zhou, Wei Wang
Date: 2018-10-29 00:00:00
Tasks: Autonomous Driving, Benchmarking, object-detection, Object Detection, Object Recognition

Neural networks has been successfully used in the processing of Lidar data,
especially in the scenario of autonomous driving. However, existing methods
heavily rely on pre-processing of the pulse signals derived from Lidar sensors
and therefore result in high computational overhead and considerable latency.
In this paper, we proposed an approach utilizing Spiking Neural Network (SNN)
to address the object recognition problem directly with raw temporal pulses. To
help with the evaluation and benchmarking, a comprehensive temporal pulses
data-set was created to simulate Lidar reflection in different road scenarios.
Being tested with regard to recognition accuracy and time efficiency under
different noise conditions, our proposed method shows remarkable performance
with the inference accuracy up to 99.83% (with 10% noise) and the average
recognition delay as low as 265 ns. It highlights the potential of SNN in
autonomous driving and some related applications. In particular, to our best
knowledge, this is the first attempt to use SNN to directly perform object
recognition on raw Lidar temporal pulses.
Concealing the identity of faces in oblique images with adaptive hopping Gaussian mixtures
Authors: Omair Sarwar, Bernhard Rinner, Andrea Cavallaro
Date: 2018-10-29 00:00:00
Tasks: Face Recognition, parameter estimation

Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for
recreational photography. However, aerial photographs of public places often
contain faces of bystanders thus leading to a perceived or actual violation of
privacy. To address this issue, we propose to pseudo-randomly modify the
appearance of face regions in the images using a privacy filter that prevents a
human or a face recogniser from inferring the identities of people. The filter,
which is applied only when the resolution is high enough for a face to be
recognisable, adaptively distorts the face appearance as a function of its
resolution. Moreover, the proposed filter locally changes its parameters to
discourage attacks that use parameter estimation. The filter exploits both
global adaptiveness to reduce distortion and local hopping of the parameters to
make their estimation difficult for an attacker. In order to evaluate the
efficiency of the proposed approach, we use a state-of-the-art face recognition
algorithm and synthetically generated face data with 3D geometric image
transformations that mimic faces captured from an MAV at different heights and
pitch angles. Experimental results show that the proposed filter protects
privacy while reducing distortion and exhibits resilience against attacks.
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Authors: Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12

We consider the off-policy estimation problem of estimating the expected
reward of a target policy using samples collected by a different behavior
policy. Importance sampling (IS) has been a key technique to derive (nearly)
unbiased estimators, but is known to suffer from an excessively high variance
in long-horizon problems. In the extreme case of in infinite-horizon problems,
the variance of an IS-based estimator may even be unbounded. In this paper, we
propose a new off-policy estimation method that applies IS directly on the
stationary state-visitation distributions to avoid the exploding variance issue
faced by existing estimators.Our key contribution is a novel approach to
estimating the density ratio of two stationary distributions, with trajectories
sampled from only the behavior distribution. We develop a mini-max loss
function for the estimation problem, and derive a closed-form solution for the
case of RKHS. We support our method with both theoretical and empirical
analyses.
Parallel Attention Mechanisms in Neural Machine Translation
Authors: Julian Richard Medina, Jugal Kalita
Date: 2018-10-29 00:00:00
Tasks: Decoder, Machine Translation, Translation

Recent papers in neural machine translation have proposed the strict use of
attention mechanisms over previous standards such as recurrent and
convolutional neural networks (RNNs and CNNs). We propose that by running
traditionally stacked encoding branches from encoder-decoder attention- focused
architectures in parallel, that even more sequential operations can be removed
from the model and thereby decrease training time. In particular, we modify the
recently published attention-based architecture called Transformer by Google,
by replacing sequential attention modules with parallel ones, reducing the
amount of training time and substantially improving BLEU scores at the same
time. Experiments over the English to German and English to French translation
tasks show that our model establishes a new state of the art.
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Authors: Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar
Date: 2018-10-29 00:00:00
Tasks: Decision Making, Multi-Armed Bandits, Sequential Decision Making

Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications, a "reneging" phenomenon, where participants may disengage from future interactions after observing an unsatisfiable outcome, is rather prevalent. To address the above issue, this paper proposes a model of heteroscedastic linear bandits with reneging, which allows each participant to have a distinct "satisfaction level," with any interaction outcome falling short of that level resulting in that participant reneging. Moreover, it allows the variance of the outcome to be context-dependent. Based on this model, we develop a UCB-type policy, namely HR-UCB, and prove that it achieves $\mathcal{O}\big(\sqrt{{T}(\log({T}))^{3}}\big)$ regret. Finally, we validate the performance of HR-UCB via simulations.
Eigenvalue analogy for confidence estimation in item-based recommender systems
Authors: Maurizio Ferrari Dacrema, Paolo Cremonesi
Date: 2018-08-31 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Item-item collaborative filtering (CF) models are a well known and studied
family of recommender systems, however current literature does not provide any
theoretical explanation of the conditions under which item-based
recommendations will succeed or fail.
  We investigate the existence of an ideal item-based CF method able to make
perfect recommendations. This CF model is formalized as an eigenvalue problem,
where estimated ratings are equivalent to the true (unknown) ratings multiplied
by a user-specific eigenvalue of the similarity matrix. Preliminary experiments
show that the magnitude of the eigenvalue is proportional to the accuracy of
recommendations for that user and therefore it can provide reliable measure of
confidence.
Counting in Language with RNNs
Authors: Heng xin Fun, Sergiy V Bokhnyak, Francesco Saverio Zuppichini
Date: 2018-10-29 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In this paper we examine a possible reason for the LSTM outperforming the GRU
on language modeling and more specifically machine translation. We hypothesize
that this has to do with counting. This is a consistent theme across the
literature of long term dependence, counting, and language modeling for RNNs.
Using the simplified forms of language -- Context-Free and Context-Sensitive
Languages -- we show how exactly the LSTM performs its counting based on their
cell states during inference and why the GRU cannot perform as well.
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Authors: Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh
Date: 2018-10-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Clustering, Machine Translation, Prediction, Translation

Neural language models have been widely used in various NLP tasks, including
machine translation, next word prediction and conversational agents. However,
it is challenging to deploy these models on mobile devices due to their slow
prediction speed, where the bottleneck is to compute top candidates in the
softmax layer. In this paper, we introduce a novel softmax layer approximation
algorithm by exploiting the clustering structure of context vectors. Our
algorithm uses a light-weight screening model to predict a much smaller set of
candidate words based on the given context, and then conducts an exact softmax
only within that subset. Training such a procedure end-to-end is challenging as
traditional clustering methods are discrete and non-differentiable, and thus
unable to be used with back-propagation in the training process. Using the
Gumbel softmax, we are able to train the screening model end-to-end on the
training set to exploit data distribution. The algorithm achieves an order of
magnitude faster inference than the original softmax layer for predicting
top-$k$ words in various tasks such as beam search in machine translation or
next words prediction. For example, for machine translation task on German to
English dataset with around 25K vocabulary, we can achieve 20.4 times speed up
with 98.9\% precision@1 and 99.3\% precision@5 with the original softmax layer
prediction, while state-of-the-art ~\citep{MSRprediction} only achieves 6.7x
speedup with 98.7\% precision@1 and 98.1\% precision@5 for the same task.
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
Authors: Rachad Atat, Lingjia Liu, Jinsong Wu, Guangyu Li, Chunxuan Ye, Yang Yi
Date: 2018-10-29 00:00:00
Tasks: Survey

The world is witnessing an unprecedented growth of cyber-physical systems
(CPS), which are foreseen to revolutionize our world {via} creating new
services and applications in a variety of sectors such as environmental
monitoring, mobile-health systems, intelligent transportation systems and so
on. The {information and communication technology }(ICT) sector is experiencing
a significant growth in { data} traffic, driven by the widespread usage of
smartphones, tablets and video streaming, along with the significant growth of
sensors deployments that are anticipated in the near future. {It} is expected
to outstandingly increase the growth rate of raw sensed data. In this paper, we
present the CPS taxonomy {via} providing a broad overview of data collection,
storage, access, processing and analysis. Compared with other survey papers,
this is the first panoramic survey on big data for CPS, where our objective is
to provide a panoramic summary of different CPS aspects. Furthermore, CPS
{require} cybersecurity to protect {them} against malicious attacks and
unauthorized intrusion, which {become} a challenge with the enormous amount of
data that is continuously being generated in the network. {Thus, we also}
provide an overview of the different security solutions proposed for CPS big
data storage, access and analytics. We also discuss big data meeting green
challenges in the contexts of CPS.
Language Modeling with Sparse Product of Sememe Experts
Authors: Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin
Date: 2018-10-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling

Most language modeling methods rely on large-scale data to statistically
learn the sequential patterns of words. In this paper, we argue that words are
atomic language units but not necessarily atomic semantic units. Inspired by
HowNet, we use sememes, the minimum semantic units in human languages, to
represent the implicit semantics behind words for language modeling, named
Sememe-Driven Language Model (SDLM). More specifically, to predict the next
word, SDLM first estimates the sememe distribution gave textual context.
Afterward, it regards each sememe as a distinct semantic expert, and these
experts jointly identify the most probable senses and the corresponding word.
In this way, SDLM enables language models to work beyond word-level
manipulation to fine-grained sememe-level semantics and offers us more powerful
tools to fine-tune language models and improve the interpretability as well as
the robustness of language models. Experiments on language modeling and the
downstream application of headline gener- ation demonstrate the significant
effect of SDLM. Source code and data used in the experiments can be accessed at
https:// github.com/thunlp/SDLM-pytorch.
Conditionals in Homomorphic Encryption and Machine Learning Applications
Authors: Diego Chialva, Ann Dooms
Date: 2018-10-29 00:00:00
Tasks: BIG-bench Machine Learning

Homomorphic encryption aims at allowing computations on encrypted data without decryption other than that of the final result. This could provide an elegant solution to the issue of privacy preservation in data-based applications, such as those using machine learning, but several open issues hamper this plan. In this work we assess the possibility for homomorphic encryption to fully implement its program without relying on other techniques, such as multiparty computation (SMPC), which may be impossible in many use cases (for instance due to the high level of communication required). We proceed in two steps: i) on the basis of the structured program theorem (Bohm-Jacopini theorem) we identify the relevant minimal set of operations homomorphic encryption must be able to perform to implement any algorithm; and ii) we analyse the possibility to solve -- and propose an implementation for -- the most fundamentally relevant issue as it emerges from our analysis, that is, the implementation of conditionals (requiring comparison and selection/jump operations). We show how this issue clashes with the fundamental requirements of homomorphic encryption and could represent a drawback for its use as a complete solution for privacy preservation in data-based applications, in particular machine learning ones. Our approach for comparisons is novel and entirely embedded in homomorphic encryption, while previous studies relied on other techniques, such as SMPC, demanding high level of communication among parties, and decryption of intermediate results from data-owners. Our protocol is also provably safe (sharing the same safety as the homomorphic encryption schemes), differently from other techniques such as Order-Preserving/Revealing-Encryption (OPE/ORE).
Learning and Inference in Hilbert Space with Quantum Graphical Models
Authors: Siddarth Srinivasan, Carlton Downey, Byron Boots
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference

Quantum Graphical Models (QGMs) generalize classical graphical models by
adopting the formalism for reasoning about uncertainty from quantum mechanics.
Unlike classical graphical models, QGMs represent uncertainty with density
matrices in complex Hilbert spaces. Hilbert space embeddings (HSEs) also
generalize Bayesian inference in Hilbert spaces. We investigate the link
between QGMs and HSEs and show that the sum rule and Bayes rule for QGMs are
equivalent to the kernel sum rule in HSEs and a special case of Nadaraya-Watson
kernel regression, respectively. We show that these operations can be
kernelized, and use these insights to propose a Hilbert Space Embedding of
Hidden Quantum Markov Models (HSE-HQMM) to model dynamics. We present
experimental results showing that HSE-HQMMs are competitive with
state-of-the-art models like LSTMs and PSRNNs on several datasets, while also
providing a nonparametric method for maintaining a probability distribution
over continuous-valued features.