pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
Authors: Kaiyang Zhou, Yu Qiao, Tao Xiang
Date: 2017-12-29 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Supervised Video Summarization, Unsupervised Video Summarization, Video Summarization

Video summarization aims to facilitate large-scale video browsing by
producing short, concise summaries that are diverse and representative of
original videos. In this paper, we formulate video summarization as a
sequential decision-making process and develop a deep summarization network
(DSN) to summarize videos. DSN predicts for each video frame a probability,
which indicates how likely a frame is selected, and then takes actions based on
the probability distributions to select frames, forming video summaries. To
train our DSN, we propose an end-to-end, reinforcement learning-based
framework, where we design a novel reward function that jointly accounts for
diversity and representativeness of generated summaries and does not rely on
labels or user interactions at all. During training, the reward function judges
how diverse and representative the generated summaries are, while DSN strives
for earning higher rewards by learning to produce more diverse and more
representative summaries. Since labels are not required, our method can be
fully unsupervised. Extensive experiments on two benchmark datasets show that
our unsupervised method not only outperforms other state-of-the-art
unsupervised methods, but also is comparable to or even superior than most of
published supervised approaches.
Quantum machine learning: a classical perspective
Authors: Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig
Date: 2017-07-26 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, Reinforcement Learning

Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.
Bidirectional deep-readout echo state networks
Authors: Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen
Date: 2017-11-17 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Memorization, Time Series, Time Series Analysis

We propose a deep architecture for the classification of multivariate time
series. By means of a recurrent and untrained reservoir we generate a vectorial
representation that embeds temporal relationships in the data. To improve the
memorization capability, we implement a bidirectional reservoir, whose last
state captures also past dependencies in the input. We apply dimensionality
reduction to the final reservoir states to obtain compressed fixed size
representations of the time series. These are subsequently fed into a deep
feedforward network trained to perform the final classification. We test our
architecture on benchmark datasets and on a real-world use-case of blood
samples classification. Results show that our method performs better than a
standard echo state network and, at the same time, achieves results comparable
to a fully-trained recurrent network, but with a faster training.
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
Authors: Mengying Sun, Fengyi Tang, Jin-Feng Yi, Fei Wang, Jiayu Zhou
Date: 2018-02-13 00:00:00

The surging availability of electronic medical records (EHR) leads to
increased research interests in medical predictive modeling. Recently many deep
learning based predicted models are also developed for EHR data and
demonstrated impressive performance. However, a series of recent studies showed
that these deep models are not safe: they suffer from certain vulnerabilities.
In short, a well-trained deep network can be extremely sensitive to inputs with
negligible changes. These inputs are referred to as adversarial examples. In
the context of medical informatics, such attacks could alter the result of a
high performance deep predictive model by slightly perturbing a patient's
medical records. Such instability not only reflects the weakness of deep
architectures, more importantly, it offers guide on detecting susceptible parts
on the inputs. In this paper, we propose an efficient and effective framework
that learns a time-preferential minimum attack targeting the LSTM model with
EHR inputs, and we leverage this attack strategy to screen medical records of
patients and identify susceptible events and measurements. The efficient
screening procedure can assist decision makers to pay extra attentions to the
locations that can cause severe consequence if not measured correctly. We
conduct extensive empirical studies on a real-world urgent care cohort and
demonstrate the effectiveness of the proposed screening approach.
Story Generation and Aviation Incident Representation
Authors: Peter Clark
Date: 2018-02-13 00:00:00
Tasks: Story Generation

This working note discusses the topic of story generation, with a view to
identifying the knowledge required to understand aviation incident narratives
(which have structural similarities to stories), following the premise that to
understand aviation incidents, one should at least be able to generate examples
of them. We give a brief overview of aviation incidents and their relation to
stories, and then describe two of our earlier attempts (using `scripts' and
`story grammars') at incident generation which did not evolve promisingly.
Following this, we describe a simple incident generator which did work (at a
`toy' level), using a `world simulation' approach. This generator is based on
Meehan's TALE-SPIN story generator (1977). We conclude with a critique of the
approach.
Stochastic Variance-Reduced Cubic Regularized Newton Method
Authors: Dongruo Zhou, Pan Xu, Quanquan Gu
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7

We propose a stochastic variance-reduced cubic regularized Newton method for
non-convex optimization. At the core of our algorithm is a novel
semi-stochastic gradient along with a semi-stochastic Hessian, which are
specifically designed for cubic regularization method. We show that our
algorithm is guaranteed to converge to an
$(\epsilon,\sqrt{\epsilon})$-approximately local minimum within
$\tilde{O}(n^{4/5}/\epsilon^{3/2})$ second-order oracle calls, which
outperforms the state-of-the-art cubic regularization algorithms including
subsampled cubic regularization. Our work also sheds light on the application
of variance reduction technique to high-order non-convex optimization methods.
Thorough experiments on various non-convex optimization problems support our
theory.
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Authors: Difan Zou, Pan Xu, Quanquan Gu
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Stochastic Optimization

We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling from a smooth and strongly log-concave distribution. At the core of our proposed method is a variance reduction technique inspired by the recent advance in stochastic optimization. We show that, to achieve $\epsilon$ accuracy in 2-Wasserstein distance, our algorithm achieves $\tilde O(n+\kappa^{2}d^{1/2}/\epsilon+\kappa^{4/3}d^{1/3}n^{2/3}/\epsilon^{2/3})$ gradient complexity (i.e., number of component gradient evaluations), which outperforms the state-of-the-art HMC and stochastic gradient HMC methods in a wide regime. We also extend our algorithm for sampling from smooth and general log-concave distributions, and prove the corresponding gradient complexity as well. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.
Exploring the Landscape of Spatial Robustness
Authors: Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry
Date: 2017-12-07 00:00:00
Tasks: Adversarial Robustness, Data Augmentation

The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural network--based classifiers to rotations and translations. While data augmentation offers relatively small robustness, we use ideas from robust optimization and test-time input aggregation to significantly improve robustness. Finally we find that, in contrast to the p-norm case, first-order methods cannot reliably find worst-case perturbations. This highlights spatial robustness as a fundamentally different setting requiring additional study. Code available at https://github.com/MadryLab/adversarial_spatial and https://github.com/MadryLab/spatial-pytorch.
Improving Quadrature for Constrained Integrands
Authors: Henry Chai, Roman Garnett
Date: 2018-02-13 00:00:00
Tasks: Bayesian Inference

We present an improved Bayesian framework for performing inference of affine
transformations of constrained functions. We focus on quadrature with
nonnegative functions, a common task in Bayesian inference. We consider
constraints on the range of the function of interest, such as nonnegativity or
boundedness. Although our framework is general, we derive explicit
approximation schemes for these constraints, and argue for the use of a log
transformation for functions with high dynamic range such as likelihood
surfaces. We propose a novel method for optimizing hyperparameters in this
framework: we optimize the marginal likelihood in the original space, as
opposed to in the transformed space. The result is a model that better explains
the actual data. Experiments on synthetic and real-world data demonstrate our
framework achieves superior estimates using less wall-clock time than existing
Bayesian quadrature procedures.
DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation
Authors: David Dao, Dan Alistarh, Claudiu Musat, Ce Zhang
Date: 2018-02-13 00:00:00
Tasks: BIG-bench Machine Learning

It is safe to assume that, for the foreseeable future, machine learning,
especially deep learning will remain both data- and computation-hungry. In this
paper, we ask: Can we build a global exchange where everyone can contribute
computation and data to train the next generation of machine learning
applications?
  We present an early, but running prototype of DataBright, a system that turns
the creation of training examples and the sharing of computation into an
investment mechanism. Unlike most crowdsourcing platforms, where the
contributor gets paid when they submit their data, DataBright pays dividends
whenever a contributor's data or hardware is used by someone to train a machine
learning model. The contributor becomes a shareholder in the dataset they
created. To enable the measurement of usage, a computation platform that
contributors can trust is also necessary. DataBright thus merges both a data
market and a trusted computation market.
  We illustrate that trusted computation can enable the creation of an AI
market, where each data point has an exact value that should be paid to its
creator. DataBright allows data creators to retain ownership of their
contribution and attaches to it a measurable value. The value of the data is
given by its utility in subsequent distributed computation done on the
DataBright computation market. The computation market allocates tasks and
subsequent payments to pooled hardware. This leads to the creation of a
decentralized AI cloud. Our experiments show that trusted hardware such as
Intel SGX can be added to the usual ML pipeline with no additional costs. We
use this setting to orchestrate distributed computation that enables the
creation of a computation market. DataBright is available for download at
https://github.com/ds3lab/databright.
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Authors: Glen Berseth, Cheng Xie, Paul Cernek, Michiel Van de Panne
Date: 2018-02-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Deep reinforcement learning has demonstrated increasing capabilities for
continuous control problems, including agents that can move with skill and
agility through their environment. An open problem in this setting is that of
developing good strategies for integrating or merging policies for multiple
skills, where each individual skill is a specialist in a specific skill and its
associated state distribution. We extend policy distillation methods to the
continuous action setting and leverage this technique to combine expert
policies, as evaluated in the domain of simulated bipedal locomotion across
different classes of terrain. We also introduce an input injection method for
augmenting an existing policy network to exploit new input features. Lastly,
our method uses transfer learning to assist in the efficient acquisition of new
skills. The combination of these methods allows a policy to be incrementally
augmented with new skills. We compare our progressive learning and integration
via distillation (PLAID) method against three alternative baselines.
Deep Predictive Coding Network for Object Recognition
Authors: Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7
Tasks: image-classification, Image Classification, Object, Object Recognition, Prediction

Based on the predictive coding theory in neuroscience, we designed a
bi-directional and recurrent neural net, namely deep predictive coding networks
(PCN). It has feedforward, feedback, and recurrent connections. Feedback
connections from a higher layer carry the prediction of its lower-layer
representation; feedforward connections carry the prediction errors to its
higher-layer. Given image input, PCN runs recursive cycles of bottom-up and
top-down computation to update its internal representations and reduce the
difference between bottom-up input and top-down prediction at every layer.
After multiple cycles of recursive updating, the representation is used for
image classification. With benchmark data (CIFAR-10/100, SVHN, and MNIST), PCN
was found to always outperform its feedforward-only counterpart: a model
without any mechanism for recurrent dynamics. Its performance tended to improve
given more cycles of computation over time. In short, PCN reuses a single
architecture to recursively run bottom-up and top-down processes. As a
dynamical system, PCN can be unfolded to a feedforward model that becomes
deeper and deeper over time, while refining it representation towards more
accurate and definitive object recognition.
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
Authors: Nadav Cohen, Ronen Tamari, Amnon Shashua
Date: 2017-03-20 00:00:00
Proceeding: ICLR 2018 1

The driving force behind deep networks is their ability to compactly
represent rich classes of functions. The primary notion for formally reasoning
about this phenomenon is expressive efficiency, which refers to a situation
where one network must grow unfeasibly large in order to realize (or
approximate) functions of another. To date, expressive efficiency analyses
focused on the architectural feature of depth, showing that deep networks are
representationally superior to shallow ones. In this paper we study the
expressive efficiency brought forth by connectivity, motivated by the
observation that modern networks interconnect their layers in elaborate ways.
We focus on dilated convolutional networks, a family of deep models delivering
state of the art performance in sequence processing tasks. By introducing and
analyzing the concept of mixed tensor decompositions, we prove that
interconnecting dilated convolutional networks can lead to expressive
efficiency. In particular, we show that even a single connection between
intermediate layers can already lead to an almost quadratic gap, which in
large-scale settings typically makes the difference between a model that is
practical and one that is not. Empirical evaluation demonstrates how the
expressive efficiency of connectivity, similarly to that of depth, translates
into gains in accuracy. This leads us to believe that expressive efficiency may
serve a key role in the development of new tools for deep network design.
A Short Survey on Sense-Annotated Corpora
Authors: Tommaso Pasini, Jose Camacho-Collados
Date: 2018-02-13 00:00:00
Proceeding: LREC 2020 5
Tasks: Survey, Word Sense Disambiguation

Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features.
Dynamic Word Embeddings for Evolving Semantic Discovery
Authors: Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, Hui Xiong
Date: 2017-03-02 00:00:00
Tasks: Representation Learning, Word Embeddings

Word evolution refers to the changing meanings and associations of words
throughout time, as a byproduct of human language evolution. By studying word
evolution, we can infer social trends and language constructs over different
periods of human history. However, traditional techniques such as word
representation learning do not adequately capture the evolving language
structure and vocabulary. In this paper, we develop a dynamic statistical model
to learn time-aware word vector representation. We propose a model that
simultaneously learns time-aware embeddings and solves the resulting "alignment
problem". This model is trained on a crawled NYTimes dataset. Additionally, we
develop multiple intuitive evaluation strategies of temporal word embeddings.
Our qualitative and quantitative tests indicate that our method not only
reliably captures this evolution over time, but also consistently outperforms
state-of-the-art temporal embedding approaches on both semantic accuracy and
alignment quality.
Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach
Authors: Amir Bennatan, Yoni Choukroun, Pavel Kisilev
Date: 2018-02-13 00:00:00

We present a novel framework for applying deep neural networks (DNN) to soft
decoding of linear codes at arbitrary block lengths. Unlike other approaches,
our framework allows unconstrained DNN design, enabling the free application of
powerful designs that were developed in other contexts. Our method is robust to
overfitting that inhibits many competing methods, which follows from the
exponentially large number of codewords required for their training. We achieve
this by transforming the channel output before feeding it to the network,
extracting only the syndrome of the hard decisions and the channel output
reliabilities. We prove analytically that this approach does not involve any
intrinsic performance penalty, and guarantees the generalization of performance
obtained during training. Our best results are obtained using a recurrent
neural network (RNN) architecture combined with simple preprocessing by
permutation. We provide simulation results that demonstrate performance that
sometimes approaches that of the ordered statistics decoding (OSD) algorithm.
Joint 3D Reconstruction of a Static Scene and Moving Objects
Authors: Sergio Caccamo, Esra Ataer-Cansizoglu, Yuichi Taguchi
Date: 2018-02-13 00:00:00
Tasks: 3D Reconstruction, Object, Object Tracking

We present a technique for simultaneous 3D reconstruction of static regions
and rigidly moving objects in a scene. An RGB-D frame is represented as a
collection of features, which are points and planes. We classify the features
into static and dynamic regions and grow separate maps, static and object maps,
for each of them. To robustly classify the features in each frame, we fuse
multiple RANSAC-based registration results obtained by registering different
groups of the features to different maps, including (1) all the features to the
static map, (2) all the features to each object map, and (3) subsets of the
features, each forming a segment, to each object map. This multi-group
registration approach is designed to overcome the following challenges: scenes
can be dominated by static regions, making object tracking more difficult; and
moving object might have larger pose variation between frames compared to the
static regions. We show qualitative results from indoor scenes with objects in
various shapes. The technique enables on-the-fly object model generation to be
used for robotic manipulation.
Semantic Scene Completion Combining Colour and Depth: preliminary experiments
Authors: Andre Bernardes Soares Guedes, Teofilo Emidio de Campos, Adrian Hilton
Date: 2018-02-13 00:00:00
Tasks: 3D Semantic Scene Completion

Semantic scene completion is the task of producing a complete 3D voxel
representation of volumetric occupancy with semantic labels for a scene from a
single-view observation. We built upon the recent work of Song et al. (CVPR
2017), who proposed SSCnet, a method that performs scene completion and
semantic labelling in a single end-to-end 3D convolutional network. SSCnet uses
only depth maps as input, even though depth maps are usually obtained from
devices that also capture colour information, such as RGBD sensors and stereo
cameras. In this work, we investigate the potential of the RGB colour channels
to improve SSCnet.
Substation Signal Matching with a Bagged Token Classifier
Authors: Qin Wang, Sandro Schoenborn, Yvonne-Anne Pignolet, Theo Widmer, Carsten Franke
Date: 2018-02-13 00:00:00
Tasks: BIG-bench Machine Learning

Currently, engineers at substation service providers match customer data with
the corresponding internally used signal names manually. This paper proposes a
machine learning method to automate this process based on substation signal
mapping data from a repository of executed projects. To this end, a bagged
token classifier is proposed, letting words (tokens) in the customer signal
name vote for provider signal names. In our evaluation, the proposed method
exhibits better performance in terms of both accuracy and efficiency over
standard classifiers.