pwc-archive/papers-with-abstracts huggingface dataset:
Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation
Authors: Shaojun Zhu, Andrew Kimmel, Abdeslam Boularias
Date: 2017-03-22 00:00:00
Tasks: Bayesian Optimization, Friction, Object, Reinforcement Learning

We consider the problem of a robot learning the mechanical properties of
objects through physical interaction with the object, and introduce a
practical, data-efficient approach for identifying the motion models of these
objects. The proposed method utilizes a physics engine, where the robot seeks
to identify the inertial and friction parameters of the object by simulating
its motion under different values of the parameters and identifying those that
result in a simulation which matches the observed real motions. The problem is
solved in a Bayesian optimization framework. The same framework is used for
both identifying the model of an object online and searching for a policy that
would minimize a given cost function according to the identified model.
Experimental results both in simulation and using a real robot indicate that
the proposed method outperforms state-of-the-art model-free reinforcement
learning approaches.
Cross-View Image Matching for Geo-localization in Urban Environments
Authors: Yicong Tian, Chen Chen, Mubarak Shah
Date: 2017-03-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Cross-View Image-to-Image Translation, geo-localization, image-classification, Image Classification, object-detection, Object Detection

In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view image geo-localization by taking advantage of the tremendous success
of deep convolutional neural networks (CNNs) in image classification and object
detection. First, we employ the Faster R-CNN to detect buildings in the query
and reference images. Next, for each building in the query image, we retrieve
the $k$ nearest neighbors from the reference buildings using a Siamese network
trained on both positive matching image pairs and negative pairs. To find the
correct NN for each query building, we develop an efficient multiple nearest
neighbors matching method based on dominant sets. We evaluate the proposed
framework on a new dataset that consists of pairs of street view and bird's eye
view images. Experimental results show that the proposed method achieves better
geo-localization accuracy than other approaches and is able to generalize to
images at unseen locations.
Learning to Partition using Score Based Compatibilities
Authors: Arun Rajkumar, Koyel Mukherjee, Theja Tulabandhula
Date: 2017-03-22 00:00:00

We study the problem of learning to partition users into groups, where one
must learn the compatibilities between the users to achieve optimal groupings.
We define four natural objectives that optimize for average and worst case
compatibilities and propose new algorithms for adaptively learning optimal
groupings. When we do not impose any structure on the compatibilities, we show
that the group formation objectives considered are $NP$ hard to solve and we
either give approximation guarantees or prove inapproximability results. We
then introduce an elegant structure, namely that of \textit{intrinsic scores},
that makes many of these problems polynomial time solvable. We explicitly
characterize the optimal groupings under this structure and show that the
optimal solutions are related to \emph{homophilous} and \emph{heterophilous}
partitions, well-studied in the psychology literature. For one of the four
objectives, we show $NP$ hardness under the score structure and give a
$\frac{1}{2}$ approximation algorithm for which no constant approximation was
known thus far. Finally, under the score structure, we propose an online low
sample complexity PAC algorithm for learning the optimal partition. We
demonstrate the efficacy of the proposed algorithm on synthetic and real world
datasets.
Supervised Typing of Big Graphs using Semantic Embeddings
Authors: Mayank Kejriwal, Pedro Szekely
Date: 2017-03-22 00:00:00
Tasks: Entity Embeddings, Feature Engineering, Vocal Bursts Type Prediction

We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.
Multitask learning and benchmarking with clinical time series data
Authors: Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, Aram Galstyan
Date: 2017-03-22 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Computational Phenotyping, General Classification, Length-of-Stay prediction, Mortality Prediction, Phenotype classification, Time Series, Time Series Analysis

Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.
Towards Evaluating the Robustness of Neural Networks
Authors: Nicholas Carlini, David Wagner
Date: 2016-08-16 00:00:00
Tasks: Adversarial Attack

Neural networks provide state-of-the-art results for most machine learning
tasks. Unfortunately, neural networks are vulnerable to adversarial examples:
given an input $x$ and any target classification $t$, it is possible to find a
new input $x'$ that is similar to $x$ but classified as $t$. This makes it
difficult to apply neural networks in security-critical areas. Defensive
distillation is a recently proposed approach that can take an arbitrary neural
network, and increase its robustness, reducing the success rate of current
attacks' ability to find adversarial examples from $95\%$ to $0.5\%$.
  In this paper, we demonstrate that defensive distillation does not
significantly increase the robustness of neural networks by introducing three
new attack algorithms that are successful on both distilled and undistilled
neural networks with $100\%$ probability. Our attacks are tailored to three
distance metrics used previously in the literature, and when compared to
previous adversarial example generation algorithms, our attacks are often much
more effective (and never worse). Furthermore, we propose using high-confidence
adversarial examples in a simple transferability test we show can also be used
to break defensive distillation. We hope our attacks will be used as a
benchmark in future defense attempts to create neural networks that resist
adversarial examples.
Predicting Process Behaviour using Deep Learning
Authors: Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
Date: 2016-12-14 00:00:00
Tasks: Deep Learning, Management, Prediction

Predicting business process behaviour is an important aspect of business
process management. Motivated by research in natural language processing, this
paper describes an application of deep learning with recurrent neural networks
to the problem of predicting the next event in a business process. This is both
a novel method in process prediction, which has largely relied on explicit
process models, and also a novel application of deep learning methods. The
approach is evaluated on two real datasets and our results surpass the
state-of-the-art in prediction precision.
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
Authors: Kartik Audhkhasi, Bhuvana Ramabhadran, George Saon, Michael Picheny, David Nahamoo
Date: 2017-03-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, English Conversational Speech Recognition, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Recent work on end-to-end automatic speech recognition (ASR) has shown that
the connectionist temporal classification (CTC) loss can be used to convert
acoustics to phone or character sequences. Such systems are used with a
dictionary and separately-trained Language Model (LM) to produce word
sequences. However, they are not truly end-to-end in the sense of mapping
acoustics directly to words without an intermediate phone representation. In
this paper, we present the first results employing direct acoustics-to-word CTC
models on two well-known public benchmark tasks: Switchboard and CallHome.
These models do not require an LM or even a decoder at run-time and hence
recognize speech with minimal complexity. However, due to the large number of
word output units, CTC word models require orders of magnitude more data to
train reliably compared to traditional systems. We present some techniques to
mitigate this issue. Our CTC word model achieves a word error rate of
13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or
decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also
present rescoring results on CTC word model lattices to quantify the
performance benefits of a LM, and contrast the performance of word and phone
CTC models.
X-ray In-Depth Decomposition: Revealing The Latent Structures
Authors: Shadi Albarqouni, Javad Fotouhi, Nassir Navab
Date: 2016-12-19 00:00:00
Tasks: Anatomy

X-ray radiography is the most readily available imaging modality and has a
broad range of applications that spans from diagnosis to intra-operative
guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation
of the hidden and obscured anatomy in X-ray images remains a challenge and
often requires high radiation dose and imaging from several perspectives. In
this work, we aim at decomposing the conventional X-ray image into d X-ray
components of independent, non-overlapped, clipped sub-volumes using deep
learning approach. Despite the challenging aspects of modeling such a highly
ill-posed problem, exciting and encouraging results are obtained paving the
path for further contributions in this direction.
ZM-Net: Real-time Zero-shot Image Manipulation Network
Authors: Hao Wang, Xiaodan Liang, Hao Zhang, Dit-yan Yeung, Eric P. Xing
Date: 2017-03-21 00:00:00
Tasks: Colorization, Descriptive, Image Manipulation, Style Transfer

Many problems in image processing and computer vision (e.g. colorization,
style transfer) can be posed as 'manipulating' an input image into a
corresponding output image given a user-specified guiding signal. A holy-grail
solution towards generic image manipulation should be able to efficiently alter
an input image with any personalized signals (even signals unseen during
training), such as diverse paintings and arbitrary descriptive attributes.
However, existing methods are either inefficient to simultaneously process
multiple signals (let alone generalize to unseen signals), or unable to handle
signals from other modalities. In this paper, we make the first attempt to
address the zero-shot image manipulation task. We cast this problem as
manipulating an input image according to a parametric model whose key
parameters can be conditionally generated from any guiding signal (even unseen
ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a
fully-differentiable architecture that jointly optimizes an
image-transformation network (TNet) and a parameter network (PNet). The PNet
learns to generate key transformation parameters for the TNet given any guiding
signal while the TNet performs fast zero-shot image manipulation according to
both signal-dependent parameters from the PNet and signal-invariant parameters
from the TNet itself. Extensive experiments show that our ZM-Net can perform
high-quality image manipulation conditioned on different forms of guiding
signals (e.g. style images and attributes) in real-time (tens of milliseconds
per image) even for unseen signals. Moreover, a large-scale style dataset with
over 20,000 style images is also constructed to promote further research.
Object Detection via Aspect Ratio and Context Aware Region-based Convolutional Networks
Authors: Bo Li, Tianfu Wu, Shuai Shao, Lun Zhang, Rufeng Chu
Date: 2016-12-02 00:00:00
Tasks: ARC, Object, object-detection, Object Detection

Jointly integrating aspect ratio and context has been extensively studied and
shown performance improvement in traditional object detection systems such as
the DPMs. It, however, has been largely ignored in deep neural network based
detection systems. This paper presents a method of integrating a mixture of
object models and region-based convolutional networks for accurate object
detection. Each mixture component accounts for both object aspect ratio and
multi-scale contextual information explicitly: (i) it exploits a mixture of
tiling configurations in the RoI pooling to remedy the warping artifacts caused
by a single type RoI pooling (e.g., with equally-sized 7 x 7 cells), and to
respect the underlying object shapes more; (ii) it "looks from both the inside
and the outside of a RoI" by incorporating contextual information at two
scales: global context pooled from the whole image and local context pooled
from the surrounding of a RoI. To facilitate accurate detection, this paper
proposes a multi-stage detection scheme for integrating the mixture of object
models, which utilizes the detection results of the model at the previous stage
as the proposals for the current in both training and testing. The proposed
method is called the aspect ratio and context aware region-based convolutional
network (ARC-R-CNN). In experiments, ARC-R-CNN shows very competitive results
with Faster R-CNN [41] and R-FCN [10] on two datasets: the PASCAL VOC and the
Microsoft COCO. It obtains significantly better mAP performance using high IoU
thresholds on both datasets.
Pixel Recursive Super Resolution
Authors: Ryan Dahl, Mohammad Norouzi, Jonathon Shlens
Date: 2017-02-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: regression, Super-Resolution

We present a pixel recursive super resolution model that synthesizes
realistic details into images while enhancing their resolution. A low
resolution image may correspond to multiple plausible high resolution images,
thus modeling the super resolution process with a pixel independent conditional
model often results in averaging different details--hence blurry edges. By
contrast, our model is able to represent a multimodal conditional distribution
by properly modeling the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We employ a PixelCNN
architecture to define a strong prior over natural images and jointly optimize
this prior with a deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look more photo realistic than a
strong L2 regression baseline.
Sequence Training and Adaptation of Highway Deep Neural Networks
Authors: Liang Lu
Date: 2016-07-07 00:00:00
Tasks: speech-recognition, Speech Recognition

Highway deep neural network (HDNN) is a type of depth-gated feedforward
neural network, which has shown to be easier to train with more hidden layers
and also generalise better compared to conventional plain deep neural networks
(DNNs). Previously, we investigated a structured HDNN architecture for speech
recognition, in which the two gate functions were tied across all the hidden
layers, and we were able to train a much smaller model without sacrificing the
recognition accuracy. In this paper, we carry on the study of this architecture
with sequence-discriminative training criterion and speaker adaptation
techniques on the AMI meeting speech recognition corpus. We show that these two
techniques improve speech recognition accuracy on top of the model trained with
the cross entropy criterion. Furthermore, we demonstrate that the two gate
functions that are tied across all the hidden layers are able to control the
information flow over the whole network, and we can achieve considerable
improvements by only updating these gate functions in both sequence training
and adaptation experiments.
Independently Controllable Features
Authors: Emmanuel Bengio, Valentin Thomas, Joelle Pineau, Doina Precup, Yoshua Bengio
Date: 2017-03-22 00:00:00

Finding features that disentangle the different causes of variation in real
data is a difficult task, that has nonetheless received considerable attention
in static domains like natural images. Interactive environments, in which an
agent can deliberately take actions, offer an opportunity to tackle this task
better, because the agent can experiment with different actions and observe
their effects. We introduce the idea that in interactive environments, latent
factors that control the variation in observed data can be identified by
figuring out what the agent can control. We propose a naive method to find
factors that explain or measure the effect of the actions of a learner, and
test it in illustrative experiments.
Hyper Temporal Networks
Authors: Carlo Comin, Roberto Posenato, Romeo Rizzi
Date: 2015-03-13 00:00:00

Simple Temporal Networks (STNs) provide a powerful and general tool for
representing conjunctions of maximum delay constraints over ordered pairs of
temporal variables. In this paper we introduce Hyper Temporal Networks (HyTNs),
a strict generalization of STNs, to overcome the limitation of considering only
conjunctions of constraints but maintaining a practical efficiency in the
consistency check of the instances. In a Hyper Temporal Network a single
temporal hyperarc constraint may be defined as a set of two or more maximum
delay constraints which is satisfied when at least one of these delay
constraints is satisfied. HyTNs are meant as a light generalization of STNs
offering an interesting compromise. On one side, there exist practical
pseudo-polynomial time algorithms for checking consistency and computing
feasible schedules for HyTNs. On the other side, HyTNs offer a more powerful
model accommodating natural constraints that cannot be expressed by STNs like
Trigger off exactly delta min before (after) the occurrence of the first (last)
event in a set., which are used to represent synchronization events in some
process aware information systems/workflow models proposed in the literature.
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Authors: Zhao Meng, Lili Mou, Zhi Jin
Date: 2017-03-22 00:00:00
Tasks: Change Detection, Sentence

Speaker change detection (SCD) is an important task in dialog modeling. Our
paper addresses the problem of text-based SCD, which differs from existing
audio-based studies and is useful in various scenarios, for example, processing
dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
and enhancing audio SCD with textual information. We formulate text-based SCD
as a matching problem of utterances before and after a certain decision point;
we propose a hierarchical recurrent neural network (RNN) with static
sentence-level attention. Experimental results show that neural networks
consistently achieve better performance than feature-based approaches, and that
our attention-based model significantly outperforms non-attention neural
networks.
Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor
Authors: Morteza Ashraphijuo, Xiaodong Wang
Date: 2017-03-22 00:00:00

In this paper, we analyze the fundamental conditions for low-rank tensor
completion given the separation or tensor-train (TT) rank, i.e., ranks of
unfoldings. We exploit the algebraic structure of the TT decomposition to
obtain the deterministic necessary and sufficient conditions on the locations
of the samples to ensure finite completability. Specifically, we propose an
algebraic geometric analysis on the TT manifold that can incorporate the whole
rank vector simultaneously in contrast to the existing approach based on the
Grassmannian manifold that can only incorporate one rank component. Our
proposed technique characterizes the algebraic independence of a set of
polynomials defined based on the sampling pattern and the TT decomposition,
which is instrumental to obtaining the deterministic condition on the sampling
pattern for finite completability. In addition, based on the proposed analysis,
assuming that the entries of the tensor are sampled independently with
probability $p$, we derive a lower bound on the sampling probability $p$, or
equivalently, the number of sampled entries that ensures finite completability
with high probability. Moreover, we also provide the deterministic and
probabilistic conditions for unique completability.
Random Forests for Big Data
Authors: Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, Nathalie Villa-Vialaneix
Date: 2015-11-26 00:00:00
Tasks: Clustering, General Classification, Multi-class Classification, regression

Big Data is one of the major challenges of statistical science and has
numerous consequences from algorithmic and theoretical viewpoints. Big Data
always involve massive data but they also often include online data and data
heterogeneity. Recently some statistical methods have been adapted to process
Big Data, like linear regression models, clustering methods and bootstrapping
schemes. Based on decision trees combined with aggregation and bootstrap ideas,
random forests were introduced by Breiman in 2001. They are a powerful
nonparametric statistical method allowing to consider in a single and versatile
framework regression problems, as well as two-class and multi-class
classification problems. Focusing on classification problems, this paper
proposes a selective review of available proposals that deal with scaling
random forests to Big Data problems. These proposals rely on parallel
environments or on online adaptations of random forests. We also describe how
related quantities -- such as out-of-bag error and variable importance -- are
addressed in these methods. Then, we formulate various remarks for random
forests in the Big Data context. Finally, we experiment five variants on two
massive datasets (15 and 120 millions of observations), a simulated one as well
as real world data. One variant relies on subsampling while three others are
related to parallel implementations of random forests and involve either
various adaptations of bootstrap to Big Data or to "divide-and-conquer"
approaches. The fifth variant relates on online learning of random forests.
These numerical experiments lead to highlight the relative performance of the
different variants, as well as some of their limitations.