pwc-archive/papers-with-abstracts huggingface dataset:
PANDA: Facilitating Usable AI Development
Authors: Jinyang Gao, Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Guoliang Li, Teck Khim Ng, Beng Chin Ooi, Sheng Wang, Jingren Zhou
Date: 2018-04-26 00:00:00
Tasks: Autonomous Driving

Recent advances in artificial intelligence (AI) and machine learning have
created a general perception that AI could be used to solve complex problems,
and in some situations over-hyped as a tool that can be so easily used.
Unfortunately, the barrier to realization of mass adoption of AI on various
business domains is too high because most domain experts have no background in
AI. Developing AI applications involves multiple phases, namely data
preparation, application modeling, and product deployment. The effort of AI
research has been spent mostly on new AI models (in the model training stage)
to improve the performance of benchmark tasks such as image recognition. Many
other factors such as usability, efficiency and security of AI have not been
well addressed, and therefore form a barrier to democratizing AI. Further, for
many real world applications such as healthcare and autonomous driving,
learning via huge amounts of possibility exploration is not feasible since
humans are involved. In many complex applications such as healthcare, subject
matter experts (e.g. Clinicians) are the ones who appreciate the importance of
features that affect health, and their knowledge together with existing
knowledge bases are critical to the end results. In this paper, we take a new
perspective on developing AI solutions, and present a solution for making AI
usable. We hope that this resolution will enable all subject matter experts
(eg. Clinicians) to exploit AI like data scientists.
Computational Approaches for Stochastic Shortest Path on Succinct MDPs
Authors: Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady, Nastaran Okati
Date: 2018-04-24 00:00:00

We consider the stochastic shortest path (SSP) problem for succinct Markov
decision processes (MDPs), where the MDP consists of a set of variables, and a
set of nondeterministic rules that update the variables. First, we show that
several examples from the AI literature can be modeled as succinct MDPs. Then
we present computational approaches for upper and lower bounds for the SSP
problem: (a)~for computing upper bounds, our method is polynomial-time in the
implicit description of the MDP; (b)~for lower bounds, we present a
polynomial-time (in the size of the implicit description) reduction to
quadratic programming. Our approach is applicable even to infinite-state MDPs.
Finally, we present experimental results to demonstrate the effectiveness of
our approach on several classical examples from the AI literature.
Recommending Outfits from Personal Closet
Authors: Pongsate Tangseng, Kota Yamaguchi, Takayuki Okatani
Date: 2018-04-26 00:00:00
Tasks: General Classification

We consider grading a fashion outfit for recommendation, where we assume that
users have a closet of items and we aim at producing a score for an arbitrary
combination of items in the closet. The challenge in outfit grading is that the
input to the system is a bag of item pictures that are unordered and vary in
size. We build a deep neural network-based system that can take variable-length
items and predict a score. We collect a large number of outfits from a popular
fashion sharing website, Polyvore, and evaluate the performance of our grading
system. We compare our model with a random-choice baseline, both on the
traditional classification evaluation and on people's judgment using a
crowdsourcing platform. With over 84% in classification accuracy and 91%
matching ratio to human annotators, our model can reliably grade the quality of
an outfit. We also build an outfit recommender on top of our grader to
demonstrate the practical application of our model for a personal closet
assistant.
A Geometric Framework for Stochastic Shape Analysis
Authors: Alexis Arnaudon, Darryl D. Holm, Stefan Sommer
Date: 2017-03-29 00:00:00
Tasks: Image Registration

We introduce a stochastic model of diffeomorphisms, whose action on a variety
of data types descends to stochastic evolution of shapes, images and landmarks.
The stochasticity is introduced in the vector field which transports the data
in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework for
shape analysis and image registration. The stochasticity thereby models errors
or uncertainties of the flow in following the prescribed deformation velocity.
The approach is illustrated in the example of finite dimensional landmark
manifolds, whose stochastic evolution is studied both via the Fokker-Planck
equation and by numerical simulations. We derive two approaches for inferring
parameters of the stochastic model from landmark configurations observed at
discrete time points. The first of the two approaches matches moments of the
Fokker-Planck equation to sample moments of the data, while the second approach
employs an Expectation-Maximisation based algorithm using a Monte Carlo bridge
sampling scheme to optimise the data likelihood. We derive and numerically test
the ability of the two approaches to infer the spatial correlation length of
the underlying noise.
Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism
Authors: Adam Bielski, Tomasz Trzcinski
Date: 2018-04-26 00:00:00

Predicting popularity of social media videos before they are published is a
challenging task, mainly due to the complexity of content distribution network
as well as the number of factors that play part in this process. As solving
this task provides tremendous help for media content creators, many successful
methods were proposed to solve this problem with machine learning. In this
work, we change the viewpoint and postulate that it is not only the predicted
popularity that matters, but also, maybe even more importantly, understanding
of how individual parts influence the final popularity score. To that end, we
propose to combine the Grad-CAM visualization method with a soft attention
mechanism. Our preliminary results show that this approach allows for more
intuitive interpretation of the content impact on video popularity, while
achieving competitive results in terms of prediction accuracy.
Gradient Estimators for Implicit Models
Authors: Yingzhen Li, Richard E. Turner
Date: 2017-05-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Diversity, Image Generation, Meta-Learning

Implicit models, which allow for the generation of samples but not for
point-wise evaluation of probabilities, are omnipresent in real-world problems
tackled by machine learning and a hot topic of current research. Some examples
include data simulators that are widely used in engineering and scientific
research, generative adversarial networks (GANs) for image synthesis, and
hot-off-the-press approximate inference techniques relying on implicit
distributions. The majority of existing approaches to learning implicit models
rely on approximating the intractable distribution or optimisation objective
for gradient-based optimisation, which is liable to produce inaccurate updates
and thus poor models. This paper alleviates the need for such approximations by
proposing the Stein gradient estimator, which directly estimates the score
function of the implicitly defined distribution. The efficacy of the proposed
estimator is empirically demonstrated by examples that include meta-learning
for approximate inference, and entropy regularised GANs that provide improved
sample diversity.
Sign-Full Random Projections
Authors: Ping Li
Date: 2018-04-26 00:00:00

The method of 1-bit ("sign-sign") random projections has been a popular tool
for efficient search and machine learning on large datasets. Given two $D$-dim
data vectors $u$, $v\in\mathbb{R}^D$, one can generate $x = \sum_{i=1}^D u_i
r_i$, and $y = \sum_{i=1}^D v_i r_i$, where $r_i\sim N(0,1)$ iid. The
"collision probability" is ${Pr}\left(sgn(x)=sgn(y)\right) =
1-\frac{\cos^{-1}\rho}{\pi}$, where $\rho = \rho(u,v)$ is the cosine
similarity.
  We develop "sign-full" random projections by estimating $\rho$ from (e.g.,)
the expectation $E(sgn(x)y)=\sqrt{\frac{2}{\pi}} \rho$, which can be further
substantially improved by normalizing $y$. For nonnegative data, we recommend
an interesting estimator based on $E\left(y_- 1_{x\geq 0} + y_+ 1_{x<0}\right)$
and its normalized version. The recommended estimator almost matches the
accuracy of the (computationally expensive) maximum likelihood estimator. At
high similarity ($\rho\rightarrow1$), the asymptotic variance of recommended
estimator is only $\frac{4}{3\pi} \approx 0.4$ of the estimator for sign-sign
projections. At small $k$ and high similarity, the improvement would be even
much more substantial.
A Benchmark and Evaluation of Non-Rigid Structure from Motion
Authors: Sebastian Hoppe Nesgaard Jensen, Mads Emil Brix Doest, Henrik Aanaes, Alessio Del Bue
Date: 2018-01-25 00:00:00

Non-Rigid structure from motion (NRSfM), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of NRSfM, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse NRSfM. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
Authors: Okan Köpüklü, Neslihan Köse, Gerhard Rigoll
Date: 2018-04-19 00:00:00
Tasks: Action Classification, General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Acquiring spatio-temporal states of an action is the most crucial step for
action classification. In this paper, we propose a data level fusion strategy,
Motion Fused Frames (MFFs), designed to fuse motion information into static
images as better representatives of spatio-temporal states of an action. MFFs
can be used as input to any deep learning architecture with very little
modification on the network. We evaluate MFFs on hand gesture recognition tasks
using three video datasets - Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand
Gesture Datasets - which require capturing long-term temporal relations of hand
movements. Our approach obtains very competitive performance on Jester and
ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%,
respectively, while achieving state-of-the-art performance with 84.7% accuracy
on NVIDIA benchmark.
Sounding Board: A User-Centric and Content-Driven Social Chatbot
Authors: Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, Mari Ostendorf
Date: 2018-04-26 00:00:00
Proceeding: NAACL 2018 6
Tasks: Chatbot, Dialogue Management, Management, Text Generation

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa
Prize. The system architecture consists of several components including spoken
language processing, dialogue management, language generation, and content
management, with emphasis on user-centric and content-driven design. We also
share insights gained from large-scale online logs based on 160,000
conversations with real-world users.
Global Convergence of the (1+1) Evolution Strategy
Authors: Tobias Glasmachers
Date: 2017-06-09 00:00:00
Tasks: Evolutionary Algorithms

We establish global convergence of the (1+1) evolution strategy, i.e., convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a critical point to measurable functions. At its core, the analysis is based on a novel progress guarantee for elitist, rank-based evolutionary algorithms. By applying it to the (1+1) evolution strategy we are able to provide an accurate characterization of whether global convergence is guaranteed with full probability, or whether premature convergence is possible. We illustrate our results on a number of example applications ranging from smooth (non-convex) cases over different types of saddle points and ridge functions to discontinuous and extremely rugged problems.
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool
Date: 2018-01-25 00:00:00
Tasks: Classification, Clustering, General Classification, Management

In this work we demonstrate a rapidly deployable weed classification system
that uses visual data to enable autonomous precision weeding without making
prior assumptions about which weed species are present in a given field.
Previous work in this area relies on having prior knowledge of the weed species
present in the field. This assumption cannot always hold true for every field,
and thus limits the use of weed classification systems based on this
assumption. In this work, we obviate this assumption and introduce a rapidly
deployable approach able to operate on any field without any weed species
assumptions prior to deployment. We present a three stage pipeline for the
implementation of our weed classification system consisting of initial field
surveillance, offline processing and selective labelling, and automated
precision weeding. The key characteristic of our approach is the combination of
plant clustering and selective labelling which is what enables our system to
operate without prior weed species knowledge. Testing using field data we are
able to label 12.3 times fewer images than traditional full labelling whilst
reducing classification accuracy by only 14%.
Tied Multitask Learning for Neural Speech Translation
Authors: Antonios Anastasopoulos, David Chiang
Date: 2018-02-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Translation

We explore multitask models for neural translation of speech, augmenting them
in order to reflect two intuitive notions. First, we introduce a model where
the second task decoder receives information from the decoder of the first
task, since higher-level intermediate representations should provide useful
information. Second, we apply regularization that encourages transitivity and
invertibility. We show that the application of these notions on jointly trained
models improves performance on the tasks of low-resource speech transcription
and translation. It also leads to better performance when using attention
information for word discovery over unsegmented input.
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
Authors: Florian Piewak, Peter Pinggera, Manuel Schäfer, David Peter, Beate Schwarz, Nick Schneider, David Pfeiffer, Markus Enzweiler, Marius Zöllner
Date: 2018-04-26 00:00:00
Tasks: Autonomous Vehicles

Mobile robots and autonomous vehicles rely on multi-modal sensor setups to
perceive and understand their surroundings. Aside from cameras, LiDAR sensors
represent a central component of state-of-the-art perception systems. In
addition to accurate spatial perception, a comprehensive semantic understanding
of the environment is essential for efficient and safe operation. In this paper
we present a novel deep neural network architecture called LiLaNet for
point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network
utilizes virtual image projections of the 3D point clouds for efficient
inference. Further, we propose an automated process for large-scale cross-modal
training data generation called Autolabeling, in order to boost semantic
labeling performance while keeping the manual annotation effort low. The
effectiveness of the proposed network architecture as well as the automated
data generation process is demonstrated on a manually annotated ground truth
dataset. LiLaNet is shown to significantly outperform current state-of-the-art
CNN architectures for LiDAR data. Applying our automatically generated
large-scale training data yields a boost of up to 14 percentage points compared
to networks trained on manually annotated data only.
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
Authors: Haichao Zhang, Haonan Yu, Wei Xu
Date: 2018-04-26 00:00:00
Proceeding: ACL 2018 7
Tasks: Grounded language learning, Language Acquisition

Building intelligent agents that can communicate with and learn from humans
in natural language is of great value. Supervised language learning is limited
by the ability of capturing mainly the statistics of training data, and is
hardly adaptive to new scenarios or flexible for acquiring new knowledge
without inefficient retraining or catastrophic forgetting. We highlight the
perspective that conversational interaction serves as a natural interface both
for language learning and for novel knowledge acquisition and propose a joint
imitation and reinforcement approach for grounded language learning through an
interactive conversational game. The agent trained with this approach is able
to actively acquire information by asking questions about novel objects and use
the just-learned knowledge in subsequent conversations in a one-shot fashion.
Results compared with other methods verified the effectiveness of the proposed
approach.
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
Authors: Thibaut Kulak, Michael Garcia Ortiz
Date: 2018-03-01 00:00:00
Tasks: Prediction, Representation Learning

In order to explore and act autonomously in an environment, an agent needs to
learn from the sensorimotor information that is captured while acting. By
extracting the regularities in this sensorimotor stream, it can learn a model
of the world, which in turn can be used as a basis for action and exploration.
  This requires the acquisition of compact representations from a possibly high
dimensional raw observation, which is noisy and ambiguous. In this paper, we
learn sensory representations from sensorimotor prediction. We propose a model
which integrates sensorimotor information over time, and project it in a
sensory representation which is useful for prediction. We emphasize on a simple
example the role of motor and memory for learning sensory representations.
High-dimensional Penalty Selection via Minimum Description Length Principle
Authors: Kohei Miyaguchi, Kenji Yamanishi
Date: 2018-04-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We tackle the problem of penalty selection of regularization on the basis of
the minimum description length (MDL) principle. In particular, we consider that
the design space of the penalty function is high-dimensional. In this
situation, the luckiness-normalized-maximum-likelihood(LNML)-minimization
approach is favorable, because LNML quantifies the goodness of regularized
models with any forms of penalty functions in view of the minimum description
length principle, and guides us to a good penalty function through the
high-dimensional space. However, the minimization of LNML entails two major
challenges: 1) the computation of the normalizing factor of LNML and 2) its
minimization in high-dimensional spaces. In this paper, we present a novel
regularization selection method (MDL-RS), in which a tight upper bound of LNML
(uLNML) is minimized with local convergence guarantee. Our main contribution is
the derivation of uLNML, which is a uniform-gap upper bound of LNML in an
analytic expression. This solves the above challenges in an approximate manner
because it allows us to accurately approximate LNML and then efficiently
minimize it. The experimental results show that MDL-RS improves the
generalization performance of regularized estimates specifically when the model
has redundant parameters.
Optimal-margin evolutionary classifier
Authors: Mohammad Reza Bonyadi, David C. Reutens
Date: 2018-04-26 00:00:00
Tasks: Classification, Evolutionary Algorithms, General Classification, Multi-class Classification

We introduce a novel approach for discriminative classification using
evolutionary algorithms. We first propose an algorithm to optimize the total
loss value using a modified 0-1 loss function in a one-dimensional space for
classification. We then extend this algorithm for multi-dimensional
classification using an evolutionary algorithm. The proposed evolutionary
algorithm aims to find a hyperplane which best classifies instances while
minimizes the classification risk. We test particle swarm optimization,
evolutionary strategy, and covariance matrix adaptation evolutionary strategy
for optimization purpose. Finally, we compare our results with well-established
and state-of-the-art classification algorithms, for both binary and multi-class
classification, on 19 benchmark classification problems, with and without noise
and outliers. Results show that the performance of the proposed algorithm is
significantly (t-test) better than all other methods in almost all problems
tested. We also show that the proposed algorithm is significantly more robust
against noise and outliers comparing to other methods. The running time of the
algorithm is within a reasonable range for the solution of real-world
classification problems.
DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild
Authors: Skand Vishwanath Peri, Abhinav Dhall
Date: 2018-04-25 00:00:00
Tasks: Disguised Face Verification, Face Verification

This paper describes our approach for the Disguised Faces in the Wild (DFW)
2018 challenge. The task here is to verify the identity of a person among
disguised and impostors images. Given the importance of the task of face
verification it is essential to compare methods across a common platform. Our
approach is based on VGG-face architecture paired with Contrastive loss based
on cosine distance metric. For augmenting the data set, we source more data
from the internet. The experiments show the effectiveness of the approach on
the DFW data. We show that adding extra data to the DFW dataset with noisy
labels also helps in increasing the generalization performance of the network.
The proposed network achieves 27.13% absolute increase in accuracy over the DFW
baseline.
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models
Authors: Yingxiang Yang, Adams Wei Yu, Zhaoran Wang, Tuo Zhao
Date: 2018-03-11 00:00:00
Tasks: Additive models, Model Selection, Time Series, Time Series Analysis

We propose a nonparametric method for detecting nonlinear causal relationship
within a set of multidimensional discrete time series, by using sparse additive
models (SpAMs). We show that, when the input to the SpAM is a $\beta$-mixing
time series, the model can be fitted by first approximating each unknown
function with a linear combination of a set of B-spline bases, and then solving
a group-lasso-type optimization problem with nonconvex regularization.
Theoretically, we characterize the oracle statistical properties of the
proposed sparse estimator in function estimation and model selection.
Numerically, we propose an efficient pathwise iterative shrinkage thresholding
algorithm (PISTA), which tames the nonconvexity and guarantees linear
convergence towards the desired sparse estimator with high probability.