pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Social Pooling for Vehicle Trajectory Prediction
Authors: Nachiket Deo, Mohan M. Trivedi
Date: 2018-05-15 00:00:00
Tasks: Decoder, Prediction, Trajectory Prediction

Forecasting the motion of surrounding vehicles is a critical ability for an
autonomous vehicle deployed in complex traffic. Motion of all vehicles in a
scene is governed by the traffic context, i.e., the motion and relative spatial
configuration of neighboring vehicles. In this paper we propose an LSTM
encoder-decoder model that uses convolutional social pooling as an improvement
to social pooling layers for robustly learning interdependencies in vehicle
motion. Additionally, our model outputs a multi-modal predictive distribution
over future trajectories based on maneuver classes. We evaluate our model using
the publicly available NGSIM US-101 and I-80 datasets. Our results show
improvement over the state of the art in terms of RMS values of prediction
error and negative log-likelihoods of true future trajectories under the
model's predictive distribution. We also present a qualitative analysis of the
model's predicted distributions for various traffic scenarios.
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
Authors: Nachiket Deo, Mohan M. Trivedi
Date: 2018-05-15 00:00:00
Tasks: Autonomous Vehicles, motion prediction, Navigate, Prediction, Trajectory Prediction

To safely and efficiently navigate through complex traffic scenarios,
autonomous vehicles need to have the ability to predict the future motion of
surrounding vehicles. Multiple interacting agents, the multi-modal nature of
driver behavior, and the inherent uncertainty involved in the task make motion
prediction of surrounding vehicles a challenging problem. In this paper, we
present an LSTM model for interaction aware motion prediction of surrounding
vehicles on freeways. Our model assigns confidence values to maneuvers being
performed by vehicles and outputs a multi-modal distribution over future motion
based on them. We compare our approach with the prior art for vehicle motion
prediction on the publicly available NGSIM US-101 and I-80 datasets. Our
results show an improvement in terms of RMS values of prediction error. We also
present an ablative analysis of the components of our proposed model and
analyze the predictions made by the model in complex traffic scenarios.
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
Authors: David Hallac, Sagar Vare, Stephen Boyd, Jure Leskovec
Date: 2017-06-10 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Subsequence clustering of multivariate time series is a useful tool for
discovering repeated patterns in temporal data. Once these patterns have been
discovered, seemingly complicated datasets can be interpreted as a temporal
sequence of only a small number of states, or clusters. For example, raw sensor
data from a fitness-tracking application can be expressed as a timeline of a
select few actions (i.e., walking, sitting, running). However, discovering
these patterns is challenging because it requires simultaneous segmentation and
clustering of the time series. Furthermore, interpreting the resulting clusters
is difficult, especially when the data is high-dimensional. Here we propose a
new method of model-based clustering, which we call Toeplitz Inverse
Covariance-based Clustering (TICC). Each cluster in the TICC method is defined
by a correlation network, or Markov random field (MRF), characterizing the
interdependencies between different observations in a typical subsequence of
that cluster. Based on this graphical representation, TICC simultaneously
segments and clusters the time series data. We solve the TICC problem through
alternating minimization, using a variation of the expectation maximization
(EM) algorithm. We derive closed-form solutions to efficiently solve the two
resulting subproblems in a scalable way, through dynamic programming and the
alternating direction method of multipliers (ADMM), respectively. We validate
our approach by comparing TICC to several state-of-the-art baselines in a
series of synthetic experiments, and we then demonstrate on an automobile
sensor dataset how TICC can be used to learn interpretable clusters in
real-world scenarios.
Did the Model Understand the Question?
Authors: Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, Kedar Dhamdhere
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: model, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We analyze state-of-the-art deep learning models for three tasks: question
answering on (1) images, (2) tables, and (3) passages of text. Using the notion
of \emph{attribution} (word importance), we find that these deep networks often
ignore important question terms. Leveraging such behavior, we perturb questions
to craft a variety of adversarial examples. Our strongest attacks drop the
accuracy of a visual question answering model from $61.1\%$ to $19\%$, and that
of a tabular question answering model from $33.5\%$ to $3.3\%$. Additionally,
we show how attributions can strengthen attacks proposed by Jia and Liang
(2017) on paragraph comprehension models. Our results demonstrate that
attributions can augment standard measures of accuracy and empower
investigation of model performance. When a model is accurate but for the wrong
reasons, attributions can surface erroneous logic in the model that indicates
inadequacies in the test data.
Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing
Authors: Martin Mueller, Marcel Salathé
Date: 2018-05-14 00:00:00

In the past decade, tracking health trends using social media data has shown
great promise, due to a powerful combination of massive adoption of social
media around the world, and increasingly potent hardware and software that
enables us to work with these new big data streams. At the same time, many
challenging problems have been identified. First, there is often a mismatch
between how rapidly online data can change, and how rapidly algorithms are
updated, which means that there is limited reusability for algorithms trained
on past data as their performance decreases over time. Second, much of the work
is focusing on specific issues during a specific past period in time, even
though public health institutions would need flexible tools to assess multiple
evolving situations in real time. Third, most tools providing such capabilities
are proprietary systems with little algorithmic or data transparency, and thus
little buy-in from the global public health and research community. Here, we
introduce Crowdbreaks, an open platform which allows tracking of health trends
by making use of continuous crowdsourced labelling of public social media
content. The system is built in a way which automatizes the typical workflow
from data collection, filtering, labelling and training of machine learning
classifiers and therefore can greatly accelerate the research process in the
public health domain. This work introduces the technical aspects of the
platform and explores its future use cases.
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Authors: Rafael Izbicki, Ann B. Lee, Taylor Pospisil
Date: 2018-05-14 00:00:00
Tasks: Density Estimation

Approximate Bayesian Computation (ABC) is typically used when the likelihood
is either unavailable or intractable but where data can be simulated under
different parameter settings using a forward model. Despite the recent interest
in ABC, high-dimensional data and costly simulations still remain a bottleneck
in some applications. There is also no consensus as to how to best assess the
performance of such methods without knowing the true posterior. We show how a
nonparametric conditional density estimation (CDE) framework, which we refer to
as ABC-CDE, help address three nontrivial challenges in ABC: (i) how to
efficiently estimate the posterior distribution with limited simulations and
different types of data, (ii) how to tune and compare the performance of ABC
and related methods in estimating the posterior itself, rather than just
certain properties of the density, and (iii) how to efficiently choose among a
large set of summary statistics based on a CDE surrogate loss. We provide
theoretical and empirical evidence that justify ABC-CDE procedures that {\em
directly} estimate and assess the posterior based on an initial ABC sample, and
we describe settings where standard ABC and regression-based approaches are
inadequate.
Wearable Audio and IMU Based Shot Detection in Racquet Sports
Authors: Manish Sharma, Akash Anand, Rupika Srivastava, Lakshmi Kaligounder
Date: 2018-05-14 00:00:00

Wearables like smartwatches which are embedded with sensors and powerful
processors, provide a strong platform for development of analytics solutions in
sports domain. To analyze players' games, while motion sensor based shot
detection has been extensively studied in sports like Tennis, Golf, Baseball;
Table Tennis and Badminton are relatively less explored due to possible less
intense hand motion during shots. In our paper, we propose a novel,
computationally inexpensive and real-time system for shot detection in table
tennis, based on fusion of Inertial Measurement Unit (IMU) and audio sensor
data embedded in a wrist-worn wearable. The system builds upon our presented
methodology for synchronizing IMU and audio sensor input in time using detected
shots and achieves 95.6% accuracy. To our knowledge, it is the first
fusion-based solution for sports analysis in wearables. Shot detectors for
other racquet sports as well as further analytics to provide features like shot
classification, rally analysis and recommendations, can easily be built over
our proposed solution.
Faithfully Explaining Rankings in a News Recommender System
Authors: Maartje ter Hoeve, Anne Schuth, Daan Odijk, Maarten de Rijke
Date: 2018-05-14 00:00:00
Tasks: News Recommendation, Recommendation Systems

There is an increasing demand for algorithms to explain their outcomes. So
far, there is no method that explains the rankings produced by a ranking
algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to
explain rankings produced by a ranking algorithm. To efficiently use LISTEN in
production, we train a neural network to learn the underlying explanation space
created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces
faithful explanations and that Q-LISTEN is able to learn these explanations.
Moreover, we show that LISTEN is safe to use in a real world environment: users
of a news recommendation system do not behave significantly differently when
they are exposed to explanations generated by LISTEN instead of manually
generated explanations.
Preserved Structure Across Vector Space Representations
Authors: Andrei Amatuni, Estelle He, Elika Bergelson
Date: 2018-02-02 00:00:00

Certain concepts, words, and images are intuitively more similar than others
(dog vs. cat, dog vs. spoon), though quantifying such similarity is notoriously
difficult. Indeed, this kind of computation is likely a critical part of
learning the category boundaries for words within a given language. Here, we
use a set of 27 items (e.g. 'dog') that are highly common in infants' input,
and use both image- and word-based algorithms to independently compute
similarity among them. We find three key results. First, the pairwise item
similarities derived within image-space and word-space are correlated,
suggesting preserved structure among these extremely different representational
formats. Second, the closest 'neighbors' for each item, within each space,
showed significant overlap (e.g. both found 'egg' as a neighbor of 'apple').
Third, items with the most overlapping neighbors are later-learned by infants
and toddlers. We conclude that this approach, which does not rely on human
ratings of similarity, may nevertheless reflect stable within-class structure
across these two spaces. We speculate that such invariance might aid lexical
acquisition, by serving as an informative marker of category boundaries.
Exploiting Treewidth for Projected Model Counting and its Limits
Authors: Johannes K. Fichte, Michael Morak, Markus Hecher, Stefan Woltran
Date: 2018-05-14 00:00:00

In this paper, we introduce a novel algorithm to solve projected model
counting (PMC). PMC asks to count solutions of a Boolean formula with respect
to a given set of projected variables, where multiple solutions that are
identical when restricted to the projected variables count as only one
solution. Our algorithm exploits small treewidth of the primal graph of the
input instance. It runs in time $O({2^{2^{k+4}} n^2})$ where k is the treewidth
and n is the input size of the instance. In other words, we obtain that the
problem PMC is fixed-parameter tractable when parameterized by treewidth.
Further, we take the exponential time hypothesis (ETH) into consideration and
establish lower bounds of bounded treewidth algorithms for PMC, yielding
asymptotically tight runtime bounds of our algorithm.
Energy Efficient Hadamard Neural Networks
Authors: T. Ceren Deveci, Serdar Cakir, A. Enis Cetin
Date: 2018-05-14 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, object-detection, Object Detection, Object Recognition

Deep learning has made significant improvements at many image processing
tasks in recent years, such as image classification, object recognition and
object detection. Convolutional neural networks (CNN), which is a popular deep
learning architecture designed to process data in multiple array form, show
great success to almost all detection \& recognition problems and computer
vision tasks. However, the number of parameters in a CNN is too high such that
the computers require more energy and larger memory size. In order to solve
this problem, we propose a novel energy efficient model Binary Weight and
Hadamard-transformed Image Network (BWHIN), which is a combination of Binary
Weight Network (BWN) and Hadamard-transformed Image Network (HIN). It is
observed that energy efficiency is achieved with a slight sacrifice at
classification accuracy. Among all energy efficient networks, our novel
ensemble model outperforms other energy efficient models.
Synthesis in pMDPs: A Tale of 1001 Parameters
Authors: Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ufuk Topcu
Date: 2018-03-05 00:00:00

This paper considers parametric Markov decision processes (pMDPs) whose
transitions are equipped with affine functions over a finite set of parameters.
The synthesis problem is to find a parameter valuation such that the
instantiated pMDP satisfies a specification under all strategies. We show that
this problem can be formulated as a quadratically-constrained quadratic program
(QCQP) and is non-convex in general. To deal with the NP-hardness of such
problems, we exploit a convex-concave procedure (CCP) to iteratively obtain
local optima. An appropriate interplay between CCP solvers and probabilistic
model checkers creates a procedure --- realized in the open-source tool
PROPhESY --- that solves the synthesis problem for models with thousands of
parameters.
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
Authors: Tongfei Chen, Jiří Navrátil, Vijay Iyengar, Karthikeyan Shanmugam
Date: 2018-05-14 00:00:00

We propose a novel confidence scoring mechanism for deep neural networks
based on a two-model paradigm involving a base model and a meta-model. The
confidence score is learned by the meta-model observing the base model
succeeding/failing at its task. As features to the meta-model, we investigate
linear classifier probes inserted between the various layers of the base model.
Our experiments demonstrate that this approach outperforms various baselines in
a filtering task, i.e., task of rejecting samples with low confidence.
Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with
and without added noise. We discuss the importance of confidence scoring to
bridge the gap between experimental and real-world applications.
An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features
Authors: Caroline Q. Cordeiro, Sergio O. Ioshii, Jeovane H. Alves, Lucas F. Oliveira
Date: 2018-05-14 00:00:00
Tasks: Diagnostic

Breast cancer (BC) is the most common cancer among women world-wide,
approximately 20-25% of BCs are HER-2 positive. Analysis of HER-2 is
fundamental to defining the appropriate therapy for patients with breast
cancer. Inter-pathologist variability in the test results can affect diagnostic
accuracy. The present study intends to propose an automatic scoring HER-2
algorithm. Based on color features, the technique is fully-automated and avoids
segmentation, showing a concordance higher than 90% with a pathologist in the
experiments realized.
Deep Attentional Structured Representation Learning for Visual Recognition
Authors: Krishna Kanth Nakka, Mathieu Salzmann
Date: 2018-05-14 00:00:00
Tasks: Representation Learning, Scene Recognition

Structured representations, such as Bags of Words, VLAD and Fisher Vectors,
have proven highly effective to tackle complex visual recognition tasks. As
such, they have recently been incorporated into deep architectures. However,
while effective, the resulting deep structured representation learning
strategies typically aggregate local features from the entire image, ignoring
the fact that, in complex recognition tasks, some regions provide much more
discriminative information than others.
  In this paper, we introduce an attentional structured representation learning
framework that incorporates an image-specific attention mechanism within the
aggregation process. Our framework learns to predict jointly the image class
label and an attention map in an end-to-end fashion and without any other
supervision than the target label. As evidenced by our experiments, this
consistently outperforms attention-less structured representation learning and
yields state-of-the-art results on standard scene recognition and fine-grained
categorization benchmarks.
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Authors: Mikhail Khodak, Nikunj Saunshi, YIngyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Classification, Domain Adaptation, Sentiment Analysis, Text Classification, Transfer Learning

Motivations like domain adaptation, transfer learning, and feature learning
have fueled interest in inducing embeddings for rare or unseen words, n-grams,
synsets, and other textual features. This paper introduces a la carte
embedding, a simple and general alternative to the usual word2vec-based
approaches for building such representations that is based upon recent
theoretical results for GloVe-like embeddings. Our method relies mainly on a
linear transformation that is efficiently learnable using pretrained word
vectors and linear regression. This transform is applicable on the fly in the
future when a new text feature or rare word is encountered, even if only a
single usage example is available. We introduce a new dataset showing how the a
la carte method requires fewer examples of words in context to learn
high-quality embeddings and we obtain state-of-the-art results on a nonce task
and some unsupervised document classification tasks.
Visual Global Localization with a Hybrid WNN-CNN Approach
Authors: Avelino Forechi, Thiago Oliveira-Santos, Claudine Badue, Alberto F. de Souza
Date: 2018-05-08 00:00:00
Tasks: Self-Driving Cars, Simultaneous Localization and Mapping, Visual Localization

Currently, self-driving cars rely greatly on the Global Positioning System
(GPS) infrastructure, albeit there is an increasing demand for alternative
methods for GPS-denied environments. One of them is known as place recognition,
which associates images of places with their corresponding positions. We
previously proposed systems based on Weightless Neural Networks (WNN) to
address this problem as a classification task. This encompasses solely one part
of the global localization, which is not precise enough for driverless cars.
Instead of just recognizing past places and outputting their poses, it is
desired that a global localization system estimates the pose of current place
images. In this paper, we propose to tackle this problem as follows. Firstly,
given a live image, the place recognition system returns the most similar image
and its pose. Then, given live and recollected images, a visual localization
system outputs the relative camera pose represented by those images. To
estimate the relative camera pose between the recollected and the current
images, a Convolutional Neural Network (CNN) is trained with the two images as
input and a relative pose vector as output. Together, these systems solve the
global localization problem using the topological and metric information to
approximate the current vehicle pose. The full approach is compared to a Real-
Time Kinematic GPS system and a Simultaneous Localization and Mapping (SLAM)
system. Experimental results show that the proposed approach correctly
localizes a vehicle 90% of the time with a mean error of 1.20m compared to
1.12m of the SLAM system and 0.37m of the GPS, 89% of the time.
Large-Scale QA-SRL Parsing
Authors: Nicholas FitzGerald, Julian Michael, Luheng He, Luke Zettlemoyer
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Role Labeling

We present a new large-scale corpus of Question-Answer driven Semantic Role
Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our
corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for
over 64,000 sentences across 3 domains and was gathered with a new
crowd-sourcing scheme that we show has high precision and good recall at modest
cost. We also present neural models for two QA-SRL subtasks: detecting argument
spans for a predicate and generating questions to label the semantic
relationship. The best models achieve question accuracy of 82.6% and span-level
accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL
prediction task. They can also, as we show, be used to gather additional
annotations at low cost.
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
Authors: Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc, Matthijs Westera, Gemma Boleda
Date: 2018-05-14 00:00:00
Proceeding: SEMEVAL 2018 6

This paper describes our winning contribution to SemEval 2018 Task 4:
Character Identification on Multiparty Dialogues. It is a simple, standard
model with one key innovation, an entity library. Our results show that this
innovation greatly facilitates the identification of infrequent characters.
Because of the generic nature of our model, this finding is potentially
relevant to any task that requires effective learning from sparse or unbalanced
data.
Deep Forest
Authors: Zhi-Hua Zhou, Ji Feng
Date: 2017-02-28 00:00:00

Current deep learning models are mostly build upon neural networks, i.e., multiple layers of parameterized differentiable nonlinear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules. We conjecture that the mystery behind the success of deep neural networks owes much to three characteristics, i.e., layer-by-layer processing, in-model feature transformation and sufficient model complexity. We propose the gcForest approach, which generates \textit{deep forest} holding these characteristics. This is a decision tree ensemble approach, with much less hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to get excellent performance by using the same default setting. This study opens the door of deep learning based on non-differentiable modules, and exhibits the possibility of constructing deep models without using backpropagation.