pwc-archive/papers-with-abstracts huggingface dataset:
MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation
Authors: Lex Fridman, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik, Jack Terwilliger, Aleksandr Patsekin, Julia Kindelsberger, Li Ding, Sean Seaman, Alea Mehler, Andrew Sipperley, Anthony Pettinato, Bobbie Seppelt, Linda Angell, Bruce Mehler, Bryan Reimer
Date: 2017-11-19 00:00:00

For the foreseeble future, human beings will likely remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving. The governing objectives of the MIT Autonomous Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning based internal and external perception systems, (2) gain a holistic understanding of how human beings interact with vehicle automation technology by integrating video data with vehicle state data, driver characteristics, mental models, and self-reported experiences with technology, and (3) identify how technology and other factors related to automation adoption and use can be improved in ways that save lives. In pursuing these objectives, we have instrumented 23 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6 vehicles for both long-term (over a year per driver) and medium term (one month per driver) naturalistic driving data collection. Furthermore, we are continually developing new methods for analysis of the massive-scale dataset collected from the instrumented vehicle fleet. The recorded data streams include IMU, GPS, CAN messages, and high-definition video streams of the driver face, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). The study is on-going and growing. To date, we have 122 participants, 15,610 days of participation, 511,638 miles, and 7.1 billion video frames. This paper presents the design of the study, the data collection hardware, the processing of the data, and the computer vision algorithms currently being used to extract actionable knowledge from the data.
Discriminant Projection Representation-based Classification for Vision Recognition
Authors: Qingxiang Feng, Yicong Zhou
Date: 2017-11-19 00:00:00
Tasks: Classification, General Classification, Sparse Representation-based Classification

Representation-based classification methods such as sparse
representation-based classification (SRC) and linear regression classification
(LRC) have attracted a lot of attentions. In order to obtain the better
representation, a novel method called projection representation-based
classification (PRC) is proposed for image recognition in this paper. PRC is
based on a new mathematical model. This model denotes that the 'ideal
projection' of a sample point $x$ on the hyper-space $H$ may be gained by
iteratively computing the projection of $x$ on a line of hyper-space $H$ with
the proper strategy. Therefore, PRC is able to iteratively approximate the
'ideal representation' of each subject for classification. Moreover, the
discriminant PRC (DPRC) is further proposed, which obtains the discriminant
information by maximizing the ratio of the between-class reconstruction error
over the within-class reconstruction error. Experimental results on five
typical databases show that the proposed PRC and DPRC are effective and
outperform other state-of-the-art methods on several vision recognition tasks.
Color Face Recognition using High-Dimension Quaternion-based Adaptive Representation
Authors: Qingxiang Feng, Yicong Zhou
Date: 2017-11-19 00:00:00
Tasks: Face Recognition, General Classification, Sparse Representation-based Classification, Vocal Bursts Intensity Prediction

Recently, quaternion collaborative representation-based classification (QCRC)
and quaternion sparse representation-based classification (QSRC) have been
proposed for color face recognition. They can obtain correlation information
among different color channels. However, their performance is unstable in
different conditions. For example, QSRC performs better than than QCRC on some
situations but worse on other situations. To benefit from quaternion-based
$e_2$-norm minimization in QCRC and quaternion-based $e_1$-norm minimization in
QSRC, we propose the quaternion-based adaptive representation (QAR) that uses a
quaternion-based $e_p$-norm minimization ($1 \le p \le 2$) for color face
recognition. To obtain the high dimension correlation information among
different color channels, we further propose the high-dimension
quaternion-based adaptive representation (HD-QAR). The experimental results
demonstrate that the proposed QAR and HD-QAR achieve better recognition rates
than QCRC, QSRC and several state-of-the-art methods.
A Novel Framework for Robustness Analysis of Visual QA Models
Authors: Jia-Hong Huang, Cuong Duc Dao, Modar Alfadly, Bernard Ghanem
Date: 2017-11-16 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Deep neural networks have been playing an essential role in many computer
vision tasks including Visual Question Answering (VQA). Until recently, the
study of their accuracy was the main focus of research but now there is a trend
toward assessing the robustness of these models against adversarial attacks by
evaluating their tolerance to varying noise levels. In VQA, adversarial attacks
can target the image and/or the proposed main question and yet there is a lack
of proper analysis of the later. In this work, we propose a flexible framework
that focuses on the language part of VQA that uses semantically relevant
questions, dubbed basic questions, acting as controllable noise to evaluate the
robustness of VQA models. We hypothesize that the level of noise is positively
correlated to the similarity of a basic question to the main question. Hence,
to apply noise on any given main question, we rank a pool of basic questions
based on their similarity by casting this ranking task as a LASSO optimization
problem. Then, we propose a novel robustness measure, R_score, and two
large-scale basic question datasets (BQDs) in order to standardize robustness
analysis for VQA models.
How much is my car worth? A methodology for predicting used cars prices using Random Forest
Authors: Nabarun Pal, Priya Arora, Dhanasekar Sundararaman, Puneet Kohli, Sai Sumanth Palakurthy
Date: 2017-11-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Cars are being sold more than ever. Developing countries adopt the lease
culture instead of buying a new car due to affordability. Therefore, the rise
of used cars sales is exponentially increasing. Car sellers sometimes take
advantage of this scenario by listing unrealistic prices owing to the demand.
Therefore, arises a need for a model that can assign a price for a vehicle by
evaluating its features taking the prices of other cars into consideration. In
this paper, we use supervised learning method namely Random Forest to predict
the prices of used cars. The model has been chosen after careful exploratory
data analysis to determine the impact of each feature on price. A Random Forest
with 500 Decision Trees were created to train the data. From experimental
results, the training accuracy was found out to be 95.82%, and the testing
accuracy was 83.63%. The the model can predict the price of cars accurately by
choosing the most correlated features.
Intelligent Word Embeddings of Free-Text Radiology Reports
Authors: Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin
Date: 2017-11-19 00:00:00
Tasks: Word Embeddings

Radiology reports are a rich resource for advancing deep learning
applications in medicine by leveraging the large volume of data continuously
being updated, integrated, and shared. However, there are significant
challenges as well, largely due to the ambiguity and subtlety of natural
language. We propose a hybrid strategy that combines semantic-dictionary
mapping and word2vec modeling for creating dense vector embeddings of free-text
radiology reports. Our method leverages the benefits of both
semantic-dictionary mapping as well as unsupervised learning. Using the vector
representation, we automatically classify the radiology reports into three
classes denoting confidence in the diagnosis of intracranial hemorrhage by the
interpreting radiologist. We performed experiments with varying hyperparameter
settings of the word embeddings and a range of different classifiers. Best
performance achieved was a weighted precision of 88% and weighted recall of
90%. Our work offers the potential to leverage unstructured electronic health
record data by allowing direct analysis of narrative clinical notes.
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning
Authors: Ziming Zhang, Yuanwei Wu, Guanghui Wang
Date: 2017-11-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object Recognition

Understanding the global optimality in deep learning (DL) has been attracting
more and more attention recently. Conventional DL solvers, however, have not
been developed intentionally to seek for such global optimality. In this paper
we propose a novel approximation algorithm, BPGrad, towards optimizing deep
models globally via branch and pruning. Our BPGrad algorithm is based on the
assumption of Lipschitz continuity in DL, and as a result it can adaptively
determine the step size for current gradient given the history of previous
updates, wherein theoretically no smaller steps can achieve the global
optimality. We prove that, by repeating such branch-and-pruning procedure, we
can locate the global optimality within finite iterations. Empirically an
efficient solver based on BPGrad for DL is proposed as well, and it outperforms
conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the
tasks of object recognition, detection, and segmentation.
A Tutorial on Thompson Sampling
Authors: Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
Date: 2017-07-07 00:00:00
Tasks: Active Learning, Product Recommendation, Reinforcement Learning, Thompson Sampling

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions. We will also discuss when and why Thompson sampling is or is not effective and relations to alternative algorithms.
Object Manipulation Learning by Imitation
Authors: Zhen Zeng, Benjamin Kuipers
Date: 2016-03-03 00:00:00
Tasks: Object, Reinforcement Learning

We aim to enable robot to learn object manipulation by imitation. Given
external observations of demonstrations on object manipulations, we believe
that two underlying problems to address in learning by imitation is 1) segment
a given demonstration into skills that can be individually learned and reused,
and 2) formulate the correct RL (Reinforcement Learning) problem that only
considers the relevant aspects of each skill so that the policy for each skill
can be effectively learned. Previous works made certain progress in this
direction, but none has taken private information into account. The public
information is the information that is available in the external observations
of demonstration, and the private information is the information that are only
available to the agent that executes the actions, such as tactile sensations.
Our contribution is that we provide a method for the robot to automatically
segment the demonstration of object manipulations into multiple skills, and
formulate the correct RL problem for each skill, and automatically decide
whether the private information is an important aspect of each skill based on
interaction with the world. Our experiment shows that our robot learns to pick
up a block, and stack it onto another block by imitating an observed
demonstration. The evaluation is based on 1) whether the demonstration is
reasonably segmented, 2) whether the correct RL problems are formulated, 3) and
whether a good policy is learned.
A novel total variation model based on kernel functions and its application
Authors: Zhizheng Liang, Lei Zhang, Jin Liu, Yong Zhou
Date: 2017-11-19 00:00:00

The total variation (TV) model and its related variants have already been
proposed for image processing in previous literature. In this paper a novel
total variation model based on kernel functions is proposed. In this novel
model, we first map each pixel value of an image into a Hilbert space by using
a nonlinear map, and then define a coupled image of an original image in order
to construct a kernel function. Finally, the proposed model is solved in a
kernel function space instead of in the projecting space from a nonlinear map.
For the proposed model, we theoretically show under what conditions the mapping
image is in the space of bounded variation when the original image is in the
space of bounded variation. It is also found that the proposed model further
extends the generalized TV model and the information from three different
channels of color images can be fused by adopting various kernel functions. A
series of experiments on some gray and color images are carried out to
demonstrate the effectiveness of the proposed model.
Triangle Generative Adversarial Networks
Authors: Zhe Gan, Liqun Chen, Wei-Yao Wang, Yunchen Pu, Yizhe Zhang, Hao liu, Chunyuan Li, Lawrence Carin
Date: 2017-09-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Attribute, Generative Adversarial Network, image-classification, Image Classification, Image Generation, Image-to-Image Translation, Semi-Supervised Image Classification, Translation

A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for
semi-supervised cross-domain joint distribution matching, where the training
data consists of samples from each domain, and supervision of domain
correspondence is provided by only a few paired samples. $\Delta$-GAN consists
of four neural networks, two generators and two discriminators. The generators
are designed to learn the two-way conditional distributions between the two
domains, while the discriminators implicitly define a ternary discriminative
function, which is trained to distinguish real data pairs and two kinds of fake
data pairs. The generators and discriminators are trained together using
adversarial learning. Under mild assumptions, in theory the joint distributions
characterized by the two generators concentrate to the data distribution. In
experiments, three different kinds of domain pairs are considered, image-label,
image-image and image-attribute pairs. Experiments on semi-supervised image
classification, image-to-image translation and attribute-based image generation
demonstrate the superiority of the proposed approach.
Robust Synthetic Control
Authors: Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
Date: 2017-11-18 00:00:00
Tasks: counterfactual

We present a robust generalization of the synthetic control method for
comparative case studies. Like the classical method, we present an algorithm to
estimate the unobservable counterfactual of a treatment unit. A distinguishing
feature of our algorithm is that of de-noising the data matrix via singular
value thresholding, which renders our approach robust in multiple facets: it
automatically identifies a good subset of donors, overcomes the challenges of
missing data, and continues to work well in settings where covariate
information may not be provided. To begin, we establish the condition under
which the fundamental assumption in synthetic control-like approaches holds,
i.e. when the linear relationship between the treatment unit and the donor pool
prevails in both the pre- and post-intervention periods. We provide the first
finite sample analysis for a broader class of models, the Latent Variable
Model, in contrast to Factor Models previously considered in the literature.
Further, we show that our de-noising procedure accurately imputes missing
entries, producing a consistent estimator of the underlying signal matrix
provided $p = \Omega( T^{-1 + \zeta})$ for some $\zeta > 0$; here, $p$ is the
fraction of observed data and $T$ is the time interval of interest. Under the
same setting, we prove that the mean-squared-error (MSE) in our prediction
estimation scales as $O(\sigma^2/p + 1/\sqrt{T})$, where $\sigma^2$ is the
noise variance. Using a data aggregation method, we show that the MSE can be
made as small as $O(T^{-1/2+\gamma})$ for any $\gamma \in (0, 1/2)$, leading to
a consistent estimator. We also introduce a Bayesian framework to quantify the
model uncertainty through posterior probabilities. Our experiments, using both
real-world and synthetic datasets, demonstrate that our robust generalization
yields an improvement over the classical synthetic control method.
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Authors: Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham
Date: 2017-09-12 00:00:00
Tasks: Image Classification

Recently, deep residual networks have been successfully applied in many
computer vision and natural language processing tasks, pushing the
state-of-the-art performance with deeper and wider architectures. In this work,
we interpret deep residual networks as ordinary differential equations (ODEs),
which have long been studied in mathematics and physics with rich theoretical
and empirical success. From this interpretation, we develop a theoretical
framework on stability and reversibility of deep neural networks, and derive
three reversible neural network architectures that can go arbitrarily deep in
theory. The reversibility property allows a memory-efficient implementation,
which does not need to store the activations for most hidden layers. Together
with the stability of our architectures, this enables training deeper networks
using only modest computational resources. We provide both theoretical analyses
and empirical results. Experimental results demonstrate the efficacy of our
architectures against several strong baselines on CIFAR-10, CIFAR-100 and
STL-10 with superior or on-par state-of-the-art performance. Furthermore, we
show our architectures yield superior results when trained using fewer training
data.
Computational Results for Extensive-Form Adversarial Team Games
Authors: Andrea Celli, Nicola Gatti
Date: 2017-11-18 00:00:00
Tasks: Form

We provide, to the best of our knowledge, the first computational study of
extensive-form adversarial team games. These games are sequential, zero-sum
games in which a team of players, sharing the same utility function, faces an
adversary. We define three different scenarios according to the communication
capabilities of the team. In the first, the teammates can communicate and
correlate their actions both before and during the play. In the second, they
can only communicate before the play. In the third, no communication is
possible at all. We define the most suitable solution concepts, and we study
the inefficiency caused by partial or null communication, showing that the
inefficiency can be arbitrarily large in the size of the game tree.
Furthermore, we study the computational complexity of the equilibrium-finding
problem in the three scenarios mentioned above, and we provide, for each of the
three scenarios, an exact algorithm. Finally, we empirically evaluate the
scalability of the algorithms in random games and the inefficiency caused by
partial or null communication.
Deep Gaussian Mixture Models
Authors: Cinzia Viroli, Geoffrey J. McLachlan
Date: 2017-11-18 00:00:00
Tasks: Dimensionality Reduction

Deep learning is a hierarchical inference method formed by subsequent
multiple layers of learning able to more efficiently describe complex
relationships. In this work, Deep Gaussian Mixture Models are introduced and
discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers
of latent variables, where, at each layer, the variables follow a mixture of
Gaussian distributions. Thus, the deep mixture model consists of a set of
nested mixtures of linear models, which globally provide a nonlinear model able
to describe the data in a very flexible way. In order to avoid
overparameterized solutions, dimension reduction by factor models can be
applied at each layer of the architecture thus resulting in deep mixtures of
factor analysers.
Optimal Shrinkage of Singular Values Under Random Data Contamination
Authors: Danny Barash, Matan Gavish
Date: 2017-10-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Missing Values

A low rank matrix X has been contaminated by uniformly distributed noise,
missing values, outliers and corrupt entries. Reconstruction of X from the
singular values and singular vectors of the contaminated matrix Y is a key
problem in machine learning, computer vision and data science. In this paper we
show that common contamination models (including arbitrary combinations of
uniform noise,missing values, outliers and corrupt entries) can be described
efficiently using a single framework. We develop an asymptotically optimal
algorithm that estimates X by manipulation of the singular values of Y , which
applies to any of the contamination models considered. Finally, we find an
explicit signal-to-noise cutoff, below which estimation of X from the singular
value decomposition of Y must fail, in a well-defined sense.
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks
Authors: Roby Velez, Jeff Clune
Date: 2017-05-20 00:00:00
Tasks: Diagnostic

A long-term goal of AI is to produce agents that can learn a diversity of
skills throughout their lifetimes and continuously improve those skills via
experience. A longstanding obstacle towards that goal is catastrophic
forgetting, which is when learning new information erases previously learned
information. Catastrophic forgetting occurs in artificial neural networks
(ANNs), which have fueled most recent advances in AI. A recent paper proposed
that catastrophic forgetting in ANNs can be reduced by promoting modularity,
which can limit forgetting by isolating task information to specific clusters
of nodes and connections (functional modules). While the prior work did show
that modular ANNs suffered less from catastrophic forgetting, it was not able
to produce ANNs that possessed task-specific functional modules, thereby
leaving the main theory regarding modularity and forgetting untested. We
introduce diffusion-based neuromodulation, which simulates the release of
diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up
or down regulate) learning in a spatial region. On the simple diagnostic
problem from the prior work, diffusion-based neuromodulation 1) induces
task-specific learning in groups of nodes and connections (task-specific
localized learning), which 2) produces functional modules for each subtask, and
3) yields higher performance by eliminating catastrophic forgetting. Overall,
our results suggest that diffusion-based neuromodulation promotes task-specific
localized learning and functional modularity, which can help solve the
challenging, but important problem of catastrophic forgetting.