pwc-archive/papers-with-abstracts huggingface dataset:
Pragmatic-Pedagogic Value Alignment
Authors: Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
Date: 2017-07-20 00:00:00
Tasks: Decision Making, Reinforcement Learning

As intelligent systems gain autonomy and capability, it becomes vital to
ensure that their objectives match those of their human users; this is known as
the value-alignment problem. In robotics, value alignment is key to the design
of collaborative robots that can integrate into human workflows, successfully
inferring and adapting to their users' objectives as they go. We argue that a
meaningful solution to value alignment must combine multi-agent decision theory
with rich mathematical models of human cognition, enabling robots to tap into
people's natural collaborative capabilities. We present a solution to the
cooperative inverse reinforcement learning (CIRL) dynamic game based on
well-established cognitive models of decision making and theory of mind. The
solution captures a key reciprocity relation: the human will not plan her
actions in isolation, but rather reason pedagogically about how the robot might
learn from them; the robot, in turn, can anticipate this and interpret the
human's actions pragmatically. To our knowledge, this work constitutes the
first formal analysis of value alignment grounded in empirically validated
cognitive models.
Learning Low-Dimensional Metrics
Authors: Lalit Jain, Blake Mason, Robert Nowak
Date: 2017-09-18 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Metric Learning

This paper investigates the theoretical foundations of metric learning,
focused on three key questions that are not fully addressed in prior work: 1)
we consider learning general low-dimensional (low-rank) metrics as well as
sparse metrics; 2) we develop upper and lower (minimax)bounds on the
generalization error; 3) we quantify the sample complexity of metric learning
in terms of the dimension of the feature space and the dimension/rank of the
underlying metric;4) we also bound the accuracy of the learned metric relative
to the underlying true generative metric. All the results involve novel
mathematical approaches to the metric learning problem, and lso shed new light
on the special case of ordinal embedding (aka non-metric multidimensional
scaling).
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Authors: Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally
Date: 2017-12-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Federated Learning, image-classification, Image Classification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile. Code is available at: https://github.com/synxlin/deep-gradient-compression.
Re-Weighted Learning for Sparsifying Deep Neural Networks
Authors: Igor Fedorov, Bhaskar D. Rao
Date: 2018-02-05 00:00:00

This paper addresses the topic of sparsifying deep neural networks (DNN's).
While DNN's are powerful models that achieve state-of-the-art performance on a
large number of tasks, the large number of model parameters poses serious
storage and computational challenges. To combat these difficulties, a growing
line of work focuses on pruning network weights without sacrificing
performance. We propose a general affine scaling transformation (AST) algorithm
to sparsify DNN's. Our approach follows in the footsteps of popular sparse
recovery techniques, which have yet to be explored in the context of DNN's. We
describe a principled framework for transforming densely connected DNN's into
sparsely connected ones without sacrificing network performance. Unlike
existing methods, our approach is able to learn sparse connections at each
layer simultaneously, and achieves comparable pruning results on the
architecture tested.
Fast and accurate approximation of the full conditional for gamma shape parameters
Authors: Jeffrey W. Miller
Date: 2018-02-05 00:00:00
Tasks: Numerical Integration

The gamma distribution arises frequently in Bayesian models, but there is not
an easy-to-use conjugate prior for the shape parameter of a gamma. This
inconvenience is usually dealt with by using either Metropolis-Hastings moves,
rejection sampling methods, or numerical integration. However, in models with a
large number of shape parameters, these existing methods are slower or more
complicated than one would like, making them burdensome in practice. It turns
out that the full conditional distribution of the gamma shape parameter is well
approximated by a gamma distribution, even for small sample sizes, when the
prior on the shape parameter is also a gamma distribution. This article
introduces a quick and easy algorithm for finding a gamma distribution that
approximates the full conditional distribution of the shape parameter. We
empirically demonstrate the speed and accuracy of the approximation across a
wide range of conditions. If exactness is required, the approximation can be
used as a proposal distribution for Metropolis-Hastings.
An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation
Authors: Kevan Yuen, Mohan M. Trivedi
Date: 2018-02-05 00:00:00
Tasks: Face Alignment, Face Detection, Occlusion Estimation

A key step to driver safety is to observe the driver's activities with the
face being a key step in this process to extracting information such as head
pose, blink rate, yawns, talking to passenger which can then help derive higher
level information such as distraction, drowsiness, intent, and where they are
looking. In the context of driving safety, it is important for the system
perform robust estimation under harsh lighting and occlusion but also be able
to detect when the occlusion occurs so that information predicted from occluded
parts of the face can be taken into account properly. This paper introduces the
Occluded Stacked Hourglass, based on the work of original Stacked Hourglass
network for body pose joint estimation, which is retrained to process a
detected face window and output 68 occlusion heat maps, each corresponding to a
facial landmark. Landmark location, occlusion levels and a refined face
detection score, to reject false positives, are extracted from these heat maps.
Using the facial landmark locations, features such as head pose and eye/mouth
openness can be extracted to derive driver attention and activity. The system
is evaluated for face detection, head pose, and occlusion estimation on various
datasets in the wild, both quantitatively and qualitatively, and shows
state-of-the-art results.
A learning framework for winner-take-all networks with stochastic synapses
Authors: Hesham Mostafa, Gert Cauwenberghs
Date: 2017-08-14 00:00:00
Tasks: All

Many recent generative models make use of neural networks to transform the
probability distribution of a simple low-dimensional noise process into the
complex distribution of the data. This raises the question of whether
biological networks operate along similar principles to implement a
probabilistic model of the environment through transformations of intrinsic
noise processes. The intrinsic neural and synaptic noise processes in
biological networks, however, are quite different from the noise processes used
in current abstract generative networks. This, together with the discrete
nature of spikes and local circuit interactions among the neurons, raises
several difficulties when using recent generative modeling frameworks to train
biologically motivated models. In this paper, we show that a biologically
motivated model based on multi-layer winner-take-all (WTA) circuits and
stochastic synapses admits an approximate analytical description. This allows
us to use the proposed networks in a variational learning setting where
stochastic backpropagation is used to optimize a lower bound on the data log
likelihood, thereby learning a generative model of the data. We illustrate the
generality of the proposed networks and learning technique by using them in a
structured output prediction task, and in a semi-supervised learning task. Our
results extend the domain of application of modern stochastic network
architectures to networks where synaptic transmission failure is the principal
noise mechanism.
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries
Authors: Chandrayee Basu, Mukesh Singhal, Anca D. Dragan
Date: 2018-02-05 00:00:00

We focus on learning the desired objective function for a robot. Although
trajectory demonstrations can be very informative of the desired objective,
they can also be difficult for users to provide. Answers to comparison queries,
asking which of two trajectories is preferable, are much easier for users, and
have emerged as an effective alternative. Unfortunately, comparisons are far
less informative. We propose that there is much richer information that users
can easily provide and that robots ought to leverage. We focus on augmenting
comparisons with feature queries, and introduce a unified formalism for
treating all answers as observations about the true desired reward. We derive
an active query selection algorithm, and test these queries in simulation and
on real users. We find that richer, feature-augmented queries can extract more
information faster, leading to robots that better match user preferences in
their behavior.
Learning to Singulate Objects using a Push Proposal Network
Authors: Andreas Eitel, Nico Hauff, Wolfram Burgard
Date: 2017-07-25 00:00:00

Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.de
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Authors: Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine
Date: 2018-02-05 00:00:00
Tasks: Meta-Learning, One-Shot Learning

Humans and animals are capable of learning a new behavior by observing others
perform the skill just once. We consider the problem of allowing a robot to do
the same -- learning from a raw video pixels of a human, even when there is
substantial domain shift in the perspective, environment, and embodiment
between the robot and the observed human. Prior approaches to this problem have
hand-specified how human and robot actions correspond and often relied on
explicit human pose detection systems. In this work, we present an approach for
one-shot learning from a video of a human by using human and robot
demonstration data from a variety of previous tasks to build up prior knowledge
through meta-learning. Then, combining this prior knowledge and only a single
video demonstration from a human, the robot can perform the task that the human
demonstrated. We show experiments on both a PR2 arm and a Sawyer arm,
demonstrating that after meta-learning, the robot can learn to place, push, and
pick-and-place new objects using just one video of a human performing the
manipulation.
Generating Compact Tree Ensembles via Annealing
Authors: Gitesh Dawer, Yangzi Guo, Adrian Barbu
Date: 2017-09-16 00:00:00

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic behavior of selecting the same tree again and again due to the way the loss is optimized. At the same time, Random Forest is not based on loss optimization and obtains a more complex and less interpretable model. In this paper we present a novel method for obtaining compact tree ensembles by growing a large pool of trees in parallel with many independent boosting threads and then selecting a small subset and updating their leaf weights by loss optimization. We allow for the trees in the initial pool to have different depths which further helps with generalization. Experiments on real datasets show that the obtained model has usually a smaller loss than boosting, which is also reflected in a lower misclassification error on the test set.
Real-time Prediction of Intermediate-Horizon Automotive Collision Risk
Authors: Blake Wulfe, Sunil Chintakindi, Sou-Cheng T. Choi, Rory Hartong-Redden, Anuradha Kodali, Mykel J. Kochenderfer
Date: 2018-02-05 00:00:00
Tasks: Collision Avoidance, Domain Adaptation, Prediction

Advanced collision avoidance and driver hand-off systems can benefit from the
ability to accurately predict, in real time, the probability a vehicle will be
involved in a collision within an intermediate horizon of 10 to 20 seconds. The
rarity of collisions in real-world data poses a significant challenge to
developing this capability because, as we demonstrate empirically,
intermediate-horizon risk prediction depends heavily on high-dimensional driver
behavioral features. As a result, a large amount of data is required to fit an
effective predictive model. In this paper, we assess whether simulated data can
help alleviate this issue. Focusing on highway driving, we present a three-step
approach for generating data and fitting a predictive model capable of
real-time prediction. First, high-risk automotive scenes are generated using
importance sampling on a learned Bayesian network scene model. Second,
collision risk is estimated through Monte Carlo simulation. Third, a neural
network domain adaptation model is trained on real and simulated data to
address discrepancies between the two domains. Experiments indicate that
simulated data can mitigate issues resulting from collision rarity, thereby
improving risk prediction in real-world data.
Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and $ β$-Mixing
Authors: Kam Chung Wong, Zifan Li, Ambuj Tewari
Date: 2016-02-12 00:00:00
Tasks: Time Series, Time Series Analysis

Many theoretical results on estimation of high dimensional time series
require specifying an underlying data generating model (DGM). Instead, along
the footsteps of~\cite{wong2017lasso}, this paper relies only on (strict)
stationarity and $ \beta $-mixing condition to establish consistency of lasso
when data comes from a $\beta$-mixing process with marginals having subgaussian
tails. Because of the general assumptions, the data can come from DGMs
different than standard time series models such as VAR or ARCH. When the true
DGM is not VAR, the lasso estimates correspond to those of the best linear
predictors using the past observations. We establish non-asymptotic
inequalities for estimation and prediction errors of the lasso estimates.
Together with~\cite{wong2017lasso}, we provide lasso guarantees that cover full
spectrum of the parameters in specifications of $ \beta $-mixing subgaussian
time series. Applications of these results potentially extend to non-Gaussian,
non-Markovian and non-linear times series models as the examples we provide
demonstrate. In order to prove our results, we derive a novel Hanson-Wright
type concentration inequality for $\beta$-mixing subgaussian random vectors
that may be of independent interest.
Abstractly Interpreting Argumentation Frameworks for Sharpening Extensions
Authors: Ryuta Arisaka, Jeremie Dauphin
Date: 2018-02-05 00:00:00

Cycles of attacking arguments pose non-trivial issues in Dung style
argumentation theory, apparent behavioural difference between odd and even
length cycles being a notable one. While a few methods were proposed for
treating them, to - in particular - enable selection of acceptable arguments in
an odd-length cycle when Dung semantics could select none, so far the issues
have been observed from a purely argument-graph-theoretic perspective. Per
contra, we consider argument graphs together with a certain lattice like
semantic structure over arguments e.g. ontology. As we show, the
semantic-argumentgraphic hybrid theory allows us to apply abstract
interpretation, a widely known methodology in static program analysis, to
formal argumentation. With this, even where no arguments in a cycle could be
selected sensibly, we could say more about arguments acceptability of an
argument framework that contains it. In a certain sense, we can verify Dung
extensions with respect to a semantic structure in this hybrid theory, to
consolidate our confidence in their suitability. By defining the theory, and by
making comparisons to existing approaches, we ultimately discover that whether
Dung semantics, or an alternative semantics such as cf2, is adequate or
problematic depends not just on an argument graph but also on the semantic
relation among the arguments in the graph.
Background subtraction using the factored 3-way restricted Boltzmann machines
Authors: Soonam Lee, Daekeun Kim
Date: 2018-02-05 00:00:00
Tasks: Translation

In this paper, we proposed a method for reconstructing the 3D model based on
continuous sensory input. The robot can draw on extremely large data from the
real world using various sensors. However, the sensory inputs are usually too
noisy and high-dimensional data. It is very difficult and time consuming for
robot to process using such raw data when the robot tries to construct 3D
model. Hence, there needs to be a method that can extract useful information
from such sensory inputs. To address this problem our method utilizes the
concept of Object Semantic Hierarchy (OSH). Different from the previous work
that used this hierarchy framework, we extract the motion information using the
Deep Belief Network technique instead of applying classical computer vision
approaches. We have trained on two large sets of random dot images (10,000)
which are translated and rotated, respectively, and have successfully extracted
several bases that explain the translation and rotation motion. Based on this
translation and rotation bases, background subtraction have become possible
using Object Semantic Hierarchy.
Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
Authors: Isaac J. Sledge, Matthew S. Emigh, Jose C. Principe
Date: 2018-02-05 00:00:00
Tasks: Reinforcement Learning

Reinforcement learning in environments with many action-state pairs is
challenging. At issue is the number of episodes needed to thoroughly search the
policy space. Most conventional heuristics address this search problem in a
stochastic manner. This can leave large portions of the policy space unvisited
during the early training stages. In this paper, we propose an
uncertainty-based, information-theoretic approach for performing guided
stochastic searches that more effectively cover the policy space. Our approach
is based on the value of information, a criterion that provides the optimal
trade-off between expected costs and the granularity of the search process. The
value of information yields a stochastic routine for choosing actions during
learning that can explore the policy space in a coarse to fine manner. We
augment this criterion with a state-transition uncertainty factor, which guides
the search process into previously unexplored regions of the policy space.
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
Authors: Victor Campos, Brendan Jou, Xavier Giro-i-Nieto, Jordi Torres, Shih-Fu Chang
Date: 2017-08-22 00:00:00
Proceeding: ICLR 2018 1

Recurrent Neural Networks (RNNs) continue to show outstanding performance in
sequence modeling tasks. However, training RNNs on long sequences often face
challenges like slow inference, vanishing gradients and difficulty in capturing
long term dependencies. In backpropagation through time settings, these issues
are tightly coupled with the large, sequential computational graph resulting
from unfolding the RNN in time. We introduce the Skip RNN model which extends
existing RNN models by learning to skip state updates and shortens the
effective size of the computational graph. This model can also be encouraged to
perform fewer state updates through a budget constraint. We evaluate the
proposed model on various tasks and show how it can reduce the number of
required RNN updates while preserving, and sometimes even improving, the
performance of the baseline RNN models. Source code is publicly available at
https://imatge-upc.github.io/skiprnn-2017-telecombcn/ .
3D non-rigid registration using color: Color Coherent Point Drift
Authors: Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Victor Villena-Martinez, Robert B. Fisher
Date: 2018-02-05 00:00:00

Research into object deformations using computer vision techniques has been
under intense study in recent years. A widely used technique is 3D non-rigid
registration to estimate the transformation between two instances of a
deforming structure. Despite many previous developments on this topic, it
remains a challenging problem. In this paper we propose a novel approach to
non-rigid registration combining two data spaces in order to robustly calculate
the correspondences and transformation between two data sets. In particular, we
use point color as well as 3D location as these are the common outputs of RGB-D
cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an
extension of the CPD method [1]). Evaluation is performed using synthetic and
real data. The synthetic data includes easy shapes that allow evaluation of the
effect of noise, outliers and missing data. Moreover, an evaluation of
realistic figures obtained using Blensor is carried out. Real data acquired
using a general purpose Primesense Carmine sensor is used to validate the CCPD
for real shapes. For all tests, the proposed method is compared to the original
CPD showing better results in registration accuracy in most cases.