pwc-archive/papers-with-abstracts huggingface dataset:
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Authors: Yuanming Hu, Jian-Cheng Liu, Andrew Spielberg, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu, Daniela Rus, Wojciech Matusik
Date: 2018-10-02 00:00:00
Tasks: Motion Planning

Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.
A Unified Framework for Clustering Constrained Data without Locality Property
Authors: Hu Ding, Jinhui Xu
Date: 2018-10-02 00:00:00
Tasks: Clustering, Constrained Clustering, LEMMA

In this paper, we consider a class of constrained clustering problems of
points in $\mathbb{R}^{d}$, where $d$ could be rather high. A common feature of
these problems is that their optimal clusterings no longer have the locality
property (due to the additional constraints), which is a key property required
by many algorithms for their unconstrained counterparts. To overcome the
difficulty caused by the loss of locality, we present in this paper a unified
framework, called {\em Peeling-and-Enclosing (PnE)}, to iteratively solve two
variants of the constrained clustering problems, {\em constrained $k$-means
clustering} ($k$-CMeans) and {\em constrained $k$-median clustering}
($k$-CMedian). Our framework is based on two standalone geometric techniques,
called {\em Simplex Lemma} and {\em Weaker Simplex Lemma}, for $k$-CMeans and
$k$-CMedian, respectively. The simplex lemma (or weaker simplex lemma) enables
us to efficiently approximate the mean (or median) point of an unknown set of
points by searching a small-size grid, independent of the dimensionality of the
space, in a simplex (or the surrounding region of a simplex), and thus can be
used to handle high dimensional data. If $k$ and $\frac{1}{\epsilon}$ are fixed
numbers, our framework generates, in nearly linear time ({\em i.e.,} $O(n(\log
n)^{k+1}d)$), $O((\log n)^{k})$ $k$-tuple candidates for the $k$ mean or median
points, and one of them induces a $(1+\epsilon)$-approximation for $k$-CMeans
or $k$-CMedian, where $n$ is the number of points. Combining this unified
framework with a problem-specific selection algorithm (which determines the
best $k$-tuple candidate), we obtain a $(1+\epsilon)$-approximation for each of
the constrained clustering problems. We expect that our technique will be
applicable to other constrained clustering problems without locality.
Interpretable Neuron Structuring with Graph Spectral Regularization
Authors: Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio, Kristina Yim, Rebecca Muhle, James Noonan, Guy Wolf, Smita Krishnaswamy
Date: 2018-09-30 00:00:00
Proceeding: ICLR 2019 5

While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly impacting performance on the primary task. Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer. This penalty encourages activations to be smooth either on a predetermined graph or on a feature-space graph learned from the data via co-activations of a hidden layer of the neural network. We show numerous uses for this additional structure including cluster indication and visualization in biological and image data sets.
Reinforcement Learning with Perturbed Rewards
Authors: Jingkang Wang, Yang Liu, Bo Li
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in practice (e.g., when rewards are collected through sensors), and is therefore not credible. In addition, for applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors by receiving corrupted rewards. In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix. We develop a robust RL framework that enables agents to learn in noisy environments where only perturbed rewards are observed. Our solution framework builds on existing RL/DRL algorithms and firstly addresses the biased noisy reward setting without any assumptions on the true distribution (e.g., zero-mean Gaussian noise as made in previous works). The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that trained policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 84.6% and 80.8% improvements on average score for five Atari games, with error rates as 10% and 30% respectively.
Large batch size training of neural networks with adversarial training and second-order information
Authors: Zhewei Yao, Amir Gholami, Daiyaan Arfeen, Richard Liaw, Joseph Gonzalez, Kurt Keutzer, Michael Mahoney
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Second-order methods

The most straightforward method to accelerate Stochastic Gradient Descent (SGD) computation is to distribute the randomly selected batch of inputs over multiple processors. To keep the distributed processors fully utilized requires commensurately growing the batch size. However, large batch training often leads to poorer generalization. A recently proposed solution for this problem is to use adaptive batch sizes in SGD. In this case, one starts with a small number of processes and scales the processes as training progresses. Two major challenges with this approach are (i) that dynamically resizing the cluster can add non-trivial overhead, in part since it is currently not supported, and (ii) that the overall speed up is limited by the initial phase with smaller batches. In this work, we address both challenges by developing a new adaptive batch size framework, with autoscaling based on the Ray framework. This allows very efficient elastic scaling with negligible resizing overhead (0.32\% of time for ResNet18 ImageNet training). Furthermore, we propose a new adaptive batch size training scheme using second order methods and adversarial training. These enable increasing batch sizes earlier during training, which leads to better training time. We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple neural networks, including ResNets and smaller networks such as SqueezeNext. Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively). Importantly, this is achieved without any additional hyper-parameter tuning to tailor our method in any of these experiments.
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Authors: Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme
Date: 2018-09-29 00:00:00
Tasks: Motion Planning

Personal robots assisting humans must perform complex manipulation tasks that
are typically difficult to specify in traditional motion planning pipelines,
where multiple objectives must be met and the high-level context be taken into
consideration. Learning from demonstration (LfD) provides a promising way to
learn these kind of complex manipulation skills even from non-technical users.
However, it is challenging for existing LfD methods to efficiently learn skills
that can generalize to task specifications that are not covered by
demonstrations. In this paper, we introduce a state transition model (STM) that
generates joint-space trajectories by imitating motions from expert behavior.
Given a few demonstrations, we show in real robot experiments that the learned
STM can quickly generalize to unseen tasks and synthesize motions having longer
time horizons than the expert trajectories. Compared to conventional motion
planners, our approach enables the robot to accomplish complex behaviors from
high-level instructions without laborious hand-engineering of planning
objectives, while being able to adapt to changing goals during the skill
execution. In conjunction with a trajectory optimizer, our STM can construct a
high-quality skeleton of a trajectory that can be further improved in
smoothness and precision. In combination with a learned inverse dynamics model,
we additionally present results where the STM is used as a high-level planner.
  A video of our experiments is available at https://youtu.be/85DX9Ojq-90
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation
Authors: Zhezhi He, Deliang Fan
Date: 2018-10-02 00:00:00
Proceeding: CVPR 2019 6

In the past years, Deep convolution neural network has achieved great success
in many artificial intelligence applications. However, its enormous model size
and massive computation cost have become the main obstacle for deployment of
such powerful algorithm in the low power and resource-limited mobile systems.
As the countermeasure to this problem, deep neural networks with ternarized
weights (i.e. -1, 0, +1) have been widely explored to greatly reduce the model
size and computational cost, with limited accuracy degradation. In this work,
we propose a novel ternarized neural network training method which
simultaneously optimizes both weights and quantizer during training,
differentiating from prior works. Instead of fixed and uniform weight
ternarization, we are the first to incorporate the thresholds of weight
ternarization into a closed-form representation using the truncated Gaussian
approximation, enabling simultaneous optimization of weights and quantizer
through back-propagation training. With both of the first and last layer
ternarized, the experiments on the ImageNet classification task show that our
ternarized ResNet-18/34/50 only has 3.9/2.52/2.16% accuracy degradation in
comparison to the full-precision counterparts.
Convergence Rates for Empirical Estimation of Binary Classification Bounds
Authors: Salimeh Yasaei Sekeh, Morteza Noshad, Kevin R. Moon, Alfred O. Hero
Date: 2018-10-01 00:00:00
Tasks: Binary Classification, Classification, General Classification

Bounding the best achievable error probability for binary classification
problems is relevant to many applications including machine learning, signal
processing, and information theory. Many bounds on the Bayes binary
classification error rate depend on information divergences between the pair of
class distributions. Recently, the Henze-Penrose (HP) divergence has been
proposed for bounding classification error probability. We consider the problem
of empirically estimating the HP-divergence from random samples. We derive a
bound on the convergence rate for the Friedman-Rafsky (FR) estimator of the
HP-divergence, which is related to a multivariate runs statistic for testing
between two distributions. The FR estimator is derived from a multicolored
Euclidean minimal spanning tree (MST) that spans the merged samples. We obtain
a concentration inequality for the Friedman-Rafsky estimator of the
Henze-Penrose divergence. We validate our results experimentally and illustrate
their application to real datasets.
Bayesian Policy Optimization for Model Uncertainty
Authors: Gilwoo Lee, Brian Hou, Aditya Mandalika, Jeongseok Lee, Sanjiban Choudhury, Siddhartha S. Srinivasa
Date: 2018-10-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: model

Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history of observations and maximizes its expected long-term reward with respect to this belief distribution. Our algorithm, Bayesian Policy Optimization, builds on recent policy optimization algorithms to learn a universal policy that navigates the exploration-exploitation trade-off to maximize the Bayesian value function. To address challenges from discretizing the continuous latent parameter space, we propose a new policy network architecture that encodes the belief distribution independently from the observable state. Our method significantly outperforms algorithms that address model uncertainty without explicitly reasoning about belief distributions and is competitive with state-of-the-art Partially Observable Markov Decision Process solvers.
CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
Authors: Shing Yan Loo, Ali Jahani Amiri, Syamsiah Mashohor, Sai Hong Tang, Hong Zhang
Date: 2018-10-01 00:00:00
Tasks: Depth Estimation, Depth Prediction, Motion Estimation, Prediction, Simultaneous Localization and Mapping, Visual Odometry

Reliable feature correspondence between frames is a critical step in visual
odometry (VO) and visual simultaneous localization and mapping (V-SLAM)
algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct
visual odometry (SVO) has two main advantages that lead to state-of-the-art
frame rate camera motion estimation: direct pixel correspondence and efficient
implementation of probabilistic mapping method. This paper improves the SVO
mapping by initializing the mean and the variance of the depth at a feature
location according to the depth prediction from a single-image depth prediction
network. By significantly reducing the depth uncertainty of the initialized map
point (i.e., small variance centred about the depth prediction), the benefits
are twofold: reliable feature correspondence between views and fast convergence
to the true depth in order to create new map points. We evaluate our method
with two outdoor datasets: KITTI dataset and Oxford Robotcar dataset. The
experimental results indicate that the improved SVO mapping results in
increased robustness and camera tracking accuracy.
Learning Hash Codes via Hamming Distance Targets
Authors: Martin Loncaric, Bowei Liu, Ryan Weber
Date: 2018-10-01 00:00:00
Tasks: Information Retrieval, Retrieval

We present a powerful new loss function and training scheme for learning
binary hash codes with any differentiable model and similarity function. Our
loss function improves over prior methods by using log likelihood loss on top
of an accurate approximation for the probability that two inputs fall within a
Hamming distance target. Our novel training scheme obtains a good estimate of
the true gradient by better sampling inputs and evaluating loss terms between
all pairs of inputs in each minibatch. To fully leverage the resulting hashes,
we use multi-indexing. We demonstrate that these techniques provide large
improvements to a similarity search tasks. We report the best results to date
on competitive information retrieval tasks for ImageNet and SIFT 1M, improving
MAP from 73% to 84% and reducing query cost by a factor of 2-8, respectively.
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
Authors: Peter L. Bartlett, Victor Gabillon, Michal Valko
Date: 2018-10-01 00:00:00

We study the problem of optimizing a function under a \emph{budgeted number
of evaluations}. We only assume that the function is \emph{locally} smooth
around one of its global optima. The difficulty of optimization is measured in
terms of 1) the amount of \emph{noise} $b$ of the function evaluation and 2)
the local smoothness, $d$, of the function. A smaller $d$ results in smaller
optimization error. We come with a new, simple, and parameter-free approach.
First, for all values of $b$ and $d$, this approach recovers at least the
state-of-the-art regret guarantees. Second, our approach additionally obtains
these results while being \textit{agnostic} to the values of both $b$ and $d$.
This leads to the first algorithm that naturally adapts to an \textit{unknown}
range of noise $b$ and leads to significant improvements in a moderate and
low-noise regime. Third, our approach also obtains a remarkable improvement
over the state-of-the-art SOO algorithm when the noise is very low which
includes the case of optimization under deterministic feedback ($b=0$). There,
under our minimal local smoothness assumption, this improvement is of
exponential magnitude and holds for a class of functions that covers the vast
majority of functions that practitioners optimize ($d=0$). We show that our
algorithmic improvement is borne out in experiments as we empirically show
faster convergence on common benchmarks.
Gyroscope-Aided Motion Deblurring with Deep Networks
Authors: Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä
Date: 2018-10-01 00:00:00
Tasks: Deblurring

We propose a deblurring method that incorporates gyroscope measurements into
a convolutional neural network (CNN). With the help of such measurements, it
can handle extremely strong and spatially-variant motion blur. At the same
time, the image data is used to overcome the limitations of gyro-based blur
estimation. To train our network, we also introduce a novel way of generating
realistic training data using the gyroscope. The evaluation shows a clear
improvement in visual quality over the state-of-the-art while achieving
real-time performance. Furthermore, the method is shown to improve the
performance of existing feature detectors and descriptors against the motion
blur.
Neural Regression Trees
Authors: Shahan Ali Memon, Wenbo Zhao, Bhiksha Raj, Rita Singh
Date: 2018-10-01 00:00:00
Tasks: Classification, General Classification, regression

Regression-via-Classification (RvC) is the process of converting a regression
problem to a classification one. Current approaches for RvC use ad-hoc
discretization strategies and are suboptimal. We propose a neural regression
tree model for RvC. In this model, we employ a joint optimization framework
where we learn optimal discretization thresholds while simultaneously
optimizing the features for each node in the tree. We empirically show the
validity of our model by testing it on two challenging regression tasks where
we establish the state of the art.
Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History
Authors: Aysenur Bilgin, Laura Hollink, Jacco van Ossenbruggen, Erik Tjong Kim Sang, Kim Smeenk, Frank Harbers, Marcel Broersma
Date: 2018-10-01 00:00:00
Tasks: Articles, General Classification, Genre classification

With the growing abundance of unlabeled data in real-world tasks, researchers
have to rely on the predictions given by black-boxed computational models.
However, it is an often neglected fact that these models may be scoring high on
accuracy for the wrong reasons. In this paper, we present a practical impact
analysis of enabling model transparency by various presentation forms. For this
purpose, we developed an environment that empowers non-computer scientists to
become practicing data scientists in their own research field. We demonstrate
the gradually increasing understanding of journalism historians through a
real-world use case study on automatic genre classification of newspaper
articles. This study is a first step towards trusted usage of machine learning
pipelines in a responsible way.
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
Authors: Nithya Attaluri, Ahmed Nasir, Carolynne Powe, Harold Racz, Ben Covington, Li Yao, Jordan Prosky, Eric Poblenz, Tobi Olatunji, Kevin Lyman
Date: 2018-10-01 00:00:00
Tasks: Sentiment Analysis

Obtaining datasets labeled to facilitate model development is a challenge for
most machine learning tasks. The difficulty is heightened for medical imaging,
where data itself is limited in accessibility and labeling requires costly time
and effort by trained medical specialists. Medical imaging studies, however,
are often accompanied by a medical report produced by a radiologist,
identifying important features on the corresponding scan for other physicians
not specifically trained in radiology. We propose a methodology for
approximating image-level labels for radiology studies from associated reports
using a general purpose language processing tool for medical concept extraction
and sentiment analysis, and simple manually crafted heuristics for false
positive reduction. Using this approach, we label more than 175,000 Head CT
studies for the presence of 33 features indicative of 11 clinically relevant
conditions. For 27 of the 30 keywords that yielded positive results (3 had no
occurrences), the lower bound of the confidence intervals created to estimate
the percentage of accurately labeled reports was above 85%, with the average
being above 95%. Though noisier then manual labeling, these results suggest
this method to be a viable means of labeling medical images at scale.
Natural measures of alignment
Authors: R. A. Kycia, Z. Tabor
Date: 2018-10-01 00:00:00

Natural coordinate system will be proposed. In this coordinate system
alignment procedure of a device and a detector can be easily performed. This
approach is generalization of previous specific formulas in the field of
calibration and provide top level description of the procedure. A basic example
application to linac therapy plan is also provided.
Challenges of Using Text Classifiers for Causal Inference
Authors: Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Causal Inference, Decision Making

Causal understanding is essential for many kinds of decision-making, but
causal inference from observational data has typically only been applied to
structured, low-dimensional datasets. While text classifiers produce
low-dimensional outputs, their use in causal inference has not previously been
studied. To facilitate causal analyses based on language data, we consider the
role that text classifiers can play in causal inference through established
modeling mechanisms from the causality literature on missing data and
measurement error. We demonstrate how to conduct causal analyses using text
classifiers on simulated and Yelp data, and discuss the opportunities and
challenges of future work that uses text data in causal inference.