pwc-archive/papers-with-abstracts huggingface dataset:
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Authors: Wonjoon Goo, Scott Niekum
Date: 2018-06-29 00:00:00
Tasks: One-Shot Learning, Reinforcement Learning, Task 2

Due to burdensome data requirements, learning from demonstration often falls
short of its promise to allow users to quickly and naturally program robots.
Demonstrations are inherently ambiguous and incomplete, making correct
generalization to unseen situations difficult without a large number of
demonstrations in varying conditions. By contrast, humans are often able to
learn complex tasks from a single demonstration (typically observations without
action labels) by leveraging context learned over a lifetime. Inspired by this
capability, our goal is to enable robots to perform one-shot learning of
multi-step tasks from observation by leveraging auxiliary video data as
context. Our primary contribution is a novel system that achieves this goal by:
(1) using a single user-segmented demonstration to define the primitive actions
that comprise a task, (2) localizing additional examples of these actions in
unsegmented auxiliary videos via a metalearning-based approach, (3) using these
additional examples to learn a reward function for each action, and (4)
performing reinforcement learning on top of the inferred reward functions to
learn action policies that can be combined to accomplish the task. We
empirically demonstrate that a robot can learn multi-step tasks more
effectively when provided auxiliary video, and that performance greatly
improves when localizing individual actions, compared to learning from
unsegmented videos.
Human Action Recognition and Prediction: A Survey
Authors: Yu Kong, Yun Fu
Date: 2018-06-28 00:00:00
Tasks: Action Recognition, Autonomous Driving, Prediction, Retrieval, Survey, Temporal Action Localization, Video Retrieval

Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce
Authors: Murium Iqbal, Adair Kovac, Kamelia Aryafar
Date: 2018-06-28 00:00:00
Tasks: Recommendation Systems, Transfer Learning

E-commerce platforms surface interesting products largely through product
recommendations that capture users' styles and aesthetic preferences. Curating
recommendations as a complete complementary set, or assortment, is critical for
a successful e-commerce experience, especially for product categories such as
furniture, where items are selected together with the overall theme, style or
ambiance of a space in mind. In this paper, we propose two visually-aware
recommender systems that can automatically curate an assortment of living room
furniture around a couple of pre-selected seed pieces for the room. The first
system aims to maximize the visual-based style compatibility of the entire
selection by making use of transfer learning and topic modeling. The second
system extends the first by incorporating text data and applying polylingual
topic modeling to infer style over both modalities. We review the production
pipeline for surfacing these visually-aware recommender systems and compare
them through offline validations and large-scale online A/B tests on Overstock.
Our experimental results show that complimentary style is best discovered over
product sets when both visual and textual data are incorporated.
Active query-driven visual search using probabilistic bisection and convolutional neural networks
Authors: Athanasios Tsiligkaridis, Theodoros Tsiligkaridis
Date: 2018-06-28 00:00:00
Tasks: Object, object-detection, Object Detection

We present a novel efficient object detection and localization framework based on the probabilistic bisection algorithm. A Convolutional Neural Network (CNN) is trained and used as a noisy oracle that provides answers to input query images. The responses along with error probability estimates obtained from the CNN are used to update beliefs on the object location along each dimension. We show that querying along each dimension achieves the same lower bound on localization error as the joint query design. Finally, we compare our approach to the traditional sliding window technique on a real world face localization task and show speed improvements by at least an order of magnitude while maintaining accurate localization.
Tight Prediction Intervals Using Expanded Interval Minimization
Authors: Dongqi Su, Ying Yin Ting, Jason Ansel
Date: 2018-06-28 00:00:00
Tasks: Prediction, Prediction Intervals

Prediction intervals are a valuable way of quantifying uncertainty in
regression problems. Good prediction intervals should be both correct,
containing the actual value between the lower and upper bound at least a target
percentage of the time; and tight, having a small mean width of the bounds.
Many prior techniques for generating prediction intervals make assumptions on
the distribution of error, which causes them to work poorly for problems with
asymmetric distributions.
  This paper presents Expanded Interval Minimization (EIM), a novel loss
function for generating prediction intervals using neural networks. This loss
function uses minibatch statistics to estimate the coverage and optimize the
width of the prediction intervals. It does not make the same assumptions on the
distributions of data and error as prior work. We compare to three published
techniques and show EIM produces on average 1.37x tighter prediction intervals
and in the worst case 1.06x tighter intervals across two large real-world
datasets and varying coverage levels.
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Authors: Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank Sciurba, Barnabas Poczos, Kayhan N. Batmanghelich
Date: 2018-06-28 00:00:00
Tasks: Anatomy

We propose an attention-based method that aggregates local image features to
a subject-level representation for predicting disease severity. In contrast to
classical deep learning that requires a fixed dimensional input, our method
operates on a set of image patches; hence it can accommodate variable length
input image without image resizing. The model learns a clinically interpretable
subject-level representation that is reflective of the disease severity. Our
model consists of three mutually dependent modules which regulate each other:
(1) a discriminative network that learns a fixed-length representation from
local features and maps them to disease severity; (2) an attention mechanism
that provides interpretability by focusing on the areas of the anatomy that
contribute the most to the prediction task; and (3) a generative network that
encourages the diversity of the local latent features. The generative term
ensures that the attention weights are non-degenerate while maintaining the
relevance of the local regions to the disease severity. We train our model
end-to-end in the context of a large-scale lung CT study of Chronic Obstructive
Pulmonary Disease (COPD). Our model gives state-of-the art performance in
predicting clinical measures of severity for COPD. The distribution of the
attention provides the regional relevance of lung tissue to the clinical
measurements.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Authors: Maximilian Seitzer, Guang Yang, Jo Schlemper, Ozan Oktay, Tobias Würfl, Vincent Christlein, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Daniel Rueckert, Andreas Maier
Date: 2018-06-28 00:00:00
Tasks: compressed sensing, MRI Reconstruction, Open-Ended Question Answering

Deep learning approaches have shown promising performance for compressed
sensing-based Magnetic Resonance Imaging. While deep neural networks trained
with mean squared error (MSE) loss functions can achieve high peak signal to
noise ratio, the reconstructed images are often blurry and lack sharp details,
especially for higher undersampling rates. Recently, adversarial and perceptual
loss functions have been shown to achieve more visually appealing results.
However, it remains an open question how to (1) optimally combine these loss
functions with the MSE loss function and (2) evaluate such a perceptual
enhancement. In this work, we propose a hybrid method, in which a visual
refinement component is learnt on top of an MSE loss-based reconstruction
network. In addition, we introduce a semantic interpretability score, measuring
the visibility of the region of interest in both ground truth and reconstructed
images, which allows us to objectively quantify the usefulness of the image
quality for image post-processing and analysis. Applied on a large cardiac MRI
dataset simulated with 8-fold undersampling, we demonstrate significant
improvements ($p<0.01$) over the state-of-the-art in both a human observer
study and the semantic interpretability score.
Proxy Fairness
Authors: Maya Gupta, Andrew Cotter, Mahdi Milani Fard, Serena Wang
Date: 2018-06-28 00:00:00
Tasks: Fairness

We consider the problem of improving fairness when one lacks access to a
dataset labeled with protected groups, making it difficult to take advantage of
strategies that can improve fairness but require protected group labels, either
at training or runtime. To address this, we investigate improving fairness
metrics for proxy groups, and test whether doing so results in improved
fairness for the true sensitive groups. Results on benchmark and real-world
datasets demonstrate that such a proxy fairness strategy can work well in
practice. However, we caution that the effectiveness likely depends on the
choice of fairness metric, as well as how aligned the proxy groups are with the
true protected groups in terms of the constrained model parameters.
Polynomial-time probabilistic reasoning with partial observations via implicit learning in probability logics
Authors: Brendan Juba
Date: 2018-06-28 00:00:00

Standard approaches to probabilistic reasoning require that one possesses an
explicit model of the distribution in question. But, the empirical learning of
models of probability distributions from partial observations is a problem for
which efficient algorithms are generally not known. In this work we consider
the use of bounded-degree fragments of the "sum-of-squares" logic as a
probability logic. Prior work has shown that we can decide refutability for
such fragments in polynomial-time. We propose to use such fragments to answer
queries about whether a given probability distribution satisfies a given system
of constraints and bounds on expected values. We show that in answering such
queries, such constraints and bounds can be implicitly learned from partial
observations in polynomial-time as well. It is known that this logic is capable
of deriving many bounds that are useful in probabilistic analysis. We show here
that it furthermore captures useful polynomial-time fragments of resolution.
Thus, these fragments are also quite expressive.
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Authors: Serena Wang, Maya Gupta, Seungil You
Date: 2018-06-28 00:00:00
Tasks: Combinatorial Optimization, CPU

Given a classifier ensemble and a set of examples to be classified, many
examples may be confidently and accurately classified after only a subset of
the base models in the ensemble are evaluated. This can reduce both mean
latency and CPU while maintaining the high accuracy of the original ensemble.
To achieve such gains, we propose jointly optimizing a fixed evaluation order
of the base models and early-stopping thresholds. Our proposed objective is a
combinatorial optimization problem, but we provide a greedy algorithm that
achieves a 4-approximation of the optimal solution for certain cases. For those
cases, this is also the best achievable polynomial time approximation bound
unless $P = NP$. Experiments on benchmark and real-world problems show that the
proposed Quit When You Can (QWYC) algorithm can speed-up average evaluation
time by $2$x--$4$x, and is around $1.5$x faster than prior work. QWYC's joint
optimization of ordering and thresholds also performed better in experiments
than various fixed orderings, including gradient boosted trees' ordering.
CR-GAN: Learning Complete Representations for Multi-view Generation
Authors: Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas
Date: 2018-06-28 00:00:00
Tasks: Decoder, Generative Adversarial Network, Self-Supervised Learning

Generating multi-view images from a single-view input is an essential yet
challenging problem. It has broad applications in vision, graphics, and
robotics. Our study indicates that the widely-used generative adversarial
network (GAN) may learn "incomplete" representations due to the single-pathway
framework: an encoder-decoder network followed by a discriminator network. We
propose CR-GAN to address this problem. In addition to the single
reconstruction path, we introduce a generation sideway to maintain the
completeness of the learned embedding space. The two learning pathways
collaborate and compete in a parameter-sharing manner, yielding considerably
improved generalization ability to "unseen" dataset. More importantly, the
two-pathway framework makes it possible to combine both labeled and unlabeled
data for self-supervised learning, which further enriches the embedding space
for realistic generations. The experimental results prove that CR-GAN
significantly outperforms state-of-the-art methods, especially when generating
from "unseen" inputs in wild conditions.
A New Angle on L2 Regularization
Authors: Thomas Tanay, Lewis D. Griffin
Date: 2018-06-28 00:00:00
Tasks: General Classification, L2 Regularization

Imagine two high-dimensional clusters and a hyperplane separating them.
Consider in particular the angle between: the direction joining the two
clusters' centroids and the normal to the hyperplane. In linear classification,
this angle depends on the level of L2 regularization used. Can you explain why?
Cross-Discourse and Multilingual Exploration of Textual Corpora with the DualNeighbors Algorithm
Authors: Taylor Arnold, Lauren Tilton
Date: 2018-06-28 00:00:00
Proceeding: COLING 2018 8

Word choice is dependent on the cultural context of writers and their
subjects. Different words are used to describe similar actions, objects, and
features based on factors such as class, race, gender, geography and political
affinity. Exploratory techniques based on locating and counting words may,
therefore, lead to conclusions that reinforce culturally inflected boundaries.
We offer a new method, the DualNeighbors algorithm, for linking thematically
similar documents both within and across discursive and linguistic barriers to
reveal cross-cultural connections. Qualitative and quantitative evaluations of
this technique are shown as applied to two cultural datasets of interest to
researchers across the humanities and social sciences. An open-source
implementation of the DualNeighbors algorithm is provided to assist in its
application.
3D Normal Coordinate Systems for Cortical Areas
Authors: J. Tilak Ratnanather, Sylvain Arguillère, Kwame S. Kutten, Peter Hubka, Andrej Kral, Laurent Younes
Date: 2018-06-28 00:00:00

A surface-based diffeomorphic algorithm to generate 3D coordinate grids in the cortical ribbon is described. In the grid, normal coordinate lines are generated by the diffeomorphic evolution from the grey/white (inner) surface to the grey/csf (outer) surface. Specifically, the cortical ribbon is described by two triangulated surfaces with open boundaries. Conceptually, the inner surface sits on top of the white matter structure and the outer on top of the gray matter. It is assumed that the cortical ribbon consists of cortical columns which are orthogonal to the white matter surface. This might be viewed as a consequence of the development of the columns in the embryo. It is also assumed that the columns are orthogonal to the outer surface so that the resultant vector field is orthogonal to the evolving surface. Then the distance of the normal lines from the vector field such that the inner surface evolves diffeomorphically towards the outer one can be construed as a measure of thickness. Applications are described for the auditory cortices in human adults and cats with normal hearing or hearing loss. The approach offers great potential for cortical morphometry.
Adversarial Reprogramming of Neural Networks
Authors: Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein
Date: 2018-06-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, General Classification

Deep neural networks are susceptible to \emph{adversarial} attacks. In
computer vision, well-crafted perturbations to images can cause neural networks
to make mistakes such as confusing a cat with a computer. Previous adversarial
attacks have been designed to degrade performance of models or cause machine
learning models to produce specific outputs chosen ahead of time by the
attacker. We introduce attacks that instead {\em reprogram} the target model to
perform a task chosen by the attacker---without the attacker needing to specify
or compute the desired output for each test-time input. This attack finds a
single adversarial perturbation, that can be added to all test-time inputs to a
machine learning model in order to cause the model to perform a task chosen by
the adversary---even if the model was not trained to do this task. These
perturbations can thus be considered a program for the new task. We demonstrate
adversarial reprogramming on six ImageNet classification models, repurposing
these models to perform a counting task, as well as classification tasks:
classification of MNIST and CIFAR-10 examples presented as inputs to the
ImageNet model.
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation
Authors: Zhuo Zhao, Lin Yang, Hao Zheng, Ian H. Guldner, Si-Yuan Zhang, Danny Z. Chen
Date: 2018-06-28 00:00:00
Tasks: 3D Instance Segmentation, Deep Learning, Instance Segmentation, Segmentation, Semantic Segmentation

Instance segmentation in 3D images is a fundamental task in biomedical image
analysis. While deep learning models often work well for 2D instance
segmentation, 3D instance segmentation still faces critical challenges, such as
insufficient training data due to various annotation difficulties in 3D
biomedical images. Common 3D annotation methods (e.g., full voxel annotation)
incur high workloads and costs for labeling enough instances for training deep
learning 3D instance segmentation models. In this paper, we propose a new weak
annotation approach for training a fast deep learning 3D instance segmentation
model without using full voxel mask annotation. Our approach needs only 3D
bounding boxes for all instances and full voxel annotation for a small fraction
of the instances, and uses a novel two-stage 3D instance segmentation model
utilizing these two kinds of annotation, respectively. We evaluate our approach
on several biomedical image datasets, and the experimental results show that
(1) with full annotated boxes and a small amount of masks, our approach can
achieve similar performance as the best known methods using full annotation,
and (2) with similar annotation time, our approach outperforms the best known
methods that use full annotation.
Recursive Neural Networks in Quark/Gluon Tagging
Authors: Taoli Cheng
Date: 2017-11-07 00:00:00
Tasks: Clustering

Since the machine learning techniques are improving rapidly, it has been
shown that the image recognition techniques in deep neural networks can be used
to detect jet substructure. And it turns out that deep neural networks can
match or outperform traditional approach of expert features. However, there are
disadvantages such as sparseness of jet images. Based on the natural tree-like
structure of jet sequential clustering, the recursive neural networks (RecNNs),
which embed jet clustering history recursively as in natural language
processing, have a better behavior when confronted with these problems. We thus
try to explore the performance of RecNNs in quark/gluon discrimination. The
results show that RecNNs work better than the baseline boosted decision tree
(BDT) by a few percent in gluon rejection rate. However, extra implementation
of particle flow identification only increases the performance slightly. We
also experimented on some relevant aspects which might influence the
performance of the networks. It shows that even taking only particle flow
identification as input feature without any extra information on momentum or
angular position is already giving a fairly good result, which indicates that
the most of the information for quark/gluon discrimination is already included
in the tree-structure itself. As a bonus, a rough up/down quark jets
discrimination is also explored.
Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access
Authors: Fatemeh Shah-Mohammadi, Andres Kwasinski
Date: 2018-06-28 00:00:00

Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Simulation results show that the ability of the cognitive engine in estimating the effect of a CR transmission on the full adaptive modulation and coding (AMC) mode leads to a much more fine underlay transmit power control. This ability also provides higher transmission opportunities for the CRs, compared to a scheme that can only estimate the modulation scheme used at the PN link.
Comment on: Decomposition of structural learning about directed acyclic graphs [1]
Authors: Mohammad Ali Javidian, Marco Valtorta
Date: 2018-06-27 00:00:00
Tasks: regression

We propose an alternative proof concerning necessary and sufficient
conditions to split the problem of searching for d-separators and building the
skeleton of a DAG into small problems for every node of a separation tree T.
The proof is simpler than the original [1]. The same proof structure has been
used in [2] for learning the structure of multivariate regression chain graphs
(MVR CGs).