pwc-archive/papers-with-abstracts huggingface dataset:
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
Authors: Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Date: 2019-03-15 00:00:00

Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing complex representations while training deep SNNs using input spikes has not been successful so far. Diverse methods have been proposed to get around this issue such as converting off-the-shelf trained deep Artificial Neural Networks (ANNs) to SNNs. However, the ANN-SNN conversion scheme fails to capture the temporal dynamics of a spiking system. On the other hand, it is still a difficult problem to directly train deep SNNs using input spike events due to the discontinuous, non-differentiable nature of the spike generation function. To overcome this problem, we propose an approximate derivative method that accounts for the leaky behavior of LIF neurons. This method enables training deep convolutional SNNs directly (with input spike events) using spike-based backpropagation. Our experiments show the effectiveness of the proposed spike-based learning on deep networks (VGG and Residual architectures) by achieving the best classification accuracies in MNIST, SVHN and CIFAR-10 datasets compared to other SNNs trained with a spike-based learning. Moreover, we analyze sparse event-based computations to demonstrate the efficacy of the proposed SNN training method for inference operation in the spiking domain.
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
Authors: Wesley Suttle, Zhuoran Yang, Kaiqing Zhang, Zhaoran Wang, Tamer Basar, Ji Liu
Date: 2019-03-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-agent version of emphatic temporal difference learning for off-policy policy evaluation, and proves convergence under linear function approximation. The paper then leverages this result, in conjunction with a novel multi-agent off-policy policy gradient theorem and recent work in both multi-agent on-policy and single-agent off-policy actor-critic methods, to develop and give convergence guarantees for a new multi-agent off-policy actor-critic algorithm.
Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation
Authors: Wei Feng, Wentao Liu, Tong Li, Jing Peng, Chen Qian, Xiaolin Hu
Date: 2019-03-15 00:00:00
Tasks: Human-Object Interaction Detection, Pose Estimation

Human-object interactions (HOI) recognition and pose estimation are two
closely related tasks. Human pose is an essential cue for recognizing actions
and localizing the interacted objects. Meanwhile, human action and their
interacted objects' localizations provide guidance for pose estimation. In this
paper, we propose a turbo learning framework to perform HOI recognition and
pose estimation simultaneously. First, two modules are designed to enforce
message passing between the tasks, i.e. pose aware HOI recognition module and
HOI guided pose estimation module. Then, these two modules form a closed loop
to utilize the complementary information iteratively, which can be trained in
an end-to-end manner. The proposed method achieves the state-of-the-art
performance on two public benchmarks including Verbs in COCO (V-COCO) and
HICO-DET datasets.
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
Authors: Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim
Date: 2019-03-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Medical Image Classification

The accuracy and robustness of image classification with supervised deep
learning are dependent on the availability of large-scale, annotated training
data. However, there is a paucity of annotated data available due to the
complexity of manual annotation. To overcome this problem, a popular approach
is to use transferable knowledge across different domains by: 1) using a
generic feature extractor that has been pre-trained on large-scale general
images (i.e., transfer-learned) but which not suited to capture characteristics
from medical images; or 2) fine-tuning generic knowledge with a relatively
smaller number of annotated images. Our aim is to reduce the reliance on
annotated training data by using a new hierarchical unsupervised feature
extractor with a convolutional auto-encoder placed atop of a pre-trained
convolutional neural network. Our approach constrains the rich and generic
image features from the pre-trained domain to a sophisticated representation of
the local image characteristics from the unannotated medical image domain. Our
approach has a higher classification accuracy than transfer-learned approaches
and is competitive with state-of-the-art supervised fine-tuned methods.
On Target Shift in Adversarial Domain Adaptation
Authors: Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson
Date: 2019-03-15 00:00:00
Tasks: Domain Adaptation

Discrepancy between training and testing domains is a fundamental problem in
the generalization of machine learning techniques. Recently, several approaches
have been proposed to learn domain invariant feature representations through
adversarial deep learning. However, label shift, where the percentage of data
in each class is different between domains, has received less attention. Label
shift naturally arises in many contexts, especially in behavioral studies where
the behaviors are freely chosen. In this work, we propose a method called
Domain Adversarial nets for Target Shift (DATS) to address label shift while
learning a domain invariant representation. This is accomplished by using
distribution matching to estimate label proportions in a blind test set. We
extend this framework to handle multiple domains by developing a scheme to
upweight source domains most similar to the target domain. Empirical results
show that this framework performs well under large label shift in synthetic and
real experiments, demonstrating the practical importance.
Successive Refinement of Images with Deep Joint Source-Channel Coding
Authors: David Burth Kurka, Deniz Gunduz
Date: 2019-03-15 00:00:00

We introduce deep learning based communication methods for successive refinement of images over wireless channels. We present three different strategies for progressive image transmission with deep JSCC, with different complexity-performance trade-offs, all based on convolutional autoencoders. Numerical results show that deep JSCC not only provides graceful degradation with channel signal-to-noise ratio (SNR) and improved performance in low SNR and low bandwidth regimes compared to state-of-the-art digital communication techniques, but can also successfully learn a layered representation, achieving performance close to a single-layer scheme. These results suggest that natural images encoded with deep JSCC over Gaussian channels are almost successively refinable.
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
Authors: Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
Date: 2019-03-15 00:00:00
Tasks: Image Super-Resolution, Stereo Image Super-Resolution, Super-Resolution

With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs. However, the lack of high-quality stereo datasets has limited the research in this area. To facilitate the training and evaluation of novel stereo SR algorithms, in this paper, we present a large-scale stereo dataset named Flickr1024, which contains 1024 pairs of high-quality images and covers diverse scenarios. We first introduce the data acquisition and processing pipeline, and then compare several popular stereo datasets. Finally, we conduct crossdataset experiments to investigate the potential benefits introduced by our dataset. Experimental results show that, as compared to the KITTI and Middlebury datasets, our Flickr1024 dataset can help to handle the over-fitting problem and significantly improves the performance of stereo SR methods. The Flickr1024 dataset is available online at: https://yingqianwang.github.io/Flickr1024.
Toward Imitating Visual Attention of Experts in Software Development Tasks
Authors: Yoshiharu Ikutani, Nishanth Koganti, Hideaki Hata, Takatomi Kubo, Kenichi Matsumoto
Date: 2019-03-15 00:00:00
Tasks: Code Generation, Comment Generation, Decoder, Imitation Learning

Expert programmers' eye-movements during source code reading are valuable
sources that are considered to be associated with their domain expertise. We
advocate a vision of new intelligent systems incorporating expertise of experts
for software development tasks, such as issue localization, comment generation,
and code generation. We present a conceptual framework of neural autonomous
agents based on imitation learning (IL), which enables agents to mimic the
visual attention of an expert via his/her eye movement. In this framework, an
autonomous agent is constructed as a context-based attention model that
consists of encoder/decoder network and trained with state-action sequences
generated by an experts' demonstration. Challenges to implement an IL-based
autonomous agent specialized for software development task are discussed in
this paper.
Stitching Videos from a Fisheye Lens Camera and a Wide-Angle Lens Camera for Telepresence Robots
Authors: Yanmei Dong, Mingtao Pei, Lijia Zhang, Bin Xu, Yuwei Wu, Yunde Jia
Date: 2019-03-15 00:00:00
Tasks: distortion correction

Many telepresence robots are equipped with a forward-facing camera for video
communication and a downward-facing camera for navigation. In this paper, we
propose to stitch videos from the FF-camera with a wide-angle lens and the
DF-camera with a fisheye lens for telepresence robots. We aim at providing more
compact and efficient visual feedback for the user interface of telepresence
robots with user-friendly interactive experiences. To this end, we present a
multi-homography-based video stitching method which stitches videos from a
wide-angle camera and a fisheye camera. The method consists of video image
alignment, seam cutting, and image blending. We directly align the wide-angle
video image and the fisheye video image based on the multi-homography alignment
without calibration, distortion correction, and unwarping procedures. Thus, we
can obtain a stitched video with shape preservation in the non-overlapping
regions and alignment in the overlapping area for telepresence. To alleviate
ghosting effects caused by moving objects and/or moving cameras during
telepresence robot driving, an optimal seam is found for aligned video
composition, and the optimal seam will be updated in subsequent frames,
considering spatial and temporal coherence. The final stitched video is created
by image blending based on the optimal seam. We conducted a user study to
demonstrate the effectiveness of our method and the superiority of telepresence
robots with a stitched video as visual feedback.
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
Authors: Yang Li, Yoshitaka Ushiku, Tatsuya Harada
Date: 2019-03-15 00:00:00
Tasks: Loop Closure Detection, Monocular Visual Odometry, Visual Odometry

Unsupervised Learning based monocular visual odometry (VO) has lately drawn
significant attention for its potential in label-free leaning ability and
robustness to camera parameters and environmental variations. However,
partially due to the lack of drift correction technique, these methods are
still by far less accurate than geometric approaches for large-scale odometry
estimation. In this paper, we propose to leverage graph optimization and loop
closure detection to overcome limitations of unsupervised learning based
monocular visual odometry. To this end, we propose a hybrid VO system which
combines an unsupervised monocular VO called NeuralBundler with a pose graph
optimization back-end. NeuralBundler is a neural network architecture that uses
temporal and spatial photometric loss as main supervision and generates a
windowed pose graph consists of multi-view 6DoF constraints. We propose a novel
pose cycle consistency loss to relieve the tensions in the windowed pose graph,
leading to improved performance and robustness. In the back-end, a global pose
graph is built from local and loop 6DoF constraints estimated by NeuralBundler
and is optimized over SE(3). Empirical evaluation on the KITTI odometry dataset
demonstrates that 1) NeuralBundler achieves state-of-the-art performance on
unsupervised monocular VO estimation, and 2) our whole approach can achieve
efficient loop closing and show favorable overall translational accuracy
compared to established monocular SLAM systems.
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Authors: Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
Date: 2017-06-05 00:00:00
Tasks: Recommendation Systems

Exact Maximum Inner Product Search (MIPS) is an important task that is widely
pertinent to recommender systems and high-dimensional similarity search. The
brute-force approach to solving exact MIPS is computationally expensive, thus
spurring recent development of novel indexes and pruning techniques for this
task. In this paper, we show that a hardware-efficient brute-force approach,
blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers
by over an order of magnitude, for some -- but not all -- inputs.
  In this paper, we also present a novel MIPS solution, MAXIMUS, that takes
advantage of hardware efficiency and pruning of the search space. Like BMM,
MAXIMUS is faster than other solvers by up to an order of magnitude, but again
only for some inputs. Since no single solution offers the best runtime
performance for all inputs, we introduce a new data-dependent optimizer,
OPTIMUS, that selects online with minimal overhead the best MIPS solver for a
given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS
solvers by 3.2$\times$ on average, and up to 10.9$\times$, on widely studied
MIPS datasets.
Adaptive Variance for Changing Sparse-Reward Environments
Authors: Xingyu Lin, Pengsheng Guo, Carlos Florensa, David Held
Date: 2019-03-15 00:00:00

Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks
Authors: Ian Goodfellow
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning

In this article I describe a research agenda for securing machine learning
models against adversarial inputs at test time. This article does not present
results but instead shares some of my thoughts about where I think that the
field needs to go. Modern machine learning works very well on I.I.D. data: data
for which each example is drawn {\em independently} and for which the
distribution generating each example is {\em identical}. When these assumptions
are relaxed, modern machine learning can perform very poorly. When machine
learning is used in contexts where security is a concern, it is desirable to
design models that perform well even when the input is designed by a malicious
adversary. So far most research in this direction has focused on an adversary
who violates the {\em identical} assumption, and imposes some kind of
restricted worst-case distribution shift. I argue that machine learning
security researchers should also address the problem of relaxing the {\em
independence} assumption and that current strategies designed for robustness to
distribution shift will not do so. I recommend {\em dynamic models} that change
each time they are run as a potential solution path to this problem, and show
an example of a simple attack using correlated data that can be mitigated by a
simple dynamic defense. This is not intended as a real-world security measure,
but as a recommendation to explore this research direction and develop more
realistic defenses.
ROS2Learn: a reinforcement learning framework for ROS 2
Authors: Yue Leire Erro Nuin, Nestor Gonzalez Lopez, Elias Barba Moral, Lander Usategui San Juan, Alejandro Solano Rueda, Víctor Mayoral Vilches, Risto Kojcev
Date: 2019-03-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a novel framework for Deep Reinforcement Learning (DRL) in modular
robotics to train a robot directly from joint states, using traditional robotic
tools. We use an state-of-the-art implementation of the Proximal Policy
Optimization, Trust Region Policy Optimization and Actor-Critic
Kronecker-Factored Trust Region algorithms to learn policies in four different
Modular Articulated Robotic Arm (MARA) environments. We support this process
using a framework that communicates with typical tools used in robotics, such
as Gazebo and Robot Operating System 2 (ROS 2). We evaluate several algorithms
in modular robots with an empirical study in simulation.
Theories of Parenting and their Application to Artificial Intelligence
Authors: Sky Croeser, Peter Eckersley
Date: 2019-03-14 00:00:00
Tasks: Ethics

As machine learning (ML) systems have advanced, they have acquired more power
over humans' lives, and questions about what values are embedded in them have
become more complex and fraught. It is conceivable that in the coming decades,
humans may succeed in creating artificial general intelligence (AGI) that
thinks and acts with an open-endedness and autonomy comparable to that of
humans. The implications would be profound for our species; they are now widely
debated not just in science fiction and speculative research agendas but
increasingly in serious technical and policy conversations.
  Much work is underway to try to weave ethics into advancing ML research. We
think it useful to add the lens of parenting to these efforts, and specifically
radical, queer theories of parenting that consciously set out to nurture agents
whose experiences, objectives and understanding of the world will necessarily
be very different from their parents'. We propose a spectrum of principles
which might underpin such an effort; some are relevant to current ML research,
while others will become more important if AGI becomes more likely. These
principles may encourage new thinking about the development, design, training,
and release into the world of increasingly autonomous agents.
Show, Translate and Tell
Authors: Dheeraj Peri, Shagan Sah, Raymond Ptucha
Date: 2019-03-14 00:00:00
Tasks: Cross-Modal Retrieval, Image Captioning, Retrieval, Sentence

Humans have an incredible ability to process and understand information from
multiple sources such as images, video, text, and speech. Recent success of
deep neural networks has enabled us to develop algorithms which give machines
the ability to understand and interpret this information. There is a need to
both broaden their applicability and develop methods which correlate visual
information along with semantic content. We propose a unified model which
jointly trains on images and captions, and learns to generate new captions
given either an image or a caption query. We evaluate our model on three
different tasks namely cross-modal retrieval, image captioning, and sentence
paraphrasing. Our model gains insight into cross-modal vector embeddings,
generalizes well on multiple tasks and is competitive to state of the art
methods on retrieval.
DysLexML: Screening Tool for Dyslexia Using Machine Learning
Authors: Thomais Asvestopoulou, Victoria Manousaki, Antonis Psistakis, Ioannis Smyrnakis, Vassilios Andreadakis, Ioannis M. Aslanides, Maria Papadopouli
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

Eye movements during text reading can provide insights about reading
disorders. Via eye-trackers, we can measure when, where and how eyes move with
relation to the words they read. Machine Learning (ML) algorithms can decode
this information and provide differential analysis. This work developed
DysLexML, a screening tool for developmental dyslexia that applies various ML
algorithms to analyze fixation points recorded via eye-tracking during silent
reading of children. It comparatively evaluated its performance using
measurements collected in a systematic field study with 69 native Greek
speakers, children, 32 of which were diagnosed as dyslexic by the official
governmental agency for diagnosing learning and reading difficulties in Greece.
We examined a large set of features based on statistical properties of
fixations and saccadic movements and identified the ones with prominent
predictive power, performing dimensionality reduction. Specifically, DysLexML
achieves its best performance using linear SVM, with an a accuracy of 97 %,
with a small feature set, namely saccade length, number of short forward
movements, and number of multiply fixated words. Furthermore, we analyzed the
impact of noise on the fixation positions and showed that DysLexML is accurate
and robust in the presence of noise. These encouraging results set the basis
for developing screening tools in less controlled, larger-scale environments,
with inexpensive eye-trackers, potentially reaching a larger population for
early intervention.
Inefficiency of K-FAC for Large Batch Size Training
Authors: Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
Date: 2019-03-14 00:00:00
Tasks: Stochastic Optimization

In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch. However, for both training loss and testing error, recent results analyzing large batch Stochastic Gradient Descent (SGD) have found sharp diminishing returns, beyond a certain critical batch size. In the hopes of addressing this, it has been suggested that the Kronecker-Factored Approximate Curvature (\mbox{K-FAC}) method allows for greater scalability to large batch sizes, for non-convex machine learning problems such as neural network optimization, as well as greater robustness to variation in model hyperparameters. Here, we perform a detailed empirical analysis of large batch size training %of these two hypotheses, for both \mbox{K-FAC} and SGD, evaluating performance in terms of both wall-clock time and aggregate computational cost. Our main results are twofold: first, we find that both \mbox{K-FAC} and SGD doesn't have ideal scalability behavior beyond a certain batch size, and that \mbox{K-FAC} does not exhibit improved large-batch scalability behavior, as compared to SGD; and second, we find that \mbox{K-FAC}, in addition to requiring more hyperparameters to tune, suffers from similar hyperparameter sensitivity behavior as does SGD. We discuss extensive results using ResNet and AlexNet on \mbox{CIFAR-10} and SVHN, respectively, as well as more general implications of our findings.