pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Authors: Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata
Date: 2018-12-05 00:00:00
Tasks: Few-Shot Learning, Generalized Few-Shot Learning, Generalized Zero-Shot Learning, Zero-Shot Learning, Zero Shot Skeletal Action Recognition

Many approaches in generalized zero-shot learning rely on cross-modal mapping
between the image feature space and the class embedding space. As labeled
images are expensive, one direction is to augment the dataset by generating
either images or image features. However, the former misses fine-grained
details and the latter requires learning a mapping associated with class
embeddings. In this work, we take feature generation one step further and
propose a model where a shared latent space of image features and class
embeddings is learned by modality-specific aligned variational autoencoders.
This leaves us with the required discriminative information about the image and
classes in the latent features, on which we train a softmax classifier. The key
to our approach is that we align the distributions learned from images and from
side-information to construct latent features that contain the essential
multi-modal information associated with unseen classes. We evaluate our learned
latent features on several benchmark datasets, i.e. CUB, SUN, AWA1 and AWA2,
and establish a new state of the art on generalized zero-shot as well as on
few-shot learning. Moreover, our results on ImageNet with various zero-shot
splits show that our latent features generalize well in large-scale settings.
Capture Dense: Markerless Motion Capture Meets Dense Pose Estimation
Authors: Xiu Li, Yebin Liu, Hanbyul Joo, Qionghai Dai, Yaser Sheikh
Date: 2018-12-05 00:00:00
Tasks: Human Parsing, Markerless Motion Capture, Pose Estimation

We present a method to combine markerless motion capture and dense pose
feature estimation into a single framework. We demonstrate that dense pose
information can help for multiview/single-view motion capture, and multiview
motion capture can help the collection of a high-quality dataset for training
the dense pose detector. Specifically, we first introduce a novel markerless
motion capture method that can take advantage of dense parsing capability
provided by the dense pose detector. Thanks to the introduced dense human
parsing ability, our method is demonstrated much more efficient, and accurate
compared with the available state-of-the-art markerless motion capture
approach. Second, we improve the performance of available dense pose detector
by using multiview markerless motion capture data. Such dataset is beneficial
to dense pose training because they are more dense and accurate and consistent,
and can compensate for the corner cases such as unusual viewpoints. We
quantitatively demonstrate the improved performance of our dense pose detector
over the available DensePose. Our dense pose dataset and detector will be made
public.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Authors: Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu
Date: 2018-12-05 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection

Decomposing complex time series into trend, seasonality, and remainder
components is an important task to facilitate time series anomaly detection and
forecasting. Although numerous methods have been proposed, there are still many
time series characteristics exhibiting in real-world data which are not
addressed properly, including 1) ability to handle seasonality fluctuation and
shift, and abrupt change in trend and reminder; 2) robustness on data with
anomalies; 3) applicability on time series with long seasonality period. In the
paper, we propose a novel and generic time series decomposition algorithm to
address these challenges. Specifically, we extract the trend component robustly
by solving a regression problem using the least absolute deviations loss with
sparse regularization. Based on the extracted trend, we apply the the non-local
seasonal filtering to extract the seasonality component. This process is
repeated until accurate decomposition is obtained. Experiments on different
synthetic and real-world time series datasets demonstrate that our method
outperforms existing solutions.
Deep Positron: A Deep Neural Network Using the Posit Number System
Authors: Zachariah Carmichael, Hamed F. Langroudi, Char Khazanov, Jeffrey Lillie, John L. Gustafson, Dhireesha Kudithipudi
Date: 2018-12-05 00:00:00

The recent surge of interest in Deep Neural Networks (DNNs) has led to
increasingly complex networks that tax computational and memory resources. Many
DNNs presently use 16-bit or 32-bit floating point operations. Significant
performance and power gains can be obtained when DNN accelerators support
low-precision numerical formats. Despite considerable research, there is still
a knowledge gap on how low-precision operations can be realized for both DNN
training and inference. In this work, we propose a DNN architecture, Deep
Positron, with posit numerical format operating successfully at $\leq$8 bits
for inference. We propose a precision-adaptable FPGA soft core for exact
multiply-and-accumulate for uniform comparison across three numerical formats,
fixed, floating-point and posit. Preliminary results demonstrate that 8-bit
posit has better accuracy than 8-bit fixed or floating-point for three
different low-dimensional datasets. Moreover, the accuracy is comparable to
32-bit floating-point on a Xilinx Virtex-7 FPGA device. The trade-offs between
DNN performance and hardware resources, i.e. latency, power, and resource
utilization, show that posit outperforms in accuracy and latency at 8-bit and
below.
Moment Matching for Multi-Source Domain Adaptation
Authors: Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo wang
Date: 2018-12-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Benchmarking, Domain Adaptation, Multi-Source Unsupervised Domain Adaptation, Unsupervised Domain Adaptation

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We make three major contributions towards addressing this problem. First, we collect and annotate by far the largest UDA dataset, called DomainNet, which contains six domains and about 0.6 million images distributed among 345 categories, addressing the gap in data availability for multi-source UDA research. Second, we propose a new deep learning approach, Moment Matching for Multi-Source Domain Adaptation M3SDA, which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Third, we provide new theoretical insights specifically for moment matching approaches in both single and multiple source domain adaptation. Extensive experiments are conducted to demonstrate the power of our new dataset in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. Dataset and Code are available at \url{http://ai.bu.edu/M3SDA/}.
Cerebrovascular Network Segmentation on MRA Images with Deep Learning
Authors: Pedro Sanches, Cyril Meyer, Vincent Vigon, Benoît Naegel
Date: 2018-12-04 00:00:00
Tasks: Cerebrovascular Network Segmentation, Deep Learning, Segmentation

Deep learning has been shown to produce state of the art results in many
tasks in biomedical imaging, especially in segmentation. Moreover, segmentation
of the cerebrovascular structure from magnetic resonance angiography is a
challenging problem because its complex geometry and topology have a large
inter-patient variability. Therefore, in this work, we present a convolutional
neural network approach for this problem. Particularly, a new network topology
inspired by the U-net 3D and by the Inception modules, entitled Uception. In
addition, a discussion about the best objective function for sparse data also
guided most choices during the project. State of the art models are also
implemented for a comparison purpose and final results show that the proposed
architecture has the best performance in this particular context.
Complete the Look: Scene-based Complementary Product Recommendation
Authors: Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, Julian McAuley
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Product Recommendation

Modeling fashion compatibility is challenging due to its complexity and
subjectivity. Existing work focuses on predicting compatibility between product
images (e.g. an image containing a t-shirt and an image containing a pair of
jeans). However, these approaches ignore real-world 'scene' images (e.g.
selfies); such images are hard to deal with due to their complexity, clutter,
variations in lighting and pose (etc.) but on the other hand could potentially
provide key context (e.g. the user's body type, or the season) for making more
accurate recommendations. In this work, we propose a new task called 'Complete
the Look', which seeks to recommend visually compatible products based on scene
images. We design an approach to extract training data for this task, and
propose a novel way to learn the scene-product compatibility from fashion or
interior design images. Our approach measures compatibility both globally and
locally via CNNs and attention mechanisms. Extensive experiments show that our
method achieves significant performance gains over alternative systems. Human
evaluation and qualitative analysis are also conducted to further understand
model behavior. We hope this work could lead to useful applications which link
large corpora of real-world scenes with shoppable products.
Domain-Adaptive Single-View 3D Reconstruction
Authors: Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn
Date: 2018-12-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Domain Adaptation, Single-View 3D Reconstruction

Single-view 3D shape reconstruction is an important but challenging problem, mainly for two reasons. First, as shape annotation is very expensive to acquire, current methods rely on synthetic data, in which ground-truth 3D annotation is easy to obtain. However, this results in domain adaptation problem when applied to natural images. The second challenge is that there are multiple shapes that can explain a given 2D image. In this paper, we propose a framework to improve over these challenges using adversarial training. On one hand, we impose domain confusion between natural and synthetic image representations to reduce the distribution gap. On the other hand, we impose the reconstruction to be `realistic' by forcing it to lie on a (learned) manifold of realistic object shapes. Our experiments show that these constraints improve performance by a large margin over baseline reconstruction models. We achieve results competitive with the state of the art with a much simpler architecture.
Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
Authors: Peter Blouw, Xuan Choo, Eric Hunsberger, Chris Eliasmith
Date: 2018-12-04 00:00:00
Tasks: Benchmarking, CPU, GPU, Keyword Spotting

Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning
toolkit, we analyze the inference speed, dynamic power consumption, and energy
cost per inference of a two-layer neural network keyword spotter trained to
recognize a single phrase. We perform comparative analyses of this keyword
spotter running on more conventional hardware devices including a CPU, a GPU,
Nvidia's Jetson TX1, and the Movidius Neural Compute Stick. Our results
indicate that for this inference application, Loihi outperforms all of these
alternatives on an energy cost per inference basis while maintaining equivalent
inference accuracy. Furthermore, an analysis of tradeoffs between network size,
inference speed, and energy cost indicates that Loihi's comparative advantage
over other low-power computing devices improves for larger networks.
Multiview Cross-supervision for Semantic Segmentation
Authors: Yuan Yao, Hyun Soo Park
Date: 2018-12-04 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Camera Calibration, Segmentation, Semantic Segmentation

This paper presents a semi-supervised learning framework for a customized
semantic segmentation task using multiview image streams. A key challenge of
the customized task lies in the limited accessibility of the labeled data due
to the requirement of prohibitive manual annotation effort. We hypothesize that
it is possible to leverage multiview image streams that are linked through the
underlying 3D geometry, which can provide an additional supervisionary signal
to train a segmentation model. We formulate a new cross-supervision method
using a shape belief transfer---the segmentation belief in one image is used to
predict that of the other image through epipolar geometry analogous to
shape-from-silhouette. The shape belief transfer provides the upper and lower
bounds of the segmentation for the unlabeled data where its gap approaches
asymptotically to zero as the number of the labeled views increases. We
integrate this theory to design a novel network that is agnostic to camera
calibration, network model, and semantic category and bypasses the intermediate
process of suboptimal 3D reconstruction. We validate this network by
recognizing a customized semantic category per pixel from realworld visual data
including non-human species and a subject of interest in social videos where
attaining large-scale annotation data is infeasible.
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
Authors: Si-Qi Liu, Eli Gibson, Sasa Grbic, Zhoubing Xu, Arnaud Arindra Adiyoso Setio, Jie Yang, Bogdan Georgescu, Dorin Comaniciu
Date: 2018-12-04 00:00:00
Tasks: Diagnostic, Image Generation, Medical Image Analysis

The performance of medical image analysis systems is constrained by the
quantity of high-quality image annotations. Such systems require data to be
annotated by experts with years of training, especially when diagnostic
decisions are involved. Such datasets are thus hard to scale up. In this
context, it is hard for supervised learning systems to generalize to the cases
that are rare in the training set but would be present in real-world clinical
practices. We believe that the synthetic image samples generated by a system
trained on the real data can be useful for improving the supervised learning
tasks in the medical image analysis applications. Allowing the image synthesis
to be manipulable could help synthetic images provide complementary information
to the training data rather than simply duplicating the real-data manifold. In
this paper, we propose a framework for synthesizing 3D objects, such as
pulmonary nodules, in 3D medical images with manipulable properties. The
manipulation is enabled by decomposing of the object of interests into its
segmentation mask and a 1D vector containing the residual information. The
synthetic object is refined and blended into the image context with two
adversarial discriminators. We evaluate the proposed framework on lung nodules
in 3D chest CT images and show that the proposed framework could generate
realistic nodules with manipulable shapes, textures and locations, etc. By
sampling from both the synthetic nodules and the real nodules from 2800 3D CT
volumes during the classifier training, we show the synthetic patches could
improve the overall nodule detection performance by average 8.44% competition
performance metric (CPM) score.
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Authors: Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adria Perez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noe, Cecilia Clementi
Date: 2018-12-04 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Learning Theory

Atomistic or ab-initio molecular dynamics simulations are widely used to
predict thermodynamics and kinetics and relate them to molecular structure. A
common approach to go beyond the time- and length-scales accessible with such
computationally expensive simulations is the definition of coarse-grained
molecular models. Existing coarse-graining approaches define an effective
interaction potential to match defined properties of high-resolution models or
experimental data. In this paper, we reformulate coarse-graining as a
supervised machine learning problem. We use statistical learning theory to
decompose the coarse-graining error and cross-validation to select and compare
the performance of different models. We introduce CGnets, a deep learning
approach, that learns coarse-grained free energy functions and can be trained
by a force matching scheme. CGnets maintain all physically relevant invariances
and allow one to incorporate prior physics knowledge to avoid sampling of
unphysical structures. We show that CGnets can capture all-atom
explicit-solvent free energy surfaces with models using only a few
coarse-grained beads and no solvent, while classical coarse-graining methods
fail to capture crucial features of the free energy surface. Thus, CGnets are
able to capture multi-body terms that emerge from the dimensionality reduction.
Learning Cheap and Novel Flight Itineraries
Authors: Dmytro Karamshuk, David Matthews
Date: 2018-12-04 00:00:00

We consider the problem of efficiently constructing cheap and novel round
trip flight itineraries by combining legs from different airlines. We analyse
the factors that contribute towards the price of such itineraries and find that
many result from the combination of just 30% of airlines and that the closer
the departure of such itineraries is to the user's search date the more likely
they are to be cheaper than the tickets from one airline. We use these insights
to formulate the problem as a trade-off between the recall of cheap itinerary
constructions and the costs associated with building them.
  We propose a supervised learning solution with location embeddings which
achieves an AUC=80.48, a substantial improvement over simpler baselines. We
discuss various practical considerations for dealing with the staleness and the
stability of the model and present the design of the machine learning pipeline.
Finally, we present an analysis of the model's performance in production and
its impact on Skyscanner's users.
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Authors: Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Date: 2018-12-04 00:00:00

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot", vast computational effort is invested for simulating these system in small steps, e.g., using Molecular Dynamics. Combining deep learning and statistical mechanics, we here develop Boltzmann Generators, that are shown to generate unbiased one-shot equilibrium samples of representative condensed matter systems and proteins. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates.
Impact of Sentiment Detection to Recognize Toxic and Subversive Online Comments
Authors: Éloi Brassard-Gourdeau, Richard Khoury
Date: 2018-12-04 00:00:00

The presence of toxic content has become a major problem for many online
communities. Moderators try to limit this problem by implementing more and more
refined comment filters, but toxic users are constantly finding new ways to
circumvent them. Our hypothesis is that while modifying toxic content and
keywords to fool filters can be easy, hiding sentiment is harder. In this
paper, we explore various aspects of sentiment detection and their correlation
to toxicity, and use our results to implement a toxicity detection tool. We
then test how adding the sentiment information helps detect toxicity in three
different real-world datasets, and incorporate subversion to these datasets to
simulate a user trying to circumvent the system. Our results show sentiment
information has a positive impact on toxicity detection against a subversive
user.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data
Authors: Haraldur T. Hallgrímsson, Filip Jankovic, Tim Althoff, Luca Foschini
Date: 2018-12-04 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

We consider the problem of modeling cardiovascular responses to physical
activity and sleep changes captured by wearable sensors in free living
conditions. We use an attentional convolutional neural network to learn
parsimonious signatures of individual cardiovascular response from data
recorded at the minute level resolution over several months on a cohort of 80k
people. We demonstrate internal validity by showing that signatures generated
on an individual's 2017 data generalize to predict minute-level heart rate from
physical activity and sleep for the same individual in 2018, outperforming
several time-series forecasting baselines. We also show external validity
demonstrating that signatures outperform plain resting heart rate (RHR) in
predicting variables associated with cardiovascular functions, such as age and
Body Mass Index (BMI). We believe that the computed cardiovascular signatures
have utility in monitoring cardiovascular health over time, including detecting
abnormalities and quantifying recovery from acute events.
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning
Authors: Paul Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina
Date: 2018-12-04 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

On-device CNN inference for real-time computer vision applications can result
in computational demands that far exceed the energy budgets of mobile devices.
This paper proposes FixyNN, a co-designed hardware accelerator platform which
splits a CNN model into two parts: a set of layers that are fixed in the
hardware platform as a front-end fixed-weight feature extractor, and the
remaining layers which become a back-end classifier running on a conventional
programmable CNN accelerator. The common front-end provides ubiquitous CNN
features for all FixyNN models, while the back-end is programmable and specific
to a given dataset. Image classification models for FixyNN are trained
end-to-end via transfer learning, with front-end layers fixed for the shared
feature extractor, and back-end layers fine-tuned for a specific task. Over a
suite of six datasets, we trained models via transfer learning with an accuracy
loss of <1%, resulting in a FixyNN hardware platform with nearly 2 times better
energy efficiency than a conventional programmable CNN accelerator of the same
silicon area (i.e. hardware cost).