pwc-archive/papers-with-abstracts huggingface dataset:
Smart "Predict, then Optimize"
Authors: Adam N. Elmachtoub, Paul Grigas
Date: 2017-10-22 00:00:00
Tasks: Portfolio Optimization, Prediction

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in the downstream optimization problem. In contrast, we propose a new and very general framework, called Smart "Predict, then Optimize" (SPO), which directly leverages the optimization problem structure, i.e., its objective and constraints, for designing better prediction models. A key component of our framework is the SPO loss function which measures the decision error induced by a prediction. Training a prediction model with respect to the SPO loss is computationally challenging, and thus we derive, using duality theory, a convex surrogate loss function which we call the SPO+ loss. Most importantly, we prove that the SPO+ loss is statistically consistent with respect to the SPO loss under mild conditions. Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective. Numerical experiments on shortest path and portfolio optimization problems show that the SPO framework can lead to significant improvement under the predict-then-optimize paradigm, in particular when the prediction model being trained is misspecified. We find that linear models trained using SPO+ loss tend to dominate random forest algorithms, even when the ground truth is highly nonlinear.
Meta-Learning MCMC Proposals
Authors: Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell
Date: 2017-08-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Meta-Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Effective implementations of sampling-based probabilistic inference often
require manually constructed, model-specific proposals. Inspired by recent
progresses in meta-learning for training learning agents that can generalize to
unseen environments, we propose a meta-learning approach to building effective
and generalizable MCMC proposals. We parametrize the proposal as a neural
network to provide fast approximations to block Gibbs conditionals. The learned
neural proposals generalize to occurrences of common structural motifs across
different models, allowing for the construction of a library of learned
inference primitives that can accelerate inference on unseen models with no
model-specific training required. We explore several applications including
open-universe Gaussian mixture models, in which our learned proposals
outperform a hand-tuned sampler, and a real-world named entity recognition
task, in which our sampler yields higher final F1 scores than classical
single-site Gibbs sampling.
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies
Authors: Elizabeth Hou, Kumar Sricharan, Alfred O. Hero
Date: 2017-02-16 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection, Vocal Bursts Intensity Prediction

Data-driven anomaly detection methods suffer from the drawback of detecting
all instances that are statistically rare, irrespective of whether the detected
instances have real-world significance or not. In this paper, we are interested
in the problem of specifically detecting anomalous instances that are known to
have high real-world utility, while ignoring the low-utility statistically
anomalous instances. To this end, we propose a novel method called Latent
Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution.
This method uses the EM algorithm to simultaneously incorporate the Geometric
Entropy Minimization principle for identifying statistical anomalies, and the
Maximum Entropy Discrimination principle to incorporate utility labels, in
order to detect high-utility anomalies. We apply our method in both simulated
and real datasets to demonstrate that it has superior performance over existing
alternatives that independently pre-process with unsupervised anomaly detection
algorithms before classifying.
Learning Binary Residual Representations for Domain-specific Video Streaming
Authors: Yi-Hsuan Tsai, Ming-Yu Liu, Deqing Sun, Ming-Hsuan Yang, Jan Kautz
Date: 2017-12-14 00:00:00
Tasks: Video Compression

We study domain-specific video streaming. Specifically, we target a streaming
setting where the videos to be streamed from a server to a client are all in
the same domain and they have to be compressed to a small size for low-latency
transmission. Several popular video streaming services, such as the video game
streaming services of GeForce Now and Twitch, fall in this category. While
conventional video compression standards such as H.264 are commonly used for
this task, we hypothesize that one can leverage the property that the videos
are all in the same domain to achieve better video quality. Based on this
hypothesis, we propose a novel video compression pipeline. Specifically, we
first apply H.264 to compress domain-specific videos. We then train a novel
binary autoencoder to encode the leftover domain-specific residual information
frame-by-frame into binary representations. These binary representations are
then compressed and sent to the client together with the H.264 stream. In our
experiments, we show that our pipeline yields consistent gains over standard
H.264 compression across several benchmark datasets while using the same
channel bandwidth.
Learning to Navigate by Growing Deep Networks
Authors: Thushan Ganegedara, Lionel Ott, Fabio Ramos
Date: 2017-12-14 00:00:00
Tasks: Denoising, Navigate, Reinforcement Learning

Adaptability is central to autonomy. Intuitively, for high-dimensional
learning problems such as navigating based on vision, internal models with
higher complexity allow to accurately encode the information available.
However, most learning methods rely on models with a fixed structure and
complexity. In this paper, we present a self-supervised framework for robots to
learn to navigate, without any prior knowledge of the environment, by
incrementally building the structure of a deep network as new data becomes
available. Our framework captures images from a monocular camera and self
labels the images to continuously train and predict actions from a
computationally efficient adaptive deep architecture based on Autoencoders
(AE), in a self-supervised fashion. The deep architecture, named Reinforced
Adaptive Denoising Autoencoders (RA-DAE), uses reinforcement learning to
dynamically change the network structure by adding or removing neurons.
Experiments were conducted in simulation and real-world indoor and outdoor
environments to assess the potential of self-supervised navigation. RA-DAE
demonstrates better performance than equivalent non-adaptive deep learning
alternatives and can continue to expand its knowledge, trading-off past and
present information.
Deep Learning for Sensor-based Activity Recognition: A Survey
Authors: Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, Lisha Hu
Date: 2017-07-12 00:00:00
Tasks: Activity Recognition, Deep Learning, Incremental Learning, Survey

Sensor-based activity recognition seeks the profound high-level knowledge
about human activities from multitudes of low-level sensor readings.
Conventional pattern recognition approaches have made tremendous progress in
the past years. However, those methods often heavily rely on heuristic
hand-crafted feature extraction, which could hinder their generalization
performance. Additionally, existing methods are undermined for unsupervised and
incremental learning tasks. Recently, the recent advancement of deep learning
makes it possible to perform automatic high-level feature extraction thus
achieves promising performance in many areas. Since then, deep learning based
methods have been widely adopted for the sensor-based activity recognition
tasks. This paper surveys the recent advance of deep learning based
sensor-based activity recognition. We summarize existing literature from three
aspects: sensor modality, deep model, and application. We also present detailed
insights on existing work and propose grand challenges for future research.
OSU Multimodal Machine Translation System Report
Authors: Mingbo Ma, Dapeng Li, Kai Zhao, Liang Huang
Date: 2017-10-07 00:00:00
Proceeding: WS 2017 9
Tasks: Image Captioning, Machine Translation, Multimodal Machine Translation, Sentence, Translation

This paper describes Oregon State University's submissions to the shared
WMT'17 task "multimodal translation task I". In this task, all the sentence
pairs are image captions in different languages. The key difference between
this task and conventional machine translation is that we have corresponding
images as additional information for each sentence pair. In this paper, we
introduce a simple but effective system which takes an image shared between
different languages, feeding it into the both encoding and decoding side. We
report our system's performance for English-French and English-German with
Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves
the best performance in TER for English-German for MSCOCO dataset.
Neural networks catching up with finite differences in solving partial differential equations in higher dimensions
Authors: V. I. Avrutskiy
Date: 2017-12-14 00:00:00

Fully connected multilayer perceptrons are used for obtaining numerical
solutions of partial differential equations in various dimensions. Independent
variables are fed into the input layer, and the output is considered as
solution's value. To train such a network one can use square of equation's
residual as a cost function and minimize it with respect to weights by gradient
descent. Following previously developed method, derivatives of the equation's
residual along random directions in space of independent variables are also
added to cost function. Similar procedure is known to produce nearly machine
precision results using less than 8 grid points per dimension for 2D case. The
same effect is observed here for higher dimensions: solutions are obtained on
low density grids, but maintain their precision in the entire region. Boundary
value problems for linear and nonlinear Poisson equations are solved inside 2,
3, 4, and 5 dimensional balls. Grids for linear cases have 40, 159, 512 and
1536 points and for nonlinear 64, 350, 1536 and 6528 points respectively. In
all cases maximum error is less than $8.8\cdot10^{-6}$, and median error is
less than $2.4\cdot10^{-6}$. Very weak grid requirements enable neural networks
to obtain solution of 5D linear problem within 22 minutes, whereas projected
solving time for finite differences on the same hardware is 50 minutes. Method
is applied to second order equation, but requires little to none modifications
to solve systems or higher order PDEs.
Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework
Authors: Ugo Rosolia, Francesco Borrelli
Date: 2016-09-06 00:00:00
Tasks: Model Predictive Control

A Learning Model Predictive Controller (LMPC) for iterative tasks is
presented. The controller is reference-free and is able to improve its
performance by learning from previous iterations. A safe set and a terminal
cost function are used in order to guarantee recursive feasibility and
non-increasing performance at each iteration. The paper presents the control
design approach, and shows how to recursively construct terminal set and
terminal cost from state and input trajectories of previous iterations.
Simulation results show the effectiveness of the proposed control logic.
MoCoGAN: Decomposing Motion and Content for Video Generation
Authors: Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, Jan Kautz
Date: 2017-07-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generative Adversarial Network, Video Generation

Visual signals in a video can be divided into content and motion. While
content specifies which objects are in the video, motion describes their
dynamics. Based on this prior, we propose the Motion and Content decomposed
Generative Adversarial Network (MoCoGAN) framework for video generation. The
proposed framework generates a video by mapping a sequence of random vectors to
a sequence of video frames. Each random vector consists of a content part and a
motion part. While the content part is kept fixed, the motion part is realized
as a stochastic process. To learn motion and content decomposition in an
unsupervised manner, we introduce a novel adversarial learning scheme utilizing
both image and video discriminators. Extensive experimental results on several
challenging datasets with qualitative and quantitative comparison to the
state-of-the-art approaches, verify effectiveness of the proposed framework. In
addition, we show that MoCoGAN allows one to generate videos with same content
but different motion as well as videos with different content and same motion.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Authors: Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei
Date: 2017-12-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Image Classification

Recent deep networks are capable of memorizing the entire data even when the
labels are completely random. To overcome the overfitting on corrupted labels,
we propose a novel technique of learning another neural network, called
MentorNet, to supervise the training of the base deep networks, namely,
StudentNet. During training, MentorNet provides a curriculum (sample weighting
scheme) for StudentNet to focus on the sample the label of which is probably
correct. Unlike the existing curriculum that is usually predefined by human
experts, MentorNet learns a data-driven curriculum dynamically with StudentNet.
Experimental results demonstrate that our approach can significantly improve
the generalization performance of deep networks trained on corrupted training
data. Notably, to the best of our knowledge, we achieve the best-published
result on WebVision, a large benchmark containing 2.2 million images of
real-world noisy labels. The code are at https://github.com/google/mentornet
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Authors: Vladimir Iglovikov, Alexander Rakhlin, Alexandr Kalinin, Alexey Shvets
Date: 2017-12-13 00:00:00

Skeletal bone age assessment is a common clinical practice to diagnose
endocrine and metabolic disorders in child development. In this paper, we
describe a fully automated deep learning approach to the problem of bone age
assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017.
The dataset for this competition is consisted of 12.6k radiological images of
left hand labeled by the bone age and sex of patients. Our approach utilizes
several deep learning architectures: U-Net, ResNet-50, and custom VGG-style
neural networks trained end-to-end. We use images of whole hands as well as
specific parts of a hand for both training and inference. This approach allows
us to measure importance of specific hand bones for the automated bone age
analysis. We further evaluate performance of the method in the context of
skeletal development stages. Our approach outperforms other common methods for
bone age assessment.
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification
Authors: Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen
Date: 2017-12-13 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional auto-encoders have shown their remarkable performance in
stacking to deep convolutional neural networks for classifying image data
during past several years. However, they are unable to construct the
state-of-the-art convolutional neural networks due to their intrinsic
architectures. In this regard, we propose a flexible convolutional auto-encoder
by eliminating the constraints on the numbers of convolutional layers and
pooling layers from the traditional convolutional auto-encoder. We also design
an architecture discovery method by using particle swarm optimization, which is
capable of automatically searching for the optimal architectures of the
proposed flexible convolutional auto-encoder with much less computational
resource and without any manual intervention. We use the designed architecture
optimization algorithm to test the proposed flexible convolutional auto-encoder
through utilizing one graphic processing unit card on four extensively used
image classification datasets. Experimental results show that our work in this
paper significantly outperform the peer competitors including the
state-of-the-art algorithm.
Unsupervised Histopathology Image Synthesis
Authors: Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurc, Rajarsi R. Gupta, Joel H. Saltz
Date: 2017-12-13 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Hematoxylin and Eosin stained histopathology image analysis is essential for
the diagnosis and study of complicated diseases such as cancer. Existing
state-of-the-art approaches demand extensive amount of supervised training data
from trained pathologists. In this work we synthesize in an unsupervised
manner, large histopathology image datasets, suitable for supervised training
tasks. We propose a unified pipeline that: a) generates a set of initial
synthetic histopathology images with paired information about the nuclei such
as segmentation masks; b) refines the initial synthetic images through a
Generative Adversarial Network (GAN) to reference styles; c) trains a
task-specific CNN and boosts the performance of the task-specific CNN with
on-the-fly generated adversarial examples. Our main contribution is that the
synthetic images are not only realistic, but also representative (in reference
styles) and relatively challenging for training task-specific CNNs. We test our
method for nucleus segmentation using images from four cancer types. When no
supervised data exists for a cancer type, our method without supervision cost
significantly outperforms supervised methods which perform across-cancer
generalization. Even when supervised data exists for all cancer types, our
approach without supervision cost performs better than supervised methods.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Authors: Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang
Date: 2017-10-23 00:00:00
Tasks: Benchmarking, Knowledge Distillation, Model Compression, Quantization

Deep neural networks (DNNs) have recently achieved great success in many visual recognition tasks. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. During the past five years, tremendous progress has been made in this area. In this paper, we review the recent techniques for compacting and accelerating DNN models. In general, these techniques are divided into four categories: parameter pruning and quantization, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. Methods of parameter pruning and quantization are described first, after that the other techniques are introduced. For each category, we also provide insightful analysis about the performance, related applications, advantages, and drawbacks. Then we go through some very recent successful methods, for example, dynamic capacity networks and stochastic depths networks. After that, we survey the evaluation matrices, the main datasets used for evaluating the model performance, and recent benchmark efforts. Finally, we conclude this paper, discuss remaining the challenges and possible directions for future work.
Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
Authors: Lei Lin, Zhengbing He, Srinivas Peeta
Date: 2017-12-13 00:00:00

This study proposes a novel Graph Convolutional Neural Network with
Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous
pairwise correlations between stations to predict station-level hourly demand
in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model
are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the
convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a
recurrent block from the Long Short-term Memory neural network architecture to
capture temporal dependencies in the bike-sharing demand series. Furthermore,
four types of GCNN models are proposed whose adjacency matrices are based on
various bike-sharing system data, including Spatial Distance matrix (SD),
Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation
matrix (DC). These six types of GCNN models and seven other benchmark models
are built and compared on a Citi Bike dataset from New York City which includes
272 stations and over 28 million transactions from 2013 to 2016. Results show
that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error,
the Mean Absolute Error and the coefficient of determination (R2), followed by
the GCNNreg-DDGF. They outperform the other models. Through a more detailed
graph network analysis based on the learned DDGF, insights are obtained on the
black box of the GCNN-DDGF model. It is found to capture some information
similar to details embedded in the SD, DE and DC matrices. More importantly, it
also uncovers hidden heterogeneous pairwise correlations between stations that
are not revealed by any of those matrices.
Real-time Egocentric Gesture Recognition on Mobile Head Mounted Displays
Authors: Rohit Pandey, Marie White, Pavel Pidlypenskyi, Xue Wang, Christine Kaeser-Chen
Date: 2017-12-13 00:00:00
Tasks: Gesture Recognition, Mixed Reality

Mobile virtual reality (VR) head mounted displays (HMD) have become popular
among consumers in recent years. In this work, we demonstrate real-time
egocentric hand gesture detection and localization on mobile HMDs. Our main
contributions are: 1) A novel mixed-reality data collection tool to automatic
annotate bounding boxes and gesture labels; 2) The largest-to-date egocentric
hand gesture and bounding box dataset with more than 400,000 annotated frames;
3) A neural network that runs real time on modern mobile CPUs, and achieves
higher than 76% precision on gesture recognition across 8 classes.
Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications
Authors: Pin-Yu Chen, Baichuan Zhang, Mohammad Al Hasan
Date: 2017-12-13 00:00:00
Tasks: Clustering, Community Detection

The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs)
of a graph Laplacian matrix have been widely used in spectral clustering and
community detection. However, in real-life applications the number of clusters
or communities (say, $K$) is generally unknown a-priori. Consequently, the
majority of the existing methods either choose $K$ heuristically or they repeat
the clustering method with different choices of $K$ and accept the best
clustering result. The first option, more often, yields suboptimal result,
while the second option is computationally expensive. In this work, we propose
an incremental method for constructing the eigenspectrum of the graph Laplacian
matrix. This method leverages the eigenstructure of graph Laplacian matrix to
obtain the $K$-th smallest eigenpair of the Laplacian matrix given a collection
of all previously computed $K-1$ smallest eigenpairs. Our proposed method
adapts the Laplacian matrix such that the batch eigenvalue decomposition
problem transforms into an efficient sequential leading eigenpair computation
problem. As a practical application, we consider user-guided spectral
clustering. Specifically, we demonstrate that users can utilize the proposed
incremental method for effective eigenpair computation and for determining the
desired number of clusters based on multiple clustering metrics.