pwc-archive/papers-with-abstracts huggingface dataset:
A more globally accurate dimensionality reduction method using triplets
Authors: Ehsan Amid, Manfred K. Warmuth
Date: 2018-03-01 00:00:00
Tasks: Dimensionality Reduction, Triplet

We first show that the commonly used dimensionality reduction (DR) methods
such as t-SNE and LargeVis poorly capture the global structure of the data in
the low dimensional embedding. We show this via a number of tests for the DR
methods that can be easily applied by any practitioner to the dataset at hand.
Surprisingly enough, t-SNE performs the best w.r.t. the commonly used measures
that reward the local neighborhood accuracy such as precision-recall while
having the worst performance in our tests for global structure. We then
contrast the performance of these two DR method against our new method called
TriMap. The main idea behind TriMap is to capture higher orders of structure
with triplet information (instead of pairwise information used by t-SNE and
LargeVis), and to minimize a robust loss function for satisfying the chosen
triplets. We provide compelling experimental evidence on large natural datasets
for the clear advantage of the TriMap DR results. As LargeVis, TriMap scales
linearly with the number of data points.
Random Hinge Forest for Differentiable Learning
Authors: Nathan Lay, Adam P. Harrison, Sharon Schreiber, Gitesh Dawer, Adrian Barbu
Date: 2018-02-12 00:00:00

We propose random hinge forests, a simple, efficient, and novel variant of
decision forests. Importantly, random hinge forests can be readily incorporated
as a general component within arbitrary computation graphs that are optimized
end-to-end with stochastic gradient descent or variants thereof. We derive
random hinge forest and ferns, focusing on their sparse and efficient nature,
their min-max margin property, strategies to initialize them for arbitrary
network architectures, and the class of optimizers most suitable for optimizing
random hinge forest. The performance and versatility of random hinge forests
are demonstrated by experiments incorporating a variety of of small and large
UCI machine learning data sets and also ones involving the MNIST, Letter, and
USPS image datasets. We compare random hinge forests with random forests and
the more recent backpropagating deep neural decision forests.
WRPN & Apprentice: Methods for Training and Inference using Low-Precision Numerics
Authors: Asit Mishra, Debbie Marr
Date: 2018-03-01 00:00:00

Today's high performance deep learning architectures involve large models
with numerous parameters. Low precision numerics has emerged as a popular
technique to reduce both the compute and memory requirements of these large
models. However, lowering precision often leads to accuracy degradation. We
describe three schemes whereby one can both train and do efficient inference
using low precision numerics without hurting accuracy. Finally, we describe an
efficient hardware accelerator that can take advantage of the proposed low
precision numerics.
Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics
Authors: Yang Hu, Guihua Wen, Huiqiang Liao, Changjun Wang, Dan Dai, Zhiwen Yu
Date: 2018-01-23 00:00:00

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.
Optimal Vehicle Dispatching Schemes via Dynamic Pricing
Authors: Mengjing Chen, Weiran Shen, Pingzhong Tang, Song Zuo
Date: 2017-07-06 00:00:00

Over the past few years, ride-sharing has emerged as an effective way to
relieve traffic congestion. A key problem for these platforms is to come up
with a revenue-optimal (or GMV-optimal) pricing scheme and an induced vehicle
dispatching policy that incorporate geographic and temporal information. In
this paper, we aim to tackle this problem via an economic approach.
  Modeled naively, the underlying optimization problem may be non-convex and
thus hard to compute. To this end, we use a so-called "ironing" technique to
convert the problem into an equivalent convex optimization one via a clean
Markov decision process (MDP) formulation, where the states are the driver
distributions and the decision variables are the prices for each pair of
locations. Our main finding is an efficient algorithm that computes the exact
revenue-optimal (or GMV-optimal) randomized pricing schemes. We characterize
the optimal solution of the MDP by a primal-dual analysis of a corresponding
convex program. We also conduct empirical evaluations of our solution through
real data of a major ride-sharing platform and show its advantages over fixed
pricing schemes as well as several prevalent surge-based pricing schemes.
Tongue image constitution recognition based on Complexity Perception method
Authors: Jiajiong Ma, Guihua Wen, Yang Hu, Tianyuan Chang, Haibin Zeng, Lijun Jiang, Jianzeng Qin
Date: 2018-03-01 00:00:00
Tasks: Classification, General Classification

Background and Object: In China, body constitution is highly related to
physiological and pathological functions of human body and determines the
tendency of the disease, which is of great importance for treatment in clinical
medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine
inspection, is an important way to recognize the type of constitution.In order
to deploy tongue image constitution recognition system on non-invasive mobile
device to achieve fast, efficient and accurate constitution recognition, an
efficient method is required to deal with the challenge of this kind of complex
environment. Methods: In this work, we perform the tongue area detection,
tongue area calibration and constitution classification using methods which are
based on deep convolutional neural network. Subject to the variation of
inconstant environmental condition, the distribution of the picture is uneven,
which has a bad effect on classification performance. To solve this problem, we
propose a method based on the complexity of individual instances to divide
dataset into two subsets and classify them separately, which is capable of
improving classification accuracy. To evaluate the performance of our proposed
method, we conduct experiments on three sizes of tongue datasets, in which deep
convolutional neural network method and traditional digital image analysis
method are respectively applied to extract features for tongue images. The
proposed method is combined with the base classifier Softmax, SVM, and
DecisionTree respectively. Results: As the experiments results shown, our
proposed method improves the classification accuracy by 1.135% on average and
achieves 59.99% constitution classification accuracy. Conclusions: Experimental
results on three datasets show that our proposed method can effectively improve
the classification accuracy of tongue constitution recognition.
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values
Authors: Hiroyuki Hanada, Toshiyuki Takada, Jun Sakuma, Ichiro Takeuchi
Date: 2018-03-01 00:00:00
Tasks: Imputation, Missing Values, Prediction, regression

The problem of machine learning with missing values is common in many areas.
A simple approach is to first construct a dataset without missing values simply
by discarding instances with missing entries or by imputing a fixed value for
each missing entry, and then train a prediction model with the new dataset. A
drawback of this naive approach is that the uncertainty in the missing entries
is not properly incorporated in the prediction. In order to evaluate prediction
uncertainty, the multiple imputation (MI) approach has been studied, but the
performance of MI is sensitive to the choice of the probabilistic model of the
true values in the missing entries, and the computational cost of MI is high
because multiple models must be trained. In this paper, we propose an
alternative approach called the Interval-based Prediction Uncertainty Bounding
(IPUB) method. The IPUB method represents the uncertainties due to missing
entries as intervals, and efficiently computes the lower and upper bounds of
the prediction results when all possible training sets constructed by imputing
arbitrary values in the intervals are considered. The IPUB method can be
applied to a wide class of convex learning algorithms including penalized
least-squares regression, support vector machine (SVM), and logistic
regression. We demonstrate the advantages of the IPUB method by comparing it
with an existing method in numerical experiment with benchmark datasets.
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Authors: Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk
Date: 2018-03-01 00:00:00
Proceeding: ICML 2018 7
Tasks: Denoising, Retrieval

Phase retrieval algorithms have become an important component in many modern
computational imaging systems. For instance, in the context of ptychography and
speckle correlation imaging, they enable imaging past the diffraction limit and
through scattering media, respectively. Unfortunately, traditional phase
retrieval algorithms struggle in the presence of noise. Progress has been made
recently on more robust algorithms using signal priors, but at the expense of
limiting the range of supported measurement models (e.g., to Gaussian or coded
diffraction patterns). In this work we leverage the regularization-by-denoising
framework and a convolutional neural network denoiser to create prDeep, a new
phase retrieval algorithm that is both robust and broadly applicable. We test
and validate prDeep in simulation to demonstrate that it is robust to noise and
can handle a variety of system models.
  A MatConvNet implementation of prDeep is available at
https://github.com/ricedsp/prDeep.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Authors: Mengying Sun, Inci M. Baytas, Liang Zhan, Zhangyang Wang, Jiayu Zhou
Date: 2018-02-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Multi-Task Learning, regression, Transfer Learning

Over the past decade a wide spectrum of machine learning models have been
developed to model the neurodegenerative diseases, associating biomarkers,
especially non-intrusive neuroimaging markers, with key clinical scores
measuring the cognitive status of patients. Multi-task learning (MTL) has been
commonly utilized by these studies to address high dimensionality and small
cohort size challenges. However, most existing MTL approaches are based on
linear models and suffer from two major limitations: 1) they cannot explicitly
consider upper/lower bounds in these clinical scores; 2) they lack the
capability to capture complicated non-linear interactions among the variables.
In this paper, we propose Subspace Network, an efficient deep modeling approach
for non-linear multi-task censored regression. Each layer of the subspace
network performs a multi-task censored regression to improve upon the
predictions from the last layer via sketching a low-dimensional subspace to
perform knowledge transfer among learning tasks. Under mild assumptions, for
each layer the parametric subspace can be recovered using only one pass of
training data. Empirical results demonstrate that the proposed subspace network
quickly picks up the correct parameter subspaces, and outperforms
state-of-the-arts in predicting neurodegenerative clinical scores using
information in brain imaging.
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
Authors: Adria Ruiz, Ognjen Rudovic, Xavier Binefa, Maja Pantic
Date: 2018-03-01 00:00:00
Tasks: Temporal Sequences, Weakly-supervised Learning

We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised
learning problems, where a training set is formed by bags (sets of feature
vectors or instances) and only labels at bag-level are provided. Specifically,
we consider the Multi-Instance Dynamic-Ordinal-Regression (MI-DOR) setting,
where the instance labels are naturally represented as ordinal variables and
bags are structured as temporal sequences. To this end, we propose
Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this framework, we
treat instance-labels as temporally-dependent latent variables in an Undirected
Graphical Model. Different MIL assumptions are modelled via newly introduced
high-order potentials relating bag and instance-labels within the energy
function of the model. We also extend our framework to address the
Partially-Observed MI-DOR problems, where a subset of instance labels are
available during training. We show on the tasks of weakly-supervised facial
behavior analysis, Facial Action Unit (DISFA dataset) and Pain (UNBC dataset)
Intensity estimation, that the proposed framework outperforms alternative
learning approaches. Furthermore, we show that MIDORF can be employed to reduce
the data annotation efforts in this context by large-scale.
Scalar Quantization as Sparse Least Square Optimization
Authors: Chen Wang, Xiaomei Yang, Shaomin Fei, Kai Zhou, Xiao-Feng Gong, Miao Du, Ruisen Luo
Date: 2018-03-01 00:00:00
Tasks: Clustering, Quantization

Quantization can be used to form new vectors/matrices with shared values close to the original. In recent years, the popularity of scalar quantization for value-sharing applications has been soaring as it has been found huge utilities in reducing the complexity of neural networks. Existing clustering-based quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for a large number of clusters. To overcome these problems, in this paper, the problem of scalar quantization is examined from a new perspective, namely sparse least square optimization. Specifically, inspired by the property of sparse least square regression, several quantization algorithms based on $l_1$ least square are proposed. In addition, similar schemes with $l_1 + l_2$ and $l_0$ regularization are proposed. Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square. The paper shows that the latter method is mathematically equivalent to an improved version of k-means clustering-based quantization algorithm, although the two algorithms originated from different intuitions. The algorithms proposed were tested with three types of data and their computational performances, including information loss, time consumption, and the distribution of the values of the sparse vectors, were compared and analyzed. The paper offers a new perspective to probe the area of quantization, and the algorithms proposed can outperform existing methods especially under some bit-width reduction scenarios, when the required post-quantization resolution (number of values) is not significantly lower than the original number.
Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems
Authors: Aidin Ferdowsi, Walid Saad
Date: 2018-03-01 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning

Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT) environment, due to the large-scale nature of the system and its susceptibility to man-in-the-middle and eavesdropping attacks. In this paper, a novel deep learning method is proposed for dynamic authentication of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the cloud, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the cloud cannot authenticate all the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the cloud's decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to a large set of available actions for the cloud, it is shown that analytically deriving the MSNE is challenging and, thus, a learning algorithm proposed that converges to the MSNE. Moreover, in order to cope with the incomplete information case in which the cloud cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the cloud to decide on which IoTDs to authenticate. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.
Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases
Authors: Miguel Campo, JJ Espinoza, Julie Rieger, Abhinav Taliyan
Date: 2018-03-01 00:00:00
Tasks: Collaborative Filtering, Metric Learning, Product Recommendation, Recommendation Systems

Product recommendation systems are important for major movie studios during
the movie greenlight process and as part of machine learning personalization
pipelines. Collaborative Filtering (CF) models have proved to be effective at
powering recommender systems for online streaming services with explicit
customer feedback data. CF models do not perform well in scenarios in which
feedback data is not available, in cold start situations like new product
launches, and situations with markedly different customer tiers (e.g., high
frequency customers vs. casual customers). Generative natural language models
that create useful theme-based representations of an underlying corpus of
documents can be used to represent new product descriptions, like new movie
plots. When combined with CF, they have shown to increase the performance in
cold start situations. Outside of those cases though in which explicit customer
feedback is available, recommender engines must rely on binary purchase data,
which materially degrades performance. Fortunately, purchase data can be
combined with product descriptions to generate meaningful representations of
products and customer trajectories in a convenient product space in which
proximity represents similarity. Learning to measure the distance between
points in this space can be accomplished with a deep neural network that trains
on customer histories and on dense vectorizations of product descriptions. We
developed a system based on Collaborative (Deep) Metric Learning (CML) to
predict the purchase probabilities of new theatrical releases. We trained and
evaluated the model using a large dataset of customer histories, and tested the
model for a set of movies that were released outside of the training window.
Initial experiments show gains relative to models that do not train on
collaborative preferences.
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
Authors: Brian Yang, Grant Wang, Roberto Calandra, Daniel Contreras, Sergey Levine, Kristofer Pister
Date: 2018-03-01 00:00:00
Tasks: Navigate

The design of gaits for robot locomotion can be a daunting process which
requires significant expert knowledge and engineering. This process is even
more challenging for robots that do not have an accurate physical model, such
as compliant or micro-scale robots. Data-driven gait optimization provides an
automated alternative to analytical gait design. In this paper, we propose a
novel approach to efficiently learn a wide range of locomotion tasks with
walking robots. This approach formalizes locomotion as a contextual policy
search task to collect data, and subsequently uses that data to learn
multi-objective locomotion primitives that can be used for planning. As a
proof-of-concept we consider a simulated hexapod modeled after a recently
developed microrobot, and we thoroughly evaluate the performance of this
microrobot on different tasks and gaits. Our results validate the proposed
controller and learning scheme on single and multi-objective locomotion tasks.
Moreover, the experimental simulations show that without any prior knowledge
about the robot used (e.g., dynamics model), our approach is capable of
learning locomotion primitives within 250 trials and subsequently using them to
successfully navigate through a maze.
Growing Story Forest Online from Massive Breaking News
Authors: Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
Date: 2018-03-01 00:00:00
Tasks: Graph Generation, Information Threading

We describe our experience of implementing a news content organization system
at Tencent that discovers events from vast streams of breaking news and evolves
news story structures in an online fashion. Our real-world system has distinct
requirements in contrast to previous studies on topic detection and tracking
(TDT) and event timeline or graph generation, in that we 1) need to accurately
and quickly extract distinguishable events from massive streams of long text
documents that cover diverse topics and contain highly redundant information,
and 2) must develop the structures of event stories in an online manner,
without repeatedly restructuring previously formed stories, in order to
guarantee a consistent user viewing experience. In solving these challenges, we
propose Story Forest, a set of online schemes that automatically clusters
streaming documents into events, while connecting related events in growing
trees to tell evolving stories. We conducted extensive evaluation based on 60
GB of real-world Chinese news data, although our ideas are not
language-dependent and can easily be extended to other languages, through
detailed pilot user experience studies. The results demonstrate the superior
capability of Story Forest to accurately identify events and organize news text
into a logical structure that is appealing to human readers, compared to
multiple existing algorithm frameworks.
XNMT: The eXtensible Neural Machine Translation Toolkit
Authors: Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, Liming Wang
Date: 2018-03-01 00:00:00
Proceeding: WS 2018 3
Tasks: Machine Translation, NMT, speech-recognition, Speech Recognition, Translation

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit.
XNMT distin- guishes itself from other open-source NMT toolkits by its focus on
modular code design, with the purpose of enabling fast iteration in research
and replicable, reliable results. In this paper we describe the design of XNMT
and its experiment configuration system, and demonstrate its utility on the
tasks of machine translation, speech recognition, and multi-tasked machine
translation/parsing. XNMT is available open-source at
https://github.com/neulab/xnmt