pwc-archive/papers-with-abstracts huggingface dataset:
RDPD: Rich Data Helps Poor Data via Imitation
Authors: Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun
Date: 2018-09-06 00:00:00
Tasks: Knowledge Distillation

In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-feature representations. On the other hand, an environment with poor observation equipment (e.g., at home) only provides low-quality, uni-modal data with poor-feature representations. To deploy a competitive model in a poor-data environment without requiring direct access to multi-modal data acquired from a rich-data environment, this paper develops and presents a knowledge distillation (KD) method (RDPD) to enhance a predictive model trained on poor data using knowledge distilled from a high-complexity model trained on rich, private data. We evaluated RDPD on three real-world datasets and shown that its distilled model consistently outperformed all baselines across all datasets, especially achieving the greatest performance improvement over a model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and 4.44% on ROC-AUC.
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I
Authors: Kshitij Tiwari
Date: 2018-09-06 00:00:00
Tasks: Gaussian Processes

This document serves to complement our website which was developed with the
aim of exposing the students to Gaussian Processes (GPs). GPs are
non-parametric Bayesian regression models that are largely used by
statisticians and geospatial data scientists for modeling spatial data. Several
open source libraries spanning from Matlab [1], Python [2], R [3] etc., are
already available for simple plug-and-use. The objective of this handout and in
turn the website was to allow the users to develop stand-alone GPs in Python by
relying on minimal external dependencies. To this end, we only use the default
python modules and assist the users in developing their own GPs from scratch
giving them an in-depth knowledge of what goes on under the hood. The module
covers GP inference using maximum likelihood estimation (MLE) and gives
examples of 1D (dummy) spatial data.
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks
Authors: Felix Leibfried, Peter Vrancx
Date: 2018-09-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a new optimization objective for value-based deep
reinforcement learning. We extend conventional Deep Q-Networks (DQNs) by adding
a model-learning component yielding a transcoder network. The prediction errors
for the model are included in the basic DQN loss as additional regularizers.
This augmented objective leads to a richer training signal that provides
feedback at every time step. Moreover, because learning an environment model
shares a common structure with the RL problem, we hypothesize that the
resulting objective improves both sample efficiency and performance. We
empirically confirm our hypothesis on a range of 20 games from the Atari
benchmark attaining superior results over vanilla DQN without model-based
regularization.
Propheticus: Generalizable Machine Learning Framework
Authors: João R. Campos, Marco Vieira, Ernesto Costa
Date: 2018-09-06 00:00:00
Tasks: BIG-bench Machine Learning

Due to recent technological developments, Machine Learning (ML), a subfield
of Artificial Intelligence (AI), has been successfully used to process and
extract knowledge from a variety of complex problems. However, a thorough ML
approach is complex and highly dependent on the problem at hand. Additionally,
implementing the logic required to execute the experiments is no small nor
trivial deed, consequentially increasing the probability of faulty code which
can compromise the results. Propheticus is a data-driven framework which
results of the need for a tool that abstracts some of the inherent complexity
of ML, whilst being easy to understand and use, as well as to adapt and expand
to assist the user's specific needs. Propheticus systematizes and enforces
various complex concepts of an ML experiment workflow, taking into account the
nature of both the problem and the data. It contains functionalities to execute
all the different tasks, from data preprocessing, to results analysis and
comparison. Notwithstanding, it can be fairly easily adapted to different
problems due to its flexible architecture, and customized as needed to address
the user's needs.
Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks
Authors: Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, Yusuke Uchida
Date: 2018-09-06 00:00:00
Tasks: Unity, Video Generation

We propose Progressive Structure-conditional Generative Adversarial Networks
(PSGAN), a new framework that can generate full-body and high-resolution
character images based on structural information. Recent progress in generative
adversarial networks with progressive training has made it possible to generate
high-resolution images. However, existing approaches have limitations in
achieving both high image quality and structural consistency at the same time.
Our method tackles the limitations by progressively increasing the resolution
of both generated images and structural conditions during training. In this
paper, we empirically demonstrate the effectiveness of this method by showing
the comparison with existing approaches and video generation results of diverse
anime characters at 1024x1024 based on target pose sequences. We also create a
novel dataset containing full-body 1024x1024 high-resolution images and exact
2D pose keypoints using Unity 3D Avatar models.
Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework
Authors: Wei Wang, Xucheng Li
Date: 2018-09-06 00:00:00
Tasks: GPU, Traffic Prediction

Advanced travel information and warning, if provided accurately, can help
road users avoid traffic congestion through dynamic route planning and behavior
change. It also enables traffic control centres mitigate the impact of
congestion by activating Intelligent Transport System (ITS) proactively. Deep
learning has become increasingly popular in recent years, following a surge of
innovative GPU technology, high-resolution, big datasets and thriving machine
learning algorithms. However, there are few examples exploiting this emerging
technology to develop applications for traffic prediction. This is largely due
to the difficulty in capturing random, seasonal, non-linear, and
spatio-temporal correlated nature of traffic data. In this paper, we propose a
data-driven modelling approach with a novel hierarchical D-CLSTM-t deep
learning model for short-term traffic speed prediction, a framework combined
with convolutional neural network (CNN) and long short-term memory (LSTM)
models. A deep CNN model is employed to learn the spatio-temporal traffic
patterns of the input graphs, which are then fed into a deep LSTM model for
sequence learning. To capture traffic seasonal variations, time of the day and
day of the week indicators are fused with trained features. The model is
trained end-to-end to predict travel speed in 15 to 90 minutes in the future.
We compare the model performance against other baseline models including CNN,
LGBM, LSTM, and traditional speed-flow curves. Experiment results show that the
D-CLSTM-t outperforms other models considerably. Model tests show that speed
upstream also responds sensibly to a sudden accident occurring downstream. Our
D-CLSTM-t model framework is also highly scalable for future extension such as
for network-wide traffic prediction, which can also be improved by including
additional features such as weather, long term seasonality and accident
information.
Deep Learning-Based Decoding for Constrained Sequence Codes
Authors: Congzhe Cao, Duanshun Li, Ivan Fair
Date: 2018-09-06 00:00:00
Tasks: Deep Learning

Constrained sequence codes have been widely used in modern communication and
data storage systems. Sequences encoded with constrained sequence codes satisfy
constraints imposed by the physical channel, hence enabling efficient and
reliable transmission of coded symbols. Traditional encoding and decoding of
constrained sequence codes rely on table look-up, which is prone to errors that
occur during transmission. In this paper, we introduce constrained sequence
decoding based on deep learning. With multiple layer perception (MLP) networks
and convolutional neural networks (CNNs), we are able to achieve low bit error
rates that are close to maximum a posteriori probability (MAP) decoding as well
as improve the system throughput. Moreover, implementation of
capacity-achieving fixed-length codes, where the complexity is prohibitively
high with table look-up decoding, becomes practical with deep learning-based
decoding.
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing
Authors: Jetic Gū, Hassan S. Shavarani, Anoop Sarkar
Date: 2018-09-06 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Constituency Parsing, Decoder, Dependency Parsing, Machine Translation, NMT, Sentence, Text Generation, Translation

The addition of syntax-aware decoding in Neural Machine Translation (NMT)
systems requires an effective tree-structured neural network, a syntax-aware
attention model and a language generation model that is sensitive to sentence
structure. We exploit a top-down tree-structured model called DRNN
(Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola
(2017) to create an NMT model called Seq2DRNN that combines a sequential
encoder with tree-structured decoding augmented with a syntax-aware attention
model. Unlike previous approaches to syntax-based NMT which use dependency
parsing models our method uses constituency parsing which we argue provides
useful information for translation. In addition, we use the syntactic structure
of the sentence to add new connections to the tree-structured decoder neural
network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of
the art syntax-based NMT models and show that our model produces more fluent
translations with better reordering. Since our model is capable of doing
translation and constituency parsing at the same time we also compare our
parsing accuracy against other neural parsing models.
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Authors: Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
Date: 2018-09-06 00:00:00

Recent progress in deep learning is revolutionizing the healthcare domain
including providing solutions to medication recommendations, especially
recommending medication combination for patients with complex health
conditions. Existing approaches either do not customize based on patient health
history, or ignore existing knowledge on drug-drug interactions (DDI) that
might lead to adverse outcomes. To fill this gap, we propose the Graph
Augmented Memory Networks (GAMENet), which integrates the drug-drug
interactions knowledge graph by a memory module implemented as a graph
convolutional networks, and models longitudinal patient records as the query.
It is trained end-to-end to provide safe and personalized recommendation of
medication combination. We demonstrate the effectiveness and safety of GAMENet
by comparing with several state-of-the-art methods on real EHR data. GAMENet
outperformed all baselines in all effectiveness measures, and also achieved
3.60% DDI rate reduction from existing EHR data.
Yes, IoU loss is submodular - as a function of the mispredictions
Authors: Maxim Berman, Matthew B. Blaschko, Amal Rannen Triki, Jiaqian Yu
Date: 2018-09-06 00:00:00

This note is a response to [7] in which it is claimed that [13, Proposition
11] is false. We demonstrate here that this assertion in [7] is false, and is
based on a misreading of the notion of set membership in [13, Proposition 11].
We maintain that [13, Proposition 11] is true.
  ([7] = arXiv:1809.00593, [13] = arXiv:1512.07797)
Unsupervised Learning of View-invariant Action Representations
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli
Date: 2018-09-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Action Recognition, Representation Learning, Temporal Action Localization

The recent success in human action recognition with deep learning methods
mostly adopt the supervised learning paradigm, which requires significant
amount of manually labeled data to achieve good performance. However, label
collection is an expensive and time-consuming process. In this work, we propose
an unsupervised learning framework, which exploits unlabeled data to learn
video representations. Different from previous works in video representation
learning, our unsupervised learning task is to predict 3D motion in multiple
target views using video representation from a source view. By learning to
extrapolate cross-view motions, the representation can capture view-invariant
motion dynamics which is discriminative for the action. In addition, we propose
a view-adversarial training method to enhance learning of view-invariant
features. We demonstrate the effectiveness of the learned representations for
action recognition on multiple datasets.
How to Combine Tree-Search Methods in Reinforcement Learning
Authors: Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
Date: 2018-09-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Finite-horizon lookahead policies are abundantly used in Reinforcement
Learning and demonstrate impressive empirical success. Usually, the lookahead
policies are implemented with specific planning methods such as Monte Carlo
Tree Search (e.g. in AlphaZero). Referring to the planning problem as tree
search, a reasonable practice in these implementations is to back up the value
only at the leaves while the information obtained at the root is not leveraged
other than for updating the policy. Here, we question the potency of this
approach. Namely, the latter procedure is non-contractive in general, and its
convergence is not guaranteed. Our proposed enhancement is straightforward and
simple: use the return from the optimal tree path to back up the values at the
descendants of the root. This leads to a $\gamma^h$-contracting procedure,
where $\gamma$ is the discount factor and $h$ is the tree depth. To establish
our results, we first introduce a notion called \emph{multiple-step greedy
consistency}. We then provide convergence rates for two algorithmic
instantiations of the above enhancement in the presence of noise injected to
both the tree search stage and value estimation stage.
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Authors: Tingran Gao, Shahab Asoodeh, Yi Huang, James Evans
Date: 2018-09-06 00:00:00
Tasks: PAC learning

Inspired by recent interests of developing machine learning and data mining
algorithms on hypergraphs, we investigate in this paper the semi-supervised
learning algorithm of propagating "soft labels" (e.g. probability
distributions, class membership scores) over hypergraphs, by means of optimal
transportation. Borrowing insights from Wasserstein propagation on graphs
[Solomon et al. 2014], we re-formulate the label propagation procedure as a
message-passing algorithm, which renders itself naturally to a generalization
applicable to hypergraphs through Wasserstein barycenters. Furthermore, in a
PAC learning framework, we provide generalization error bounds for propagating
one-dimensional distributions on graphs and hypergraphs using 2-Wasserstein
distance, by establishing the \textit{algorithmic stability} of the proposed
semi-supervised learning algorithm. These theoretical results also shed new
lights upon deeper understandings of the Wasserstein propagation on graphs.
Adversarial Reprogramming of Text Classification Neural Networks
Authors: Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, Farinaz Koushanfar
Date: 2018-09-06 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, text-classification, Text Classification

Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network. An adversary in such an attack scenario trains an additive contribution to the inputs to repurpose the neural network for the new classification task. While this reprogramming approach works for neural networks with a continuous input space such as that of images, it is not directly applicable to neural networks trained for tasks such as text classification, where the input space is discrete. Repurposing such classification networks would require the attacker to learn an adversarial program that maps inputs from one discrete space to the other. In this work, we introduce a context-based vocabulary remapping model to reprogram neural networks trained on a specific sequence classification task, for a new sequence classification task desired by the adversary. We propose training procedures for this adversarial program in both white-box and black-box settings. We demonstrate the application of our model by adversarially repurposing various text-classification models including LSTM, bi-directional LSTM and CNN for alternate classification tasks.
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
Authors: Ding Liu, Bihan Wen, Jianbo Jiao, Xian-Ming Liu, Zhangyang Wang, Thomas S. Huang
Date: 2018-09-06 00:00:00
Tasks: Denoising, Image Denoising, Vocal Bursts Intensity Prediction

Image denoising and high-level vision tasks are usually handled independently
in the conventional practice of computer vision, and their connection is
fragile. In this paper, we cope with the two jointly and explore the mutual
influence between them with the focus on two questions, namely (1) how image
denoising can help improving high-level vision tasks, and (2) how the semantic
information from high-level vision tasks can be used to guide image denoising.
First for image denoising we propose a convolutional neural network in which
convolutions are conducted in various spatial resolutions via downsampling and
upsampling operations in order to fuse and exploit contextual information on
different scales. Second we propose a deep neural network solution that
cascades two modules for image denoising and various high-level tasks,
respectively, and use the joint loss for updating only the denoising network
via back-propagation. We experimentally show that on one hand, the proposed
denoiser has the generality to overcome the performance degradation of
different high-level vision tasks. On the other hand, with the guidance of
high-level vision information, the denoising network produces more visually
appealing results. Extensive experiments demonstrate the benefit of exploiting
image semantics simultaneously for image denoising and high-level vision tasks
via deep learning. The code is available online:
https://github.com/Ding-Liu/DeepDenoising
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars
Authors: Dooseop Choi, Taeg-Hyun An, Kyounghwan Ahn, Jeongdan Choi
Date: 2018-09-06 00:00:00
Tasks: Self-Driving Cars, Transfer Learning

In this paper, we present a transfer learning method for the end-to-end
control of self-driving cars, which enables a convolutional neural network
(CNN) trained on a source domain to be utilized for the same task in a
different target domain. A conventional CNN for the end-to-end control is
designed to map a single front-facing camera image to a steering command. To
enable the transfer learning, we let the CNN produce not only a steering
command but also a lane departure level (LDL) by adding a new task module,
which takes the output of the last convolutional layer as input. The CNN
trained on the source domain, called source network, is then utilized to train
another task module called target network, which also takes the output of the
last convolutional layer of the source network and is trained to produce a
steering command for the target domain. The steering commands from the source
and target network are finally merged according to the LDL and the merged
command is utilized for controlling a car in the target domain. To demonstrate
the effectiveness of the proposed method, we utilized two simulators, TORCS and
GTAV, for the source and the target domains, respectively. Experimental results
show that the proposed method outperforms other baseline methods in terms of
stable and safe control of cars.
MASA: Motif-Aware State Assignment in Noisy Time Series Data
Authors: Saachi Jain, David Hallac, Rok Sosic, Jure Leskovec
Date: 2018-09-06 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Complex systems, such as airplanes, cars, or financial markets, produce multivariate time series data consisting of a large number of system measurements over a period of time. Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior. An important problem in this domain is to identify repeated sequences of states, known as motifs. Such motifs correspond to complex behaviors that capture common sequences of state transitions. For example, in automotive data, a motif of "making a turn" might manifest as a sequence of states: slowing down, turning the wheel, and then speeding back up. However, discovering these motifs is challenging, because the individual states and state assignments are unknown, have different durations, and need to be jointly learned from the noisy time series. Here we develop motif-aware state assignment (MASA), a method to discover common motifs in noisy time series data and leverage those motifs to more robustly assign states to measurements. We formulate the problem of motif discovery as a large optimization problem, which we solve using an expectation-maximization type approach. MASA performs well in the presence of noise in the input data and is scalable to very large datasets. Experiments on synthetic data show that MASA outperforms state-of-the-art baselines by up to 38.2%, and two case studies demonstrate how our approach discovers insightful motifs in the presence of noise in real-world time series data.
Improving Explorability in Variational Inference with Annealed Variational Objectives
Authors: Chin-wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville
Date: 2018-09-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Despite the advances in the representational capacity of approximate
distributions for variational inference, the optimization process can still
limit the density that is ultimately learned. We demonstrate the drawbacks of
biasing the true posterior to be unimodal, and introduce Annealed Variational
Objectives (AVO) into the training of hierarchical variational methods.
Inspired by Annealed Importance Sampling, the proposed method facilitates
learning by incorporating energy tempering into the optimization objective. In
our experiments, we demonstrate our method's robustness to deterministic warm
up, and the benefits of encouraging exploration in the latent space.