pwc-archive/papers-with-abstracts huggingface dataset:
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces
Authors: Haifang Li, Yingce Xia, Wensheng Zhang
Date: 2018-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy evaluation with linear function approximation is an important problem
in reinforcement learning. When facing high-dimensional feature spaces, such a
problem becomes extremely hard considering the computation efficiency and
quality of approximations. We propose a new algorithm, LSTD($\lambda$)-RP,
which leverages random projection techniques and takes eligibility traces into
consideration to tackle the above two challenges. We carry out theoretical
analysis of LSTD($\lambda$)-RP, and provide meaningful upper bounds of the
estimation error, approximation error and total generalization error. These
results demonstrate that LSTD($\lambda$)-RP can benefit from random projection
and eligibility traces strategies, and LSTD($\lambda$)-RP can achieve better
performances than prior LSTD-RP and LSTD($\lambda$) algorithms.
Masked Conditional Neural Networks for Environmental Sound Classification
Authors: Fady Medhat, David Chesmore, John Robinson
Date: 2018-05-25 00:00:00
Tasks: Classification, Environmental Sound Classification, General Classification, Sound Classification

The ConditionaL Neural Network (CLNN) exploits the nature of the temporal
sequencing of the sound signal represented in a spectrogram, and its variant
the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in
frequency bands by embedding a filterbank-like sparseness over the network's
links using a binary mask. Additionally, the masking automates the exploration
of different feature combinations concurrently analogous to handcrafting the
optimum combination of features for a recognition task. We have evaluated the
MCLNN performance using the Urbansound8k dataset of environmental sounds.
Additionally, we present a collection of manually recorded sounds for rail and
road traffic, YorNoise, to investigate the confusion rates among machine
generated sounds possessing low-frequency components. MCLNN has achieved
competitive results without augmentation and using 12% of the trainable
parameters utilized by an equivalent model based on state-of-the-art
Convolutional Neural Networks on the Urbansound8k. We extended the Urbansound8k
dataset with YorNoise, where experiments have shown that common tonal
properties affect the classification performance.
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Authors: Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang
Date: 2018-05-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, graph construction, Meta-Learning

The goal of few-shot learning is to learn a classifier that generalizes well
even when trained with a limited number of training instances per class. The
recently introduced meta-learning approaches tackle this problem by learning a
generic classifier across a large number of multiclass classification tasks and
generalizing the model to a new task. Yet, even with such meta-learning, the
low-data problem in the novel classification task still remains. In this paper,
we propose Transductive Propagation Network (TPN), a novel meta-learning
framework for transductive inference that classifies the entire test set at
once to alleviate the low-data problem. Specifically, we propose to learn to
propagate labels from labeled instances to unlabeled test instances, by
learning a graph construction module that exploits the manifold structure in
the data. TPN jointly learns both the parameters of feature embedding and the
graph construction in an end-to-end manner. We validate TPN on multiple
benchmark datasets, on which it largely outperforms existing few-shot learning
approaches and achieves the state-of-the-art results.
Implicit Weight Uncertainty in Neural Networks
Authors: Nick Pawlowski, Andrew Brock, Matthew C. H. Lee, Martin Rajchl, Ben Glocker
Date: 2017-11-03 00:00:00
Tasks: Deep Learning, Normalising Flows

Modern neural networks tend to be overconfident on unseen, noisy or
incorrectly labelled data and do not produce meaningful uncertainty measures.
Bayesian deep learning aims to address this shortcoming with variational
approximations (such as Bayes by Backprop or Multiplicative Normalising Flows).
However, current approaches have limitations regarding flexibility and
scalability. We introduce Bayes by Hypernet (BbH), a new method of variational
approximation that interprets hypernetworks as implicit distributions. It
naturally uses neural networks to model arbitrarily complex distributions and
scales to modern deep learning architectures. In our experiments, we
demonstrate that our method achieves competitive accuracies and predictive
uncertainties on MNIST and a CIFAR5 task, while being the most robust against
adversarial attacks.
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Authors: Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng
Date: 2018-05-25 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Applying reinforcement learning in physical-world tasks is extremely
challenging. It is commonly infeasible to sample a large number of trials, as
required by current reinforcement learning methods, in a physical environment.
This paper reports our project on using reinforcement learning for better
commodity search in Taobao, one of the largest online retail platforms and
meanwhile a physical environment with a high sampling cost. Instead of training
reinforcement learning in Taobao directly, we present our approach: first we
build Virtual Taobao, a simulator learned from historical customer behavior
data through the proposed GAN-SD (GAN for Simulating Distributions) and MAIL
(multi-agent adversarial imitation learning), and then we train policies in
Virtual Taobao with no physical costs in which ANC (Action Norm Constraint)
strategy is proposed to reduce over-fitting. In experiments, Virtual Taobao is
trained from hundreds of millions of customers' records, and its properties are
compared with the real environment. The results disclose that Virtual Taobao
faithfully recovers important properties of the real environment. We also show
that the policies trained in Virtual Taobao can have significantly superior
online performance to the traditional supervised approaches. We hope our work
could shed some light on reinforcement learning applications in complex
physical environments.
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm
Authors: Mina Yazdandoost, Peyman Khazaei, Salar Saadatian, Rahim Kamali
Date: 2018-05-25 00:00:00
Tasks: Distributed Optimization

A new adopted evolutionary algorithm is presented in this paper to solve the
non-smooth, non-convex and non-linear multi-area economic dispatch (MAED). MAED
includes some areas which contains its own power generation and loads. By
transmitting the power from the area with lower cost to the area with higher
cost, the total cost function can be minimized greatly. The tie line capacity,
multi-fuel generator and the prohibited operating zones are satisfied in this
study. In addition, a new algorithm based on electro search optimization
algorithm (ESOA) is proposed to solve the MAED optimization problem with
considering all the constraints. In ESOA algorithm all probable moving states
for individuals to get away from or move towards the worst or best solution
needs to be considered. To evaluate the performance of the ESOA algorithm, the
algorithm is applied to both the original economic dispatch with 40 generator
systems and the multi-area economic dispatch with 3 different systems such as:
6 generators in 2 areas; and 40 generators in 4 areas. It can be concluded
that, ESOA algorithm is more accurate and robust in comparison with other
methods.
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Authors: Tam Le, Makoto Yamada
Date: 2018-02-10 00:00:00
Proceeding: NeurIPS 2018 12

Algebraic topology methods have recently played an important role for
statistical analysis with complicated geometric structured data such as shapes,
linked twist maps, and material data. Among them, \textit{persistent homology}
is a well-known tool to extract robust topological features, and outputs as
\textit{persistence diagrams} (PDs). However, PDs are point multi-sets which
can not be used in machine learning algorithms for vector data. To deal with
it, an emerged approach is to use kernel methods, and an appropriate geometry
for PDs is an important factor to measure the similarity of PDs. A popular
geometry for PDs is the \textit{Wasserstein metric}. However, Wasserstein
distance is not \textit{negative definite}. Thus, it is limited to build
positive definite kernels upon the Wasserstein distance \textit{without
approximation}. In this work, we rely upon the alternative \textit{Fisher
information geometry} to propose a positive definite kernel for PDs
\textit{without approximation}, namely the Persistence Fisher (PF) kernel.
Then, we analyze eigensystem of the integral operator induced by the proposed
kernel for kernel machines. Based on that, we derive generalization error
bounds via covering numbers and Rademacher averages for kernel machines with
the PF kernel. Additionally, we show some nice properties such as stability and
infinite divisibility for the proposed kernel. Furthermore, we also propose a
linear time complexity over the number of points in PDs for an approximation of
our proposed kernel with a bounded error. Throughout experiments with many
different tasks on various benchmark datasets, we illustrate that the PF kernel
compares favorably with other baseline kernels for PDs.
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Authors: Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, Vadim Sheinin
Date: 2018-04-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Graph-to-Sequence, SQL-to-Text, Text Generation

The celebrated Sequence to Sequence learning (Seq2Seq) technique and its
numerous variants achieve excellent performance on many tasks. However, many
machine learning tasks have inputs naturally represented as graphs; existing
Seq2Seq models face a significant challenge in achieving accurate conversion
from graph form to the appropriate sequence. To address this challenge, we
introduce a novel general end-to-end graph-to-sequence neural encoder-decoder
model that maps an input graph to a sequence of vectors and uses an
attention-based LSTM method to decode the target sequence from these vectors.
Our method first generates the node and graph embeddings using an improved
graph-based neural network with a novel aggregation strategy to incorporate
edge direction information in the node embeddings. We further introduce an
attention mechanism that aligns node embeddings and the decoding sequence to
better cope with large graphs. Experimental results on bAbI, Shortest Path, and
Natural Language Generation tasks demonstrate that our model achieves
state-of-the-art performance and significantly outperforms existing graph
neural networks, Seq2Seq, and Tree2Seq models; using the proposed
bi-directional node embedding aggregation strategy, the model can converge
rapidly to the optimal performance.
Lifelong Domain Word Embedding via Meta-Learning
Authors: Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Date: 2018-05-25 00:00:00
Tasks: Lifelong learning, Meta-Learning, Word Embeddings

Learning high-quality domain word embeddings is important for achieving good
performance in many NLP tasks. General-purpose embeddings trained on
large-scale corpora are often sub-optimal for domain-specific applications.
However, domain-specific tasks often do not have large in-domain corpora for
training high-quality domain embeddings. In this paper, we propose a novel
lifelong learning setting for domain embedding. That is, when performing the
new domain embedding, the system has seen many past domains, and it tries to
expand the new in-domain corpus by exploiting the corpora from the past domains
via meta-learning. The proposed meta-learner characterizes the similarities of
the contexts of the same word in many domain corpora, which helps retrieve
relevant data from the past domains to expand the new domain corpus.
Experimental results show that domain embeddings produced from such a process
improve the performance of the downstream tasks.
D2KE: From Distance to Kernel and Embedding
Authors: Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock
Date: 2018-02-14 00:00:00
Tasks: Time Series Analysis

For many machine learning problem settings, particularly with structured
inputs such as sequences or sets of objects, a distance measure between inputs
can be specified more naturally than a feature representation. However, most
standard machine models are designed for inputs with a vector feature
representation. In this work, we consider the estimation of a function
$f:\mathcal{X} \rightarrow \R$ based solely on a dissimilarity measure
$d:\mathcal{X}\times\mathcal{X} \rightarrow \R$ between inputs. In particular,
we propose a general framework to derive a family of \emph{positive definite
kernels} from a given dissimilarity measure, which subsumes the widely-used
\emph{representative-set method} as a special case, and relates to the
well-known \emph{distance substitution kernel} in a limiting case. We show that
functions in the corresponding Reproducing Kernel Hilbert Space (RKHS) are
Lipschitz-continuous w.r.t. the given distance metric. We provide a tractable
algorithm to estimate a function from this RKHS, and show that it enjoys better
generalizability than Nearest-Neighbor estimates. Our approach draws from the
literature of Random Features, but instead of deriving feature maps from an
existing kernel, we construct novel kernels from a random feature map, that we
specify given the distance measure. We conduct classification experiments with
such disparate domains as strings, time series, and sets of vectors, where our
proposed framework compares favorably to existing distance-based learning
methods such as $k$-nearest-neighbors, distance-substitution kernels,
pseudo-Euclidean embedding, and the representative-set method.
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
Authors: Zheng Xu, Michael Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin
Date: 2018-05-25 00:00:00
Tasks: Style Transfer

We propose a fast feed-forward network for arbitrary style transfer, which
can generate stylized image for previously unseen content and style image
pairs. Besides the traditional content and style representation based on deep
features and statistics for textures, we use adversarial networks to regularize
the generation of stylized images. Our adversarial network learns the intrinsic
property of image styles from large-scale multi-domain artistic images. The
adversarial training is challenging because both the input and output of our
generator are diverse multi-domain images. We use a conditional generator that
stylized content by shifting the statistics of deep features, and a conditional
discriminator based on the coarse category of styles. Moreover, we propose a
mask module to spatially decide the stylization level and stabilize adversarial
training by avoiding mode collapse. As a side effect, our trained discriminator
can be applied to rank and select representative stylized images. We
qualitatively and quantitatively evaluate the proposed method, and compare with
recent style transfer methods. We release our code and model at
https://github.com/nightldj/behance_release.
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
Authors: Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell
Date: 2017-08-22 00:00:00

This work considers the trade-off between accuracy and test-time
computational cost of deep neural networks (DNNs) via \emph{anytime}
predictions from auxiliary predictions. Specifically, we optimize auxiliary
losses jointly in an \emph{adaptive} weighted sum, where the weights are
inversely proportional to average of each loss. Intuitively, this balances the
losses to have the same scale. We demonstrate theoretical considerations that
motivate this approach from multiple viewpoints, including connecting it to
optimizing the geometric mean of the expectation of each loss, an objective
that ignores the scale of losses. Experimentally, the adaptive weights induce
more competitive anytime predictions on multiple recognition data-sets and
models than non-adaptive approaches including weighing all losses equally. In
particular, anytime neural networks (ANNs) can achieve the same accuracy faster
using adaptive weights on a small network than using static constant weights on
a large one. For problems with high performance saturation, we also show a
sequence of exponentially deepening ANNscan achieve near-optimal anytime
results at any budget, at the cost of a const fraction of extra computation.
Coordinated Multi-Agent Imitation Learning
Authors: Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
Date: 2017-03-09 00:00:00
Proceeding: ICML 2017 8
Tasks: Imitation Learning

We study the problem of imitation learning from demonstrations of multiple
coordinating agents. One key challenge in this setting is that learning a good
model of coordination can be difficult, since coordination is often implicit in
the demonstrations and must be inferred as a latent variable. We propose a
joint approach that simultaneously learns a latent coordination model along
with the individual policies. In particular, our method integrates unsupervised
structure learning with conventional imitation learning. We illustrate the
power of our approach on a difficult problem of learning multiple policies for
fine-grained behavior modeling in team sports, where different players occupy
different roles in the coordinated team strategy. We show that having a
coordination model to infer the roles of players yields substantially improved
imitation loss compared to conventional baselines.
Deep Graph Translation
Authors: Xiaojie Guo, Lingfei Wu, Liang Zhao
Date: 2018-05-25 00:00:00
Tasks: Management, Translation

Inspired by the tremendous success of deep generative models on generating
continuous data like image and audio, in the most recent year, few deep graph
generative models have been proposed to generate discrete data such as graphs.
They are typically unconditioned generative models which has no control on
modes of the graphs being generated. Differently, in this paper, we are
interested in a new problem named \emph{Deep Graph Translation}: given an input
graph, we want to infer a target graph based on their underlying (both global
and local) translation mapping. Graph translation could be highly desirable in
many applications such as disaster management and rare event forecasting, where
the rare and abnormal graph patterns (e.g., traffic congestions and terrorism
events) will be inferred prior to their occurrence even without historical data
on the abnormal patterns for this graph (e.g., a road network or human contact
network). To achieve this, we propose a novel Graph-Translation-Generative
Adversarial Networks (GT-GAN) which will generate a graph translator from input
to target graphs. GT-GAN consists of a graph translator where we propose new
graph convolution and deconvolution layers to learn the global and local
translation mapping. A new conditional graph discriminator has also been
proposed to classify target graphs by conditioning on input graphs. Extensive
experiments on multiple synthetic and real-world datasets demonstrate the
effectiveness and scalability of the proposed GT-GAN.
Scalable Spectral Clustering Using Random Binning Features
Authors: Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu Aggarwal
Date: 2018-05-25 00:00:00
Tasks: Clustering, graph construction, Graph Similarity, Image/Document Clustering

Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods have been proposed to accelerate spectral clustering, most of them compromise considerable information loss in the original data for reducing computational bottlenecks. In this paper, we present a novel scalable spectral clustering method using Random Binning features (RB) to simultaneously accelerate both similarity graph construction and the eigendecomposition. Specifically, we implicitly approximate the graph similarity (kernel) matrix by the inner product of a large sparse feature matrix generated by RB. Then we introduce a state-of-the-art SVD solver to effectively compute eigenvectors of this large matrix for spectral clustering. Using these two building blocks, we reduce the computational cost from quadratic to linear in the number of data points while achieving similar accuracy. Our theoretical analysis shows that spectral clustering via RB converges faster to the exact spectral clustering than the standard Random Feature approximation. Extensive experiments on 8 benchmarks show that the proposed method either outperforms or matches the state-of-the-art methods in both accuracy and runtime. Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators
Authors: Krzysztof Janowicz, Armin Haller, Simon J D Cox, Danh Le Phuoc, Maxime Lefrancois
Date: 2018-05-25 00:00:00

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a
formal but lightweight general-purpose specification for modeling the
interaction between the entities involved in the acts of observation,
actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic
Sensor Network (SSN) ontology based on changes in scope and target audience,
technical developments, and lessons learned over the past years. SOSA also acts
as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been
developed by the first joint working group of the Open Geospatial Consortium
(OGC) and the World Wide Web Consortium (W3C) on \emph{Spatial Data on the
Web}. In this work, we motivate the need for SOSA, provide an overview of the
main classes and properties, and briefly discuss its integration with the new
release of the SSN ontology as well as various other alignments to
specifications such as OGC's Observations and Measurements (O\&M),
Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon
common modeling problems and application areas related to publishing and
searching observation, sampling, and actuation data on the Web. The SOSA
ontology and standard can be accessed at
\url{https://www.w3.org/TR/vocab-ssn/}.
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
Authors: Shitong Wei, Oscar Hernan Madrid-Padilla, James Sharpnack
Date: 2018-05-25 00:00:00
Tasks: Denoising, Graphon Estimation

We study an extention of total variation denoising over images to over
Cartesian power graphs and its applications to estimating non-parametric
network models. The power graph fused lasso (PGFL) segments a matrix by
exploiting a known graphical structure, $G$, over the rows and columns. Our
main results shows that for any connected graph, under subGaussian noise, the
PGFL achieves the same mean-square error rate as 2D total variation denoising
for signals of bounded variation. We study the use of the PGFL for denoising an
observed network $H$, where we learn the graph $G$ as the $K$-nearest
neighborhood graph of an estimated metric over the vertices. We provide
theoretical and empirical results for estimating graphons, a non-parametric
exchangeable network model, and compare to the state of the art graphon
estimation methods.