pwc-archive/papers-with-abstracts huggingface dataset:
Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning
Authors: Jiayao Zhang, Guangxu Zhu, Robert W. Heath Jr., Kaibin Huang
Date: 2018-08-07 00:00:00
Tasks: Deep Learning

Modern machine learning algorithms have been adopted in a range of
signal-processing applications spanning computer vision, natural language
processing, and artificial intelligence. Many relevant problems involve
subspace-structured features, orthogonality constrained or low-rank constrained
objective functions, or subspace distances. These mathematical characteristics
are expressed naturally using the Grassmann manifold. Unfortunately, this fact
is not yet explored in many traditional learning algorithms. In the last few
years, there have been growing interests in studying Grassmann manifold to
tackle new learning problems. Such attempts have been reassured by substantial
performance improvements in both classic learning and learning using deep
neural networks. We term the former as shallow and the latter deep Grassmannian
learning. The aim of this paper is to introduce the emerging area of
Grassmannian learning by surveying common mathematical problems and primary
solution approaches, and overviewing various applications. We hope to inspire
practitioners in different fields to adopt the powerful tool of Grassmannian
learning in their research.
Segmental Audio Word2Vec: Representing Utterances as Sequences of Vectors with Applications in Spoken Term Detection
Authors: Yu-Hsuan Wang, Hung-Yi Lee, Lin-shan Lee
Date: 2018-08-07 00:00:00
Tasks: Reinforcement Learning, Segmentation

While Word2Vec represents words (in text) as vectors carrying semantic
information, audio Word2Vec was shown to be able to represent signal segments
of spoken words as vectors carrying phonetic structure information. Audio
Word2Vec can be trained in an unsupervised way from an unlabeled corpus, except
the word boundaries are needed. In this paper, we extend audio Word2Vec from
word-level to utterance-level by proposing a new segmental audio Word2Vec, in
which unsupervised spoken word boundary segmentation and audio Word2Vec are
jointly learned and mutually enhanced, so an utterance can be directly
represented as a sequence of vectors carrying phonetic structure information.
This is achieved by a segmental sequence-to-sequence autoencoder (SSAE), in
which a segmentation gate trained with reinforcement learning is inserted in
the encoder. Experiments on English, Czech, French and German show very good
performance in both unsupervised spoken word segmentation and spoken term
detection applications (significantly better than frame-based DTW).
Hierarchical Clustering better than Average-Linkage
Authors: Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh
Date: 2018-08-07 00:00:00
Tasks: Clustering, Open-Ended Question Answering

Hierarchical Clustering (HC) is a widely studied problem in exploratory data
analysis, usually tackled by simple agglomerative procedures like
average-linkage, single-linkage or complete-linkage. In this paper we focus on
two objectives, introduced recently to give insight into the performance of
average-linkage clustering: a similarity based HC objective proposed by
[Moseley and Wang, 2017] and a dissimilarity based HC objective proposed by
[Cohen-Addad et al., 2018]. In both cases, we present tight counterexamples
showing that average-linkage cannot obtain better than 1/3 and 2/3
approximations respectively (in the worst-case), settling an open question
raised in [Moseley and Wang, 2017]. This matches the approximation ratio of a
random solution, raising a natural question: can we beat average-linkage for
these objectives? We answer this in the affirmative, giving two new algorithms
based on semidefinite programming with provably better guarantees.
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications
Authors: Paul J. Atzberger
Date: 2018-08-07 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Inductive Bias, Learning Theory

There has been a lot of recent interest in adopting machine learning methods
for scientific and engineering applications. This has in large part been
inspired by recent successes and advances in the domains of Natural Language
Processing (NLP) and Image Classification (IC). However, scientific and
engineering problems have their own unique characteristics and requirements
raising new challenges for effective design and deployment of machine learning
approaches. There is a strong need for further mathematical developments on the
foundations of machine learning methods to increase the level of rigor of
employed methods and to ensure more reliable and interpretable results. Also as
reported in the recent literature on state-of-the-art results and indicated by
the No Free Lunch Theorems of statistical learning theory incorporating some
form of inductive bias and domain knowledge is essential to success.
Consequently, even for existing and widely used methods there is a strong need
for further mathematical work to facilitate ways to incorporate prior
scientific knowledge and related inductive biases into learning frameworks and
algorithms. We briefly discuss these topics and discuss some ideas proceeding
in this direction.
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
Authors: Rameswar Panda, Jianming Zhang, Haoxiang Li, Joon-Young Lee, Xin Lu, Amit K. Roy-Chowdhury
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Emotion Recognition

While machine learning approaches to visual emotion recognition offer great
promise, current methods consider training and testing models on small scale
datasets covering limited visual emotion concepts. Our analysis identifies an
important but long overlooked issue of existing visual emotion benchmarks in
the form of dataset biases. We design a series of tests to show and measure how
such dataset biases obstruct learning a generalizable emotion recognition
model. Based on our analysis, we propose a webly supervised approach by
leveraging a large quantity of stock image data. Our approach uses a simple yet
effective curriculum guided training strategy for learning discriminative
emotion features. We discover that the models learned using our large scale
stock image dataset exhibit significantly better generalization ability than
the existing datasets without the manual collection of even a single label.
Moreover, visual representation learned using our approach holds a lot of
promise across a variety of tasks on different image and video datasets.
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
Authors: Siyuan Huang, Siyuan Qi, Yixin Zhu, Yinxue Xiao, Yuanlu Xu, Song-Chun Zhu
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Object Detection, Monocular 3D Object Detection, Object, object-detection, Object Detection, Object Localization, Room Layout Estimation, Scene Parsing, Scene Understanding, Semantic Segmentation

We propose a computational framework to jointly parse a single RGB image and
reconstruct a holistic 3D configuration composed by a set of CAD models using a
stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar
(HSG) to represent the 3D scene structure, which characterizes a joint
distribution over the functional and geometric space of indoor scenes. The
proposed HSG captures three essential and often latent dimensions of the indoor
scenes: i) latent human context, describing the affordance and the
functionality of a room arrangement, ii) geometric constraints over the scene
configurations, and iii) physical constraints that guarantee physically
plausible parsing and reconstruction. We solve this joint parsing and
reconstruction problem in an analysis-by-synthesis fashion, seeking to minimize
the differences between the input image and the rendered images generated by
our 3D representation, over the space of depth, surface normal, and object
segmentation map. The optimal configuration, represented by a parse graph, is
inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses
through the non-differentiable solution space, jointly optimizing object
localization, 3D layout, and hidden human context. Experimental results
demonstrate that the proposed algorithm improves the generalization ability and
significantly outperforms prior methods on 3D layout estimation, 3D object
detection, and holistic scene understanding.
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Authors: Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas
Date: 2018-08-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Alignment, Pose Estimation

In this paper, we propose quantized densely connected U-Nets for efficient
visual landmark localization. The idea is that features of the same semantic
meanings are globally reused across the stacked U-Nets. This dense connectivity
largely improves the information flow, yielding improved localization accuracy.
However, a vanilla dense design would suffer from critical efficiency issue in
both training and testing. To solve this problem, we first propose order-K
dense connectivity to trim off long-distance shortcuts; then, we use a
memory-efficient implementation to significantly boost the training efficiency
and investigate an iterative refinement that may slice the model size in half.
Finally, to reduce the memory consumption and high precision operations both in
training and testing, we further quantize weights, inputs, and gradients of our
localization network to low bit-width numbers. We validate our approach in two
tasks: human pose estimation and face alignment. The results show that our
approach achieves state-of-the-art localization accuracy, but using ~70% fewer
parameters, ~98% less model size and saving ~75% training memory compared with
other benchmark localizers. The code is available at
https://github.com/zhiqiangdon/CU-Net.
Round-Table Group Optimization for Sequencing Problems
Authors: Xiao-Feng Xie
Date: 2018-08-07 00:00:00
Tasks: Scheduling

In this paper, a round-table group optimization (RTGO) algorithm is
presented. RTGO is a simple metaheuristic framework using the insights of
research on group creativity. In a cooperative group, the agents work in
iterative sessions to search innovative ideas in a common problem landscape.
Each agent has one base idea stored in its individual memory, and one social
idea fed by a round-table group support mechanism in each session. The idea
combination and improvement processes are respectively realized by using a
recombination search (XS) strategy and a local search (LS) strategy, to build
on the base and social ideas. RTGO is then implemented for solving two
difficult sequencing problems, i.e., the flowshop scheduling problem and the
quadratic assignment problem. The domain-specific LS strategies are adopted
from existing algorithms, whereas a general XS class, called socially biased
combination (SBX), is realized in a modular form. The performance of RTGO is
then evaluated on commonly-used benchmark datasets. Good performance on
different problems can be achieved by RTGO using appropriate SBX operators.
Furthermore, RTGO is able to outperform some existing methods, including
methods using the same LS strategies.
Instance-Dependent PU Learning by Bayesian Optimal Relabeling
Authors: Fengxiang He, Tongliang Liu, Geoffrey I. Webb, DaCheng Tao
Date: 2018-08-07 00:00:00

When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many real-world applications, the observed positive examples are dependent on the conditional probability $P(Y = 1|X)$ and should be sampled biasedly. In this paper, we assume that a positive example with a higher $P(Y = 1|X)$ is more likely to be labelled and propose a probabilistic-gap based PU learning algorithms. Specifically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classifier with a consistency guarantee. The relabelled examples have a biased domain, which is remedied by the kernel mean matching technique. The proposed algorithm is model-free and thus do not have any parameters to tune. Experimental results demonstrate that our method works well on both generated and real-world datasets.
Test without Trust: Optimal Locally Private Distribution Testing
Authors: Jayadev Acharya, Clément L. Canonne, Cody Freitag, Himanshu Tyagi
Date: 2018-08-07 00:00:00

We study the problem of distribution testing when the samples can only be
accessed using a locally differentially private mechanism and focus on two
representative testing questions of identity (goodness-of-fit) and independence
testing for discrete distributions. We are concerned with two settings: First,
when we insist on using an already deployed, general-purpose locally
differentially private mechanism such as the popular RAPPOR or the recently
introduced Hadamard Response for collecting data, and must build our tests
based on the data collected via this mechanism; and second, when no such
restriction is imposed, and we can design a bespoke mechanism specifically for
testing. For the latter purpose, we introduce the Randomized Aggregated Private
Testing Optimal Response (RAPTOR) mechanism which is remarkably simple and
requires only one bit of communication per sample.
  We propose tests based on these mechanisms and analyze their sample
complexities. Each proposed test can be implemented efficiently. In each case
(barring one), we complement our performance bounds for algorithms with
information-theoretic lower bounds and establish sample optimality of our
proposed algorithm. A peculiar feature that emerges is that our sample-optimal
algorithm based on RAPTOR uses public-coins, and any test based on RAPPOR or
Hadamard Response, which are both private-coin mechanisms, requires
significantly more samples.
Dialog-context aware end-to-end speech recognition
Authors: Suyoun Kim, Florian Metze
Date: 2018-08-07 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition

Existing speech recognition systems are typically built at the sentence
level, although it is known that dialog context, e.g. higher-level knowledge
that spans across sentences or speakers, can help the processing of long
conversations. The recent progress in end-to-end speech recognition systems
promises to integrate all available information (e.g. acoustic, language
resources) into a single model, which is then jointly optimized. It seems
natural that such dialog context information should thus also be integrated
into the end-to-end models to improve further recognition accuracy. In this
work, we present a dialog-context aware speech recognition model, which
explicitly uses context information beyond sentence-level information, in an
end-to-end fashion. Our dialog-context model captures a history of
sentence-level context so that the whole system can be trained with
dialog-context information in an end-to-end manner. We evaluate our proposed
approach on the Switchboard conversational speech corpus and show that our
system outperforms a comparable sentence-level end-to-end speech recognition
system.
Fast Variance Reduction Method with Stochastic Batch Size
Authors: Xuanqing Liu, Cho-Jui Hsieh
Date: 2018-08-07 00:00:00
Proceeding: ICML 2018 7

In this paper we study a family of variance reduction methods with randomized
batch size---at each step, the algorithm first randomly chooses the batch size
and then selects a batch of samples to conduct a variance-reduced stochastic
update. We give the linear convergence rate for this framework for composite
functions, and show that the optimal strategy to achieve the optimal
convergence rate per data access is to always choose batch size of 1, which is
equivalent to the SAGA algorithm. However, due to the presence of cache/disk IO
effect in computer architecture, the number of data access cannot reflect the
running time because of 1) random memory access is much slower than sequential
access, 2) when data is too big to fit into memory, disk seeking takes even
longer time. After taking these into account, choosing batch size of $1$ is no
longer optimal, so we propose a new algorithm called SAGA++ and show how to
calculate the optimal average batch size theoretically. Our algorithm
outperforms SAGA and other existing batched and stochastic solvers on real
datasets. In addition, we also conduct a precise analysis to compare different
update rules for variance reduction methods, showing that SAGA++ converges
faster than SVRG in theory.
Designing Adaptive Neural Networks for Energy-Constrained Image Classification
Authors: Dimitrios Stamoulis, Ting-Wu Chin, Anand Krishnan Prakash, Haocheng Fang, Sribhuvan Sajja, Mitchell Bognar, Diana Marculescu
Date: 2018-08-05 00:00:00
Tasks: Bayesian Optimization, Classification, General Classification, image-classification, Image Classification

As convolutional neural networks (CNNs) enable state-of-the-art computer
vision applications, their high energy consumption has emerged as a key
impediment to their deployment on embedded and mobile devices. Towards
efficient image classification under hardware constraints, prior work has
proposed adaptive CNNs, i.e., systems of networks with different accuracy and
computation characteristics, where a selection scheme adaptively selects the
network to be evaluated for each input image. While previous efforts have
investigated different network selection schemes, we find that they do not
necessarily result in energy savings when deployed on mobile systems. The key
limitation of existing methods is that they learn only how data should be
processed among the CNNs and not the network architectures, with each network
being treated as a blackbox.
  To address this limitation, we pursue a more powerful design paradigm where
the architecture settings of the CNNs are treated as hyper-parameters to be
globally optimized. We cast the design of adaptive CNNs as a hyper-parameter
optimization problem with respect to energy, accuracy, and communication
constraints imposed by the mobile device. To efficiently solve this problem, we
adapt Bayesian optimization to the properties of the design space, reaching
near-optimal configurations in few tens of function evaluations. Our method
reduces the energy consumed for image classification on a mobile device by up
to 6x, compared to the best previously published work that uses CNNs as
blackboxes. Finally, we evaluate two image classification practices, i.e.,
classifying all images locally versus over the cloud under energy and
communication constraints.
Efficient Fusion of Sparse and Complementary Convolutions
Authors: Chun-Fu Chen, Quanfu Fan, Marco Pistoia, Gwo Giun Lee
Date: 2018-08-07 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection

We propose a new method to create compact convolutional neural networks
(CNNs) by exploiting sparse convolutions. Different from previous works that
learn sparsity in models, we directly employ hand-crafted kernels with regular
sparse patterns, which result in the computational gain in practice without
sophisticated and dedicated software or hardware. The core of our approach is
an efficient network module that linearly combines sparse kernels to yield
feature representations as strong as those from regular kernels. We integrate
this module into various network architectures and demonstrate its
effectiveness on three vision tasks, object classification, localization and
detection. For object classification and localization, our approach achieves
comparable or better performance than several baselines and related works while
providing lower computational costs with fewer parameters (on average, a
$2-4\times$ reduction of convolutional parameters and computation). For object
detection, our approach leads to a VGG-16-based Faster RCNN detector that is
12.4$\times$ smaller and about 3$\times$ faster than the baseline.
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data
Authors: Ben Eckart, Kihwan Kim, Jan Kautz
Date: 2018-08-06 00:00:00
Tasks: Point Cloud Registration

We present an iterative overlap estimation technique to augment existing
point cloud registration algorithms that can achieve high performance in
difficult real-world situations where large pose displacement and
non-overlapping geometry would otherwise cause traditional methods to fail. Our
approach estimates overlapping regions through an iterative Expectation
Maximization procedure that encodes the sensor field-of-view into the
registration process. The proposed technique, Expected Overlap Estimation
(EOE), is derived from the observation that differences in field-of-view
violate the iid assumption implicitly held by all maximum likelihood based
registration techniques. We demonstrate how our approach can augment many
popular registration methods with minimal computational overhead. Through
experimentation on both synthetic and real-world datasets, we find that adding
an explicit overlap estimation step can aid robust outlier handling and
increase the accuracy of both ICP-based and GMM-based registration methods,
especially in large unstructured domains and where the amount of overlap
between point clouds is very small.
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
Authors: Tao Hu, Jizheng Xu, Cong Huang, Honggang Qi, Qingming Huang, Yan Lu
Date: 2018-08-06 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification

For fine-grained visual classification, objects usually share similar
geometric structure but present variant local appearance and different pose.
Therefore, localizing and extracting discriminative local features play a
crucial role in accurate category prediction. Existing works either pay
attention to limited object parts or train isolated networks for locating and
classification. In this paper, we propose Weakly Supervised Bilinear Attention
Network (WS-BAN) to solve these issues. It jointly generates a set of attention
maps (region-of-interest maps) to indicate the locations of object's parts and
extracts sequential part features by Bilinear Attention Pooling (BAP). Besides,
we propose attention regularization and attention dropout to weakly supervise
the generating process of attention maps. WS-BAN can be trained end-to-end and
achieves the state-of-the-art performance on multiple fine-grained
classification datasets, including CUB-200-2011, Stanford Car and
FGVC-Aircraft, which demonstrated its effectiveness.