pwc-archive/papers-with-abstracts huggingface dataset:
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
Authors: Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
Date: 2017-03-27 00:00:00
Tasks: Clustering, Computed Tomography (CT), Image Reconstruction

The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure while maintaining high image
quality is an important area of research in low-dose CT (LDCT) imaging. We
propose a new penalized weighted least squares (PWLS) reconstruction method
that exploits regularization based on an efficient Union of Learned TRAnsforms
(PWLS-ULTRA). The union of square transforms is pre-learned from numerous image
patches extracted from a dataset of CT images or volumes. The proposed
PWLS-based cost function is optimized by alternating between a CT image
reconstruction step, and a sparse coding and clustering step. The CT image
reconstruction step is accelerated by a relaxed linearized augmented Lagrangian
method with ordered-subsets that reduces the number of forward and back
projections. Simulations with 2-D and 3-D axial CT scans of the extended
cardiac-torso phantom and 3D helical chest and abdomen scans show that for both
normal-dose and low-dose levels, the proposed method significantly improves the
quality of reconstructed images compared to PWLS reconstruction with a
nonadaptive edge-preserving regularizer (PWLS-EP). PWLS with regularization
based on a union of learned transforms leads to better image reconstructions
than using a single learned square transform. We also incorporate patch-based
weights in PWLS-ULTRA that enhance image quality and help improve image
resolution uniformity. The proposed approach achieves comparable or better
image quality compared to learned overcomplete synthesis dictionaries, but
importantly, is much faster (computationally more efficient).
ASR-based Features for Emotion Recognition: A Transfer Learning Approach
Authors: Noé Tits, Kevin El Haddad, Thierry Dutoit
Date: 2018-05-23 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotional Speech Synthesis, Emotion Recognition, speech-recognition, Speech Recognition, Speech Synthesis, Transfer Learning

During the last decade, the applications of signal processing have
drastically improved with deep learning. However areas of affecting computing
such as emotional speech synthesis or emotion recognition from spoken language
remains challenging. In this paper, we investigate the use of a neural
Automatic Speech Recognition (ASR) as a feature extractor for emotion
recognition. We show that these features outperform the eGeMAPS feature set to
predict the valence and arousal emotional dimensions, which means that the
audio-to-text mapping learning by the ASR system contain information related to
the emotional dimensions in spontaneous speech. We also examine the
relationship between first layers (closer to speech) and last layers (closer to
text) of the ASR and valence/arousal.
Risk and parameter convergence of logistic regression
Authors: Ziwei Ji, Matus Telgarsky
Date: 2018-03-20 00:00:00
Tasks: regression

Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data. The direction of this ray is the maximum margin predictor of a maximal linearly separable subset of the data; the gradient descent iterates converge to this ray in direction at the rate $\mathcal{O}(\ln\ln t / \ln t)$. The ray does not pass through the origin in general, and its offset is the bounded global optimum of the risk over the remaining data; gradient descent recovers this offset at a rate $\mathcal{O}((\ln t)^2 / \sqrt{t})$.
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Authors: Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran
Date: 2018-05-31 00:00:00
Tasks: Attribute, Classification, General Classification

Given a graph where every node has certain attributes associated with it and
some nodes have labels associated with them, Collective Classification (CC) is
the task of assigning labels to every unlabeled node using information from the
node as well as its neighbors. It is often the case that a node is not only
influenced by its immediate neighbors but also by higher order neighbors,
multiple hops away. Recent state-of-the-art models for CC learn end-to-end
differentiable variations of Weisfeiler-Lehman (WL) kernels to aggregate
multi-hop neighborhood information. In this work, we propose a Higher Order
Propagation Framework, HOPF, which provides an iterative inference mechanism
for these powerful differentiable kernels. Such a combination of classical
iterative inference mechanism with recent differentiable kernels allows the
framework to learn graph convolutional filters that simultaneously exploit the
attribute and label information available in the neighborhood. Further, these
iterative differentiable kernels can scale to larger hops beyond the memory
limitations of existing differentiable kernels. We also show that existing WL
kernel-based models suffer from the problem of Node Information Morphing where
the information of the node is morphed or overwhelmed by the information of its
neighbors when considering multiple hops. To address this, we propose a
specific instantiation of HOPF, called the NIP models, which preserves the node
information at every propagation step. The iterative formulation of NIP models
further helps in incorporating distant hop information concisely as summaries
of the inferred labels. We do an extensive evaluation across 11 datasets from
different domains. We show that existing CC models do not provide consistent
performance across datasets, while the proposed NIP model with iterative
inference is more robust.
Improved Mixed-Example Data Augmentation
Authors: Cecilia Summers, Michael J. Dinneen
Date: 2018-05-29 00:00:00
Tasks: Data Augmentation, Image Augmentation

In order to reduce overfitting, neural networks are typically trained with
data augmentation, the practice of artificially generating additional training
data via label-preserving transformations of existing training examples. While
these types of transformations make intuitive sense, recent work has
demonstrated that even non-label-preserving data augmentation can be
surprisingly effective, examining this type of data augmentation through linear
combinations of pairs of examples. Despite their effectiveness, little is known
about why such methods work. In this work, we aim to explore a new, more
generalized form of this type of data augmentation in order to determine
whether such linearity is necessary. By considering this broader scope of
"mixed-example data augmentation", we find a much larger space of practical
augmentation techniques, including methods that improve upon previous
state-of-the-art. This generalization has benefits beyond the promise of
improved performance, revealing a number of types of mixed-example data
augmentation that are radically different from those considered in prior work,
which provides evidence that current theories for the effectiveness of such
methods are incomplete and suggests that any such theory must explain a much
broader phenomenon. Code is available at
https://github.com/ceciliaresearch/MixedExample.
Document Chunking and Learning Objective Generation for Instruction Design
Authors: Khoi-Nguyen Tran, Jey Han Lau, Danish Contractor, Utkarsh Gupta, Bikram Sengupta, Christopher J. Butler, Mukesh Mohania
Date: 2018-06-01 00:00:00
Tasks: Chunking, Descriptive

Instructional Systems Design is the practice of creating of instructional
experiences that make the acquisition of knowledge and skill more efficient,
effective, and appealing. Specifically in designing courses, an hour of
training material can require between 30 to 500 hours of effort in sourcing and
organizing reference data for use in just the preparation of course material.
In this paper, we present the first system of its kind that helps reduce the
effort associated with sourcing reference material and course creation. We
present algorithms for document chunking and automatic generation of learning
objectives from content, creating descriptive content metadata to improve
content-discoverability. Unlike existing methods, the learning objectives
generated by our system incorporate pedagogically motivated Bloom's verbs. We
demonstrate the usefulness of our methods using real world data from the
banking industry and through a live deployment at a large pharmaceutical
company.
Visual to Sound: Generating Natural Sound for Videos in the Wild
Authors: Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, Tamara L. Berg
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6

As two of the five traditional human senses (sight, hearing, taste, smell,
and touch), vision and sound are basic sources through which humans understand
the world. Often correlated during natural events, these two modalities combine
to jointly affect human perception. In this paper, we pose the task of
generating sound given visual input. Such capabilities could help enable
applications in virtual reality (generating sound for virtual scenes
automatically) or provide additional accessibility to images or videos for
people with visual impairments. As a first step in this direction, we apply
learning-based methods to generate raw waveform samples given input video
frames. We evaluate our models on a dataset of videos containing a variety of
sounds (such as ambient sounds and sounds from people/animals). Our experiments
show that the generated sounds are fairly realistic and have good temporal
synchronization with the visual inputs.
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Authors: Zehong Hu, Yitao Liang, Yang Liu, Jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Incentive mechanisms for crowdsourcing are designed to incentivize
financially self-interested workers to generate and report high-quality labels.
Existing mechanisms are often developed as one-shot static solutions, assuming
a certain level of knowledge about worker models (expertise levels, costs of
exerting efforts, etc.). In this paper, we propose a novel inference aided
reinforcement mechanism that learns to incentivize high-quality data
sequentially and requires no such prior assumptions. Specifically, we first
design a Gibbs sampling augmented Bayesian inference algorithm to estimate
workers' labeling strategies from the collected labels at each step. Then we
propose a reinforcement incentive learning (RIL) method, building on top of the
above estimates, to uncover how workers respond to different payments. RIL
dynamically determines the payment without accessing any ground-truth labels.
We theoretically prove that RIL is able to incentivize rational workers to
provide high-quality labels. Empirical results show that our mechanism performs
consistently well under both rational and non-fully rational (adaptive
learning) worker models. Besides, the payments offered by RIL are more robust
and have lower variances compared to the existing one-shot mechanisms.
Being curious about the answers to questions: novelty search with learned attention
Authors: Nicholas Guttenberg, Martin Biehl, Nathaniel Virgo, Ryota Kanai
Date: 2018-06-01 00:00:00
Tasks: Reinforcement Learning

We investigate the use of attentional neural network layers in order to learn
a `behavior characterization' which can be used to drive novelty search and
curiosity-based policies. The space is structured towards answering a
particular distribution of questions, which are used in a supervised way to
train the attentional neural network. We find that in a 2d exploration task,
the structure of the space successfully encodes local sensory-motor
contingencies such that even a greedy local `do the most novel action' policy
with no reinforcement learning or evolution can explore the space quickly. We
also apply this to a high/low number guessing game task, and find that guessing
according to the learned attention profile performs active inference and can
discover the correct number more quickly than an exact but passive approach.
Private Sequential Learning
Authors: John N. Tsitsiklis, Kuang Xu, Zhi Xu
Date: 2018-05-06 00:00:00

We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, $v^*$, by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes the learner's queries, though not the responses, and tries to infer from them the value of $v^*$. The objective of the learner is to obtain an accurate estimate of $v^*$ using only a small number of queries, while simultaneously protecting her privacy by making $v^*$ provably difficult to learn for the adversary. Our main results provide tight upper and lower bounds on the learner's query complexity as a function of desired levels of privacy and estimation accuracy. We also construct explicit query strategies whose complexity is optimal up to an additive constant.
Learning a Latent Space of Multitrack Measures
Authors: Ian Simon, Adam Roberts, Colin Raffel, Jesse Engel, Curtis Hawthorne, Douglas Eck
Date: 2018-06-01 00:00:00

Discovering and exploring the underlying structure of multi-instrumental
music using learning-based approaches remains an open problem. We extend the
recent MusicVAE model to represent multitrack polyphonic measures as vectors in
a latent space. Our approach enables several useful operations such as
generating plausible measures from scratch, interpolating between measures in a
musically meaningful way, and manipulating specific musical attributes. We also
introduce chord conditioning, which allows all of these operations to be
performed while keeping harmony fixed, and allows chords to be changed while
maintaining musical "style". By generating a sequence of measures over a
predefined chord progression, our model can produce music with convincing
long-term structure. We demonstrate that our latent space model makes it
possible to intuitively control and generate musical sequences with rich
instrumentation (see https://goo.gl/s2N7dV for generated audio).
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Authors: Chen Huang, Yining Li, Chen Change Loy, Xiaoou Tang
Date: 2018-06-01 00:00:00
Tasks: Attribute, Face Recognition, Prediction, Representation Learning

Data for face analysis often exhibit highly-skewed class distribution, i.e.,
most data belong to a few majority classes, while the minority classes only
contain a scarce amount of instances. To mitigate this issue, contemporary deep
learning methods typically follow classic strategies such as class re-sampling
or cost-sensitive training. In this paper, we conduct extensive and systematic
experiments to validate the effectiveness of these classic schemes for
representation learning on class-imbalanced data. We further demonstrate that
more discriminative deep representation can be learned by enforcing a deep
network to maintain inter-cluster margins both within and between classes. This
tight constraint effectively reduces the class imbalance inherent in the local
data neighborhood, thus carving much more balanced class boundaries locally. We
show that it is easy to deploy angular margins between the cluster
distributions on a hypersphere manifold. Such learned Cluster-based Large
Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster
algorithm, shows significant improvements in accuracy over existing methods on
both face recognition and face attribute prediction tasks that exhibit
imbalanced class distribution.
An Interpretable Reasoning Network for Multi-Relation Question Answering
Authors: Mantong Zhou, Minlie Huang, Xiaoyan Zhu
Date: 2018-01-15 00:00:00
Proceeding: COLING 2018 8
Tasks: Question Answering, Relation

Multi-relation Question Answering is a challenging task, due to the
requirement of elaborated analysis on questions and reasoning over multiple
fact triples in knowledge base. In this paper, we present a novel model called
Interpretable Reasoning Network that employs an interpretable, hop-by-hop
reasoning process for question answering. The model dynamically decides which
part of an input question should be analyzed at each hop; predicts a relation
that corresponds to the current parsed results; utilizes the predicted relation
to update the question representation and the state of the reasoning process;
and then drives the next-hop reasoning. Experiments show that our model yields
state-of-the-art results on two datasets. More interestingly, the model can
offer traceable and observable intermediate predictions for reasoning analysis
and failure diagnosis, thereby allowing manual manipulation in predicting the
final answer.
Video Description: A Survey of Methods, Datasets and Evaluation Metrics
Authors: Nayyer Aafaq, Ajmal Mian, Wei Liu, Syed Zulqarnain Gilani, Mubarak Shah
Date: 2018-06-01 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Video Description

Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing. Numerous methods, datasets and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction. This paper fills the gap by surveying the state of the art approaches with a focus on deep learning models; comparing benchmark datasets in terms of their domains, number of classes, and repository size; and identifying the pros and cons of various evaluation metrics like SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical video description approaches combined subject, object and verb detection with template based language models to generate sentences. However, the release of large datasets revealed that these methods can not cope with the diversity in unconstrained open domain videos. Classical approaches were followed by a very short era of statistical methods which were soon replaced with deep learning, the current state of the art in video description. Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons. Analysis of video description models is challenging because it is difficult to ascertain the contributions, towards accuracy or errors, of the visual features and the adopted language model in the final description. Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures. Finally, current evaluation metrics ...
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Authors: Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
Date: 2017-11-06 00:00:00
Tasks: Network Pruning

Deep neural networks (DNNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal
DNN architecture for large applications is challenging. Common approaches go
for deeper and larger DNN architectures but may incur substantial redundancy.
To address these problems, we introduce a network growth algorithm that
complements network pruning to learn both weights and compact DNN architectures
during training. We propose a DNN synthesis tool (NeST) that combines both
methods to automate the generation of compact and accurate DNNs. NeST starts
with a randomly initialized sparse network called the seed architecture. It
iteratively tunes the architecture with gradient-based growth and
magnitude-based pruning of neurons and connections. Our experimental results
show that NeST yields accurate, yet very compact DNNs, with a wide range of
seed architecture selection. For the LeNet-300-100 (LeNet-5) architecture, we
reduce network parameters by 70.2x (74.3x) and floating-point operations
(FLOPs) by 79.4x (43.7x). For the AlexNet and VGG-16 architectures, we reduce
network parameters (FLOPs) by 15.7x (4.6x) and 30.2x (8.6x), respectively.
NeST's grow-and-prune paradigm delivers significant additional parameter and
FLOPs reduction relative to pruning-only methods.
The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks
Authors: George Philipp, Jaime G. Carbonell
Date: 2018-06-01 00:00:00
Proceeding: ICLR 2019 5

For a long time, designing neural architectures that exhibit high performance
was considered a dark art that required expert hand-tuning. One of the few
well-known guidelines for architecture design is the avoidance of exploding
gradients, though even this guideline has remained relatively vague and
circumstantial. We introduce the nonlinearity coefficient (NLC), a measurement
of the complexity of the function computed by a neural network that is based on
the magnitude of the gradient. Via an extensive empirical study, we show that
the NLC is a powerful predictor of test error and that attaining a right-sized
NLC is essential for optimal performance.
  The NLC exhibits a range of intriguing and important properties. It is
closely tied to the amount of information gained from computing a single
network gradient. It is tied to the error incurred when replacing the
nonlinearity operations in the network with linear operations. It is not
susceptible to the confounders of multiplicative scaling, additive bias and
layer width. It is stable from layer to layer. Hence, we argue that the NLC is
the first robust predictor of overfitting in deep networks.
Understanding Batch Normalization
Authors: Johan Bjorck, Carla Gomes, Bart Selman, Kilian Q. Weinberger
Date: 2018-06-01 00:00:00
Proceeding: NeurIPS 2018 12

Batch normalization (BN) is a technique to normalize activations in
intermediate layers of deep neural networks. Its tendency to improve accuracy
and speed up training have established BN as a favorite technique in deep
learning. Yet, despite its enormous success, there remains little consensus on
the exact reason and mechanism behind these improvements. In this paper we take
a step towards a better understanding of BN, following an empirical approach.
We conduct several experiments, and show that BN primarily enables training
with larger learning rates, which is the cause for faster convergence and
better generalization. For networks without BN we demonstrate how large
gradient updates can result in diverging loss and activations growing
uncontrollably with network depth, which limits possible learning rates. BN
avoids this problem by constantly correcting activations to be zero-mean and of
unit standard deviation, which enables larger gradient steps, yields faster
convergence and may help bypass sharp local minima. We further show various
ways in which gradients and activations of deep unnormalized networks are
ill-behaved. We contrast our results against recent findings in random matrix
theory, shedding new light on classical initialization schemes and their
consequences.