pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
Authors: Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, Larry Heck
Date: 2017-11-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a neural network based task-oriented dialogue
system that can be optimized end-to-end with deep reinforcement learning (RL).
The system is able to track dialogue state, interface with knowledge bases, and
incorporate query results into agent's responses to successfully complete
task-oriented dialogues. Dialogue policy learning is conducted with a hybrid
supervised and deep RL methods. We first train the dialogue agent in a
supervised manner by learning directly from task-oriented dialogue corpora, and
further optimize it with deep RL during its interaction with users. In the
experiments on two different dialogue task domains, our model demonstrates
robust performance in tracking dialogue state and producing reasonable system
responses. We show that deep RL based optimization leads to significant
improvement on task success rate and reduction in dialogue length comparing to
supervised training model. We further show benefits of training task-oriented
dialogue model end-to-end comparing to component-wise optimization with
experiment results on dialogue simulations and human evaluations.
Pedagogical learning
Authors: Long Ouyang, Michael C. Frank
Date: 2017-11-26 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

A common assumption in machine learning is that training data are i.i.d.
samples from some distribution. Processes that generate i.i.d. samples are, in
a sense, uninformative---they produce data without regard to how good this data
is for learning. By contrast, cognitive science research has shown that when
people generate training data for others (i.e., teaching), they deliberately
select examples that are helpful for learning. Because the data is more
informative, learning can require less data. Interestingly, such examples are
most effective when learners know that the data were pedagogically generated
(as opposed to randomly generated). We call this pedagogical learning---when a
learner assumes that evidence comes from a helpful teacher. In this work, we
ask how pedagogical learning might work for machine learning algorithms.
Studying this question requires understanding how people actually teach complex
concepts with examples, so we conducted a behavioral study examining how people
teach regular expressions using example strings. We found that teachers'
examples contain powerful clustering structure that can greatly facilitate
learning. We then develop a model of teaching and show a proof of concept that
using this model inside of a learner can improve performance.
Sparse Coding on Stereo Video for Object Detection
Authors: Sheng Y. Lundquist, Melanie Mitchell, Garrett T. Kenyon
Date: 2017-05-19 00:00:00
Tasks: image-classification, Image Classification, Object, object-detection, Object Detection

Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alleviate the need for large amounts of labeled data. We show that
replacing a typical supervised convolutional layer with an unsupervised
sparse-coding layer within a DCNN allows for better performance on a car
detection task when only a limited number of labeled training examples is
available. Furthermore, the network that incorporates sparse coding allows for
more consistent performance over varying initializations and ordering of
training examples when compared to a fully supervised DCNN. Finally, we compare
activations between the unsupervised sparse-coding layer and the supervised
convolutional layer, and show that the sparse representation exhibits an
encoding that is depth selective, whereas encodings from the convolutional
layer do not exhibit such selectivity. These result indicates promise for using
unsupervised sparse-coding approaches in real-world computer vision tasks in
domains with limited labeled training data.
PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network
Authors: Seungkyun Hong, Seongchan Kim, Minsu Joh, Sa-kwang Song
Date: 2017-11-29 00:00:00
Tasks: Decoder, Management

Predicting unseen weather phenomena is an important issue for disaster
management. In this paper, we suggest a model for a convolutional
sequence-to-sequence autoencoder for predicting undiscovered weather situations
from previous satellite images. We also propose a symmetric skip connection
between encoder and decoder modules to produce more comprehensive image
predictions. To examine our model performance, we conducted experiments for
each suggested model to predict future satellite images from historical
satellite images. A specific combination of skip connection and
sequence-to-sequence autoencoder was able to generate closest prediction from
the ground truth image.
Differentially Private Dropout
Authors: Beyza Ermis, Ali Taylan Cemgil
Date: 2017-11-30 00:00:00
Tasks: Privacy Preserving

Large data collections required for the training of neural networks often
contain sensitive information such as the medical histories of patients, and
the privacy of the training data must be preserved. In this paper, we introduce
a dropout technique that provides an elegant Bayesian interpretation to
dropout, and show that the intrinsic noise added, with the primary goal of
regularization, can be exploited to obtain a degree of differential privacy.
The iterative nature of training neural networks presents a challenge for
privacy-preserving estimation since multiple iterations increase the amount of
noise added. We overcome this by using a relaxed notion of differential
privacy, called concentrated differential privacy, which provides tighter
estimates on the overall privacy loss. We demonstrate the accuracy of our
privacy-preserving dropout algorithm on benchmark datasets.
Beyond Parity: Fairness Objectives for Collaborative Filtering
Authors: Sirui Yao, Bert Huang
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Collaborative Filtering, Fairness, Recommendation Systems

We study fairness in collaborative-filtering recommender systems, which are
sensitive to discrimination that exists in historical data. Biased data can
lead collaborative-filtering methods to make unfair predictions for users from
minority groups. We identify the insufficiency of existing fairness metrics and
propose four new metrics that address different forms of unfairness. These
fairness metrics can be optimized by adding fairness terms to the learning
objective. Experiments on synthetic and real data show that our new metrics can
better measure fairness than the baseline, and that the fairness objectives
effectively help reduce unfairness.
Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
Authors: Quentin Feltgen, Benjamin Fagard, Jean-Pierre Nadal
Date: 2017-03-01 00:00:00

It is generally believed that, when a linguistic item acquires a new meaning,
its overall frequency of use in the language rises with time with an S-shaped
growth curve. Yet, this claim has only been supported by a limited number of
case studies. In this paper, we provide the first corpus-based quantitative
confirmation of the genericity of the S-curve in language change. Moreover, we
uncover another generic pattern, a latency phase of variable duration preceding
the S-growth, during which the frequency of use of the semantically expanding
word remains low and more or less constant. We also propose a usage-based model
of language change supported by cognitive considerations, which predicts that
both phases, the latency and the fast S-growth, take place. The driving
mechanism is a stochastic dynamics, a random walk in the space of frequency of
use. The underlying deterministic dynamics highlights the role of a control
parameter, the strength of the cognitive impetus governing the onset of change,
which tunes the system at the vicinity of a saddle-node bifurcation. In the
neighborhood of the critical point, the latency phase corresponds to the
diffusion time over the critical region, and the S-growth to the fast
convergence that follows. The duration of the two phases is computed as
specific first passage times of the random walk process, leading to
distributions that fit well the ones extracted from our dataset. We argue that
our results are not specific to the studied corpus, but apply to semantic
change in general.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
Authors: Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation, Video Frame Interpolation, Vocal Bursts Intensity Prediction

Given two consecutive frames, video interpolation aims at generating
intermediate frame(s) to form both spatially and temporally coherent video
sequences. While most existing methods focus on single-frame interpolation, we
propose an end-to-end convolutional neural network for variable-length
multi-frame video interpolation, where the motion interpretation and occlusion
reasoning are jointly modeled. We start by computing bi-directional optical
flow between the input images using a U-Net architecture. These flows are then
linearly combined at each time step to approximate the intermediate
bi-directional optical flows. These approximate flows, however, only work well
in locally smooth regions and produce artifacts around motion boundaries. To
address this shortcoming, we employ another U-Net to refine the approximated
flow and also predict soft visibility maps. Finally, the two input images are
warped and linearly fused to form each intermediate frame. By applying the
visibility maps to the warped images before fusion, we exclude the contribution
of occluded pixels to the interpolated intermediate frame to avoid artifacts.
Since none of our learned network parameters are time-dependent, our approach
is able to produce as many intermediate frames as needed. We use 1,132 video
clips with 240-fps, containing 300K individual video frames, to train our
network. Experimental results on several datasets, predicting different numbers
of interpolated frames, demonstrate that our approach performs consistently
better than existing methods.
Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis
Authors: Ryan Gary Kim, Janardhan Rao Doppa, Partha Pratim Pande, Diana Marculescu, Radu Marculescu
Date: 2017-11-30 00:00:00
Tasks: BIG-bench Machine Learning

Tight collaboration between experts of machine learning and manycore system
design is necessary to create a data-driven manycore design framework that
integrates both learning and expert knowledge. Such a framework will be
necessary to address the rising complexity of designing large-scale manycore
systems and machine learning techniques.
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness
Authors: Shuo Liu, Zheng Liu
Date: 2017-11-30 00:00:00
Tasks: Computational Efficiency, Object, object-detection, Object Detection, Transfer Learning

Object Detection is critical for automatic military operations. However, the
performance of current object detection algorithms is deficient in terms of the
requirements in military scenarios. This is mainly because the object presence
is hard to detect due to the indistinguishable appearance and dramatic changes
of object's size which is determined by the distance to the detection sensors.
Recent advances in deep learning have achieved promising results in many
challenging tasks. The state-of-the-art in object detection is represented by
convolutional neural networks (CNNs), such as the fast R-CNN algorithm. These
CNN-based methods improve the detection performance significantly on several
public generic object detection datasets. However, their performance on
detecting small objects or undistinguishable objects in visible spectrum images
is still insufficient. In this study, we propose a novel detection algorithm
for military objects by fusing multi-channel CNNs. We combine spatial, temporal
and thermal information by generating a three-channel image, and they will be
fused as CNN feature maps in an unsupervised manner. The backbone of our object
detection framework is from the fast R-CNN algorithm, and we utilize
cross-domain transfer learning technique to fine-tune the CNN model on
generated multi-channel images. In the experiments, we validated the proposed
method with the images from SENSIAC (Military Sensing Information Analysis
Centre) database and compared it with the state-of-the-art. The experimental
results demonstrated the effectiveness of the proposed method on both accuracy
and computational efficiency.
On the importance of normative data in speech-based assessment
Authors: Zeinab Noorian, Chloé Pou-Prom, Frank Rudzicz
Date: 2017-11-30 00:00:00
Tasks: Binary Classification, General Classification

Data sets for identifying Alzheimer's disease (AD) are often relatively
sparse, which limits their ability to train generalizable models. Here, we
augment such a data set, DementiaBank, with each of two normative data sets,
the Wisconsin Longitudinal Study and Talk2Me, each of which employs a
speech-based picture-description assessment. Through minority class
oversampling with ADASYN, we outperform state-of-the-art results in binary
classification of people with and without AD in DementiaBank. This work
highlights the effectiveness of combining sparse and difficult-to-acquire
patient data with relatively large and easily accessible normative datasets.
Universal Joint Image Clustering and Registration using Partition Information
Authors: Ravi Kiran Raman, Lav R. Varshney
Date: 2017-01-10 00:00:00
Tasks: Clustering, Image Clustering, Image Registration

We consider the problem of universal joint clustering and registration of
images and define algorithms using multivariate information functionals. We
first study registering two images using maximum mutual information and prove
its asymptotic optimality. We then show the shortcomings of pairwise
registration in multi-image registration, and design an asymptotically optimal
algorithm based on multiinformation. Further, we define a novel multivariate
information functional to perform joint clustering and registration of images,
and prove consistency of the algorithm. Finally, we consider registration and
clustering of numerous limited-resolution images, defining algorithms that are
order-optimal in scaling of number of pixels in each image with the number of
images.
Graph Centrality Measures for Boosting Popularity-Based Entity Linking
Authors: Hussam Hamdan, Jean-Gabriel Ganascia
Date: 2017-11-30 00:00:00
Tasks: Entity Linking, graph construction, Graph Ranking

Many Entity Linking systems use collective graph-based methods to
disambiguate the entity mentions within a document. Most of them have focused
on graph construction and initial weighting of the candidate entities, less
attention has been devoted to compare the graph ranking algorithms. In this
work, we focus on the graph-based ranking algorithms, therefore we propose to
apply five centrality measures: Degree, HITS, PageRank, Betweenness and
Closeness. A disambiguation graph of candidate entities is constructed for each
document using the popularity method, then centrality measures are applied to
choose the most relevant candidate to boost the results of entity popularity
method. We investigate the effectiveness of each centrality measure on the
performance across different domains and datasets. Our experiments show that a
simple and fast centrality measure such as Degree centrality can outperform
other more time-consuming measures.
An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos
Authors: Rui Hou, Chen Chen, Mubarak Shah
Date: 2017-11-30 00:00:00
Tasks: Action Detection, Action Segmentation, Decoder, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

In this paper, we propose an end-to-end 3D CNN for action detection and
segmentation in videos. The proposed architecture is a unified deep network
that is able to recognize and localize action based on 3D convolution features.
A video is first divided into equal length clips and next for each clip a set
of tube proposals are generated based on 3D CNN features. Finally, the tube
proposals of different clips are linked together and spatio-temporal action
detection is performed using these linked video proposals. This top-down action
detection approach explicitly relies on a set of good tube proposals to perform
well and training the bounding box regression usually requires a large number
of annotated samples. To remedy this, we further extend the 3D CNN to an
encoder-decoder structure and formulate the localization problem as action
segmentation. The foreground regions (i.e. action regions) for each frame are
segmented first then the segmented foreground maps are used to generate the
bounding boxes. This bottom-up approach effectively avoids tube proposal
generation by leveraging the pixel-wise annotations of segmentation. The
segmentation framework also can be readily applied to a general problem of
video object segmentation. Extensive experiments on several video datasets
demonstrate the superior performance of our approach for action detection and
video object segmentation compared to the state-of-the-arts.
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
Authors: Genevieve Flaspohler, Nicholas Roy, Yogesh Girdhar
Date: 2017-11-30 00:00:00
Tasks: Scene Understanding, Topic Models

The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
  We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.
Learning Certifiably Optimal Rule Lists for Categorical Data
Authors: Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin
Date: 2017-04-06 00:00:00

We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling.
Learning to Optimize Neural Nets
Authors: Ke Li, Jitendra Malik
Date: 2017-03-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Stochastic Optimization

Learning to Optimize is a recently proposed framework for learning
optimization algorithms using reinforcement learning. In this paper, we explore
learning an optimization algorithm for training shallow neural nets. Such
high-dimensional stochastic optimization problems present interesting
challenges for existing reinforcement learning algorithms. We develop an
extension that is suited to learning optimization algorithms in this setting
and demonstrate that the learned optimization algorithm consistently
outperforms other known optimization algorithms even on unseen tasks and is
robust to changes in stochasticity of gradients and the neural net
architecture. More specifically, we show that an optimization algorithm trained
with the proposed method on the problem of training a neural net on MNIST
generalizes to the problems of training neural nets on the Toronto Faces
Dataset, CIFAR-10 and CIFAR-100.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Authors: Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Conditional Image Generation, Fundus to Angiography Generation, Image Generation, Image-to-Image Translation, Instance Segmentation, Object, Semantic Segmentation, Sketch-to-Image Translation, Vocal Bursts Intensity Prediction

We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In this work, we generate 2048x1024 visually appealing results
with a novel adversarial loss, as well as new multi-scale generator and
discriminator architectures. Furthermore, we extend our framework to
interactive visual manipulation with two additional features. First, we
incorporate object instance segmentation information, which enables object
manipulations such as removing/adding objects and changing the object category.
Second, we propose a method to generate diverse results given the same input,
allowing users to edit the object appearance interactively. Human opinion
studies demonstrate that our method significantly outperforms existing methods,
advancing both the quality and the resolution of deep image synthesis and
editing.
Towards High Performance Video Object Detection
Authors: Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection, Video Object Detection, Vocal Bursts Intensity Prediction

There has been significant progresses for image object detection in recent
years. Nevertheless, video object detection has received little attention,
although it is more challenging and more important in practical scenarios.
  Built upon the recent works, this work proposes a unified approach based on
the principle of multi-frame end-to-end learning of features and cross-frame
motion. Our approach extends prior works with three new techniques and steadily
pushes forward the performance envelope (speed-accuracy tradeoff), towards high
performance video object detection.