pwc-archive/papers-with-abstracts huggingface dataset:
Single-channel Speech Dereverberation via Generative Adversarial Training
Authors: Chenxing Li, Tieqiang Wang, Shuang Xu, Bo Xu
Date: 2018-06-25 00:00:00
Tasks: Speech Dereverberation

In this paper, we propose a single-channel speech dereverberation system
(DeReGAT) based on convolutional, bidirectional long short-term memory and deep
feed-forward neural network (CBLDNN) with generative adversarial training
(GAT). In order to obtain better speech quality instead of only minimizing a
mean square error (MSE), GAT is employed to make the dereverberated speech
indistinguishable form the clean samples. Besides, our system can deal with
wide range reverberation and be well adapted to variant environments. The
experimental results show that the proposed model outperforms weighted
prediction error (WPE) and deep neural network-based systems. In addition,
DeReGAT is extended to an online speech dereverberation scenario, which reports
comparable performance with the offline case.
Vision-based Pose Estimation for Augmented Reality : A Comparison Study
Authors: Hayet Belghit, Abdelkader Bellarbi, Nadia Zenati, Samir Otmane
Date: 2018-06-25 00:00:00
Tasks: 3D Pose Estimation, Camera Localization, General Classification, Pose Estimation

Augmented reality aims to enrich our real world by inserting 3D virtual
objects. In order to accomplish this goal, it is important that virtual
elements are rendered and aligned in the real scene in an accurate and visually
acceptable way. The solution of this problem can be related to a pose
estimation and 3D camera localization. This paper presents a survey on
different approaches of 3D pose estimation in augmented reality and gives
classification of key-points-based techniques. The study given in this paper
may help both developers and researchers in the field of augmented reality.
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders
Authors: Esben Jannik Bjerrum, Boris Sattarov
Date: 2018-06-25 00:00:00
Tasks: Decoder, Diversity, Drug Discovery

Chemical autoencoders are attractive models as they combine chemical space
navigation with possibilities for de-novo molecule generation in areas of
interest. This enables them to produce focused chemical libraries around a
single lead compound for employment early in a drug discovery project. Here it
is shown that the choice of chemical representation, such as SMILES strings,
has a large influence on the properties of the latent space. It is further
explored to what extent translating between different chemical representations
influences the latent space similarity to the SMILES strings or circular
fingerprints. By employing SMILES enumeration for either the encoder or
decoder, it is found that the decoder has the largest influence on the
properties of the latent space. Training a sequence to sequence heteroencoder
based on recurrent neural networks(RNNs) with long short-term memory cells
(LSTM) to predict different enumerated SMILES strings from the same canonical
SMILES string gives the largest similarity between latent space distance and
molecular similarity measured as circular fingerprints similarity. Using the
output from the bottleneck in QSAR modelling of five molecular datasets shows
that heteroencoder derived vectors markedly outperforms autoencoder derived
vectors as well as models built using ECFP4 fingerprints, underlining the
increased chemical relevance of the latent space. However, the use of
enumeration during training of the decoder leads to a markedly increase in the
rate of decoding to a different molecules than encoded, a tendency that can be
counteracted with more complex network architectures.
RAM: A Region-Aware Deep Model for Vehicle Re-Identification
Authors: Xiaobin Liu, Shiliang Zhang, Qingming Huang, Wen Gao
Date: 2018-06-25 00:00:00
Tasks: Vehicle Re-Identification

Previous works on vehicle Re-ID mainly focus on extracting global features
and learning distance metrics. Because some vehicles commonly share same model
and maker, it is hard to distinguish them based on their global appearances.
Compared with the global appearance, local regions such as decorations and
inspection stickers attached to the windshield, may be more distinctive for
vehicle Re-ID. To embed the detailed visual cues in those local regions, we
propose a Region-Aware deep Model (RAM). Specifically, in addition to
extracting global features, RAM also extracts features from a series of local
regions. As each local region conveys more distinctive visual cues, RAM
encourages the deep model to learn discriminative features. We also introduce a
novel learning algorithm to jointly use vehicle IDs, types/models, and colors
to train the RAM. This strategy fuses more cues for training and results in
more discriminative global and regional features. We evaluate our methods on
two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental
results show our methods achieve promising performance in comparison with
recent works.
Framework for Opinion Mining Approach to Augment Education System Performance
Authors: Amritpal Kaur, Harkiran Kaur
Date: 2018-06-25 00:00:00
Tasks: Decision Making, Opinion Mining

The extensive expansion growth of social networking sites allows the people
to share their views and experiences freely with their peers on internet. Due
to this, huge amount of data is generated on everyday basis which can be used
for the opinion mining to extract the views of people in a particular field.
Opinion mining finds its applications in many areas such as Tourism, Politics,
education and entertainment, etc. It has not been extensively implemented in
area of education system. This paper discusses the malpractices in the present
examination system. In the present scenario, Opinion mining is vastly used for
decision making. The authors of this paper have designed a framework by
applying Na\"ive Bayes approach to the education dataset. The various phases of
Na\"ive Bayes approach include three steps: conversion of data into frequency
table, making classes of dataset and apply the Na\"ive Bayes algorithm equation
to calculate the probabilities of classes. Finally the highest probability
class is the outcome of this prediction. These predictions are used to make
improvements in the education system and help to provide better education.
Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos
Authors: Yuan Liu, Moyini Yao
Date: 2018-06-25 00:00:00
Tasks: Dense Captioning, Optical Flow Estimation, Sentence, Video Captioning

This note describes the details of our solution to the dense-captioning
events in videos task of ActivityNet Challenge 2018. Specifically, we solve
this problem with a two-stage way, i.e., first temporal event proposal and then
sentence generation. For temporal event proposal, we directly leverage the
three-stage workflow in [13, 16]. For sentence generation, we capitalize on
LSTM-based captioning framework with temporal attention mechanism (dubbed as
LSTM-T). Moreover, the input visual sequence to the LSTM-based video captioning
model is comprised of RGB and optical flow images. At inference, we adopt a
late fusion scheme to fuse the two LSTM-based captioning models for sentence
generation.
Towards Optimal Transport with Global Invariances
Authors: David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Date: 2018-06-25 00:00:00
Tasks: Translation, Word Embeddings, Word Translation

Many problems in machine learning involve calculating correspondences between
sets of objects, such as point clouds or images. Discrete optimal transport
provides a natural and successful approach to such tasks whenever the two sets
of objects can be represented in the same space, or at least distances between
them can be directly evaluated. Unfortunately neither requirement is likely to
hold when object representations are learned from data. Indeed, automatically
derived representations such as word embeddings are typically fixed only up to
some global transformations, for example, reflection or rotation. As a result,
pairwise distances across two such instances are ill-defined without specifying
their relative transformation. In this work, we propose a general framework for
optimal transport in the presence of latent global transformations. We cast the
problem as a joint optimization over transport couplings and transformations
chosen from a flexible class of invariances, propose algorithms to solve it,
and show promising results in various tasks, including a popular unsupervised
word translation benchmark.
Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision
Authors: Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese
Date: 2018-06-25 00:00:00

Tool manipulation is vital for facilitating robots to complete challenging
task goals. It requires reasoning about the desired effect of the task and thus
properly grasping and manipulating the tool to achieve the task. Task-agnostic
grasping optimizes for grasp robustness while ignoring crucial task-specific
constraints. In this paper, we propose the Task-Oriented Grasping Network
(TOG-Net) to jointly optimize both task-oriented grasping of a tool and the
manipulation policy for that tool. The training process of the model is based
on large-scale simulated self-supervision with procedurally generated tool
objects. We perform both simulated and real-world experiments on two tool-based
manipulation tasks: sweeping and hammering. Our model achieves overall 71.1%
task success rate for sweeping and 80.0% task success rate for hammering.
Supplementary material is available at: bit.ly/task-oriented-grasp
Track Xplorer: A System for Visual Analysis of Sensor-based Motor Activity Predictions
Authors: Marco Cavallo, Çağatay Demiralp
Date: 2018-06-25 00:00:00
Tasks: Temporal Sequences

With the rapid commoditization of wearable sensors, detecting human movements
from sensor datasets has become increasingly common over a wide range of
applications. To detect activities, data scientists iteratively experiment with
different classifiers before deciding which model to deploy. Effective
reasoning about and comparison of alternative classifiers are crucial in
successful model development. This is, however, inherently difficult in
developing classifiers for sensor data, where the intricacy of long temporal
sequences, high prediction frequency, and imprecise labeling make standard
evaluation methods relatively ineffective and even misleading. We introduce
Track Xplorer, an interactive visualization system to query, analyze, and
compare the predictions of sensor-data classifiers. Track Xplorer enables users
to interactively explore and compare the results of different classifiers, and
assess their accuracy with respect to the ground-truth labels and video.
Through integration with a version control system, Track Xplorer supports
tracking of models and their parameters without additional workload on model
developers. Track Xplorer also contributes an extensible algebra over track
representations to filter, compose, and compare classification outputs,
enabling users to reason effectively about classifier performance. We apply
Track Xplorer in a collaborative project to develop classifiers to detect
movements from multisensor data gathered from Parkinson's disease patients. We
demonstrate how Track Xplorer helps identify early on possible systemic data
errors, effectively track and compare the results of different classifiers, and
reason about and pinpoint the causes of misclassifications.
Color Constancy by Reweighting Image Feature Maps
Authors: Jueqin Qiu, Haisong Xu, Zhengnan Ye
Date: 2018-06-25 00:00:00
Tasks: Color Constancy, Deep Learning

In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models. The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art deep learning based models while requiring more compact model size and cheaper computational cost. In addition to local color estimation, a confidence estimation branch is also included such that the model is able to simultaneously produce point estimate and its uncertainty estimate, which provides useful clues for local estimates aggregation and multiple illumination estimation. The source code and the dataset have been made available at https://github.com/QiuJueqin/Reweight-CC.
A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast
Authors: Igor Oliveira, Renato L. F. Cunha, Bruno Silva, Marco A. S. Netto
Date: 2018-06-25 00:00:00
Tasks: BIG-bench Machine Learning

Yield forecast is essential to agriculture stakeholders and can be obtained
with the use of machine learning models and data coming from multiple sources.
Most solutions for yield forecast rely on NDVI (Normalized Difference
Vegetation Index) data, which is time-consuming to be acquired and processed.
To bring scalability for yield forecast, in the present paper we describe a
system that incorporates satellite-derived precipitation and soil properties
datasets, seasonal climate forecasting data from physical models and other
sources to produce a pre-season prediction of soybean/maize yield---with no
need of NDVI data. This system provides significantly useful results by the
exempting the need for high-resolution remote-sensing data and allowing farmers
to prepare for adverse climate influence on the crop cycle. In our studies, we
forecast the soybean and maize yields for Brazil and USA, which corresponded to
44% of the world's grain production in 2016. Results show the error metrics for
soybean and maize yield forecasts are comparable to similar systems that only
provide yield forecast information in the first weeks to months of the crop
cycle.
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information
Authors: Yulong Shi, Xiaoguang Han, Nianjuan Jiang, Kun Zhou, Kui Jia, Jiangbo Lu
Date: 2018-06-25 00:00:00
Tasks: 3D Human Pose Estimation

Although significant advances have been made in the area of human poses
estimation from images using deep Convolutional Neural Network (ConvNet), it
remains a big challenge to perform 3D pose inference in-the-wild. This is due
to the difficulty to obtain 3D pose groundtruth for outdoor environments. In
this paper, we propose a novel framework to tackle this problem by exploiting
the information of each bone indicating if it is forward or backward with
respect to the view of the camera(we term it Forwardor-Backward Information
abbreviated as FBI). Our method firstly trains a ConvNet with two branches
which maps an image of a human to both the 2D joint locations and the FBI of
bones. These information is further fed into a deep regression network to
predict the 3D positions of joints. To support the training, we also develop an
annotation user interface and labeled such FBI for around 12K in-the-wild
images which are randomly selected from MPII (a public dataset of 2D pose
annotation). Our experimental results on the standard benchmarks demonstrate
that our approach outperforms state-of-the-art methods both qualitatively and
quantitatively.
Towards a Better Understanding and Regularization of GAN Training Dynamics
Authors: Weili Nie, Ankit Patel
Date: 2018-06-24 00:00:00
Tasks: Speech Synthesis - Gujarati

Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood. By first considering a simple yet representative GAN example, we mathematically analyze its local convergence behavior in a non-asymptotic way. Furthermore, the analysis is extended to general GANs under certain assumptions. We find that in order to ensure a good convergence rate, two factors of the Jacobian in the GAN training dynamics should be simultaneously avoided, which are (i) the Phase Factor, i.e., the Jacobian has complex eigenvalues with a large imaginary-to-real ratio, and (ii) the Conditioning Factor, i.e., the Jacobian is ill-conditioned. Previous methods of regularizing the Jacobian can only alleviate one of these two factors, while making the other more severe. Thus we propose a new JAcobian REgularization (JARE) for GANs, which simultaneously addresses both factors by construction. Finally, we conduct experiments that confirm our theoretical analysis and demonstrate the advantages of JARE over previous methods in stabilizing GANs.
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Authors: Risi Kondor, Zhen Lin, Shubhendu Trivedi
Date: 2018-06-24 00:00:00

Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for
learning spherical images in a rotation invariant way by using ideas from group
representation theory and noncommutative harmonic analysis. In this paper we
propose a generalization of this work that generally exhibits improved
performace, but from an implementation point of view is actually simpler. An
unusual feature of the proposed architecture is that it uses the
Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding
repeated forward and backward Fourier transforms. The underlying ideas of the
paper generalize to constructing neural networks that are invariant to the
action of other compact groups.
Scale Space Approximation in Convolutional Neural Networks for Retinal Vessel Segmentation
Authors: Kyoung Jin Noh, Sang Jun Park, Soochahn Lee
Date: 2018-06-24 00:00:00
Tasks: Retinal Vessel Segmentation

Retinal images have the highest resolution and clarity among medical images.
Thus, vessel analysis in retinal images may facilitate early diagnosis and
treatment of many chronic diseases. In this paper, we propose a novel
multi-scale residual convolutional neural network structure based on a
\emph{scale-space approximation (SSA)} block of layers, comprising subsampling
and subsequent upsampling, for multi-scale representation. Through analysis in
the frequency domain, we show that this block structure is a close
approximation of Gaussian filtering, the operation to achieve scale variations
in scale-space theory. Experimental evaluations demonstrate that the proposed
network outperforms current state-of-the-art methods. Ablative analysis shows
that the SSA is indeed an important factor in performance improvement.
Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Authors: Junru Wu, Yue Wang, Zhen-Yu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin
Date: 2018-06-24 00:00:00
Tasks: Clustering

The current trend of pushing CNNs deeper with convolutions has created a
pressing demand to achieve higher compression gains on CNNs where convolutions
dominate the computation and parameter amount (e.g., GoogLeNet, ResNet and Wide
ResNet). Further, the high energy consumption of convolutions limits its
deployment on mobile devices. To this end, we proposed a simple yet effective
scheme for compressing convolutions though applying k-means clustering on the
weights, compression is achieved through weight-sharing, by only recording $K$
cluster centers and weight assignment indexes. We then introduced a novel
spectrally relaxed $k$-means regularization, which tends to make hard
assignments of convolutional layer weights to $K$ learned cluster centers
during re-training. We additionally propose an improved set of metrics to
estimate energy consumption of CNN hardware implementations, whose estimation
results are verified to be consistent with previously proposed energy
estimation tool extrapolated from actual hardware measurements. We finally
evaluated Deep $k$-Means across several CNN models in terms of both compression
ratio and energy consumption reduction, observing promising results without
incurring accuracy loss. The code is available at
https://github.com/Sandbox3aster/Deep-K-Means
Equalizing Financial Impact in Supervised Learning
Authors: Govind Ramnarayan
Date: 2018-06-24 00:00:00
Tasks: Fairness, General Classification

Notions of "fair classification" that have arisen in computer science
generally revolve around equalizing certain statistics across protected groups.
This approach has been criticized as ignoring societal issues, including how
errors can hurt certain groups disproportionately. We pose a modification of
one of the fairness criteria from Hardt, Price, and Srebro [NIPS, 2016] that
makes a small step towards addressing this issue in the case of financial
decisions like giving loans. We call this new notion "equalized financial
impact."
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Authors: Shengchao Liu, Mehmet Furkan Demirel, YIngyu Liang
Date: 2018-06-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Drug Design, Graph Neural Network, Molecular Property Prediction

Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite the increasing interest, the key challenge is to construct proper representations of molecules for learning algorithms. This paper introduces the N-gram graph, a simple unsupervised representation for molecules. The method first embeds the vertices in the molecule graph. It then constructs a compact representation for the graph by assembling the vertex embeddings in short walks in the graph, which we show is equivalent to a simple graph neural network that needs no training. The representations can thus be efficiently computed and then used with supervised learning methods for prediction. Experiments on 60 tasks from 10 benchmark datasets demonstrate its advantages over both popular graph neural networks and traditional representation methods. This is complemented by theoretical analysis showing its strong representation and prediction power.
Balanced News Using Constrained Bandit-based Personalization
Authors: Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis
Date: 2018-06-24 00:00:00
Tasks: Articles

We present a prototype for a news search engine that presents balanced
viewpoints across liberal and conservative articles with the goal of
de-polarizing content and allowing users to escape their filter bubble. The
balancing is done according to flexible user-defined constraints, and leverages
recent advances in constrained bandit optimization. We showcase our balanced
news feed by displaying it side-by-side with the news feed produced by a
traditional (polarized) feed.
On The Differential Privacy of Thompson Sampling With Gaussian Prior
Authors: Aristide C. Y. Tossou, Christos Dimitrakakis
Date: 2018-06-24 00:00:00
Tasks: Thompson Sampling

We show that Thompson Sampling with Gaussian Prior as detailed by Algorithm 2
in (Agrawal & Goyal, 2013) is already differentially private. Theorem 1 show
that it enjoys a very competitive privacy loss of only $\mathcal{O}(\ln^2 T)$
after T rounds. Finally, Theorem 2 show that one can control the privacy loss
to any desirable $\epsilon$ level by appropriately increasing the variance of
the samples from the Gaussian posterior. And this increases the regret only by
a term of $\mathcal{O}(\frac{\ln^2 T}{\epsilon})$. This compares favorably to
the previous result for Thompson Sampling in the literature ((Mishra &
Thakurta, 2015)) which adds a term of $\mathcal{O}(\frac{K \ln^3
T}{\epsilon^2})$ to the regret in order to achieve the same privacy level.
Furthermore, our result use the basic Thompson Sampling with few modifications
whereas the result of (Mishra & Thakurta, 2015) required sophisticated
constructions.