pwc-archive/papers-with-abstracts huggingface dataset:
Video-based Person Re-identification Using Spatial-Temporal Attention Networks
Authors: Shivansh Rao, Tanzila Rahman, Mrigank Rochan, Yang Wang
Date: 2018-10-26 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

We consider the problem of video-based person re-identification. The goal is
to identify a person from videos captured under different cameras. In this
paper, we propose an efficient spatial-temporal attention based model for
person re-identification from videos. Our method generates an attention score
for each frame based on frame-level features. The attention scores of all
frames in a video are used to produce a weighted feature vector for the input
video. Unlike most existing deep learning methods that use global
representation, our approach focuses on attention scores. Extensive experiments
on two benchmark datasets demonstrate that our method achieves the
state-of-the-art performance. This is a technical report.
From the EM Algorithm to the CM-EM Algorithm for Global Convergence of Mixture Models
Authors: Chenguang Lu
Date: 2018-10-26 00:00:00

The Expectation-Maximization (EM) algorithm for mixture models often results
in slow or invalid convergence. The popular convergence proof affirms that the
likelihood increases with Q; Q is increasing in the M -step and non-decreasing
in the E-step. The author found that (1) Q may and should decrease in some
E-steps; (2) The Shannon channel from the E-step is improper and hence the
expectation is improper. The author proposed the CM-EM algorithm (CM means
Channel's Matching), which adds a step to optimize the mixture ratios for the
proper Shannon channel and maximizes G, average log-normalized-likelihood, in
the M-step. Neal and Hinton's Maximization-Maximization (MM) algorithm use F
instead of Q to speed the convergence. Maximizing G is similar to maximizing F.
The new convergence proof is similar to Beal's proof with the variational
method. It first proves that the minimum relative entropy equals the minimum
R-G (R is mutual information), then uses variational and iterative methods that
Shannon et al. use for rate-distortion functions to prove the global
convergence. Some examples show that Q and F should and may decrease in some
E-steps. For the same example, the EM, MM, and CM-EM algorithms need about 36,
18, and 9 iterations respectively.
Building Footprint Generation Using Improved Generative Adversarial Networks
Authors: Yilei Shi, Qingyu Li, Xiao Xiang Zhu
Date: 2018-10-26 00:00:00

Building footprint information is an essential ingredient for 3-D
reconstruction of urban models. The automatic generation of building footprints
from satellite images presents a considerable challenge due to the complexity
of building shapes. In this work, we have proposed improved generative
adversarial networks (GANs) for the automatic generation of building footprints
from satellite images. We used a conditional GAN with a cost function derived
from the Wasserstein distance and added a gradient penalty term. The achieved
results indicated that the proposed method can significantly improve the
quality of building footprint generation compared to conditional generative
adversarial networks, the U-Net, and other networks. In addition, our method
nearly removes all hyperparameters tuning.
Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos
Authors: Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
Date: 2018-10-26 00:00:00
Tasks: Image and Video Forgery Detection

Recent advances in media generation techniques have made it easier for
attackers to create forged images and videos. State-of-the-art methods enable
the real-time creation of a forged version of a single video obtained from a
social network. Although numerous methods have been developed for detecting
forged images and videos, they are generally targeted at certain domains and
quickly become obsolete as new kinds of attacks appear. The method introduced
in this paper uses a capsule network to detect various kinds of spoofs, from
replay attacks using printed images or recorded videos to computer-generated
videos using deep convolutional neural networks. It extends the application of
capsule networks beyond their original intention to the solving of inverse
graphics problems.
Deep Poisson gamma dynamical systems
Authors: Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou
Date: 2018-10-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Augmentation, Time Series, Time Series Analysis

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially
observed multivariate count data, improving previously proposed models by not
only mining deep hierarchical latent structure from the data, but also
capturing both first-order and long-range temporal dependencies. Using
sophisticated but simple-to-implement data augmentation techniques, we derived
closed-form Gibbs sampling update equations by first backward and upward
propagating auxiliary latent counts, and then forward and downward sampling
latent variables. Moreover, we develop stochastic gradient MCMC inference that
is scalable to very long multivariate count time series. Experiments on both
synthetic and a variety of real-world data demonstrate that the proposed model
not only has excellent predictive performance, but also provides highly
interpretable multilayer latent structure to represent hierarchical and
temporal information propagation.
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography
Authors: Max-Heinrich Laves, Lüder A. Kahrs, Tobias Ortmaier
Date: 2018-10-26 00:00:00
Tasks: Object Tracking, Optical Flow Estimation, Scene Flow Estimation

In microsurgery, lasers have emerged as precise tools for bone ablation. A
challenge is automatic control of laser bone ablation with 4D optical coherence
tomography (OCT). OCT as high resolution imaging modality provides volumetric
images of tissue and foresees information of bone position and orientation
(pose) as well as thickness. However, existing approaches for OCT based laser
ablation control rely on external tracking systems or invasively ablated
artificial landmarks for tracking the pose of the OCT probe relative to the
tissue. This can be superseded by estimating the scene flow caused by relative
movement between OCT-based laser ablation system and patient.
  Therefore, this paper deals with 2.5D scene flow estimation of volumetric OCT
images for application in laser ablation. We present a semi-supervised
convolutional neural network based tracking scheme for subsequent 3D OCT
volumes and apply it to a realistic semi-synthetic data set of ex vivo human
temporal bone specimen. The scene flow is estimated in a two-stage approach. In
the first stage, 2D lateral scene flow is computed on census-transformed
en-face arguments-of-maximum intensity projections. Subsequent to this, the
projections are warped by predicted lateral flow and 1D depth flow is
estimated. The neural network is trained semi-supervised by combining error to
ground truth and the reconstruction error of warped images with assumptions of
spatial flow smoothness. Quantitative evaluation reveals a mean endpoint error
of $ (4.7\pm{}3.5) $ voxel or $ 27.5 \pm 20.5 \mu\mathrm{m} $ for scene flow
estimation caused by simulated relative movement between the OCT probe and
bone. The scene flow estimation for 4D OCT enables its use for markerless
tracking of mastoid bone structures for image guidance in general, and
automated laser ablation control.
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Authors: Asma Nouira, Nataliya Sokolovska, Jean-Claude Crivello
Date: 2018-10-26 00:00:00

Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data. Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data. Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications. However, in the domain of materials science, there is a need to synthesize data with higher order complexity compared to observed samples, and the state-of-the-art cross-domain GANs can not be adapted directly. In this contribution, we propose a novel GAN called CrystalGAN which generates new chemically stable crystallographic structures with increased domain complexity. We introduce an original architecture, we provide the corresponding loss functions, and we show that the CrystalGAN generates very reasonable data. We illustrate the efficiency of the proposed method on a real original problem of novel hydrides discovery that can be further used in development of hydrogen storage materials.
Lossless (and Lossy) Compression of Random Forests
Authors: Amichai Painsky, Saharon Rosset
Date: 2018-10-26 00:00:00
Tasks: Clustering

Ensemble methods are among the state-of-the-art predictive modeling
approaches. Applied to modern big data, these methods often require a large
number of sub-learners, where the complexity of each learner typically grows
with the size of the dataset. This phenomenon results in an increasing demand
for storage space, which may be very costly. This problem mostly manifests in a
subscriber based environment, where a user-specific ensemble needs to be stored
on a personal device with strict storage limitations (such as a cellular
device). In this work we introduce a novel method for lossless compression of
tree-based ensemble methods, focusing on random forests. Our suggested method
is based on probabilistic modeling of the ensemble's trees, followed by model
clustering via Bregman divergence. This allows us to find a minimal set of
models that provides an accurate description of the trees, and at the same time
is small enough to store and maintain. Our compression scheme demonstrates high
compression rates on a variety of modern datasets. Importantly, our scheme
enables predictions from the compressed format and a perfect reconstruction of
the original ensemble. In addition, we introduce a theoretically sound lossy
compression scheme, which allows us to control the trade-off between the
distortion and the coding rate.
Integrating Transformer and Paraphrase Rules for Sentence Simplification
Authors: Sanqiang Zhao, Rui Meng, Daqing He, Saptono Andi, Parmanto Bambang
Date: 2018-10-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Text Simplification, Translation

Sentence simplification aims to reduce the complexity of a sentence while
retaining its original meaning. Current models for sentence simplification
adopted ideas from ma- chine translation studies and implicitly learned
simplification mapping rules from normal- simple sentence pairs. In this paper,
we explore a novel model based on a multi-layer and multi-head attention
architecture and we pro- pose two innovative approaches to integrate the Simple
PPDB (A Paraphrase Database for Simplification), an external paraphrase
knowledge base for simplification that covers a wide range of real-world
simplification rules. The experiments show that the integration provides two
major benefits: (1) the integrated model outperforms multiple state- of-the-art
baseline models for sentence simplification in the literature (2) through
analysis of the rule utilization, the model seeks to select more accurate
simplification rules. The code and models used in the paper are available at
https://github.com/ Sanqiang/text_simplification.
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
Authors: Ajay Patel, Alexander Sands, Chris Callison-Burch, Marianna Apidianaki
Date: 2018-10-26 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Word Embeddings

Vector space embedding models like word2vec, GloVe, fastText, and ELMo are
extremely popular representations in natural language processing (NLP)
applications. We present Magnitude, a fast, lightweight tool for utilizing and
processing embeddings. Magnitude is an open source Python package with a
compact vector storage file format that allows for efficient manipulation of
huge numbers of embeddings. Magnitude performs common operations up to 60 to
6,000 times faster than Gensim. Magnitude introduces several novel features for
improved robustness like out-of-vocabulary lookups.
Fine-grained Video Categorization with Redundancy Reduction Attention
Authors: Chen Zhu, Xiao Tan, Feng Zhou, Xiao Liu, Kaiyu Yue, Errui Ding, Yi Ma
Date: 2018-10-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Video Classification

For fine-grained categorization tasks, videos could serve as a better source
than static images as videos have a higher chance of containing discriminative
patterns. Nevertheless, a video sequence could also contain a lot of redundant
and irrelevant frames. How to locate critical information of interest is a
challenging task. In this paper, we propose a new network structure, known as
Redundancy Reduction Attention (RRA), which learns to focus on multiple
discriminative patterns by sup- pressing redundant feature channels.
Specifically, it firstly summarizes the video by weight-summing all feature
vectors in the feature maps of selected frames with a spatio-temporal soft
attention, and then predicts which channels to suppress or to enhance according
to this summary with a learned non-linear transform. Suppression is achieved by
modulating the feature maps and threshing out weak activations. The updated
feature maps are then used in the next iteration. Finally, the video is
classified based on multiple summaries. The proposed method achieves out-
standing performances in multiple video classification datasets. Further- more,
we have collected two large-scale video datasets, YouTube-Birds and
YouTube-Cars, for future researches on fine-grained video categorization. The
datasets are available at http://www.cs.umd.edu/~chenzhu/fgvc.
TarMAC: Targeted Multi-Agent Communication
Authors: Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael Rabbat, Joelle Pineau
Date: 2018-10-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision. We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.
Neural Modular Control for Embodied Question Answering
Authors: Abhishek Das, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra
Date: 2018-10-26 00:00:00
Tasks: Embodied Question Answering, Imitation Learning, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and semantic, i.e. they can be sequentially combined in arbitrary orderings, and assume human-interpretable descriptions (e.g. 'exit room', 'find kitchen', 'find refrigerator', etc.). We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning. Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors. Subsequent joint hierarchical training enables the master policy to adapt to the sub-policies. On the challenging EQA (Das et al., 2018) benchmark in House3D (Wu et al., 2018), requiring navigating diverse realistic indoor environments, our approach outperforms prior work by a significant margin, both in terms of navigation and question answering.
Using solar and load predictions in battery scheduling at the residential level
Authors: Richard Bean, Hina Khan
Date: 2018-10-26 00:00:00
Tasks: Scheduling

Smart solar inverters can be used to store, monitor and manage a home's solar
energy. We describe a smart solar inverter system with battery which can either
operate in an automatic mode or receive commands over a network to charge and
discharge at a given rate. In order to make battery storage financially viable
and advantageous to the consumers, effective battery scheduling algorithms can
be employed. Particularly, when time-of-use tariffs are in effect in the region
of the inverter, it is possible in some cases to schedule the battery to save
money for the individual customer, compared to the "automatic" mode. Hence,
this paper presents and evaluates the performance of a novel battery scheduling
algorithm for residential consumers of solar energy. The proposed battery
scheduling algorithm optimizes the cost of electricity over next 24 hours for
residential consumers. The cost minimization is realized by controlling the
charging/discharging of battery storage system based on the predictions for
load and solar power generation values. The scheduling problem is formulated as
a linear programming problem. We performed computer simulations over 83
inverters using several months of hourly load and PV data. The simulation
results indicate that key factors affecting the viability of optimization are
the tariffs and the PV to Load ratio at each inverter. Depending on the tariff,
savings of between 1% and 10% can be expected over the automatic approach. The
prediction approach used in this paper is also shown to out-perform basic
"persistence" forecasting approaches. We have also examined the approaches for
improving the prediction accuracy and optimization effectiveness.
Learning sparse relational transition models
Authors: Victoria Xia, Zi Wang, Leslie Pack Kaelbling
Date: 2018-10-26 00:00:00
Proceeding: ICLR 2019 5

We present a representation for describing transition models in complex
uncertain domains using relational rules. For any action, a rule selects a set
of relevant objects and computes a distribution over properties of just those
objects in the resulting state given their properties in the previous state. An
iterative greedy algorithm is used to construct a set of deictic references
that determine which objects are relevant in any given state. Feed-forward
neural networks are used to learn the transition distribution on the relevant
objects' properties. This strategy is demonstrated to be both more versatile
and more sample efficient than learning a monolithic transition model in a
simulated domain in which a robot pushes stacks of objects on a cluttered
table.
Efficient learning of neighbor representations for boundary trees and forests
Authors: Tharindu Adikari, Stark C. Draper
Date: 2018-10-26 00:00:00
Tasks: Classification, General Classification, Retrieval, Semantic Similarity, Semantic Textual Similarity

We introduce a semiparametric approach to neighbor-based classification. We
build off the recently proposed Boundary Trees algorithm by Mathy et al.(2015)
which enables fast neighbor-based classification, regression and retrieval in
large datasets. While boundary trees use an Euclidean measure of similarity,
the Differentiable Boundary Tree algorithm by Zoran et al.(2017) was introduced
to learn low-dimensional representations of complex input data, on which
semantic similarity can be calculated to train boundary trees. As is pointed
out by its authors, the differentiable boundary tree approach contains a few
limitations that prevents it from scaling to large datasets. In this paper, we
introduce Differentiable Boundary Sets, an algorithm that overcomes the
computational issues of the differentiable boundary tree scheme and also
improves its classification accuracy and data representability. Our algorithm
is efficiently implementable with existing tools and offers a significant
reduction in training time. We test and compare the algorithms on the well
known MNIST handwritten digits dataset and the newer Fashion-MNIST dataset by
Xiao et al.(2017).
Data-specific Adaptive Threshold for Face Recognition and Authentication
Authors: Hsin-Rung Chou, Jia-Hong Lee, Yi-Ming Chan, Chu-Song Chen
Date: 2018-10-26 00:00:00
Tasks: Face Recognition

Many face recognition systems boost the performance using deep learning
models, but only a few researches go into the mechanisms for dealing with
online registration. Although we can obtain discriminative facial features
through the state-of-the-art deep model training, how to decide the best
threshold for practical use remains a challenge. We develop a technique of
adaptive threshold mechanism to improve the recognition accuracy. We also
design a face recognition system along with the registering procedure to handle
online registration. Furthermore, we introduce a new evaluation protocol to
better evaluate the performance of an algorithm for real-world scenarios. Under
our proposed protocol, our method can achieve a 22\% accuracy improvement on
the LFW dataset.
Size-Noise Tradeoffs in Generative Networks
Authors: Bolton Bailey, Matus Telgarsky
Date: 2018-10-26 00:00:00
Proceeding: NeurIPS 2018 12

This paper investigates the ability of generative networks to convert their
input noise distributions into other distributions. Firstly, we demonstrate a
construction that allows ReLU networks to increase the dimensionality of their
noise distribution by implementing a "space-filling" function based on iterated
tent maps. We show this construction is optimal by analyzing the number of
affine pieces in functions computed by multivariate ReLU networks. Secondly, we
provide efficient ways (using polylog $(1/\epsilon)$ nodes) for networks to
pass between univariate uniform and normal distributions, using a Taylor series
approximation and a binary search gadget for computing function inverses.
Lastly, we indicate how high dimensional distributions can be efficiently
transformed into low dimensional distributions.