pwc-archive/papers-with-abstracts huggingface dataset:
Building Generalizable Agents with a Realistic and Rich 3D Environment
Authors: Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian
Date: 2018-01-07 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, Reinforcement Learning

Teaching an agent to navigate in an unseen 3D environment is a challenging
task, even in the event of simulated environments. To generalize to unseen
environments, an agent needs to be robust to low-level variations (e.g. color,
texture, object changes), and also high-level variations (e.g. layout changes
of the environment). To improve overall generalization, all types of variations
in the environment have to be taken under consideration via different level of
data augmentation steps. To this end, we propose House3D, a rich, extensible
and efficient environment that contains 45,622 human-designed 3D scenes of
visually realistic houses, ranging from single-room studios to multi-storied
houses, equipped with a diverse set of fully labeled 3D objects, textures and
scene layouts, based on the SUNCG dataset (Song et.al.). The diversity in
House3D opens the door towards scene-level augmentation, while the label-rich
nature of House3D enables us to inject pixel- & task-level augmentations such
as domain randomization (Toubin et. al.) and multi-task training. Using a
subset of houses in House3D, we show that reinforcement learning agents trained
with an enhancement of different levels of augmentations perform much better in
unseen environments than our baselines with raw RGB input by over 8% in terms
of navigation success rate. House3D is publicly available at
http://github.com/facebookresearch/House3D.
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis
Authors: Xiaogang Cheng, Guoqing Liu, Anders Hedman, Kun Wang, Hai-Bo Li
Date: 2018-04-08 00:00:00
Tasks: Time Series, Time Series Analysis

Vision-based methods for visibility estimation can play a critical role in
reducing traffic accidents caused by fog and haze. To overcome the
disadvantages of current visibility estimation methods, we present a novel
data-driven approach based on Gaussian image entropy and piecewise stationary
time series analysis (SPEV). This is the first time that Gaussian image entropy
is used for estimating atmospheric visibility. To lessen the impact of
landscape and sunshine illuminance on visibility estimation, we used region of
interest (ROI) analysis and took into account relative ratios of image entropy,
to improve estimation accuracy. We assume fog and haze cause blurred images and
that fog and haze can be considered as a piecewise stationary signal. We used
piecewise stationary time series analysis to construct the piecewise causal
relationship between image entropy and visibility. To obtain a real-world
visibility measure during fog and haze, a subjective assessment was established
through a study with 36 subjects who performed visibility observations.
Finally, a total of two million videos were used for training the SPEV model
and validate its effectiveness. The videos were collected from the constantly
foggy and hazy Tongqi expressway in Jiangsu, China. The contrast model of
visibility estimation was used for algorithm performance comparison, and the
validation results of the SPEV model were encouraging as 99.14% of the relative
errors were less than 10%.
Order Effects for Queries in Intelligent Systems
Authors: Subhash Kak
Date: 2018-04-08 00:00:00
Tasks: Decision Making

This paper examines common assumptions regarding the decision-making internal
environment for intelligent agents and investigates issues related to
processing of memory and belief states to help obtain better understanding of
the responses. In specific, we consider order effects and discuss both
classical and non-classical explanations for them. We also consider implicit
cognition and explore if certain inaccessible states may be best modeled as
quantum states. We propose that the hypothesis that quantum states are at the
basis of order effects be tested on large databases such as those related to
medical treatment and drug efficacy. A problem involving a maze network is
considered and comparisons made between classical and quantum decision
scenarios for it.
An adaptive multiclass nearest neighbor classifier
Authors: Nikita Puchkin, Vladimir Spokoiny
Date: 2018-04-08 00:00:00

We consider a problem of multiclass classification, where the training sample $S_n = \{(X_i, Y_i)\}_{i=1}^n$ is generated from the model $\mathbb P(Y = m | X = x) = \eta_m(x)$, $1 \leq m \leq M$, and $\eta_1(x), \dots, \eta_M(x)$ are unknown $\alpha$-Holder continuous functions.Given a test point $X$, our goal is to predict its label. A widely used $\mathsf k$-nearest-neighbors classifier constructs estimates of $\eta_1(X), \dots, \eta_M(X)$ and uses a plug-in rule for the prediction. However, it requires a proper choice of the smoothing parameter $\mathsf k$, which may become tricky in some situations. In our solution, we fix several integers $n_1, \dots, n_K$, compute corresponding $n_k$-nearest-neighbor estimates for each $m$ and each $n_k$ and apply an aggregation procedure. We study an algorithm, which constructs a convex combination of these estimates such that the aggregated estimate behaves approximately as well as an oracle choice. We also provide a non-asymptotic analysis of the procedure, prove its adaptation to the unknown smoothness parameter $\alpha$ and to the margin and establish rates of convergence under mild assumptions.
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Authors: Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray
Date: 2018-04-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Anticipation

First-person vision is gaining interest as it offers a unique viewpoint on
people's interaction with objects, their attention, and even intention.
However, progress in this challenging domain has been relatively slow due to
the lack of sufficiently large datasets. In this paper, we introduce
EPIC-KITCHENS, a large-scale egocentric video benchmark recorded by 32
participants in their native kitchen environments. Our videos depict
nonscripted daily activities: we simply asked each participant to start
recording every time they entered their kitchen. Recording took place in 4
cities (in North America and Europe) by participants belonging to 10 different
nationalities, resulting in highly diverse cooking styles. Our dataset features
55 hours of video consisting of 11.5M frames, which we densely labeled for a
total of 39.6K action segments and 454.3K object bounding boxes. Our annotation
is unique in that we had the participants narrate their own videos (after
recording), thus reflecting true intention, and we crowd-sourced ground-truths
based on these. We describe our object, action and anticipation challenges, and
evaluate several baselines over two test splits, seen and unseen kitchens.
Dataset and Project page: http://epic-kitchens.github.io
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Authors: Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
Date: 2018-04-08 00:00:00

We present and evaluate the Fast (conditional) Independence Test (FIT) -- a
nonparametric conditional independence test. The test is based on the idea that
when $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict
$X$, as long as $Y$ is also a regressor. On the contrary, if $P(X \mid Y, Z)
\neq P(X \mid Y)$, $Z$ might improve prediction results. FIT applies to
thousand-dimensional random variables with a hundred thousand samples in a
fraction of the time required by alternative methods. We provide an extensive
evaluation that compares FIT to six extant nonparametric independence tests.
The evaluation shows that FIT has low probability of making both Type I and
Type II errors compared to other tests, especially as the number of available
samples grows. Our implementation of FIT is publicly available.
Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression
Authors: Haiping Zhu, Qi Zhou, Junping Zhang, James Z. Wang
Date: 2018-04-08 00:00:00
Tasks: Age Estimation, Generative Adversarial Network, regression

Facial aging and facial rejuvenation analyze a given face photograph to
predict a future look or estimate a past look of the person. To achieve this,
it is critical to preserve human identity and the corresponding aging
progression and regression with high accuracy. However, existing methods cannot
simultaneously handle these two objectives well. We propose a novel generative
adversarial network based approach, named the Conditional Multi-Adversarial
AutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimation
technique to control the aging accuracy and takes a high-level feature
representation to preserve personalized identity. Specifically, the face is
first mapped to a latent vector through a convolutional encoder. The latent
vector is then projected onto the face manifold conditional on the age through
a deconvolutional generator. The latent vector preserves personalized face
features and the age controls facial aging and rejuvenation. A discriminator
and an ordinal regression are imposed on the encoder and the generator in
tandem, making the generated face images to be more photorealistic while
simultaneously exhibiting desirable aging effects. Besides, a high-level
feature representation is utilized to preserve personalized identity of the
generated face. Experiments on two benchmark datasets demonstrate appealing
performance of the proposed method over the state-of-the-art.
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
Authors: Xiaolong Wang, Yufei Ye, Abhinav Gupta
Date: 2018-03-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Neural Network, Knowledge Graphs, Zero-Shot Learning

We consider the problem of zero-shot recognition: learning a visual
classifier for a category with zero training examples, just using the word
embedding of the category and its relationship to other categories, which
visual data are provided. The key to dealing with the unfamiliar or novel
category is to transfer knowledge obtained from familiar classes to describe
the unfamiliar class. In this paper, we build upon the recently introduced
Graph Convolutional Network (GCN) and propose an approach that uses both
semantic embeddings and the categorical relationships to predict the
classifiers. Given a learned knowledge graph (KG), our approach takes as input
semantic embeddings for each node (representing visual category). After a
series of graph convolutions, we predict the visual classifier for each
category. During training, the visual classifiers for a few categories are
given to learn the GCN parameters. At test time, these filters are used to
predict the visual classifiers of unseen categories. We show that our approach
is robust to noise in the KG. More importantly, our approach provides
significant improvement in performance compared to the current state-of-the-art
results (from 2 ~ 3% on some metrics to whopping 20% on a few).
NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Authors: Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, Michael Trenell
Date: 2018-04-02 00:00:00
Proceeding: SEMEVAL 2018 6

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony
detection in English tweets". We propose to use a simple neural network
architecture of Multilayer Perceptron with various types of input features
including: lexical, syntactic, semantic and polarity features. Our system
achieves very high performance in both subtasks of binary and multi-class irony
detection in tweets. In particular, we rank third using the accuracy metric and
fifth using the F1 metric. Our code is available at
https://github.com/NIHRIO/IronyDetectionInTwitter
Image Segmentation using Sparse Subset Selection
Authors: Fariba Zohrizadeh, Mohsen Kheirandishfard, Farhad Kamangar
Date: 2018-04-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

In this paper, we present a new image segmentation method based on the
concept of sparse subset selection. Starting with an over-segmentation, we
adopt local spectral histogram features to encode the visual information of the
small segments into high-dimensional vectors, called superpixel features. Then,
the superpixel features are fed into a novel convex model which efficiently
leverages the features to group the superpixels into a proper number of
coherent regions. Our model automatically determines the optimal number of
coherent regions and superpixels assignment to shape final segments. To solve
our model, we propose a numerical algorithm based on the alternating direction
method of multipliers (ADMM), whose iterations consist of two highly
parallelizable sub-problems. We show each sub-problem enjoys closed-form
solution which makes the ADMM iterations computationally very efficient.
Extensive experiments on benchmark image segmentation datasets demonstrate that
our proposed method in combination with an over-segmentation can provide high
quality and competitive results compared to the existing state-of-the-art
methods.
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Authors: Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluc, Dmitriy Morozov, Leonid Oliker, Katherine Yelick, Sang-Yun Oh
Date: 2017-10-30 00:00:00
Tasks: Clustering

Across a variety of scientific disciplines, sparse inverse covariance
estimation is a popular tool for capturing the underlying dependency
relationships in multivariate data. Unfortunately, most estimators are not
scalable enough to handle the sizes of modern high-dimensional data sets (often
on the order of terabytes), and assume Gaussian samples. To address these
deficiencies, we introduce HP-CONCORD, a highly scalable optimization method
for estimating a sparse inverse covariance matrix based on a regularized
pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal
gradient method uses a novel communication-avoiding linear algebra algorithm
and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving
parallel scalability on problems with up to ~819 billion parameters (1.28
million dimensions); even on a single node, HP-CONCORD demonstrates
scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to
estimate the underlying dependency structure of the brain from fMRI data, and
use the result to identify functional regions automatically. The results show
good agreement with a clustering from the neuroscience literature.
Discovering Process Maps from Event Streams
Authors: Volodymyr Leno, Abel Armas-Cervantes, Marlon Dumas, Marcello La Rosa, Fabrizio M. Maggi
Date: 2018-04-08 00:00:00
Tasks: Management

Automated process discovery is a class of process mining methods that allow
analysts to extract business process models from event logs. Traditional
process discovery methods extract process models from a snapshot of an event
log stored in its entirety. In some scenarios, however, events keep coming with
a high arrival rate to the extent that it is impractical to store the entire
event log and to continuously re-discover a process model from scratch. Such
scenarios require online process discovery approaches. Given an event stream
produced by the execution of a business process, the goal of an online process
discovery method is to maintain a continuously updated model of the process
with a bounded amount of memory while at the same time achieving similar
accuracy as offline methods. However, existing online discovery approaches
require relatively large amounts of memory to achieve levels of accuracy
comparable to that of offline methods. Therefore, this paper proposes an
approach that addresses this limitation by mapping the problem of online
process discovery to that of cache memory management, and applying well-known
cache replacement policies to the problem of online process discovery. The
approach has been implemented in .NET, experimentally integrated with the Minit
process mining tool and comparatively evaluated against an existing baseline
using real-life datasets.
Gradient descent revisited via an adaptive online learning rate
Authors: Mathieu Ravaut, Satya Gorti
Date: 2018-01-27 00:00:00
Tasks: BIG-bench Machine Learning

Any gradient descent optimization requires to choose a learning rate. With
deeper and deeper models, tuning that learning rate can easily become tedious
and does not necessarily lead to an ideal convergence. We propose a variation
of the gradient descent algorithm in the which the learning rate is not fixed.
Instead, we learn the learning rate itself, either by another gradient descent
(first-order method), or by Newton's method (second-order). This way, gradient
descent for any machine learning algorithm can be optimized.
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Authors: Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Indoor Localization, Pose Estimation, Retrieval, Visual Localization

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data.
A Unified Method for First and Third Person Action Recognition
Authors: Ali Javidani, Ahmad Mahmoudi-Aznaveh
Date: 2017-12-30 00:00:00
Tasks: Action Recognition, General Classification, Optical Flow Estimation, Temporal Action Localization, Time Series, Time Series Analysis, Video Classification

In this paper, a new video classification methodology is proposed which can
be applied in both first and third person videos. The main idea behind the
proposed strategy is to capture complementary information of appearance and
motion efficiently by performing two independent streams on the videos. The
first stream is aimed to capture long-term motions from shorter ones by keeping
track of how elements in optical flow images have changed over time. Optical
flow images are described by pre-trained networks that have been trained on
large scale image datasets. A set of multi-channel time series are obtained by
aligning descriptions beside each other. For extracting motion features from
these time series, PoT representation method plus a novel pooling operator is
followed due to several advantages. The second stream is accomplished to
extract appearance features which are vital in the case of video
classification. The proposed method has been evaluated on both first and
third-person datasets and results present that the proposed methodology reaches
the state of the art successfully.
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem
Authors: Takumi Ichimura, Daisuke Igaue
Date: 2018-04-08 00:00:00
Tasks: Position, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered
learning where Profit Sharing works to plan a prey position in the higher layer
and Q-learning method trains the state-actions to the target in the lower
layer. In this paper, we expanded HMRL to multi-target problem to take the
distance between targets to the consideration. The function, called `AT field',
can estimate the interests for an agent according to the distance between 2
agents and the advantage/disadvantage of the other agent. Moreover, the
knowledge related to state-action rules is extracted by C4.5. The action under
the situation is decided by using the acquired knowledge. To verify the
effectiveness of proposed method, some experimental results are reported.
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
Authors: Alberto Bietti, Julien Mairal
Date: 2017-06-09 00:00:00
Tasks: Generalization Bounds

The success of deep convolutional architectures is often attributed in part
to their ability to learn multiscale and invariant representations of natural
signals. However, a precise study of these properties and how they affect
learning guarantees is still missing. In this paper, we consider deep
convolutional representations of signals; we study their invariance to
translations and to more general groups of transformations, their stability to
the action of diffeomorphisms, and their ability to preserve signal
information. This analysis is carried by introducing a multilayer kernel based
on convolutional kernel networks and by studying the geometry induced by the
kernel mapping. We then characterize the corresponding reproducing kernel
Hilbert space (RKHS), showing that it contains a large class of convolutional
neural networks with homogeneous activation functions. This analysis allows us
to separate data representation from learning, and to provide a canonical
measure of model complexity, the RKHS norm, which controls both stability and
generalization of any learned model. In addition to models in the constructed
RKHS, our stability analysis also applies to convolutional networks with
generic activations such as rectified linear units, and we discuss its
relationship with recent generalization bounds based on spectral norms.
Personalized Classifier for Food Image Recognition
Authors: Shota Horiguchi, Sosuke Amano, Makoto Ogawa, Kiyoharu Aizawa
Date: 2018-04-08 00:00:00

Currently, food image recognition tasks are evaluated against fixed datasets.
However, in real-world conditions, there are cases in which the number of
samples in each class continues to increase and samples from novel classes
appear. In particular, dynamic datasets in which each individual user creates
samples and continues the updating process often have content that varies
considerably between different users, and the number of samples per person is
very limited. A single classifier common to all users cannot handle such
dynamic data. Bridging the gap between the laboratory environment and the real
world has not yet been accomplished on a large scale. Personalizing a
classifier incrementally for each user is a promising way to do this. In this
paper, we address the personalization problem, which involves adapting to the
user's domain incrementally using a very limited number of samples. We propose
a simple yet effective personalization framework which is a combination of the
nearest class mean classifier and the 1-nearest neighbor classifier based on
deep features. To conduct realistic experiments, we made use of a new dataset
of daily food images collected by a food-logging application. Experimental
results show that our proposed method significantly outperforms existing
methods.