pwc-archive/papers-with-abstracts huggingface dataset:
Disparity Image Segmentation For ADAS
Authors: Viktor Mukha, Inon Sharony
Date: 2018-06-27 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We present a simple solution for segmenting grayscale images using existing
Connected Component Labeling (CCL) algorithms (which are generally applied to
binary images), which was efficient enough to be implemented in a constrained
(embedded automotive) architecture. Our solution customizes the region growing
and merging approach, and is primarily targeted for stereoscopic disparity
images where nearer objects carry more relevance. We provide results from a
standard OpenCV implementation for some basic cases and an image from the
Tsukuba stereo-pair dataset.
Quantum-chemical insights from interpretable atomistic neural networks
Authors: Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
Date: 2018-06-27 00:00:00

With the rise of deep neural networks for quantum chemistry applications,
there is a pressing need for architectures that, beyond delivering accurate
predictions of chemical properties, are readily interpretable by researchers.
Here, we describe interpretation techniques for atomistic neural networks on
the example of Behler-Parrinello networks as well as the end-to-end model
SchNet. Both models obtain predictions of chemical properties by aggregating
atom-wise contributions. These latent variables can serve as local explanations
of a prediction and are obtained during training without additional cost. Due
to their correspondence to well-known chemical concepts such as atomic energies
and partial charges, these atom-wise explanations enable insights not only
about the model but more importantly about the underlying quantum-chemical
regularities. We generalize from atomistic explanations to 3d space, thus
obtaining spatially resolved visualizations which further improve
interpretability. Finally, we analyze learned embeddings of chemical elements
that exhibit a partial ordering that resembles the order of the periodic table.
As the examined neural networks show excellent agreement with chemical
knowledge, the presented techniques open up new venues for data-driven research
in chemistry, physics and materials science.
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
Authors: Haoyue Shi, Jiayuan Mao, Tete Xiao, Yuning Jiang, Jian Sun
Date: 2018-06-27 00:00:00
Proceeding: COLING 2018 8
Tasks: Adversarial Attack, Image Captioning

We study the problem of grounding distributional representations of texts on
the visual domain, namely visual-semantic embeddings (VSE for short). Begin
with an insightful adversarial attack on VSE embeddings, we show the limitation
of current frameworks and image-text datasets (e.g., MS-COCO) both
quantitatively and qualitatively. The large gap between the number of possible
constitutions of real-world semantics and the size of parallel data, to a large
extent, restricts the model to establish the link between textual semantics and
visual concepts. We alleviate this problem by augmenting the MS-COCO image
captioning datasets with textual contrastive adversarial samples. These samples
are synthesized using linguistic rules and the WordNet knowledge base. The
construction procedure is both syntax- and semantics-aware. The samples enforce
the model to ground learned embeddings to concrete concepts within the image.
This simple but powerful technique brings a noticeable improvement over the
baselines on a diverse set of downstream tasks, in addition to defending
known-type adversarial attacks. We release the codes at
https://github.com/ExplorerFreda/VSE-C.
A Multi-Task Learning Approach for Meal Assessment
Authors: Ya Lu, Dario Allegra, Marios Anthimopoulos, Filippo Stanco, Giovanni Maria Farinella, Stavroula Mougiakakou
Date: 2018-06-27 00:00:00
Tasks: Multi-Task Learning, Nutrition

Key role in the prevention of diet-related chronic diseases plays the
balanced nutrition together with a proper diet. The conventional dietary
assessment methods are time-consuming, expensive and prone to errors. New
technology-based methods that provide reliable and convenient dietary
assessment, have emerged during the last decade. The advances in the field of
computer vision permitted the use of meal image to assess the nutrient content
usually through three steps: food segmentation, recognition and volume
estimation. In this paper, we propose a use one RGB meal image as input to a
multi-task learning based Convolutional Neural Network (CNN). The proposed
approach achieved outstanding performance, while a comparison with
state-of-the-art methods indicated that the proposed approach exhibits clear
advantage in accuracy, along with a massive reduction of processing time.
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
Authors: Yi-Jie Huang, Qi Dou, Zi-Xian Wang, Li-Zhi Liu, Ying Jin, Chao-Feng Li, Lisheng Wang, Hao Chen, Rui-Hua Xu
Date: 2018-06-27 00:00:00
Tasks: GPU, Image Segmentation, Multi-Task Learning, Segmentation, Semantic Segmentation, Tumor Segmentation

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR)
images is a crucial procedure for radiotherapy which conventionally requires
accurate delineation of tumour boundaries at an expense of labor, time and
reproducibility. While deep learning based methods serve good baselines in 3D
image segmentation tasks, small applicable patch size limits effective
receptive field and degrades segmentation performance. In addition, Regions of
interest (RoIs) localization from large whole volume 3D images serves as a
preceding operation that brings about multiple benefits in terms of speed,
target completeness, reduction of false positives. Distinct from sliding window
or non-joint localization-segmentation based models, we propose a novel
multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI
localization and in-region segmentation where the two tasks share one backbone
encoder network. With the region proposals from the encoder, we crop
multi-level RoI in-region features from the encoder to form a GPU
memory-efficient decoder for detailpreserving segmentation and therefore
enlarged applicable volume size and effective receptive field. To effectively
train the model, we designed a Dice formulated loss function for the
global-to-local multi-task learning procedure. Based on the efficiency gains,
we went on to ensemble models with different receptive fields to achieve even
higher performance costing minor extra computational expensiveness. Extensive
experiments were conducted on 64 cancerous cases with a four-fold
cross-validation, and the results showed significant superiority in terms of
accuracy and efficiency over conventional frameworks. In conclusion, the
proposed method has a huge potential for extension to other 3D object
segmentation tasks from medical images due to its inherent generalizability.
The code for the proposed method is publicly available.
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning
Authors: Chi-Hung Hsu, Shu-Huan Chang, Jhao-Hong Liang, Hsin-Ping Chou, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Date: 2018-06-27 00:00:00
Tasks: General Classification, Neural Architecture Search, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent studies on neural architecture search have shown that automatically
designed neural networks perform as good as expert-crafted architectures. While
most existing works aim at finding architectures that optimize the prediction
accuracy, these architectures may have complexity and is therefore not suitable
being deployed on certain computing environment (e.g., with limited power
budgets). We propose MONAS, a framework for Multi-Objective Neural
Architectural Search that employs reward functions considering both prediction
accuracy and other important objectives (e.g., power consumption) when
searching for neural network architectures. Experimental results showed that,
compared to the state-ofthe-arts, models found by MONAS achieve comparable or
better classification accuracy on computer vision applications, while
satisfying the additional objectives such as peak power.
The Virtuous Machine - Old Ethics for New Technology?
Authors: Nicolas Berberich, Klaus Diepold
Date: 2018-06-27 00:00:00
Tasks: Autonomous Driving, Ethics, Imitation Learning

Modern AI and robotic systems are characterized by a high and ever-increasing
level of autonomy. At the same time, their applications in fields such as
autonomous driving, service robotics and digital personal assistants move
closer to humans. From the combination of both developments emerges the field
of AI ethics which recognizes that the actions of autonomous machines entail
moral dimensions and tries to answer the question of how we can build moral
machines. In this paper we argue for taking inspiration from Aristotelian
virtue ethics by showing that it forms a suitable combination with modern AI
due to its focus on learning from experience. We furthermore propose that
imitation learning from moral exemplars, a central concept in virtue ethics,
can solve the value alignment problem. Finally, we show that an intelligent
system endowed with the virtues of temperance and friendship to humans would
not pose a control problem as it would not have the desire for limitless
self-improvement.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition
Authors: Dongliang He, Fu Li, Qijie Zhao, Xiang Long, Yi Fu, Shilei Wen
Date: 2018-06-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Video Understanding

In this report, our approach to tackling the task of ActivityNet 2018
Kinetics-600 challenge is described in detail. Though spatial-temporal
modelling methods, which adopt either such end-to-end framework as I3D
\cite{i3d} or two-stage frameworks (i.e., CNN+RNN), have been proposed in
existing state-of-the-arts for this task, video modelling is far from being
well solved. In this challenge, we propose spatial-temporal network (StNet) for
better joint spatial-temporal modelling and comprehensively video
understanding. Besides, given that multi-modal information is contained in
video source, we manage to integrate both early-fusion and later-fusion
strategy of multi-modal information via our proposed improved temporal Xception
network (iTXN) for video understanding. Our StNet RGB single model achieves
78.99\% top-1 precision in the Kinetics-600 validation set and that of our
improved temporal Xception network which integrates RGB, flow and audio
modalities is up to 82.35\%. After model ensemble, we achieve top-1 precision
as high as 85.0\% on the validation set and rank No.1 among all submissions.
Adversarial Distillation of Bayesian Neural Network Posteriors
Authors: Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel
Date: 2018-06-27 00:00:00
Tasks: Active Learning, Anomaly Detection, Generative Adversarial Network

Bayesian neural networks (BNNs) allow us to reason about uncertainty in a
principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient
BNN learning by drawing samples from the BNN posterior using mini-batches.
However, SGLD and its extensions require storage of many copies of the model
parameters, a potentially prohibitive cost, especially for large neural
networks. We propose a framework, Adversarial Posterior Distillation, to
distill the SGLD samples using a Generative Adversarial Network (GAN). At
test-time, samples are generated by the GAN. We show that this distillation
framework incurs no loss in performance on recent BNN applications including
anomaly detection, active learning, and defense against adversarial attacks. By
construction, our framework not only distills the Bayesian predictive
distribution, but the posterior itself. This allows one to compute quantities
such as the approximate model variance, which is useful in downstream tasks. To
our knowledge, these are the first results applying MCMC-based BNNs to the
aforementioned downstream applications.
Matrix Completion from Non-Uniformly Sampled Entries
Authors: Yuanyu Wan, Jin-Feng Yi, Lijun Zhang
Date: 2018-06-27 00:00:00
Tasks: Matrix Completion

In this paper, we consider matrix completion from non-uniformly sampled
entries including fully observed and partially observed columns. Specifically,
we assume that a small number of columns are randomly selected and fully
observed, and each remaining column is partially observed with uniform
sampling. To recover the unknown matrix, we first recover its column space from
the fully observed columns. Then, for each partially observed column, we
recover it by finding a vector which lies in the recovered column space and
consists of the observed entries. When the unknown $m\times n$ matrix is
low-rank, we show that our algorithm can exactly recover it from merely
$\Omega(rn\ln n)$ entries, where $r$ is the rank of the matrix. Furthermore,
for a noisy low-rank matrix, our algorithm computes a low-rank approximation of
the unknown matrix and enjoys an additive error bound measured by Frobenius
norm. Experimental results on synthetic datasets verify our theoretical claims
and demonstrate the effectiveness of our proposed algorithm.
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR
Authors: Yerbolat Khassanov, Eng Siong Chng
Date: 2018-06-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In automatic speech recognition (ASR) systems, recurrent neural network
language models (RNNLM) are used to rescore a word lattice or N-best hypotheses
list. Due to the expensive training, the RNNLM's vocabulary set accommodates
only small shortlist of most frequent words. This leads to suboptimal
performance if an input speech contains many out-of-shortlist (OOS) words. An
effective solution is to increase the shortlist size and retrain the entire
network which is highly inefficient. Therefore, we propose an efficient method
to expand the shortlist set of a pretrained RNNLM without incurring expensive
retraining and using additional training data. Our method exploits the
structure of RNNLM which can be decoupled into three parts: input projection
layer, middle layers, and output projection layer. Specifically, our method
expands the word embedding matrices in projection layers and keeps the middle
layers unchanged. In this approach, the functionality of the pretrained RNNLM
will be correctly maintained as long as OOS words are properly modeled in two
embedding spaces. We propose to model the OOS words by borrowing linguistic
knowledge from appropriate in-shortlist words. Additionally, we propose to
generate the list of OOS words to expand vocabulary in unsupervised manner by
automatically extracting them from ASR output.
Research on Artificial Intelligence Ethics Based on the Evolution of Population Knowledge Base
Authors: Feng Liu, Yong Shi
Date: 2018-06-09 00:00:00
Tasks: Ethics

The unclear development direction of human society is a deep reason for that it is difficult to form a uniform ethical standard for human society and artificial intelligence. Since the 21st century, the latest advances in the Internet, brain science and artificial intelligence have brought new inspiration to the research on the development direction of human society. Through the study of the Internet brain model, AI IQ evaluation, and the evolution of the brain, this paper proposes that the evolution of population knowledge base is the key for judging the development direction of human society, thereby discussing the standards and norms for the construction of artificial intelligence ethics.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Authors: Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine
Date: 2018-06-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we study the problem of learning vision-based dynamic
manipulation skills using a scalable reinforcement learning approach. We study
this problem in the context of grasping, a longstanding challenge in robotic
manipulation. In contrast to static learning behaviors that choose a grasp
point and then execute the desired grasp, our method enables closed-loop
vision-based control, whereby the robot continuously updates its grasp strategy
based on the most recent observations to optimize long-horizon grasp success.
To that end, we introduce QT-Opt, a scalable self-supervised vision-based
reinforcement learning framework that can leverage over 580k real-world grasp
attempts to train a deep neural network Q-function with over 1.2M parameters to
perform closed-loop, real-world grasping that generalizes to 96% grasp success
on unseen objects. Aside from attaining a very high success rate, our method
exhibits behaviors that are quite distinct from more standard grasping systems:
using only RGB vision-based perception from an over-the-shoulder camera, our
method automatically learns regrasping strategies, probes objects to find the
most effective grasps, learns to reposition objects and perform other
non-prehensile pre-grasp manipulations, and responds dynamically to
disturbances and perturbations.
Attention to Head Locations for Crowd Counting
Authors: Youmei Zhang, Chunluan Zhou, Faliang Chang, Alex C. Kot
Date: 2018-06-27 00:00:00
Tasks: Crowd Counting, Density Estimation

Occlusions, complex backgrounds, scale variations and non-uniform
distributions present great challenges for crowd counting in practical
applications. In this paper, we propose a novel method using an attention model
to exploit head locations which are the most important cue for crowd counting.
The attention model estimates a probability map in which high probabilities
indicate locations where heads are likely to be present. The estimated
probability map is used to suppress non-head regions in feature maps from
several multi-scale feature extraction branches of a convolution neural network
for crowd density estimation, which makes our method robust to complex
backgrounds, scale variations and non-uniform distributions. In addition, we
introduce a relative deviation loss to compensate a commonly used training
loss, Euclidean distance, to improve the accuracy of sparse crowd density
estimation. Experiments on Shanghai-Tech, UCF_CC_50 and World-Expo'10 data sets
demonstrate the effectiveness of our method.
Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices
Authors: Yusheng Luo, Min Xian, Manish Mohanpurkar, Bishnu P. Bhattarai, Anudeep Medam, Rahul Kadavil, Rob Hovsapian
Date: 2018-06-27 00:00:00
Tasks: Scheduling

Optimal scheduling of hydrogen production in dynamic pricing power market can
maximize the profit of hydrogen producer; however, it highly depends on the
accurate forecast of hydrogen consumption. In this paper, we propose a deep
leaning based forecasting approach for predicting hydrogen consumption of fuel
cell vehicles in future taxi industry. The cost of hydrogen production is
minimized by utilizing the proposed forecasting tool to reduce the hydrogen
produced during high cost on-peak hours and guide hydrogen producer to store
sufficient hydrogen during low cost off-peak hours.
Auto-Keras: An Efficient Neural Architecture Search System
Authors: Haifeng Jin, Qingquan Song, Xia Hu
Date: 2018-06-27 00:00:00
Tasks: AutoML, Bayesian Optimization, CPU, GPU, Neural Architecture Search

Neural architecture search (NAS) has been proposed to automatically tune deep
neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually
suffer from expensive computational cost. Network morphism, which keeps the
functionality of a neural network while changing its neural architecture, could
be helpful for NAS by enabling more efficient training during the search. In
this paper, we propose a novel framework enabling Bayesian optimization to
guide the network morphism for efficient neural architecture search. The
framework develops a neural network kernel and a tree-structured acquisition
function optimization algorithm to efficiently explores the search space.
Intensive experiments on real-world benchmark datasets have been done to
demonstrate the superior performance of the developed framework over the
state-of-the-art methods. Moreover, we build an open-source AutoML system based
on our method, namely Auto-Keras. The system runs in parallel on CPU and GPU,
with an adaptive search strategy for different GPU memory limits.
Feature-less Stitching of Cylindrical Tunnel
Authors: Ramanpreet Singh Pahwa, Wei Kiat Leong, Shaohui Foong, Karianto Leman, Minh N. Do
Date: 2018-06-27 00:00:00
Tasks: Image Stitching, Position, Unity

Traditional image stitching algorithms use transforms such as homography to
combine different views of a scene. They usually work well when the scene is
planar or when the camera is only rotated, keeping its position static. This
severely limits their use in real world scenarios where an unmanned aerial
vehicle (UAV) potentially hovers around and flies in an enclosed area while
rotating to capture a video sequence. We utilize known scene geometry along
with recorded camera trajectory to create cylindrical images captured in a
given environment such as a tunnel where the camera rotates around its center.
The captured images of the inner surface of the given scene are combined to
create a composite panoramic image that is textured onto a 3D geometrical
object in Unity graphical engine to create an immersive environment for end
users.
Hierarchical Deep Co-segmentation of Primary Objects in Aerial Videos
Authors: Jia Li, Pengcheng Yuan, Daxin Gu, Yonghong Tian
Date: 2018-06-27 00:00:00
Tasks: Segmentation, Semantic Segmentation

Primary object segmentation plays an important role in understanding videos
generated by unmanned aerial vehicles. In this paper, we propose a large-scale
dataset with 500 aerial videos and manually annotated primary objects. To the
best of our knowledge, it is the largest dataset to date for primary object
segmentation in aerial videos. From this dataset, we find most aerial videos
contain large-scale scenes, small primary objects as well as consistently
varying scales and viewpoints. Inspired by that, we propose a hierarchical deep
co-segmentation approach that repeatedly divides a video into two sub-videos
formed by the odd and even frames, respectively. In this manner, the primary
objects shared by sub-videos can be co-segmented by training two-stream CNNs
and finally refined within the neighborhood reversible flows. Experimental
results show that our approach remarkably outperforms 17 state-of-the-art
methods in segmenting primary objects in various types of aerial videos.
Optimal Piecewise Local-Linear Approximations
Authors: Kartik Ahuja, William Zame, Mihaela van der Schaar
Date: 2018-06-27 00:00:00
Tasks: Clustering

Existing works on "black-box" model interpretation use local-linear approximations to explain the predictions made for each data instance in terms of the importance assigned to the different features for arriving at the prediction. These works provide instancewise explanations and thus give a local view of the model. To be able to trust the model it is important to understand the global model behavior and there are relatively fewer works which do the same. Piecewise local-linear models provide a natural way to extend local-linear models to explain the global behavior of the model. In this work, we provide a dynamic programming based framework to obtain piecewise approximations of the black-box model. We also provide provable fidelity, i.e., how well the explanations reflect the black-box model, guarantees. We carry out simulations on synthetic and real datasets to show the utility of the proposed approach. At the end, we show that the ideas developed for our framework can also be used to address the problem of clustering for one-dimensional data. We give a polynomial time algorithm and prove that it achieves optimal clustering.