pwc-archive/papers-with-abstracts huggingface dataset:
Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian
Authors: Masayuki Ohzeki
Date: 2019-01-21 00:00:00

Quantum annealing (QA) is a generic method for solving optimization problems
using fictitious quantum fluctuation. The current device performing QA involves
controlling the transverse field; it is classically simulatable by using the
standard technique for mapping the quantum spin systems to the classical ones.
In this sense, the current system for QA is not powerful despite utilizing
quantum fluctuation. Hence, we developed a system with a time-dependent
Hamiltonian consisting of a combination of the formulated Ising model and the
"driver" Hamiltonian with only quantum fluctuation. In the previous study, for
a fully connected spin model, quantum fluctuation can be addressed in a
relatively simple way. We proved that the fully connected antiferromagnetic
interaction can be transformed into a fluctuating transverse field and is thus
classically simulatable at sufficiently low temperatures. Using the fluctuating
transverse field, we established several ways to simulate part of the
nonstoquastic Hamiltonian on classical computers. We formulated a
message-passing algorithm in the present study. This algorithm is capable of
assessing the performance of QA with part of the nonstoquastic Hamiltonian
having a large number of spins. In other words, we developed a different
approach for simulating the nonstoquastic Hamiltonian without using the quantum
Monte Carlo technique. Our results were validated by comparison to the results
obtained by the replica method.
Skeleton-based Action Recognition of People Handling Objects
Authors: Sunoh Kim, Kimin Yun, Jongyoul Park, Jin Young Choi
Date: 2019-01-21 00:00:00
Tasks: Action Recognition, Object, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

In visual surveillance systems, it is necessary to recognize the behavior of
people handling objects such as a phone, a cup, or a plastic bag. In this
paper, to address this problem, we propose a new framework for recognizing
object-related human actions by graph convolutional networks using human and
object poses. In this framework, we construct skeletal graphs of reliable human
poses by selectively sampling the informative frames in a video, which include
human joints with high confidence scores obtained in pose estimation. The
skeletal graphs generated from the sampled frames represent human poses related
to the object position in both the spatial and temporal domains, and these
graphs are used as inputs to the graph convolutional networks. Through
experiments over an open benchmark and our own data sets, we verify the
validity of our framework in that our method outperforms the state-of-the-art
method for skeleton-based action recognition.
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Authors: Amr Alexandari, Anshul Kundaje, Avanti Shrikumar
Date: 2019-01-21 00:00:00
Tasks: Diabetic Retinopathy Detection, Domain Adaptation, Image Classification, Medical Diagnosis

Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different. Given estimates of p(y|x) from a predictive model, Saerens et al. proposed an efficient maximum likelihood algorithm to correct for label shift that does not require model retraining, but a limiting assumption of this algorithm is that p(y|x) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) and Regularized Learning under Label Shifts (RLLS) have emerged as state-of-the-art techniques to cope with label shift when a classifier does not output calibrated probabilities, but both methods require model retraining with importance weights and neither has been benchmarked against maximum likelihood. Here we (1) show that combining maximum likelihood with a type of calibration we call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, (2) prove that the maximum likelihood objective is concave, and (3) introduce a principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that the maximum likelihood with appropriate calibration is a formidable and efficient baseline for label shift adaptation; notebooks reproducing experiments available at https://github.com/kundajelab/labelshiftexperiments
Perception-in-the-Loop Adversarial Examples
Authors: Mahmoud Salamati, Sadegh Soudjani, Rupak Majumdar
Date: 2019-01-21 00:00:00

We present a scalable, black box, perception-in-the-loop technique to find
adversarial examples for deep neural network classifiers. Black box means that
our procedure only has input-output access to the classifier, and not to the
internal structure, parameters, or intermediate confidence values.
Perception-in-the-loop means that the notion of proximity between inputs can be
directly queried from human participants rather than an arbitrarily chosen
metric. Our technique is based on covariance matrix adaptation evolution
strategy (CMA-ES), a black box optimization approach. CMA-ES explores the
search space iteratively in a black box manner, by generating populations of
candidates according to a distribution, choosing the best candidates according
to a cost function, and updating the posterior distribution to favor the best
candidates. We run CMA-ES using human participants to provide the fitness
function, using the insight that the choice of best candidates in CMA-ES can be
naturally modeled as a perception task: pick the top $k$ inputs perceptually
closest to a fixed input. We empirically demonstrate that finding adversarial
examples is feasible using small populations and few iterations. We compare the
performance of CMA-ES on the MNIST benchmark with other black-box approaches
using $L_p$ norms as a cost function, and show that it performs favorably both
in terms of success in finding adversarial examples and in minimizing the
distance between the original and the adversarial input. In experiments on the
MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find
perceptually similar adversarial inputs with a small number of iterations and
small population sizes when using perception-in-the-loop. Finally, we show that
networks trained specifically to be robust against $L_\infty$ norm can still be
susceptible to perceptually similar adversarial examples.
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
Authors: Dongliang He, Xiang Zhao, Jizhou Huang, Fu Li, Xiao Liu, Shilei Wen
Date: 2019-01-21 00:00:00
Tasks: Decision Making, Multi-Task Learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Sequential Decision Making, Video Grounding

The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video.
A Deterministic Gradient-Based Approach to Avoid Saddle Points
Authors: Lisa Maria Kreusser, Stanley J. Osher, Bao Wang
Date: 2019-01-21 00:00:00
Tasks: BIG-bench Machine Learning

Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning models efficiently. First-order methods such as gradient descent are usually the methods of choice for training machine learning models. However, these methods converge to saddle points for certain choices of initial guesses. In this paper, we propose a modification of the recently proposed Laplacian smoothing gradient descent [Osher et al., arXiv:1806.06317], called modified Laplacian smoothing gradient descent (mLSGD), and demonstrate its potential to avoid saddle points without sacrificing the convergence rate. Our analysis is based on the attraction region, formed by all starting points for which the considered numerical scheme converges to a saddle point. We investigate the attraction region's dimension both analytically and numerically. For a canonical class of quadratic functions, we show that the dimension of the attraction region for mLSGD is floor((n-1)/2), and hence it is significantly smaller than that of the gradient descent whose dimension is n-1.
Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss
Authors: Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen
Date: 2019-01-21 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Salient object detection (SOD), which aims to identify and locate the most
salient pixels or regions in images, has been attracting more and more interest
due to its various real-world applications. However, this vision task is quite
challenging, especially under complex image scenes. Inspired by the intrinsic
reflection of natural images, in this paper we propose a novel feature learning
framework for large-scale salient object detection. Specifically, we design a
symmetrical fully convolutional network (SFCN) to effectively learn
complementary saliency features under the guidance of lossless feature
reflection. The location information, together with contextual and semantic
information, of salient objects are jointly utilized to supervise the proposed
network for more accurate saliency predictions. In addition, to overcome the
blurry boundary problem, we propose a new weighted structural loss function to
ensure clear object boundaries and spatially consistent saliency. The coarse
prediction results are effectively refined by these structural information for
performance improvements. Extensive experiments on seven saliency detection
datasets demonstrate that our approach achieves consistently superior
performance and outperforms the very recent state-of-the-art methods with a
large margin.
Straggler Resilient Serverless Computing Based on Polar Codes
Authors: Burak Bartan, Mert Pilanci
Date: 2019-01-21 00:00:00
Tasks: Decoder

We propose a serverless computing mechanism for distributed computation based on polar codes. Serverless computing is an emerging cloud based computation model that lets users run their functions on the cloud without provisioning or managing servers. Our proposed approach is a hybrid computing framework that carries out computationally expensive tasks such as linear algebraic operations involving large-scale data using serverless computing and does the rest of the processing locally. We address the limitations and reliability issues of serverless platforms such as straggling workers using coding theory, drawing ideas from recent literature on coded computation. The proposed mechanism uses polar codes to ensure straggler-resilience in a computationally effective manner. We provide extensive evidence showing polar codes outperform other coding methods. We have designed a sequential decoder specifically for polar codes in erasure channels with full-precision input and outputs. In addition, we have extended the proposed method to the matrix multiplication case where both matrices being multiplied are coded. The proposed coded computation scheme is implemented for AWS Lambda. Experiment results are presented where the performance of the proposed coded computation technique is tested in optimization via gradient descent. Finally, we introduce the idea of partial polarization which reduces the computational burden of encoding and decoding at the expense of straggler-resilience.
Active Learning with Gaussian Processes for High Throughput Phenotyping
Authors: Sumit Kumar, Wenhao Luo, George Kantor, Katia Sycara
Date: 2019-01-21 00:00:00
Tasks: Active Learning, Gaussian Processes, Plant Phenotyping, Vocal Bursts Intensity Prediction

A looming question that must be solved before robotic plant phenotyping
capabilities can have significant impact to crop improvement programs is
scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
analyze crops in order to determine species with favorable traits, however, the
current practices rely on exhaustive coverage and data collection from the
entire crop field being monitored under the breeding experiment. This works
well in relatively small agricultural fields but can not be scaled to the
larger ones, thus limiting the progress of genetics research. In this work, we
propose an active learning algorithm to enable an autonomous system to collect
the most informative samples in order to accurately learn the distribution of
phenotypes in the field with the help of a Gaussian Process model. We
demonstrate the superior performance of our proposed algorithm compared to the
current practices on sorghum phenotype data collection.
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
Authors: Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack, Mahsa Baktashmotlagh, Anders Eriksson
Date: 2019-01-21 00:00:00
Tasks: 3D Reconstruction, 3D Shape Representation

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
Learning-Based Synthesis of Safety Controllers
Authors: Daniel Neider, Oliver Markgraf
Date: 2019-01-21 00:00:00
Tasks: Motion Planning

We propose a machine learning framework to synthesize reactive controllers for systems whose interactions with their adversarial environment are modeled by infinite-duration, two-player games over (potentially) infinite graphs. Our framework targets safety games with infinitely many vertices, but it is also applicable to safety games over finite graphs whose size is too prohibitive for conventional synthesis techniques. The learning takes place in a feedback loop between a teacher component, which can reason symbolically about the safety game, and a learning algorithm, which successively learns an overapproximation of the winning region from various kinds of examples provided by the teacher. We develop a novel decision tree learning algorithm for this setting and show that our algorithm is guaranteed to converge to a reactive safety controller if a suitable overapproximation of the winning region can be expressed as a decision tree. Finally, we empirically compare the performance of a prototype implementation to existing approaches, which are based on constraint solving and automata learning, respectively.
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
Authors: Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li
Date: 2019-01-21 00:00:00

With the development of high computational devices, deep neural networks
(DNNs), in recent years, have gained significant popularity in many Artificial
Intelligence (AI) applications. However, previous efforts have shown that DNNs
were vulnerable to strategically modified samples, named adversarial examples.
These samples are generated with some imperceptible perturbations but can fool
the DNNs to give false predictions. Inspired by the popularity of generating
adversarial examples for image DNNs, research efforts on attacking DNNs for
textual applications emerges in recent years. However, existing perturbation
methods for images cannotbe directly applied to texts as text data is discrete.
In this article, we review research works that address this difference and
generatetextual adversarial examples on DNNs. We collect, select, summarize,
discuss and analyze these works in a comprehensive way andcover all the related
information to make the article self-contained. Finally, drawing on the
reviewed literature, we provide further discussions and suggestions on this
topic.
Semantic Image Networks for Human Action Recognition
Authors: Sunder Ali Khowaja, Seok-Lyong Lee
Date: 2019-01-21 00:00:00
Tasks: Action Recognition, Clustering, Temporal Action Localization

In this paper, we propose the use of a semantic image, an improved
representation for video analysis, principally in combination with Inception
networks. The semantic image is obtained by applying localized sparse
segmentation using global clustering (LSSGC) prior to the approximate rank
pooling which summarizes the motion characteristics in single or multiple
images. It incorporates the background information by overlaying a static
background from the window onto the subsequent segmented frames. The idea is to
improve the action-motion dynamics by focusing on the region which is important
for action recognition and encoding the temporal variances using the frame
ranking method. We also propose the sequential combination of
Inception-ResNetv2 and long-short-term memory network (LSTM) to leverage the
temporal variances for improved recognition performance. Extensive analysis has
been carried out on UCF101 and HMDB51 datasets which are widely used in action
recognition studies. We show that (i) the semantic image generates better
activations and converges faster than its original variant, (ii) using
segmentation prior to approximate rank pooling yields better recognition
performance, (iii) The use of LSTM leverages the temporal variance information
from approximate rank pooling to model the action behavior better than the base
network, (iv) the proposed representations can be adaptive as they can be used
with existing methods such as temporal segment networks to improve the
recognition performance, and (v) our proposed four-stream network architecture
comprising of semantic images and semantic optical flows achieves
state-of-the-art performance, 95.9% and 73.5% recognition accuracy on UCF101
and HMDB51, respectively.
Dynamic Curriculum Learning for Imbalanced Data Classification
Authors: Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, Junjie Yan
Date: 2019-01-21 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Classification, General Classification, Metric Learning

Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed. Common techniques such as re-sampling and cost-sensitive learning require prior-knowledge to train the system. To address this problem, we propose a unified framework called Dynamic Curriculum Learning (DCL) to online adaptively adjust the sampling strategy and loss learning in single batch, which resulting in better generalization and discrimination. Inspired by the curriculum learning, DCL consists of two level curriculum schedulers: (1) sampling scheduler not only manages the data distribution from imbalanced to balanced but also from easy to hard; (2) loss scheduler controls the learning importance between classification and metric learning loss. Learning from these two schedulers, we demonstrate our DCL framework with the new state-of-the-art performance on the widely used face attribute dataset CelebA and pedestrian attribute dataset RAP.
Generating Text Sequence Images for Recognition
Authors: Yanxiang Gong, Linjie Deng, Zheng Ma, Mei Xie
Date: 2019-01-21 00:00:00
Tasks: Image-to-Image Translation, Translation

Recently, methods based on deep learning have dominated the field of text
recognition. With a large number of training data, most of them can achieve the
state-of-the-art performances. However, it is hard to harvest and label
sufficient text sequence images from the real scenes. To mitigate this issue,
several methods to synthesize text sequence images were proposed, yet they
usually need complicated preceding or follow-up steps. In this work, we present
a method which is able to generate infinite training data without any auxiliary
pre/post-process. We tackle the generation task as an image-to-image
translation one and utilize conditional adversarial networks to produce
realistic text sequence images in the light of the semantic ones. Some
evaluation metrics are involved to assess our method and the results
demonstrate that the caliber of the data is satisfactory. The code and dataset
will be publicly available soon.
Hybrid coarse-fine classification for head pose estimation
Authors: Haofan Wang, Zhenghua Chen, Yi Zhou
Date: 2019-01-21 00:00:00
Tasks: 3D Reconstruction, Classification, Face Alignment, Gaze Estimation, General Classification, Head Pose Estimation, Pose Estimation, Quantization, regression

Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch, roll) from the human, is crucial for gaze estimation, face alignment, and 3D reconstruction. Traditional approaches heavily relies on the accuracy of facial landmarks. It limits their performances, especially when the visibility of the face is not in good condition. In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network. Utilizing more quantization units for the angles, a fine classifier is trained with the help of other auxiliary coarse units. Integrating regression is adopted to get the final prediction. The proposed approach is evaluated on three challenging benchmarks. It achieves the state-of-the-art on AFLW2000, BIWI and performs favorably on AFLW. The code has been released on Github.
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design
Authors: Jack Collins, Ben Cottier, David Howard
Date: 2019-01-21 00:00:00

We compare two representations used to define the morphology of legs for a
hexapod robot, which are subsequently 3D printed. A leg morphology occupies a
set of voxels in a voxel grid. One method, a direct representation, uses a
collection of Bezier splines. The second, an indirect method, utilises
CPPN-NEAT. In our first experiment, we investigate two strategies to
post-process the CPPN output and ensure leg length constraints are met. The
first uses an adaptive threshold on the output neuron, the second, previously
reported in the literature, scales the largest generated artefact to our
desired length. In our second experiment, we build on our past work that
evolves the tibia of a hexapod to provide environment-specific performance
benefits. We compare the performance of our direct and indirect legs across
three distinct environments, represented in a high-fidelity simulator. Results
are significant and support our hypothesis that the indirect representation
allows for further exploration of the design space leading to improved fitness.