pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models
Authors: Sajan Patel, Brent Griffin, Kristofer Kusano, Jason J. Corso
Date: 2018-01-12 00:00:00
Tasks: Autonomous Vehicles, Trajectory Prediction

In the event of sensor failure, autonomous vehicles need to safely execute emergency maneuvers while avoiding other vehicles on the road. To accomplish this, the sensor-failed vehicle must predict the future semantic behaviors of other drivers, such as lane changes, as well as their future trajectories given a recent window of past sensor observations. We address the first issue of semantic behavior prediction in this paper, which is a precursor to trajectory prediction, by introducing a framework that leverages the power of recurrent neural networks (RNNs) and graphical models. Our goal is to predict the future categorical driving intent, for lane changes, of neighboring vehicles up to three seconds into the future given as little as a one-second window of past LIDAR, GPS, inertial, and map data. We collect real-world data containing over 20 hours of highway driving using an autonomous Toyota vehicle. We propose a composite RNN model by adopting the methodology of Structural Recurrent Neural Networks (RNNs) to learn factor functions and take advantage of both the high-level structure of graphical models and the sequence modeling power of RNNs, which we expect to afford more transparent modeling and activity than opaque, single RNN models. To demonstrate our approach, we validate our model using authentic interstate highway driving to predict the future lane change maneuvers of other vehicles neighboring our autonomous vehicle. We find that our composite Structural RNN outperforms baselines by as much as 12% in balanced accuracy metrics.
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training
Authors: Derek Wang, Chaoran Li, Sheng Wen, Surya Nepal, Yang Xiang
Date: 2018-03-14 00:00:00
Tasks: Adversarial Attack

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of the attack as a priori knowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantages of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this paper proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on $MNIST$ and $CIFAR10$ datasets. The results showed that our defending method achieved up to $96.3\%$ classification accuracy on black-box adversarial examples, and detected up to $98.7\%$ of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by $2.1\%$ for the $CIFAR10$ dataset.
Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks
Authors: Yufan He, Aaron Carass, Bruno M. Jedynak, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince
Date: 2018-03-14 00:00:00
Tasks: Position, Segmentation

Optical coherence tomography (OCT) is a noninvasive imaging modality which
can be used to obtain depth images of the retina. The changing layer
thicknesses can thus be quantified by analyzing these OCT images, moreover
these changes have been shown to correlate with disease progression in multiple
sclerosis. Recent automated retinal layer segmentation tools use machine
learning methods to perform pixel-wise labeling and graph methods to guarantee
the layer hierarchy or topology. However, graph parameters like distance and
smoothness constraints must be experimentally assigned by retinal region and
pathology, thus degrading the flexibility and time efficiency of the whole
framework. In this paper, we develop cascaded deep networks to provide a
topologically correct segmentation of the retinal layers in a single feed
forward propagation. The first network (S-Net) performs pixel-wise labeling and
the second regression network (R-Net) takes the topologically unconstrained
S-Net results and outputs layer thicknesses for each layer and each position.
Relu activation is used as the final operation of the R-Net which guarantees
non-negativity of the output layer thickness. Since the segmentation boundary
position is acquired by summing up the corresponding non-negative layer
thicknesses, the layer ordering (i.e., topology) of the reconstructed
boundaries is guaranteed even at the fovea where the distances between
boundaries can be zero. The R-Net is trained using simulated masks and thus can
be generalized to provide topology guaranteed segmentation for other layered
structures. This deep network has achieved comparable mean absolute boundary
error (2.82 {\mu}m) to state-of-the-art graph methods (2.83 {\mu}m).
MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation
Authors: Tao Liu, Zihao Liu, Fuhong Lin, Yier Jin, Gang Quan, Wujie Wen
Date: 2018-03-14 00:00:00

Modern deep learning enabled artificial neural networks, such as Deep Neural
Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of
breaking records on a broad spectrum of recognition applications. However, the
enormous computation and storage requirements associated with such deep and
complex neural network models greatly challenge their implementations on
resource-limited platforms. Time-based spiking neural network has recently
emerged as a promising solution in Neuromorphic Computing System designs for
achieving remarkable computing and power efficiency within a single chip.
However, the relevant research activities have been narrowly concentrated on
the biological plausibility and theoretical learning approaches, causing
inefficient neural processing and impracticable multilayer extension thus
significantly limitations on speed and accuracy when handling the realistic
cognitive tasks. In this work, a practical multilayer time-based spiking
neuromorphic architecture, namely "MT-Spike", is developed to fill this gap.
With the proposed practical time-coding scheme, average delay response model,
temporal error backpropagation algorithm, and heuristic loss function,
"MT-Spike" achieves more efficient neural processing through flexible neural
model size reduction while offering very competitive classification accuracy
for realistic recognition tasks. Simulation results well validated that the
algorithmic power of deep multi-layer learning can be seamlessly merged with
the efficiency of time-based spiking neuromorphic architecture, demonstrating
great potentials of "MT-Spike" in resource and power constrained embedded
platforms.
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
Authors: Tao Liu, Lei Jiang, Yier Jin, Gang Quan, Wujie Wen
Date: 2018-03-14 00:00:00

One of the most exciting advancements in AI over the last decade is the wide
adoption of ANNs, such as DNN and CNN, in many real-world applications.
However, the underlying massive amounts of computation and storage requirement
greatly challenge their applicability in resource-limited platforms like the
drone, mobile phone, and IoT devices etc. The third generation of neural
network model--Spiking Neural Network (SNN), inspired by the working mechanism
and efficiency of human brain, has emerged as a promising solution for
achieving more impressive computing and power efficiency within light-weighted
devices (e.g. single chip). However, the relevant research activities have been
narrowly carried out on conventional rate-based spiking system designs for
fulfilling the practical cognitive tasks, underestimating SNN's energy
efficiency, throughput, and system flexibility. Although the time-based SNN can
be more attractive conceptually, its potentials are not unleashed in realistic
applications due to lack of efficient coding and practical learning schemes. In
this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture,
namely "PT-Spike", is developed to bridge this gap. Three constituent
hardware-favorable techniques: precise single-spike temporal encoding,
efficient supervised temporal learning, and fast asymmetric decoding are
proposed accordingly to boost the energy efficiency and data processing
capability of the time-based SNN at a more compact neural network model size
when executing real cognitive tasks. Simulation results show that "PT-Spike"
demonstrates significant improvements in network size, processing efficiency
and power consumption with marginal classification accuracy degradation when
compared with the rate-based SNN and ANN under the similar network
configuration.
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Authors: Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, Wujie Wen
Date: 2018-03-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, General Classification, Image Compression, Quantization

Image compression-based approaches for defending against the
adversarial-example attacks, which threaten the safety use of deep neural
networks (DNN), have been investigated recently. However, prior works mainly
rely on directly tuning parameters like compression rate, to blindly reduce
image features, thereby lacking guarantee on both defense efficiency (i.e.
accuracy of polluted images) and classification accuracy of benign images,
after applying defense methods. To overcome these limitations, we propose a
JPEG-based defensive compression framework, namely "feature distillation", to
effectively rectify adversarial examples without impacting classification
accuracy on benign data. Our framework significantly escalates the defense
efficiency with marginal accuracy reduction using a two-step method: First, we
maximize malicious features filtering of adversarial input perturbations by
developing defensive quantization in frequency domain of JPEG compression or
decompression, guided by a semi-analytical method; Second, we suppress the
distortions of benign features to restore classification accuracy through a
DNN-oriented quantization refine process. Our experimental results show that
proposed "feature distillation" can significantly surpass the latest
input-transformation based mitigations such as Quilting and TV Minimization in
three aspects, including defense efficiency (improve classification accuracy
from $\sim20\%$ to $\sim90\%$ on adversarial examples), accuracy of benign
images after defense ($\le1\%$ accuracy degradation), and processing time per
image ($\sim259\times$ Speedup). Moreover, our solution can also provide the
best defense efficiency ($\sim60\%$ accuracy) against the recent adaptive
attack with least accuracy reduction ($\sim1\%$) on benign images when compared
with other input-transformation based defense methods.
Ranking with Adaptive Neighbors
Authors: Muge Li, Liangyue Li, Feiping Nie
Date: 2018-03-14 00:00:00

Retrieving the most similar objects in a large-scale database for a given
query is a fundamental building block in many application domains, ranging from
web searches, visual, cross media, and document retrievals. State-of-the-art
approaches have mainly focused on capturing the underlying geometry of the data
manifolds. Graph-based approaches, in particular, define various diffusion
processes on weighted data graphs. Despite success, these approaches rely on
fixed-weight graphs, making ranking sensitive to the input affinity matrix. In
this study, we propose a new ranking algorithm that simultaneously learns the
data affinity matrix and the ranking scores. The proposed optimization
formulation assigns adaptive neighbors to each point in the data based on the
local connectivity, and the smoothness constraint assigns similar ranking
scores to similar data points. We develop a novel and efficient algorithm to
solve the optimization problem. Evaluations using synthetic and real datasets
suggest that the proposed algorithm can outperform the existing methods.
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
Authors: Zihao Liu, Tao Liu, Wujie Wen, Lei Jiang, Jie Xu, Yanzhi Wang, Gang Quan
Date: 2018-03-14 00:00:00
Tasks: Data Compression, General Classification, image-classification, Image Classification, Image Compression

As one of most fascinating machine learning techniques, deep neural network
(DNN) has demonstrated excellent performance in various intelligent tasks such
as image classification. DNN achieves such performance, to a large extent, by
performing expensive training over huge volumes of training data. To reduce the
data storage and transfer overhead in smart resource-limited Internet-of-Thing
(IoT) systems, effective data compression is a "must-have" feature before
transferring real-time produced dataset for training or classification. While
there have been many well-known image compression approaches (such as JPEG), we
for the first time find that a human-visual based image compression approach
such as JPEG compression is not an optimized solution for DNN systems,
especially with high compression ratios. To this end, we develop an image
compression framework tailored for DNN applications, named "DeepN-JPEG", to
embrace the nature of deep cascaded information process mechanism of DNN
architecture. Extensive experiments, based on "ImageNet" dataset with various
state-of-the-art DNNs, show that "DeepN-JPEG" can achieve ~3.5x higher
compression rate over the popular JPEG solution while maintaining the same
accuracy level for image recognition, demonstrating its great potential of
storage and power efficiency in DNN-based smart IoT system design.
Model-Agnostic Private Learning via Stability
Authors: Raef Bassily, Om Thakkar, Abhradeep Thakurta
Date: 2018-03-14 00:00:00
Tasks: Binary Classification, Classification, General Classification, model, Multi-class Classification

We design differentially private learning algorithms that are agnostic to the
learning model. Our algorithms are interactive in nature, i.e., instead of
outputting a model based on the training data, they provide predictions for a
set of $m$ feature vectors that arrive online. We show that, for the feature
vectors on which an ensemble of models (trained on random disjoint subsets of a
dataset) makes consistent predictions, there is almost no-cost of privacy in
generating accurate predictions for those feature vectors. To that end, we
provide a novel coupling of the distance to instability framework with the
sparse vector technique. We provide algorithms with formal privacy and utility
guarantees for both binary/multi-class classification, and soft-label
classification. For binary classification in the standard (agnostic) PAC model,
we show how to bootstrap from our privately generated predictions to construct
a computationally efficient private learner that outputs a final accurate
hypothesis. Our construction - to the best of our knowledge - is the first
computationally efficient construction for a label-private learner. We prove
sample complexity upper bounds for this setting. As in non-private sample
complexity bounds, the only relevant property of the given concept class is its
VC dimension. For soft-label classification, our techniques are based on
exploiting the stability properties of traditional learning algorithms, like
stochastic gradient descent (SGD). We provide a new technique to boost the
average-case stability properties of learning algorithms to strong (worst-case)
stability properties, and then exploit them to obtain private classification
algorithms. In the process, we also show that a large class of SGD methods
satisfy average-case stability properties, in contrast to a smaller class of
SGD methods that are uniformly stable as shown in prior work.
Algorithmic Social Intervention
Authors: Bryan Wilder
Date: 2018-03-14 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Social and behavioral interventions are a critical tool for governments and
communities to tackle deep-rooted societal challenges such as homelessness,
disease, and poverty. However, real-world interventions are almost always
plagued by limited resources and limited data, which creates a computational
challenge: how can we use algorithmic techniques to enhance the targeting and
delivery of social and behavioral interventions? The goal of my thesis is to
provide a unified study of such questions, collectively considered under the
name "algorithmic social intervention". This proposal introduces algorithmic
social intervention as a distinct area with characteristic technical
challenges, presents my published research in the context of these challenges,
and outlines open problems for future work. A common technical theme is
decision making under uncertainty: how can we find actions which will impact a
social system in desirable ways under limitations of knowledge and resources?
The primary application area for my work thus far is public health, e.g. HIV or
tuberculosis prevention. For instance, I have developed a series of algorithms
which optimize social network interventions for HIV prevention. Two of these
algorithms have been pilot-tested in collaboration with LA-area service
providers for homeless youth, with preliminary results showing substantial
improvement over status-quo approaches. My work also spans other topics in
infectious disease prevention and underlying algorithmic questions in robust
and risk-aware submodular optimization.
Robust Fitting in Computer Vision: Easy or Hard?
Authors: Tat-Jun Chin, Zhipeng Cai, Frank Neumann
Date: 2018-02-18 00:00:00
Proceeding: ECCV 2018 9

Robust model fitting plays a vital role in computer vision, and research into
algorithms for robust fitting continues to be active. Arguably the most popular
paradigm for robust fitting in computer vision is consensus maximisation, which
strives to find the model parameters that maximise the number of inliers.
Despite the significant developments in algorithms for consensus maximisation,
there has been a lack of fundamental analysis of the problem in the computer
vision literature. In particular, whether consensus maximisation is "tractable"
remains a question that has not been rigorously dealt with, thus making it
difficult to assess and compare the performance of proposed algorithms,
relative to what is theoretically achievable. To shed light on these issues, we
present several computational hardness results for consensus maximisation. Our
results underline the fundamental intractability of the problem, and resolve
several ambiguities existing in the literature.
Neural Lattice Language Models
Authors: Jacob Buckman, Graham Neubig
Date: 2018-03-13 00:00:00
Proceeding: TACL 2018 1
Tasks: Language Modeling, Language Modelling, Sentence

In this work, we propose a new language modeling paradigm that has the
ability to perform both prediction and moderation of information flow at
multiple granularities: neural lattice language models. These models construct
a lattice of possible paths through a sentence and marginalize across this
lattice to calculate sequence probabilities or optimize parameters. This
approach allows us to seamlessly incorporate linguistic intuitions - including
polysemy and existence of multi-word lexical items - into our language model.
Experiments on multiple language modeling tasks show that English neural
lattice language models that utilize polysemous embeddings are able to improve
perplexity by 9.95% relative to a word-level baseline, and that a Chinese model
that handles multi-character tokens is able to improve perplexity by 20.94%
relative to a character-level baseline.
A Multi-Modal Approach to Infer Image Affect
Authors: Ashok Sundaresan, Sugumar Murugesan, Sean Davis, Karthik Kappaganthu, ZhongYi Jin, Divya Jain, Anurag Maunder
Date: 2018-03-13 00:00:00

The group affect or emotion in an image of people can be inferred by
extracting features about both the people in the picture and the overall makeup
of the scene. The state-of-the-art on this problem investigates a combination
of facial features, scene extraction and even audio tonality. This paper
combines three additional modalities, namely, human pose, text-based tagging
and CNN extracted features / predictions. To the best of our knowledge, this is
the first time all of the modalities were extracted using deep neural networks.
We evaluate the performance of our approach against baselines and identify
insights throughout this paper.
A Review on Image Texture Analysis Methods
Authors: Shervan Fekri-Ershad
Date: 2018-03-13 00:00:00
Tasks: Face Recognition, General Classification, Image Retrieval, Retrieval, Sensitivity, Texture Classification

Texture classification is an active topic in image processing which plays an
important role in many applications such as image retrieval, inspection
systems, face recognition, medical image processing, etc. There are many
approaches extracting texture features in gray-level images such as local
binary patterns, gray level co-occurrence matrices, statistical features,
skeleton, scale invariant feature transform, etc. The texture analysis methods
can be categorized in 4 groups titles: statistical methods, structural methods,
filter-based and model based approaches. In many related researches, authors
have tried to extract color and texture features jointly. In this respect,
combined methods are considered as efficient image analysis descriptors. Mostly
important challenges in image texture analysis are rotation sensitivity, gray
scale variations, noise sensitivity, illumination and brightness conditions,
etc. In this paper, we review most efficient and state-of-the-art image texture
analysis methods. Also, some texture classification approaches are survived.
Learning the Base Distribution in Implicit Generative Models
Authors: Cem Subakan, Oluwasanmi Koyejo, Paris Smaragdis
Date: 2018-03-12 00:00:00

Popular generative model learning methods such as Generative Adversarial
Networks (GANs), and Variational Autoencoders (VAE) enforce the latent
representation to follow simple distributions such as isotropic Gaussian. In
this paper, we argue that learning a complicated distribution over the latent
space of an auto-encoder enables more accurate modeling of complicated data
distributions. Based on this observation, we propose a two stage optimization
procedure which maximizes an approximate implicit density model. We
experimentally verify that our method outperforms GANs and VAEs on two image
datasets (MNIST, CELEB-A). We also show that our approach is amenable to
learning generative model for sequential data, by learning to generate speech
and music.