pwc-archive/papers-with-abstracts huggingface dataset:
SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks
Authors: Ziheng Wang, Ann Majewicz Fey
Date: 2018-06-15 00:00:00
Tasks: Representation Learning

Purpose: This paper focuses on an automated analysis of surgical motion
profiles for objective skill assessment and task recognition in robot-assisted
surgery. Existing techniques heavily rely on conventional statistic measures or
shallow modelings based on hand-engineered features and gesture segmentation.
Such developments require significant expert knowledge, are prone to errors,
and are less efficient in online adaptive training systems. Methods: In this
work, we present an efficient analytic framework with a parallel deep learning
architecture, SATR-DL, to assess trainee expertise and recognize surgical
training activity. Through an end-to-end learning technique, abstract
information of spatial representations and temporal dynamics is jointly
obtained directly from raw motion sequences. Results: By leveraging a shared
high-level representation learning, the resulting model is successful in the
recognition of trainee skills and surgical tasks, suturing, needle-passing, and
knot-tying. Meanwhile, we explore the use of ensemble in classification at the
trial level, where the SATR-DL outperforms state-of-the-art performance by
achieving accuracies of 0.960 and 1.000 in skill assessment and task
recognition, respectively. Conclusion: This study highlights the potential of
SATR-DL to provide improvements for an efficient data-driven assessment in
intelligent robotic surgery.
Supervised Speech Separation Based on Deep Learning: An Overview
Authors: DeLiang Wang, Jitong Chen
Date: 2017-08-24 00:00:00
Tasks: Deep Learning, Speaker Separation, Speech Dereverberation, Speech Enhancement, Speech Separation

Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background noise are learned from training data. Over the past
decade, many supervised separation algorithms have been put forward. In
particular, the recent introduction of deep learning to supervised speech
separation has dramatically accelerated progress and boosted separation
performance. This article provides a comprehensive overview of the research on
deep learning based supervised speech separation in the last several years. We
first introduce the background of speech separation and the formulation of
supervised separation. Then we discuss three main components of supervised
separation: learning machines, training targets, and acoustic features. Much of
the overview is on separation algorithms where we review monaural methods,
including speech enhancement (speech-nonspeech separation), speaker separation
(multi-talker separation), and speech dereverberation, as well as
multi-microphone techniques. The important issue of generalization, unique to
supervised learning, is discussed. This overview provides a historical
perspective on how advances are made. In addition, we discuss a number of
conceptual issues, including what constitutes the target source.
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery
Authors: Ziheng Wang, Ann Majewicz Fey
Date: 2018-06-15 00:00:00
Tasks: Time Series, Time Series Analysis

With the advent of robot-assisted surgery, the role of data-driven approaches
to integrate statistics and machine learning is growing rapidly with prominent
interests in objective surgical skill assessment. However, most existing work
requires translating robot motion kinematics into intermediate features or
gesture segments that are expensive to extract, lack efficiency, and require
significant domain-specific knowledge. We propose an analytical deep learning
framework for skill assessment in surgical training. A deep convolutional
neural network is implemented to map multivariate time series data of the
motion kinematics to individual skill levels. We perform experiments on the
public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill
Assessment Working Set (JIGSAWS). Our proposed learning model achieved a
competitive accuracy of 92.5%, 95.4%, and 91.3%, in the standard training
tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need
of engineered features or carefully-tuned gesture segmentation, our model can
successfully decode skill information from raw motion profiles via end-to-end
learning. Meanwhile, the proposed model is able to reliably interpret skills
within 1-3 second window, without needing an observation of entire training
trial. This study highlights the potentials of deep architectures for an
proficient online skill assessment in modern surgical training.
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
Authors: Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing
Date: 2018-06-15 00:00:00
Tasks: Clustering, speech-recognition, Speech Recognition, Video Segmentation, Video Semantic Segmentation

Deep neural networks (DNN) have demonstrated effectiveness for various
applications such as image processing, video segmentation, and speech
recognition. Running state-of-the-art DNNs on current systems mostly relies on
either generalpurpose processors, ASIC designs, or FPGA accelerators, all of
which suffer from data movements due to the limited onchip memory and data
transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN,
which processes all DNN operations within the memory to minimize the cost of
data movement. To enable in-memory processing, RAPIDNN reinterprets a DNN model
and maps it into a specialized accelerator, which is designed using
non-volatile memory blocks that model four fundamental DNN operations, i.e.,
multiplication, addition, activation functions, and pooling. The framework
extracts representative operands of a DNN model, e.g., weights and input
values, using clustering methods to optimize the model for in-memory
processing. Then, it maps the extracted operands and their precomputed results
into the accelerator memory blocks. At runtime, the accelerator identifies
computation results based on efficient in-memory search capability which also
provides tunability of approximation to further improve computation efficiency.
Our evaluation shows that RAPIDNN achieves 68.4x, 49.5x energy efficiency
improvement and 48.1x, 10.9x speedup as compared to ISAAC and PipeLayer, the
state-of-the-art DNN accelerators, while ensuring less than 0.3% of quality
loss.
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
Authors: John Ray Bergado, Claudio Persello, Alfred Stein
Date: 2018-06-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Land Cover Classification, Pansharpening

Classification of very high resolution (VHR) satellite images has three major
challenges: 1) inherent low intra-class and high inter-class spectral
similarities, 2) mismatching resolution of available bands, and 3) the need to
regularize noisy classification maps. Conventional methods have addressed these
challenges by adopting separate stages of image fusion, feature extraction, and
post-classification map regularization. These processing stages, however, are
not jointly optimizing the classification task at hand. In this study, we
propose a single-stage framework embedding the processing stages in a recurrent
multiresolution convolutional network trained in an end-to-end manner. The
feedforward version of the network, called FuseNet, aims to match the
resolution of the panchromatic and multispectral bands in a VHR image using
convolutional layers with corresponding downsampling and upsampling operations.
Contextual label information is incorporated into FuseNet by means of a
recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the
use of separate processing steps for both image fusion, e.g. pansharpening and
resampling through interpolation, and map regularization such as conditional
random fields. We carried out our experiments on a land cover classification
task using a Worldview-03 image of Quezon City, Philippines and the ISPRS 2D
semantic labeling benchmark dataset of Vaihingen, Germany. FuseNet and ReuseNet
surpass the baseline approaches in both quantitative and qualitative results.
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Authors: Sheng Chen, Yang Liu, Xiang Gao, Zhen Han
Date: 2018-04-20 00:00:00
Tasks: Lightweight Face Recognition

Face Analysis Project on MXNet
Monaural source enhancement maximizing source-to-distortion ratio via automatic differentiation
Authors: Hiroaki Nakajima, Yu Takahashi, Kazunobu Kondo, Yuji Hisaminato
Date: 2018-06-15 00:00:00

Recently, deep neural network (DNN) has made a breakthrough in monaural
source enhancement. Through a training step by using a large amount of data,
DNN estimates a mapping between mixed signals and clean signals. At this time,
we use an objective function that numerically expresses the quality of a
mapping by DNN. In the conventional methods, L1 norm, L2 norm, and
Itakura-Saito divergence are often used as objective functions. Recently, an
objective function based on short-time objective intelligibility (STOI) has
also been proposed. However, these functions only indicate similarity between
the clean signal and the estimated signal by DNN. In other words, they do not
show the quality of noise reduction or source enhancement. Motivated by the
fact, this paper adopts signal-to-distortion ratio (SDR) as the objective
function. Since SDR virtually shows signal-to-noise ratio (SNR), maximizing SDR
solves the above problem. The experimental results revealed that the proposed
method achieved better performance than the conventional methods.
Image classification and retrieval with random depthwise signed convolutional neural networks
Authors: Yunzhe Xue, Usman Roshan
Date: 2018-06-15 00:00:00
Tasks: General Classification, image-classification, Image Classification, Retrieval

We propose a random convolutional neural network to generate a feature space
in which we study image classification and retrieval performance. Put briefly
we apply random convolutional blocks followed by global average pooling to
generate a new feature, and we repeat this k times to produce a k-dimensional
feature space. This can be interpreted as partitioning the space of image
patches with random hyperplanes which we formalize as a random depthwise
convolutional neural network. In the network's final layer we perform image
classification and retrieval with the linear support vector machine and
k-nearest neighbor classifiers and study other empirical properties. We show
that the ratio of image pixel distribution similarity across classes to within
classes is higher in our network's final layer compared to the input space.
When we apply the linear support vector machine for image classification we see
that the accuracy is higher than if we were to train just the final layer of
VGG16, ResNet18, and DenseNet40 with random weights. In the same setting we
compare it to an unsupervised feature learning method and find our accuracy to
be comparable on CIFAR10 but higher on CIFAR100 and STL10. We see that the
accuracy is not far behind that of trained networks, particularly in the top-k
setting. For example the top-2 accuracy of our network is near 90% on both
CIFAR10 and a 10-class mini ImageNet, and 85% on STL10. We find that k-nearest
neighbor gives a comparable precision on the Corel Princeton Image Similarity
Benchmark than if we were to use the final layer of trained networks. As with
other networks we find that our network fails to a black box attack even though
we lack a gradient and use the sign activation. We highlight sensitivity of our
network to background as a potential pitfall and an advantage. Overall our work
pushes the boundary of what can be achieved with random weights.
A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges
Authors: Yee-Hui Oh, John See, Anh Cat Le Ngo, Raphael Chung-Wei Phan, Vishnu Monn Baskaran
Date: 2018-06-15 00:00:00

Over the last few years, automatic facial micro-expression analysis has
garnered increasing attention from experts across different disciplines because
of its potential applications in various fields such as clinical diagnosis,
forensic investigation and security systems. Advances in computer algorithms
and video acquisition technology have rendered machine analysis of facial
micro-expressions possible today, in contrast to decades ago when it was
primarily the domain of psychiatrists where analysis was largely manual.
Indeed, although the study of facial micro-expressions is a well-established
field in psychology, it is still relatively new from the computational
perspective with many interesting problems. In this survey, we present a
comprehensive review of state-of-the-art databases and methods for
micro-expressions spotting and recognition. Individual stages involved in the
automation of these tasks are also described and reviewed at length. In
addition, we also deliberate on the challenges and future directions in this
growing field of automatic facial micro-expression analysis.
Surprising Negative Results for Generative Adversarial Tree Search
Authors: Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C. Lipton, Animashree Anandkumar
Date: 2018-06-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environments and propose a sample efficient and robust method to learn the model of Atari environments. We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning. While MCTS on the learned model is computationally expensive, similar to AlphaGo, GATS follows depth limited MCTS. GATS employs deep Q network (DQN) and learns a Q-function to assign values to the leaves of the tree in MCTS. We theoretical analyze GATS vis-a-vis the bias-variance trade-off and show GATS is able to mitigate the worst-case error in the Q-estimate. While we were expecting GATS to enjoy a better sample complexity and faster converges to better policies, surprisingly, GATS fails to outperform DQN. We provide a study on which we show why depth limited MCTS fails to perform desirably.
Deep Learning Approximation: Zero-Shot Neural Network Speedup
Authors: Michele Pratusevich
Date: 2018-06-15 00:00:00
Tasks: Deep Learning

Neural networks offer high-accuracy solutions to a range of problems, but are
costly to run in production systems because of computational and memory
requirements during a forward pass. Given a trained network, we propose a
techique called Deep Learning Approximation to build a faster network in a tiny
fraction of the time required for training by only manipulating the network
structure and coefficients without requiring re-training or access to the
training data. Speedup is achieved by by applying a sequential series of
independent optimizations that reduce the floating-point operations (FLOPs)
required to perform a forward pass. First, lossless optimizations are applied,
followed by lossy approximations using singular value decomposition (SVD) and
low-rank matrix decomposition. The optimal approximation is chosen by weighing
the relative accuracy loss and FLOP reduction according to a single parameter
specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an
end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can
be re-gained by finetuning.
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Authors: Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh
Date: 2017-12-03 00:00:00
Proceeding: ICML 2018 7

We consider the problem of learning a one-hidden-layer neural network with
non-overlapping convolutional layer and ReLU activation, i.e., $f(\mathbf{Z},
\mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^T\mathbf{Z}_j)$, in which
both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$
are parameters to be learned. When the labels are the outputs from a teacher
network of the same architecture with fixed weights $(\mathbf{w}^*,
\mathbf{a}^*)$, we prove that with Gaussian input $\mathbf{Z}$, there is a
spurious local minimizer. Surprisingly, in the presence of the spurious local
minimizer, gradient descent with weight normalization from randomly initialized
weights can still be proven to recover the true parameters with constant
probability, which can be boosted to probability $1$ with multiple restarts. We
also show that with constant probability, the same procedure could also
converge to the spurious local minimum, showing that the local minimum plays a
non-trivial role in the dynamics of gradient descent. Furthermore, a
quantitative analysis shows that the gradient descent dynamics has two phases:
it starts off slow, but converges much faster after several iterations.
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Authors: Simon S. Du, Jason D. Lee
Date: 2018-03-03 00:00:00
Proceeding: ICML 2018

We provide new theoretical insights on why over-parametrization is effective
in learning neural networks. For a $k$ hidden node shallow network with
quadratic activation and $n$ training data points, we show as long as $ k \ge
\sqrt{2n}$, over-parametrization enables local search algorithms to find a
\emph{globally} optimal solution for general smooth and convex loss functions.
Further, despite that the number of parameters may exceed the sample size,
using theory of Rademacher complexity, we show with weight decay, the solution
also generalizes well if the data is sampled from a regular distribution such
as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign
landscape properties, we adopt an idea from smoothed analysis, which may have
other applications in studying loss surfaces of neural networks.
Hardware Trojan Attacks on Neural Networks
Authors: Joseph Clements, Yingjie Lao
Date: 2018-06-14 00:00:00
Tasks: BIG-bench Machine Learning, Neural Network Security

With the rising popularity of machine learning and the ever increasing demand
for computational power, there is a growing need for hardware optimized
implementations of neural networks and other machine learning models. As the
technology evolves, it is also plausible that machine learning or artificial
intelligence will soon become consumer electronic products and military
equipment, in the form of well-trained models. Unfortunately, the modern
fabless business model of manufacturing hardware, while economic, leads to
deficiencies in security through the supply chain. In this paper, we illuminate
these security issues by introducing hardware Trojan attacks on neural
networks, expanding the current taxonomy of neural network security to
incorporate attacks of this nature. To aid in this, we develop a novel
framework for inserting malicious hardware Trojans in the implementation of a
neural network classifier. We evaluate the capabilities of the adversary in
this setting by implementing the attack algorithm on convolutional neural
networks while controlling a variety of parameters available to the adversary.
Our experimental results show that the proposed algorithm could effectively
classify a selected input trigger as a specified class on the MNIST dataset by
injecting hardware Trojans into $0.03\%$, on average, of neurons in the 5th
hidden layer of arbitrary 7-layer convolutional neural networks, while
undetectable under the test data. Finally, we discuss the potential defenses to
protect neural networks against hardware Trojan attacks.
Motion Planning Networks
Authors: Ahmed H. Qureshi, Anthony Simeonov, Mayur J. Bency, Michael C. Yip
Date: 2018-06-14 00:00:00
Tasks: Motion Planning, Self-Driving Cars, Transfer Learning

Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods become ineffective as their computational complexity
increases exponentially with the dimensionality of the motion planning problem.
To address this issue, we present Motion Planning Networks (MPNet), a neural
network-based novel planning algorithm. The proposed method encodes the given
workspaces directly from a point cloud measurement and generates the end-to-end
collision-free paths for the given start and goal configurations. We evaluate
MPNet on various 2D and 3D environments including the planning of a 7 DOF
Baxter robot manipulator. The results show that MPNet is not only consistently
computationally efficient in all environments but also generalizes to
completely unseen environments. The results also show that the computation time
of MPNet consistently remains less than 1 second in all presented experiments,
which is significantly lower than existing state-of-the-art motion planning
algorithms.
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Authors: Alice Lucas, Santiago Lopez Tapia, Rafael Molina, Aggelos K. Katsaggelos
Date: 2018-06-14 00:00:00
Tasks: Generative Adversarial Network, Image Restoration, SSIM, Super-Resolution, Video Super-Resolution

Video super-resolution (VSR) has become one of the most critical problems in
video processing. In the deep learning literature, recent works have shown the
benefits of using adversarial-based and perceptual losses to improve the
performance on various image restoration tasks; however, these have yet to be
applied for video super-resolution. In this work, we propose a Generative
Adversarial Network(GAN)-based formulation for VSR. We introduce a new
generator network optimized for the VSR problem, named VSRResNet, along with a
new discriminator architecture to properly guide VSRResNet during the GAN
training. We further enhance our VSR GAN formulation with two regularizers, a
distance loss in feature-space and pixel-space, to obtain our final
VSRResFeatGAN model. We show that pre-training our generator with the
Mean-Squared-Error loss only quantitatively surpasses the current
state-of-the-art VSR models. Finally, we employ the PercepDist metric (Zhang et
al., 2018) to compare state-of-the-art VSR models. We show that this metric
more accurately evaluates the perceptual quality of SR solutions obtained from
neural networks, compared with the commonly used PSNR/SSIM metrics. Finally, we
show that our proposed model, the VSRResFeatGAN model, outperforms current
state-of-the-art SR models, both quantitatively and qualitatively.