pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation
Authors: Shogo Seki, Hirokazu Kameoka, Li Li, Tomoki Toda, Kazuya Takeda
Date: 2018-09-29 00:00:00
Tasks: Audio Source Separation

This paper deals with a multichannel audio source separation problem under
underdetermined conditions. Multichannel Non-negative Matrix Factorization
(MNMF) is one of powerful approaches, which adopts the NMF concept for source
power spectrogram modeling. This concept is also employed in Independent
Low-Rank Matrix Analysis (ILRMA), a special class of the MNMF framework
formulated under determined conditions. While these methods work reasonably
well for particular types of sound sources, one limitation is that they can
fail to work for sources with spectrograms that do not comply with the NMF
model. To address this limitation, an extension of ILRMA called the
Multichannel Variational Autoencoder (MVAE) method was recently proposed, where
a Conditional VAE (CVAE) is used instead of the NMF model for source power
spectrogram modeling. This approach has shown to perform impressively in
determined source separation tasks thanks to the representation power of DNNs.
While the original MVAE method was formulated under determined mixing
conditions, this paper generalizes it so that it can also deal with
underdetermined cases. We call the proposed framework the Generalized MVAE
(GMVAE). The proposed method was evaluated on a underdetermined source
separation task of separating out three sources from two microphone inputs.
Experimental results revealed that the GMVAE method achieved better performance
than the MNMF method.
Parameter Estimation for the Single-Look $\mathcal{G}^0$ Distribution
Authors: Débora Chan, Andrea Rey, Juliana Gambini, Alejandro C. Frery
Date: 2018-09-29 00:00:00
Tasks: parameter estimation

The statistical properties of Synthetic Aperture Radar (SAR) image texture
reveals useful target characteristics. It is well-known that these images are
affected by speckle, and prone to contamination as double bounce and corner
reflectors. The $\mathcal{G}^0$ distribution is flexible enough to model
different degrees of texture in speckled data. It is indexed by three
parameters: $\alpha$, related to the texture, $\gamma$, a scale parameter, and
$L$, the number of looks which is related to the signal-to-noise ratio. Quality
estimation of $\alpha$ is essential due to its immediate interpretability. In
this article, we compare the behavior of a number of parameter estimation
techniques in the noisiest case, namely single look data. We evaluate them
using Monte Carlo methods for non-contaminated and contaminated data,
considering convergence rate, bias, mean squared error (MSE) and computational
cost. The results are verified with simulated and actual SAR images.
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression
Authors: Yiren Zhao, Ilia Shumailov, Robert Mullins, Ross Anderson
Date: 2018-09-29 00:00:00
Tasks: Neural Network Compression

As deep neural networks (DNNs) become widely used, pruned and quantised models are becoming ubiquitous on edge devices; such compressed DNNs are popular for lowering computational requirements. Meanwhile, recent studies show that adversarial samples can be effective at making DNNs misclassify. We, therefore, investigate the extent to which adversarial samples are transferable between uncompressed and compressed DNNs. We find that adversarial samples remain transferable for both pruned and quantised models. For pruning, the adversarial samples generated from heavily pruned models remain effective on uncompressed models. For quantisation, we find the transferability of adversarial samples is highly sensitive to integer precision.
Non-local NetVLAD Encoding for Video Classification
Authors: Yongyi Tang, Xing Zhang, Jingwen Wang, Shaoxiang Chen, Lin Ma, Yu-Gang Jiang
Date: 2018-09-29 00:00:00
Tasks: Classification, General Classification, Video Classification, Video Recognition, Video Understanding

This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video
understanding challenge organized by Google AI. Unlike the video recognition
benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides
pre-extracted visual and audio features instead of raw videos. In this
challenge, the submitted model is restricted to 1GB, which encourages
participants focus on constructing one powerful single model rather than
incorporating of the results from a bunch of models. Our system fuses six
different sub-models into one single computational graph, which are categorized
into three families. More specifically, the most effective family is the model
with non-local operations following the NetVLAD encoding. The other two family
models are Soft-BoF and GRU, respectively. In order to further boost single
models performance, the model parameters of different checkpoints are averaged.
Experimental results demonstrate that our proposed system can effectively
perform the video classification task, achieving 0.88763 on the public test set
and 0.88704 on the private set in terms of GAP@20, respectively. We finally
ranked at the fourth place in the YouTube-8M video understanding challenge.
Stakeholders in Explainable AI
Authors: Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, Supriyo Chakraborty
Date: 2018-09-29 00:00:00

There is general consensus that it is important for artificial intelligence
(AI) and machine learning systems to be explainable and/or interpretable.
However, there is no general consensus over what is meant by 'explainable' and
'interpretable'. In this paper, we argue that this lack of consensus is due to
there being several distinct stakeholder communities. We note that, while the
concerns of the individual communities are broadly compatible, they are not
identical, which gives rise to different intents and requirements for
explainability/interpretability. We use the software engineering distinction
between validation and verification, and the epistemological distinctions
between knowns/unknowns, to tease apart the concerns of the stakeholder
communities and highlight the areas where their foci overlap or diverge. It is
not the purpose of the authors of this paper to 'take sides' - we count
ourselves as members, to varying degrees, of multiple communities - but rather
to help disambiguate what stakeholders mean when they ask 'Why?' of an AI.
Continual Learning of Context-dependent Processing in Neural Networks
Authors: Guanxiong Zeng, Yang Chen, Bo Cui, Shan Yu
Date: 2018-09-29 00:00:00
Tasks: Continual Learning

Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it.
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
Authors: Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa, Yoshimasa Tsuruoka
Date: 2018-09-29 00:00:00
Tasks: Decision Making

Hierarchical planners that produce interpretable and appropriate plans are
desired, especially in its application to supporting human decision making. In
the typical development of the hierarchical planners, higher-level planners and
symbol grounding functions are manually created, and this manual creation
requires much human effort. In this paper, we propose a framework that can
automatically refine symbol grounding functions and a high-level planner to
reduce human effort for designing these modules. In our framework, symbol
grounding and high-level planning, which are based on manually-designed
knowledge bases, are modeled with semi-Markov decision processes. A policy
gradient method is then applied to refine the modules, in which two terms for
updating the modules are considered. The first term, called a reinforcement
term, contributes to updating the modules to improve the overall performance of
a hierarchical planner to produce appropriate plans. The second term, called a
penalty term, contributes to keeping refined modules consistent with the
manually-designed original modules. Namely, it keeps the planner, which uses
the refined modules, producing interpretable plans. We perform preliminary
experiments to solve the Mountain car problem, and its results show that a
manually-designed high-level planner and symbol grounding function were
successfully refined by our framework.
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Authors: Chaim Baskin, Natan Liss, Yoav Chai, Evgenii Zheltonozhskii, Eli Schwartz, Raja Giryes, Avi Mendelson, Alexander M. Bronstein
Date: 2018-09-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, GPU, Quantization, speech-recognition, Speech Recognition

Convolutional Neural Networks (CNN) are very popular in many fields including
computer vision, speech recognition, natural language processing, to name a
few. Though deep learning leads to groundbreaking performance in these domains,
the networks used are very demanding computationally and are far from real-time
even on a GPU, which is not power efficient and therefore does not suit low
power systems such as mobile devices. To overcome this challenge, some
solutions have been proposed for quantizing the weights and activations of
these networks, which accelerate the runtime significantly. Yet, this
acceleration comes at the cost of a larger error. The \uniqname method proposed
in this work trains quantized neural networks by noise injection and a learned
clamping, which improve the accuracy. This leads to state-of-the-art results on
various regression and classification tasks, e.g., ImageNet classification with
architectures such as ResNet-18/34/50 with low as 3-bit weights and
activations. We implement the proposed solution on an FPGA to demonstrate its
applicability for low power real-time applications. The implementation of the
paper is available at https://github.com/Lancer555/NICE
Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep learning
Authors: Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang
Date: 2018-09-29 00:00:00
Proceeding: ICLR 2019 5

Although stochastic gradient descent (SGD) is a driving force behind the
recent success of deep learning, our understanding of its dynamics in a
high-dimensional parameter space is limited. In recent years, some researchers
have used the stochasticity of minibatch gradients, or the signal-to-noise
ratio, to better characterize the learning dynamics of SGD. Inspired from these
work, we here analyze SGD from a geometrical perspective by inspecting the
stochasticity of the norms and directions of minibatch gradients. We propose a
model of the directional concentration for minibatch gradients through von
Mises-Fisher (VMF) distribution, and show that the directional uniformity of
minibatch gradients increases over the course of SGD. We empirically verify our
result using deep convolutional networks and observe a higher correlation
between the gradient stochasticity and the proposed directional uniformity than
that against the gradient norm stochasticity, suggesting that the directional
statistics of minibatch gradients is a major factor behind SGD.
M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning
Authors: Tianmin Shu, Yuandong Tian
Date: 2018-09-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most of the prior work on multi-agent reinforcement learning (MARL) achieves
optimal collaboration by directly controlling the agents to maximize a common
reward. In this paper, we aim to address this from a different angle. In
particular, we consider scenarios where there are self-interested agents (i.e.,
worker agents) which have their own minds (preferences, intentions, skills,
etc.) and can not be dictated to perform tasks they do not wish to do. For
achieving optimal coordination among these agents, we train a super agent
(i.e., the manager) to manage them by first inferring their minds based on both
current and past observations and then initiating contracts to assign suitable
tasks to workers and promise to reward them with corresponding bonuses so that
they will agree to work together. The objective of the manager is maximizing
the overall productivity as well as minimizing payments made to the workers for
ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent
Management Reinforcement Learning (M^3RL), which consists of agent modeling and
policy learning. We have evaluated our approach in two environments, Resource
Collection and Crafting, to simulate multi-agent management problems with
various task settings and multiple designs for the worker agents. The
experimental results have validated the effectiveness of our approach in
modeling worker agents' minds online, and in achieving optimal ad-hoc teaming
with good generalization and fast adaptation.
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
Authors: Fuxun Yu, ChenChen Liu, Yanzhi Wang, Liang Zhao, Xiang Chen
Date: 2018-09-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Robustness

One popular hypothesis of neural network generalization is that the flat
local minima of loss surface in parameter space leads to good generalization.
However, we demonstrate that loss surface in parameter space has no obvious
relationship with generalization, especially under adversarial settings.
Through visualizing decision surfaces in both parameter space and input space,
we instead show that the geometry property of decision surface in input space
correlates well with the adversarial robustness. We then propose an adversarial
robustness indicator, which can evaluate a neural network's intrinsic
robustness property without testing its accuracy under adversarial attacks.
Guided by it, we further propose our robust training method. Without involving
adversarial training, our method could enhance network's intrinsic adversarial
robustness against various adversarial attacks.
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Authors: Zhiming Zhou, Qingru Zhang, Guansong Lu, Hongwei Wang, Wei-Nan Zhang, Yong Yu
Date: 2018-09-29 00:00:00
Proceeding: ICLR 2019 5

Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient $g_t$ and the second-moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such biased step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$. The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.
Knowledge-guided Semantic Computing Network
Authors: Guangming Shi, Zhongqiang Zhang, Dahua Gao, Xuemei Xie, Yihao Feng, Xinrui Ma, Danhua Liu
Date: 2018-09-29 00:00:00
Tasks: Adversarial Robustness, Object Recognition

It is very useful to integrate human knowledge and experience into
traditional neural networks for faster learning speed, fewer training samples
and better interpretability. However, due to the obscured and indescribable
black box model of neural networks, it is very difficult to design its
architecture, interpret its features and predict its performance. Inspired by
human visual cognition process, we propose a knowledge-guided semantic
computing network which includes two modules: a knowledge-guided semantic tree
and a data-driven neural network. The semantic tree is pre-defined to describe
the spatial structural relations of different semantics, which just corresponds
to the tree-like description of objects based on human knowledge. The object
recognition process through the semantic tree only needs simple forward
computing without training. Besides, to enhance the recognition ability of the
semantic tree in aspects of the diversity, randomicity and variability, we use
the traditional neural network to aid the semantic tree to learn some
indescribable features. Only in this case, the training process is needed. The
experimental results on MNIST and GTSRB datasets show that compared with the
traditional data-driven network, our proposed semantic computing network can
achieve better performance with fewer training samples and lower computational
complexity. Especially, Our model also has better adversarial robustness than
traditional neural network with the help of human knowledge.
Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach
Authors: Jacky C. K. Chow, Derek Lichti, Kathleen Ang, Gregor Kuntze, Gulshan Sharma, Janet Ronsky
Date: 2018-09-29 00:00:00
Tasks: 3D Reconstruction

X-ray imaging is a fundamental tool of routine clinical diagnosis.
Fluoroscopic imaging can further acquire X-ray images at video frame rates,
thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal
tract, etc. For both the qualitative and quantitative analysis of static and
dynamic X-ray images, the data should be free of systematic biases. Besides
precise fabrication of hardware, software-based calibration solutions are
commonly used for modelling the distortions. In this primary research study, a
robust photogrammetric bundle adjustment was used to model the projective
geometry of two fluoroscopic X-ray imaging systems. However, instead of relying
on an expert photogrammetrist's knowledge and judgement to decide on a
parametric model for describing the systematic errors, a self-tuning
data-driven approach is used to model the complex non-linear distortion profile
of the sensors. Quality control from the experiment showed that 0.06 mm to 0.09
mm 3D reconstruction accuracy was achievable post-calibration using merely 15
X-ray images. As part of the bundle adjustment, the location of the virtual
fluoroscopic system relative to the target field can also be spatially resected
with an RMSE between 3.10 mm and 3.31 mm.
FusedLSTM: Fusing frame-level and video-level features for Content-based Video Relevance Prediction
Authors: Yash Bhalgat
Date: 2018-09-29 00:00:00
Tasks: Triplet

This paper describes two of my best performing approaches on the
Content-based Video Relevance Prediction challenge. In the FusedLSTM based
approach, the inception-pool3 and the C3D-pool5 features are combined using an
LSTM and a dense layer to form embeddings with the objective to minimize the
triplet loss function. In the second approach, an Online Kernel Similarity
Learning method is proposed to learn a non-linear similarity measure to adhere
the relevance training data. The last section gives a complete comparison of
all the approaches implemented during this challenge, including the one
presented in the baseline paper.
Robot Vision: Calibration of Wide-Angle Lens Cameras Using Collinearity Condition and K-Nearest Neighbour Regression
Authors: Jacky C. K. Chow, Ivan Detchev, Kathleen Ang, Kristian Morin, Karthik Mahadevan, Nicholas Louie
Date: 2018-09-29 00:00:00
Tasks: Camera Calibration

Visual perception is regularly used by humans and robots for navigation. By
either implicitly or explicitly mapping the environment, ego-motion can be
determined and a path of actions can be planned. The process of mapping and
navigation are delicately intertwined; therefore, improving one can often lead
to an improvement of the other. Both processes are sensitive to the interior
orientation parameters of the camera system and mathematically modelling these
systematic errors can often improve the precision and accuracy of the overall
solution. This paper presents an automatic camera calibration method suitable
for any lens, without having prior knowledge about the sensor. Statistical
inference is performed to map the environment and localize the camera
simultaneously. K-nearest neighbour regression is used to model the geometric
distortions of the images. A normal-angle lens Nikon camera and wide-angle lens
GoPro camera were calibrated using the proposed method, as well as the
conventional bundle adjustment with self-calibration method (for comparison).
Results showed that the mapping error was reduced from an average of 14.9 mm to
1.2 mm (i.e. a 92% improvement) and 66.6 mm to 1.5 mm (i.e. a 98% improvement)
using the proposed method for the Nikon and GoPro cameras, respectively. In
contrast, the conventional approach achieved an average 3D error of 0.9 mm
(i.e. 94% improvement) and 3.3 mm (i.e. 95% improvement) for the Nikon and
GoPro cameras, respectively. Thus, the proposed method performs well
irrespective of the lens/sensor used: it yields results that are comparable to
the conventional approach for normal-angle lens cameras, and it has the
additional benefit of improving calibration results for wide-angle lens
cameras.
Generalization and Regularization in DQN
Authors: Jesse Farebrother, Marlos C. Machado, Michael Bowling
Date: 2018-09-29 00:00:00
Tasks: Atari Games, Benchmarking, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performance on popular benchmarks, policies learned by deep reinforcement learning algorithms can struggle to generalize when evaluated in remarkably similar environments. In this paper we propose a protocol to evaluate generalization in reinforcement learning through different modes of Atari 2600 games. With that protocol we assess the generalization capabilities of DQN, one of the most traditional deep reinforcement learning algorithms, and we provide evidence suggesting that DQN overspecializes to the training environment. We then comprehensively evaluate the impact of dropout and $\ell_2$ regularization, as well as the impact of reusing learned representations to improve the generalization capabilities of DQN. Despite regularization being largely underutilized in deep reinforcement learning, we show that it can, in fact, help DQN learn more general features. These features can be reused and fine-tuned on similar tasks, considerably improving DQN's sample efficiency.