pwc-archive/papers-with-abstracts huggingface dataset:
3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects
Authors: Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Lorna Smith, H. Rolf Jäger, M. Jorge Cardoso
Date: 2018-12-21 00:00:00

Extremely small objects (ESO) have become observable on clinical routine
magnetic resonance imaging acquisitions, thanks to a reduction in acquisition
time at higher resolution. Despite their small size (usually $<$10 voxels per
object for an image of more than $10^6$ voxels), these markers reflect tissue
damage and need to be accounted for to investigate the complete phenotype of
complex pathological pathways. In addition to their very small size,
variability in shape and appearance leads to high labelling variability across
human raters, resulting in a very noisy gold standard. Such objects are notably
present in the context of cerebral small vessel disease where enlarged
perivascular spaces and lacunes, commonly observed in the ageing population,
are thought to be associated with acceleration of cognitive decline and risk of
dementia onset. In this work, we redesign the RCNN model to scale to 3D data,
and to jointly detect and characterise these important markers of age-related
neurovascular changes. We also propose training strategies enforcing the
detection of extremely small objects, ensuring a tractable and stable training
process.
LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models
Authors: Ajaya Adhikari, D. M. J Tax, Riccardo Satta, Matthias Fath
Date: 2018-12-21 00:00:00
Tasks: Feature Importance

As machine learning models become more accurate, they typically become more complex and uninterpretable by humans. The black-box character of these models holds back its acceptance in practice, especially in high-risk domains where the consequences of failure could be catastrophic such as health-care or defense. Providing understandable and useful explanations behind ML models or predictions can increase the trust of the user. Example-based reasoning, which entails leveraging previous experience with analogous tasks to make a decision, is a well known strategy for problem solving and justification. This work presents a new explanation extraction method called LEAFAGE, for a prediction made by any black-box ML model. The explanation consists of the visualization of similar examples from the training set and the importance of each feature. Moreover, these explanations are contrastive which aims to take the expectations of the user into account. LEAFAGE is evaluated in terms of fidelity to the underlying black-box model and usefulness to the user. The results showed that LEAFAGE performs overall better than the current state-of-the-art method LIME in terms of fidelity, on ML models with non-linear decision boundary. A user-study was conducted which focused on revealing the differences between example-based and feature importance-based explanations. It showed that example-based explanations performed significantly better than feature importance-based explanation, in terms of perceived transparency, information sufficiency, competence and confidence. Counter-intuitively, when the gained knowledge of the participants was tested, it showed that they learned less about the black-box model after seeing a feature importance-based explanation than seeing no explanation at all. The participants found feature importance-based explanation vague and hard to generalize it to other instances.
Low-rank Approximation of Linear Maps
Authors: Patrick Heas, Cedric Herzet
Date: 2018-12-21 00:00:00

This work provides closed-form solutions and minimum achievable errors for a large class of low-rank approximation problems in Hilbert spaces. The proposed theorem generalizes to the case of bounded linear operators the previous results obtained in the finite dimensional case for the Frobenius norm. The theorem provides the basis for the design of tractable algorithms for kernel or continuous DMD.
A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization
Authors: Guoyun Tu, Yanwei Fu, Boyang Li, Jiarui Gao, Yu-Gang Jiang, xiangyang xue
Date: 2018-12-21 00:00:00
Tasks: Classification, Emotion Recognition, General Classification

Emotional content is a crucial ingredient in user-generated videos. However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis. In this paper, we propose a new neural approach, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion analysis tasks: emotion recognition, emotion attribution, and emotion-oriented summarization, in a single integrated framework. BEAC-Net has two major constituents, an attribution network and a classification network. The attribution network extracts the main emotional segment that classification should focus on in order to mitigate the sparsity issue. The classification network utilizes both the extracted segment and the original video in a bi-stream architecture. We contribute a new dataset for the emotion attribution task with human-annotated ground-truth labels for emotion segments. Experiments on two video datasets demonstrate superior performance of the proposed framework and the complementary nature of the dual classification streams.
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Authors: Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma, Wei zhang, Liang Lin
Date: 2018-12-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning agents need exploratory behaviors to escape from local
optima. These behaviors may include both immediate dithering perturbation and
temporally consistent exploration. To achieve these, a stochastic policy model
that is inherently consistent through a period of time is in desire, especially
for tasks with either sparse rewards or long term information. In this work, we
introduce a novel on-policy temporally consistent exploration strategy - Neural
Adaptive Dropout Policy Exploration (NADPEx) - for deep reinforcement learning
agents. Modeled as a global random variable for conditional distribution,
dropout is incorporated to reinforcement learning policies, equipping them with
inherent temporal consistency, even when the reward signals are sparse. Two
factors, gradients' alignment with the objective and KL constraint in policy
space, are discussed to guarantee NADPEx policy's stable improvement. Our
experiments demonstrate that NADPEx solves tasks with sparse reward while naive
exploration and parameter noise fail. It yields as well or even faster
convergence in the standard mujoco benchmark for continuous control.
A new approach to learning in Dynamic Bayesian Networks (DBNs)
Authors: E. Benhamou, J. Atif, R. Laraki
Date: 2018-12-21 00:00:00
Tasks: Algorithmic Trading

In this paper, we revisit the parameter learning problem, namely the
estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are
directed graphical models of stochastic processes that encompasses and
generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs).
Whenever we apply these models to economics and finance, we are forced to make
some modeling assumptions about the state dynamics and the graph topology (the
DBN structure). These assumptions may be incorrectly specified and contain some
additional noise compared to reality. Trying to use a best fit approach through
maximum likelihood estimation may miss this point and try to fit at any price
these models on data. We present here a new methodology that takes a radical
point of view and instead focus on the final efficiency of our model.
Parameters are hence estimated in terms of their efficiency rather than their
distributional fit to the data. The resulting optimization problem that
consists in finding the optimal parameters is a hard problem. We rely on
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to tackle this
issue. We apply this method to the seminal problem of trend detection in
financial markets. We see on numerical results that the resulting parameters
seem less error prone to over fitting than traditional moving average cross
over trend detection and perform better. The method developed here for
algorithmic trading is general. It can be applied to other real case
applications whenever there is no physical law underlying our DBNs.
Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN
Authors: Erez Yahalomi, Michael Chernofsky, Michael Werman
Date: 2018-12-21 00:00:00
Tasks: object-detection, Object Detection

Distal radius fractures are the most common fractures of the upper extremity
in humans. As such, they account for a significant portion of the injuries that
present to emergency rooms and clinics throughout the world. We trained a
Faster R-CNN, a machine vision neural network for object detection, to identify
and locate distal radius fractures in anteroposterior X-ray images. We achieved
an accuracy of 96\% in identifying fractures and mean Average Precision, mAP,
of 0.866. This is significantly more accurate than the detection achieved by
physicians and radiologists. These results were obtained by training the deep
learning network with only 38 original images of anteroposterior hands X-ray
images with fractures. This opens the possibility to detect with this type of
neural network rare diseases or rare symptoms of common diseases , where only a
small set of diagnosed X-ray images could be collected for each disease.
Face Hallucination Revisited: An Exploratory Study on Dataset Bias
Authors: Klemen Grm, Martin Pernuš, Leo Cluzel, Walter Scheirer, Simon Dobrišek, Vitomir Štruc
Date: 2018-12-21 00:00:00
Tasks: Face Hallucination, Hallucination

Contemporary face hallucination (FH) models exhibit considerable ability to
reconstruct high-resolution (HR) details from low-resolution (LR) face images.
This ability is commonly learned from examples of corresponding HR-LR image
pairs, created by artificially down-sampling the HR ground truth data. This
down-sampling (or degradation) procedure not only defines the characteristics
of the LR training data, but also determines the type of image degradations the
learned FH models are eventually able to handle. If the image characteristics
encountered with real-world LR images differ from the ones seen during
training, FH models are still expected to perform well, but in practice may not
produce the desired results. In this paper we study this problem and explore
the bias introduced into FH models by the characteristics of the training data.
We systematically analyze the generalization capabilities of several FH models
in various scenarios, where the image the degradation function does not match
the training setup and conduct experiments with synthetically downgraded as
well as real-life low-quality images. We make several interesting findings that
provide insight into existing problems with FH models and point to future
research directions.
Feature-Wise Bias Amplification
Authors: Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, Anupam Datta
Date: 2018-12-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: feature selection, Inductive Bias

We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the importance of moderately-predictive "weak" features if insufficient training data is available. This overestimation gives rise to feature-wise bias amplification -- a previously unreported form of bias that can be traced back to the features of a trained model. Through analysis and experiments, we show that while some bias cannot be mitigated without sacrificing accuracy, feature-wise bias amplification can be mitigated through targeted feature selection. We present two new feature selection algorithms for mitigating bias amplification in linear models, and show how they can be adapted to convolutional neural networks efficiently. Our experiments on synthetic and real data demonstrate that these algorithms consistently lead to reduced bias without harming accuracy, in some cases eliminating predictive bias altogether while providing modest gains in accuracy.
Stochastic Doubly Robust Gradient
Authors: Kanghoon Lee, Jihye Choi, Moonsu Cha, Jung-Kwon Lee, Tae-Yoon Kim
Date: 2018-12-21 00:00:00
Tasks: Fairness

When training a machine learning model with observational data, it is often
encountered that some values are systemically missing. Learning from the
incomplete data in which the missingness depends on some covariates may lead to
biased estimation of parameters and even harm the fairness of decision outcome.
This paper proposes how to adjust the causal effect of covariates on the
missingness when training models using stochastic gradient descent (SGD).
Inspired by the design of doubly robust estimator and its theoretical property
of double robustness, we introduce stochastic doubly robust gradient (SDRG)
consisting of two models: weight-corrected gradients for inverse propensity
score weighting and per-covariate control variates for regression adjustment.
Also, we identify the connection between double robustness and variance
reduction in SGD by demonstrating the SDRG algorithm with a unifying framework
for variance reduced SGD. The performance of our approach is empirically tested
by showing the convergence in training image classifiers with several examples
of missing data.
The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
Authors: Li Zhou, Jianfeng Gao, Di Li, Heung-Yeung Shum
Date: 2018-12-21 00:00:00
Proceeding: CL 2020 3
Tasks: Chatbot, Decision Making

This paper describes the development of Microsoft XiaoIce, the most popular social chatbot in the world. XiaoIce is uniquely designed as an AI companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient (IQ) and emotional quotient (EQ) in system design, cast human-machine social chat as decision-making over Markov Decision Processes (MDPs), and optimize XiaoIce for long-term user engagement, measured in expected Conversation-turns Per Session (CPS). We detail the system architecture and key components including dialogue manager, core chat, skills, and an empathetic computing module. We show how XiaoIce dynamically recognizes human feelings and states, understands user intent, and responds to user needs throughout long conversations. Since her launch in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them. Analysis of large scale online logs shows that XiaoIce has achieved an average CPS of 23, which is significantly higher than that of other chatbots and even human conversations.
Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors
Authors: Yedid Hoshen, Jitendra Malik
Date: 2018-12-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Unconditional Image Generation

Unconditional image generation has recently been dominated by generative
adversarial networks (GANs). GAN methods train a generator which regresses
images from random noise vectors, as well as a discriminator that attempts to
differentiate between the generated images and a training set of real images.
GANs have shown amazing results at generating realistic looking images. Despite
their success, GANs suffer from critical drawbacks including: unstable training
and mode-dropping. The weaknesses in GANs have motivated research into
alternatives including: variational auto-encoders (VAEs), latent embedding
learning methods (e.g. GLO) and nearest-neighbor based implicit maximum
likelihood estimation (IMLE). Unfortunately at the moment, GANs still
significantly outperform the alternative methods for image generation. In this
work, we present a novel method - Generative Latent Nearest Neighbors (GLANN) -
for training generative models without adversarial training. GLANN combines the
strengths of IMLE and GLO in a way that overcomes the main drawbacks of each
method. Consequently, GLANN generates images that are far better than GLO and
IMLE. Our method does not suffer from mode collapse which plagues GAN training
and is much more stable. Qualitative results show that GLANN outperforms a
baseline consisting of 800 GANs and VAEs on commonly used datasets. Our models
are also shown to be effective for training truly non-adversarial unsupervised
image translation.
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
Authors: Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-12-21 00:00:00
Tasks: Person Re-Identification, Triplet

State-of-the-art person re-identification systems that employ a triplet based
deep network suffer from a poor generalization capability. In this paper, we
propose a four stream Siamese deep convolutional neural network for person
redetection that jointly optimises verification and identification losses over
a four image input group. Specifically, the proposed method overcomes the
weakness of the typical triplet formulation by using groups of four images
featuring two matched (i.e. the same identity) and two mismatched images. This
allows us to jointly increase the interclass variations and reduce the
intra-class variations in the learned feature space. The proposed approach also
optimises over both the identification and verification losses, further
minimising intra-class variation and maximising inter-class variation,
improving overall performance. Extensive experiments on four challenging
datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed
approach achieves state-of-the-art performance.
Multi-component Image Translation for Deep Domain Generalization
Authors: Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, Sridha Sridharan
Date: 2018-12-21 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Generative Adversarial Network, Translation

Domain adaption (DA) and domain generalization (DG) are two closely related
methods which are both concerned with the task of assigning labels to an
unlabeled data set. The only dissimilarity between these approaches is that DA
can access the target data during the training phase, while the target data is
totally unseen during the training phase in DG. The task of DG is challenging
as we have no earlier knowledge of the target samples. If DA methods are
applied directly to DG by a simple exclusion of the target data from training,
poor performance will result for a given task. In this paper, we tackle the
domain generalization challenge in two ways. In our first approach, we propose
a novel deep domain generalization architecture utilizing synthetic data
generated by a Generative Adversarial Network (GAN). The discrepancy between
the generated images and synthetic images is minimized using existing domain
discrepancy metrics such as maximum mean discrepancy or correlation alignment.
In our second approach, we introduce a protocol for applying DA methods to a DG
scenario by excluding the target data from the training phase, splitting the
source data to training and validation parts, and treating the validation data
as target data for DA. We conduct extensive experiments on four cross-domain
benchmark datasets. Experimental results signify our proposed model outperforms
the current state-of-the-art methods for DG.
Saliency Guided Hierarchical Robust Visual Tracking
Authors: Fangwen Tu, Shuzhi Sam Ge, Yazhe Tang, Chang Chieh Hang
Date: 2018-12-21 00:00:00
Tasks: Visual Tracking

A saliency guided hierarchical visual tracking (SHT) algorithm containing
global and local search phases is proposed in this paper. In global search, a
top-down saliency model is novelly developed to handle abrupt motion and
appearance variation problems. Nineteen feature maps are extracted first and
combined with online learnt weights to produce the final saliency map and
estimated target locations. After the evaluation of integration mechanism, the
optimum candidate patch is passed to the local search. In local search, a
superpixel based HSV histogram matching is performed jointly with an L2-RLS
tracker to take both color distribution and holistic appearance feature of the
object into consideration. Furthermore, a linear refinement search process with
fast iterative solver is implemented to attenuate the possible negative
influence of dominant particles. Both qualitative and quantitative experiments
are conducted on a series of challenging image sequences. The superior
performance of the proposed method over other state-of-the-art algorithms is
demonstrated by comparative study.
COSINE: Compressive Network Embedding on Large-scale Information Networks
Authors: Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, Zhichong Fang, Bo Zhang, Leyu Lin
Date: 2018-12-21 00:00:00
Tasks: General Classification, graph partitioning, Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Network Embedding, Representation Learning

There is recently a surge in approaches that learn low-dimensional embeddings
of nodes in networks. As there are many large-scale real-world networks, it's
inefficient for existing approaches to store amounts of parameters in memory
and update them edge after edge. With the knowledge that nodes having similar
neighborhood will be close to each other in embedding space, we propose COSINE
(COmpresSIve NE) algorithm which reduces the memory footprint and accelerates
the training process by parameters sharing among similar nodes. COSINE applies
graph partitioning algorithms to networks and builds parameter sharing
dependency of nodes based on the result of partitioning. With parameters
sharing among similar nodes, COSINE injects prior knowledge about higher
structural information into training process which makes network embedding more
efficient and effective. COSINE can be applied to any embedding lookup method
and learn high-quality embeddings with limited memory and shorter training
time. We conduct experiments of multi-label classification and link prediction,
where baselines and our model have the same memory usage. Experimental results
show that COSINE gives baselines up to 23% increase on classification and up to
25% increase on link prediction. Moreover, time of all representation learning
methods using COSINE decreases from 30% to 70%.