pwc-archive/papers-with-abstracts huggingface dataset:
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
Authors: Matthew C. Fontaine, Scott Lee, L. B. Soros, Fernando De Mesentier Silva, Julian Togelius, Amy K. Hoover
Date: 2019-04-24 00:00:00
Tasks: Diversity

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a
powerful alternative to traditional single-objective optimization methods. They
were initially applied to evolutionary robotics problems such as locomotion and
maze navigation, but have yet to see widespread application. We argue that
these algorithms are perfectly suited to the rich domain of video games, which
contains many relevant problems with a multitude of successful strategies and
often also multiple dimensions along which solutions can vary.
  This paper introduces a novel modification of the MAP-Elites algorithm called
MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and
rebalancing of Hearthstone, a popular collectible card game chosen for its
number of multidimensional behavior features relevant to particular styles of
play. To avoid overpopulating cells with conflated behaviors, MESB slides the
boundaries of cells based on the distribution of evolved individuals.
Experiments in this paper demonstrate the performance of MESB in Hearthstone.
Results suggest MESB finds diverse ways of playing the game well along the
selected behavioral dimensions. Further analysis of the evolved strategies
reveals common patterns that recur across behavioral dimensions and explores
how MESB can help rebalance the game.
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators
Authors: Xuan Zhu, Yue Cheng, Jinye Peng, Rongzhi Wang, Mingnan Le, Xin Liu
Date: 2019-04-24 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. In this paper, we design a new GAN-based SR framework GAN-IMC which includes generator, image discriminator, morphological component discriminator and color discriminator. The combination of multiple feature discriminators improves the accuracy of image discrimination. Adversarial training between the generator and multi-feature discriminators forces SR images to converge with HR images in terms of data and features distribution. Moreover, in some cases, feature enhancement of salient regions is also worth considering. GAN-IMC is further optimized by weighted content loss (GAN-IMCW), which effectively restores and enhances salient regions in SR images. The effectiveness and robustness of our method are confirmed by extensive experiments on public datasets. Compared with state-of-the-art methods, the proposed method not only achieves competitive Perceptual Index (PI) and Natural Image Quality Evaluator (NIQE) values but also obtains pleasant visual perception in image edge, texture, color and salient regions.
Stochastic Lipschitz Q-Learning
Authors: Xu Zhu, David Dunson
Date: 2019-04-24 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

In an episodic Markov Decision Process (MDP) problem, an online algorithm chooses from a set of actions in a sequence of $H$ trials, where $H$ is the episode length, in order to maximize the total payoff of the chosen actions. Q-learning, as the most popular model-free reinforcement learning (RL) algorithm, directly parameterizes and updates value functions without explicitly modeling the environment. Recently, [Jin et al. 2018] studies the sample complexity of Q-learning with finite states and actions. Their algorithm achieves nearly optimal regret, which shows that Q-learning can be made sample efficient. However, MDPs with large discrete states and actions [Silver et al. 2016] or continuous spaces [Mnih et al. 2013] cannot learn efficiently in this way. Hence, it is critical to develop new algorithms to solve this dilemma with provable guarantee on the sample complexity. With this motivation, we propose a novel algorithm that works for MDPs with a more general setting, which has infinitely many states and actions and assumes that the payoff function and transition kernel are Lipschitz continuous. We also provide corresponding theory justification for our algorithm. It achieves the regret $\tilde{\mathcal{O}}(K^{\frac{d+1}{d+2}}\sqrt{H^3}),$ where $K$ denotes the number of episodes and $d$ denotes the dimension of the joint space. To the best of our knowledge, this is the first analysis in the model-free setting whose established regret matches the lower bound up to a logarithmic factor.
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
Authors: Yu Chen, Tom Diethe, Neil Lawrence
Date: 2019-04-24 00:00:00
Tasks: Continual Learning, Task 2

Continual learning aims to enable machine learning models to learn a general
solution space for past and future tasks in a sequential manner. Conventional
models tend to forget the knowledge of previous tasks while learning a new
task, a phenomenon known as catastrophic forgetting. When using Bayesian models
in continual learning, knowledge from previous tasks can be retained in two
ways: 1). posterior distributions over the parameters, containing the knowledge
gained from inference in previous tasks, which then serve as the priors for the
following task; 2). coresets, containing knowledge of data distributions of
previous tasks. Here, we show that Bayesian continual learning can be
facilitated in terms of these two means through the use of natural gradients
and Stein gradients respectively.
Towards Combining On-Off-Policy Methods for Real-World Applications
Authors: Kai-Chun Hu, Chen-Huan Pi, Ting Han Wei, I-Chen Wu, Stone Cheng, Yi-Wei Dai, Wei-Yuan Ye
Date: 2019-04-24 00:00:00
Tasks: OpenAI Gym, Position, Reinforcement Learning

In this paper, we point out a fundamental property of the objective in
reinforcement learning, with which we can reformulate the policy gradient
objective into a perceptron-like loss function, removing the need to
distinguish between on and off policy training. Namely, we posit that it is
sufficient to only update a policy $\pi$ for cases that satisfy the condition
$A(\frac{\pi}{\mu}-1)\leq0$, where $A$ is the advantage, and $\mu$ is another
policy. Furthermore, we show via theoretic derivation that a perceptron-like
loss function matches the clipped surrogate objective for PPO. With our new
formulation, the policies $\pi$ and $\mu$ can be arbitrarily apart in theory,
effectively enabling off-policy training. To examine our derivations, we can
combine the on-policy PPO clipped surrogate (which we show to be equivalent
with one instance of the new reformation) with the off-policy IMPALA method. We
first verify the combined method on the OpenAI Gym pendulum toy problem. Next,
we use our method to train a quadrotor position controller in a simulator. Our
trained policy is efficient and lightweight enough to perform in a low cost
micro-controller at a minimum update rate of 500 Hz. For the quadrotor, we show
two experiments to verify our method and demonstrate performance: 1) hovering
at a fixed position, and 2) tracking along a specific trajectory. In
preliminary trials, we are also able to apply the method to a real-world
quadrotor.
Detecting Machine-Translated Paragraphs by Matching Similar Words
Authors: Hoang-Quoc Nguyen-Son, Tran Phuong Thao, Seira Hidano, Shinsaku Kiyomoto
Date: 2019-04-24 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Translation

Machine-translated text plays an important role in modern life by smoothing
communication from various communities using different languages. However,
unnatural translation may lead to misunderstanding, a detector is thus needed
to avoid the unfortunate mistakes. While a previous method measured the
naturalness of continuous words using a N-gram language model, another method
matched noncontinuous words across sentences but this method ignores such words
in an individual sentence. We have developed a method matching similar words
throughout the paragraph and estimating the paragraph-level coherence, that can
identify machine-translated text. Experiment evaluates on 2000 English
human-generated and 2000 English machine-translated paragraphs from German
showing that the coherence-based method achieves high performance (accuracy =
87.0%; equal error rate = 13.0%). It is efficiently better than previous
methods (best accuracy = 72.4%; equal error rate = 29.7%). Similar experiments
on Dutch and Japanese obtain 89.2% and 97.9% accuracy, respectively. The
results demonstrate the persistence of the proposed method in various languages
with different resource levels.
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
Authors: Yu Yu, Gang Liu, Jean-Marc Odobez
Date: 2019-04-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Gaze Estimation, gaze redirection

As an indicator of human attention gaze is a subtle behavioral cue which can
be exploited in many applications. However, inferring 3D gaze direction is
challenging even for deep neural networks given the lack of large amount of
data (groundtruthing gaze is expensive and existing datasets use different
setups) and the inherent presence of gaze biases due to person-specific
difference. In this work, we address the problem of person-specific gaze model
adaptation from only a few reference training samples. The main and novel idea
is to improve gaze adaptation by generating additional training samples through
the synthesis of gaze-redirected eye images from existing reference samples. In
doing so, our contributions are threefold: (i) we design our gaze redirection
framework from synthetic data, allowing us to benefit from aligned training
sample pairs to predict accurate inverse mapping fields; (ii) we proposed a
self-supervised approach for domain adaptation; (iii) we exploit the gaze
redirection to improve the performance of person-specific gaze estimation.
Extensive experiments on two public datasets demonstrate the validity of our
gaze retargeting and gaze estimation framework.
Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
Authors: Shuailong Liang, Olivia Nicol, Yue Zhang
Date: 2019-04-24 00:00:00
Tasks: Articles

Blame games tend to follow major disruptions, be they financial crises,
natural disasters or terrorist attacks. To study how the blame game evolves and
shapes the dominant crisis narratives is of great significance, as sense-making
processes can affect regulatory outcomes, social hierarchies, and cultural
norms. However, it takes tremendous time and efforts for social scientists to
manually examine each relevant news article and extract the blame ties (A
blames B). In this study, we define a new task, Blame Tie Extraction, and
construct a new dataset related to the United States financial crisis
(2007-2010) from The New York Times, The Wall Street Journal and USA Today. We
build a Bi-directional Long Short-Term Memory (BiLSTM) network for contexts
where the entities appear in and it learns to automatically extract such blame
ties at the document level. Leveraging the large unsupervised model such as
GloVe and ELMo, our best model achieves an F1 score of 70% on the test set for
blame tie extraction, making it a useful tool for social scientists to extract
blame ties more efficiently.
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings
Authors: Sarik Ghazarian, Johnny Tian-Zheng Wei, Aram Galstyan, Nanyun Peng
Date: 2019-04-24 00:00:00
Proceeding: WS 2019 6
Tasks: Dialogue Evaluation, valid, Word Embeddings

Despite advances in open-domain dialogue systems, automatic evaluation of
such systems is still a challenging problem. Traditional reference-based
metrics such as BLEU are ineffective because there could be many valid
responses for a given context that share no common words with reference
responses. A recent work proposed Referenced metric and Unreferenced metric
Blended Evaluation Routine (RUBER) to combine a learning-based metric, which
predicts relatedness between a generated response and a given query, with
reference-based metric; it showed high correlation with human judgments. In
this paper, we explore using contextualized word embeddings to compute more
accurate relatedness scores, thus better evaluation metrics. Experiments show
that our evaluation metrics outperform RUBER, which is trained on static
embeddings.
LFFD: A Light and Fast Face Detector for Edge Devices
Authors: Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, Chunhong Pan
Date: 2019-04-24 00:00:00
Tasks: Face Detection, Raspberry Pi 3

Face detection, as a fundamental technology for various applications, is always deployed on edge devices which have limited memory storage and low computing power. This paper introduces a Light and Fast Face Detector (LFFD) for edge devices. The proposed method is anchor-free and belongs to the one-stage category. Specifically, we rethink the importance of receptive field (RF) and effective receptive field (ERF) in the background of face detection. Essentially, the RFs of neurons in a certain layer are distributed regularly in the input image and theses RFs are natural "anchors". Combining RF "anchors" and appropriate RF strides, the proposed method can detect a large range of continuous face scales with 100% coverage in theory. The insightful understanding of relations between ERF and face scales motivates an efficient backbone for one-stage detection. The backbone is characterized by eight detection branches and common layers, resulting in efficient computation. Comprehensive and extensive experiments on popular benchmarks: WIDER FACE and FDDB are conducted. A new evaluation schema is proposed for application-oriented scenarios. Under the new schema, the proposed method can achieve superior accuracy (WIDER FACE Val/Test -- Easy: 0.910/0.896, Medium: 0.881/0.865, Hard: 0.780/0.770; FDDB -- discontinuous: 0.973, continuous: 0.724). Multiple hardware platforms are introduced to evaluate the running efficiency. The proposed method can obtain fast inference speed (NVIDIA TITAN Xp: 131.45 FPS at 640x480; NVIDIA TX2: 136.99 PFS at 160x120; Raspberry Pi 3 Model B+: 8.44 FPS at 160x120) with model size of 9 MB.
Maximum Entropy Based Significance of Itemsets
Authors: Nikolaj Tatti
Date: 2019-04-24 00:00:00

We consider the problem of defining the significance of an itemset. We say
that the itemset is significant if we are surprised by its frequency when
compared to the frequencies of its sub-itemsets. In other words, we estimate
the frequency of the itemset from the frequencies of its sub-itemsets and
compute the deviation between the real value and the estimate. For the
estimation we use Maximum Entropy and for measuring the deviation we use
Kullback-Leibler divergence.
  A major advantage compared to the previous methods is that we are able to use
richer models whereas the previous approaches only measure the deviation from
the independence model.
  We show that our measure of significance goes to zero for derivable itemsets
and that we can use the rank as a statistical test. Our empirical results
demonstrate that for our real datasets the independence assumption is too
strong but applying more flexible models leads to good results.
Low-Memory Neural Network Training: A Technical Report
Authors: Nimit S. Sohoni, Christopher R. Aberger, Megan Leszczynski, Jian Zhang, Christopher Ré
Date: 2019-04-24 00:00:00
Tasks: image-classification, Image Classification, Machine Translation, Translation

Memory is increasingly often the bottleneck when training neural network models. Despite this, techniques to lower the overall memory requirements of training have been less widely studied compared to the extensive literature on reducing the memory requirements of inference. In this paper we study a fundamental question: How much memory is actually needed to train a neural network? To answer this question, we profile the overall memory usage of training on two representative deep learning benchmarks -- the WideResNet model for image classification and the DynamicConv Transformer model for machine translation -- and comprehensively evaluate four standard techniques for reducing the training memory requirements: (1) imposing sparsity on the model, (2) using low precision, (3) microbatching, and (4) gradient checkpointing. We explore how each of these techniques in isolation affects both the peak memory usage of training and the quality of the end model, and explore the memory, accuracy, and computation tradeoffs incurred when combining these techniques. Using appropriate combinations of these techniques, we show that it is possible to the reduce the memory required to train a WideResNet-28-2 on CIFAR-10 by up to 60.7x with a 0.4% loss in accuracy, and reduce the memory required to train a DynamicConv model on IWSLT'14 German to English translation by up to 8.7x with a BLEU score drop of 0.15.
Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
Authors: Hao Sun, Xianxu Zeng, Tao Xu, Gang Peng, Yutao Ma
Date: 2019-04-24 00:00:00
Tasks: Binary Classification, Classification, General Classification, Sensitivity, Specificity

Uterine cancer, also known as endometrial cancer, can seriously affect the
female reproductive organs, and histopathological image analysis is the gold
standard for diagnosing endometrial cancer. However, due to the limited
capability of modeling the complicated relationships between histopathological
images and their interpretations, these computer-aided diagnosis (CADx)
approaches based on traditional machine learning algorithms often failed to
achieve satisfying results. In this study, we developed a CADx approach using a
convolutional neural network (CNN) and attention mechanisms, called HIENet.
Because HIENet used the attention mechanisms and feature map visualization
techniques, it can provide pathologists better interpretability of diagnoses by
highlighting the histopathological correlations of local (pixel-level) image
features to morphological characteristics of endometrial tissue. In the
ten-fold cross-validation process, the CADx approach, HIENet, achieved a 76.91
$\pm$ 1.17% (mean $\pm$ s. d.) classification accuracy for four classes of
endometrial tissue, namely normal endometrium, endometrial polyp, endometrial
hyperplasia, and endometrial adenocarcinoma. Also, HIENet achieved an
area-under-the-curve (AUC) of 0.9579 $\pm$ 0.0103 with an 81.04 $\pm$ 3.87%
sensitivity and 94.78 $\pm$ 0.87% specificity in a binary classification task
that detected endometrioid adenocarcinoma (Malignant). Besides, in the external
validation process, HIENet achieved an 84.50% accuracy in the four-class
classification task, and it achieved an AUC of 0.9829 with a 77.97% (95% CI,
65.27%-87.71%) sensitivity and 100% (95% CI, 97.42%-100.00%) specificity. In
summary, the proposed CADx approach, HIENet, outperformed three human experts
and four end-to-end CNN-based classifiers on this small-scale dataset composed
of 3,500 hematoxylin and eosin (H&E) images regarding overall classification
performance.
Objective Assessment of Social Skills Using Automated Language Analysis for Identification of Schizophrenia and Bipolar Disorder
Authors: Rohit Voleti, Stephanie Woolridge, Julie M. Liss, Melissa Milanovic, Christopher R. Bowie, Visar Berisha
Date: 2019-04-24 00:00:00
Tasks: Diagnostic

Several studies have shown that speech and language features, automatically extracted from clinical interviews or spontaneous discourse, have diagnostic value for mental disorders such as schizophrenia and bipolar disorder. They typically make use of a large feature set to train a classifier for distinguishing between two groups of interest, i.e. a clinical and control group. However, a purely data-driven approach runs the risk of overfitting to a particular data set, especially when sample sizes are limited. Here, we first down-select the set of language features to a small subset that is related to a well-validated test of functional ability, the Social Skills Performance Assessment (SSPA). This helps establish the concurrent validity of the selected features. We use only these features to train a simple classifier to distinguish between groups of interest. Linear regression reveals that a subset of language features can effectively model the SSPA, with a correlation coefficient of 0.75. Furthermore, the same feature set can be used to build a strong binary classifier to distinguish between healthy controls and a clinical group (AUC = 0.96) and also between patients within the clinical group with schizophrenia and bipolar I disorder (AUC = 0.83).
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Authors: Yunsheng Li, Lu Yuan, Nuno Vasconcelos
Date: 2019-04-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Image Segmentation, Image-to-Image Translation, Segmentation, Self-Supervised Learning, Semantic Segmentation, Synthetic-to-Real Translation, Translation

Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time
consuming. Existing domain adaptation techniques either work on limited
datasets, or yield not so good performance compared with supervised learning.
In this paper, we propose a novel bidirectional learning framework for domain
adaptation of segmentation. Using the bidirectional learning, the image
translation model and the segmentation adaptation model can be learned
alternatively and promote to each other. Furthermore, we propose a
self-supervised learning algorithm to learn a better segmentation adaptation
model and in return improve the image translation model. Experiments show that
our method is superior to the state-of-the-art methods in domain adaptation of
segmentation with a big margin. The source code is available at
https://github.com/liyunsheng13/BDL.
Reinterpreting CTC training as iterative fitting
Authors: Hongzhu Li, Weiqiang Wang
Date: 2019-04-24 00:00:00
Tasks: General Classification

The connectionist temporal classification (CTC) enables end-to-end sequence learning by maximizing the probability of correctly recognizing sequences during training. The outputs of a CTC-trained model tend to form a series of spikes separated by strongly predicted blanks, know as the spiky problem. To figure out the reason for it, we reinterpret the CTC training process as an iterative fitting task that is based on frame-wise cross-entropy loss. It offers us an intuitive way to compare target probabilities with model outputs for each iteration, and explain how the model outputs gradually turns spiky. Inspired by it, we put forward two ways to modify the CTC training. The experiments demonstrate that our method can well solve the spiky problem and moreover, lead to faster convergence over various training settings. Beside this, the reinterpretation of CTC, as a brand new perspective, may be potentially useful in other situations. The code is publicly available at https://github.com/hzli-ucas/caffe/tree/ctc.