pwc-archive/papers-with-abstracts huggingface dataset:
Towards intervention-centric causal reasoning in learning agents
Authors: Benjamin Lansdell
Date: 2020-05-26 00:00:00
Tasks: Deep Reinforcement Learning, Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interventions are central to causal learning and reasoning. Yet ultimately an intervention is an abstraction: an agent embedded in a physical environment (perhaps modeled as a Markov decision process) does not typically come equipped with the notion of an intervention -- its action space is typically ego-centric, without actions of the form `intervene on X'. Such a correspondence between ego-centric actions and interventions would be challenging to hard-code. It would instead be better if an agent learnt which sequence of actions allow it to make targeted manipulations of the environment, and learnt corresponding representations that permitted learning from observation. Here we show how a meta-learning approach can be used to perform causal learning in this challenging setting, where the action-space is not a set of interventions and the observation space is a high-dimensional space with a latent causal structure. A meta-reinforcement learning algorithm is used to learn relationships that transfer on observational causal learning tasks. This work shows how advances in deep reinforcement learning and meta-learning can provide intervention-centric causal learning in high-dimensional environments with a latent causal structure.
A comparison of Vietnamese Statistical Parametric Speech Synthesis Systems
Authors: Huy Kinh Phan, Viet Lam Phung, Tuan Anh Dinh, Bao Quoc Nguyen
Date: 2020-05-26 00:00:00
Tasks: GPU, Speech Synthesis

In recent years, statistical parametric speech synthesis (SPSS) systems have been widely utilized in many interactive speech-based systems (e.g.~Amazon's Alexa, Bose's headphones). To select a suitable SPSS system, both speech quality and performance efficiency (e.g.~decoding time) must be taken into account. In the paper, we compared four popular Vietnamese SPSS techniques using: 1) hidden Markov models (HMM), 2) deep neural networks (DNN), 3) generative adversarial networks (GAN), and 4) end-to-end (E2E) architectures, which consists of Tacontron~2 and WaveGlow vocoder in terms of speech quality and performance efficiency. We showed that the E2E systems accomplished the best quality, but required the power of GPU to achieve real-time performance. We also showed that the HMM-based system had inferior speech quality, but it was the most efficient system. Surprisingly, the E2E systems were more efficient than the DNN and GAN in inference on GPU. Surprisingly, the GAN-based system did not outperform the DNN in term of quality.
Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos
Authors: Karan Ahuja, Abhishek Bose, Mohit Jain, Kuntal Dey, Anil Joshi, Krishnaveni Achary, Blessin Varkey, Chris Harrison, Mayank Goel
Date: 2020-05-26 00:00:00
Tasks: Autism detection

Autism often remains undiagnosed in adolescents and adults. Prior research has indicated that an autistic individual often shows atypical fixation and gaze patterns. In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an autism diagnosis. We envision such automatic detection being applied during e.g., the consumption of web media, which could allow for passive screening and adaptation of user interfaces.
End-to-end Optimized Video Compression with MV-Residual Prediction
Authors: XiangJi Wu, Ziwen Zhang, Jie Feng, Lei Zhou, Junmin Wu
Date: 2020-05-26 00:00:00
Tasks: MS-SSIM, SSIM, Video Compression

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior autoencoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.
Class-Weighted Classification: Trade-offs and Robust Approaches
Authors: Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
Date: 2020-05-26 00:00:00
Proceeding: ICML 2020 1
Tasks: Classification, General Classification, imbalanced classification

We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem. Finally, we show that particular choices of the weighting set leads to a special instance of conditional value at risk (CVaR) from stochastic programming, which we call label conditional value at risk (LCVaR). Additionally, we generalize this weighting to derive a new robust risk problem that we call label heterogeneous conditional value at risk (LHCVaR). Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.
Kernel Self-Attention in Deep Multiple Instance Learning
Authors: Dawid Rymarczyk, Adriana Borowa, Jacek Tabor, Bartosz Zieliński
Date: 2020-05-25 00:00:00
Tasks: Medical Image Analysis, Multiple Instance Learning, whole slide images

Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), and to classify such bag, aggregation of information from all of the instances is needed. There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance. In this work, we introduce Self-Attention Attention-based MIL Pooling (SA-AbMILP) aggregation operation to account for the dependencies between instances. We conduct several experiments on MNIST, histological, microbiological, and retinal databases to show that SA-AbMILP performs better than other models. Additionally, we investigate kernel variations of Self-Attention and their influence on the results.
Emotion-robust EEG Classification for Motor Imagery
Authors: Abdul Moeed
Date: 2020-05-23 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

Developments in Brain Computer Interfaces (BCIs) are empowering those with severe physical afflictions through their use in assistive systems. Common methods of achieving this is via Motor Imagery (MI), which maps brain signals to code for certain commands. Electroencephalogram (EEG) is preferred for recording brain signal data on account of it being non-invasive. Despite their potential utility, MI-BCI systems are yet confined to research labs. A major cause for this is lack of robustness of such systems. As hypothesized by two teams during Cybathlon 2016, a particular source of the system's vulnerability is the sharp change in the subject's state of emotional arousal. This work aims towards making MI-BCI systems resilient to such emotional perturbations. To do so, subjects are exposed to high and low arousal-inducing virtual reality (VR) environments before recording EEG data. The advent of COVID-19 compelled us to modify our methodology. Instead of training machine learning algorithms to classify emotional arousal, we opt for classifying subjects that serve as proxy for each state. Additionally, MI models are trained for each subject instead of each arousal state. As training subjects to use MI-BCI can be an arduous and time-consuming process, reducing this variability and increasing robustness can considerably accelerate the acceptance and adoption of assistive technologies powered by BCI.
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting
Authors: Mahboobeh Parsapoor
Date: 2020-05-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis, Time Series Forecasting

Accurate forecasting of an electroencephalogram (EEG) time series is crucial for the correct diagnosis of neurological disorders such as seizures and epilepsy. Since the EEG time series is chaotic, most traditional machine learning algorithms have failed to forecast its next steps accurately. Thus, we suggest a model, which has formed by taking inspiration from the neural structures that underlie feelings (emotional states), to forecast EEG time series. The model, which is referred to as emotion-inspired deep structure (EiDS), can be used to predict both short- and long-term of EEG time series. This paper also compares the performance of EiDS with other variations of long short-term memory (LSTM) networks.
Devising Malware Characterstics using Transformers
Authors: Simra Shahid, Tanmay Singh, Yash Sharma, Kapil Sharma
Date: 2020-05-23 00:00:00
Tasks: Malware Analysis

With the increasing number of cybersecurity threats, it becomes more difficult for researchers to skim through the security reports for malware analysis. There is a need to be able to extract highly relevant sentences without having to read through the entire malware reports. In this paper, we are finding relevant malware behavior mentions from Advanced Persistent Threat Reports. This main contribution is an opening attempt to Transformer the approach for malware behavior analysis.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Authors: Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua
Date: 2020-05-23 00:00:00
Tasks: Collaborative Filtering, Conversational Recommendation, Thompson Sampling

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns
Authors: Moonseop Kim, Guang Lin
Date: 2020-05-23 00:00:00
Tasks: image-classification, Image Classification, regression

This paper uses the peridynamic theory, which is well-suited to crack studies, to predict the crack patterns in a moving disk and classify them according to the modes and finally perform regression analysis. In that way, the crack patterns are obtained according to each mode by Molecular Dynamic (MD) simulation using the peridynamics. Image classification and regression studies are conducted through Convolutional Neural Networks (CNNs) and the neural processes. First, we increased the amount and quality of the data using peridynamics, which can theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, we did the case study for the PMB, LPS, and VES models that were obtained using the peridynamic theory. Case studies were performed to classify the images using CNNs and determine the PMB, LBS, and VES models' suitability. Finally, we performed the regression analysis for the images of the crack patterns with neural processes to predict the crack patterns. In the regression problem, by representing the results of the variance according to the epochs, it can be confirmed that the result of the variance is decreased by increasing the epoch numbers through the neural processes. The most critical point of this study is that the neural processes make an accurate prediction even if there are missing or insufficient training data.
Skewness Ranking Optimization for Personalized Recommendation
Authors: Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen, Ming-Feng Tsai
Date: 2020-05-23 00:00:00

In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility of the skew normal distribution, based on which three hyperparameters are attached to the optimization criterion. Furthermore, from a theoretical point of view, we not only establish the relation between the maximization of the proposed criterion and the shape parameter in the skew normal distribution, but also provide the analogies and asymptotic analysis of the proposed criterion to maximization of the area under the ROC curve. Experimental results conducted on a range of large-scale real-world datasets show that our model significantly outperforms the state of the art and yields consistently best performance on all tested datasets.
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Authors: Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, Hongxia Yang
Date: 2020-05-20 00:00:00
Tasks: Contrastive Learning, Fairness, Language Modeling, Language Modelling, Recommendation Systems, Representation Learning

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommender systems. Standard approaches approximate maximum likelihood estimation (MLE) through sampling for better scalability and address the problem of DCG in a way similar to language modeling. However, live recommender systems face severe exposure bias and have a vocabulary several orders of magnitude larger than that of natural language, implying that MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples. In this paper, we theoretically prove that a popular choice of contrastive loss is equivalent to reducing the exposure bias via inverse propensity weighting, which provides a new perspective for understanding the effectiveness of contrastive learning. Based on the theoretical discovery, we design CLRec, a contrastive learning method to improve DCG in terms of fairness, effectiveness and efficiency in recommender systems with extremely large candidate size. We further improve upon CLRec and propose Multi-CLRec, for accurate multi-intention aware bias reduction. Our methods have been successfully deployed in Taobao, where at least four-month online A/B tests and offline analyses demonstrate its substantial improvements, including a dramatic reduction in the Matthew effect.
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
Authors: Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher
Date: 2020-05-21 00:00:00
Tasks: Attribute, Fairness, Recommendation Systems, Re-Ranking

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.
Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
Authors: Weinan Xu, Hengxu He, Minshi Tan, Yunming Li, Jun Lang, Dongbai Guo
Date: 2020-05-22 00:00:00
Tasks: Click-Through Rate Prediction

Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors. User interests intuitively follow a hierarchical pattern such that users generally show interests from a higher-level then to a lower-level abstraction. Modeling such an interest hierarchy in an attention network can fundamentally improve the representation of user behaviors. We, therefore, propose an improvement over DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical Attention Network (DHAN). In this model, a multi-dimensional hierarchical structure is introduced on the first attention layer which attends to an individual item, and the subsequent attention layers in the same dimension attend to higher-level hierarchy built on top of the lower corresponding layers. To enable modeling of multiple dimensional hierarchies, an expanding mechanism is introduced to capture one to many hierarchies. This design enables DHAN to attend different importance to different hierarchical abstractions thus can fully capture user interests at different dimensions (e.g. category, price, or brand).To validate our model, a simplified DHAN has applied to Click-Through Rate (CTR) prediction and our experimental results on three public datasets with two levels of the one-dimensional hierarchy only by category. It shows the superiority of DHAN with significant AUC uplift from 12% to 21% over DIN. DHAN is also compared with another state-of-the-art model Deep Interest Evolution Network (DIEN), which models temporal interest. The simplified DHAN also gets slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be a combination of DHAN and DIEN to model both temporal and hierarchical interests.
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
Authors: Hyeoncheol Cho, Eok Kyun Lee, Insung S. Choi
Date: 2020-05-12 00:00:00
Tasks: Drug Design, Graph Neural Network

Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the protein-ligand interactions with graph neural networks (GNNs) has experienced difficulties in the conversion of protein-ligand complex structures into the graph representation and left questions regarding whether the trained models properly learn the appropriate noncovalent interactions. Here, we proposed a GNN architecture, denoted as InteractionNet, which learns two separated molecular graphs, being covalent and noncovalent, through distinct convolution layers. We also analyzed the InteractionNet model with an explainability technique, i.e., layer-wise relevance propagation, for examination of the chemical relevance of the model's predictions. Separation of the covalent and noncovalent convolutional steps made it possible to evaluate the contribution of each step independently and analyze the graph-building strategy for noncovalent interactions. We applied InteractionNet to the prediction of protein-ligand binding affinity and showed that our model successfully predicted the noncovalent interactions in both performance and relevance in chemical interpretation.
Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction
Authors: Weiran Yao, Sean Qian
Date: 2020-05-21 00:00:00
Tasks: Decision Making, Management, Metric Learning, Traffic Prediction

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 minutes in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, we propose to decompose the end-to-end recommendation task into two hierarchical models: real-time traffic prediction and plan association. We learn the connections between the two models through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. We demonstrate the effectiveness of our approach by testing this framework on the traffic network in Cranberry Township in 2019. Results show that our recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and make recommendation of an average of 22.5 minutes lead time ahead of Waze alerts. The results suggest that our framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.