pwc-archive/papers-with-abstracts huggingface dataset:
Meta-path Free Semi-supervised Learning for Heterogeneous Networks
Authors: Shin-woo Park, Byung Jun Bae, Jinyoung Yeo, Seung-won Hwang
Date: 2020-10-18 00:00:00
Tasks: Graph Neural Network, Node Classification, Representation Learning

Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification. However, analyzing heterogeneous graph of different types of nodes and links still brings great challenges for injecting the heterogeneity into a graph neural network. A general remedy is to manually or automatically design meta-paths to transform a heterogeneous graph into a homogeneous graph, but this is suboptimal since the features from the first-order neighbors are not fully leveraged for training and inference. In this paper, we propose simple and effective graph neural networks for heterogeneous graph, excluding the use of meta-paths. Specifically, our models focus on relaxing the heterogeneity stress for model parameters by expanding model capacity of general GNNs in an effective way. Extensive experimental results on six real-world graphs not only show the superior performance of our proposed models over the state-of-the-arts, but also demonstrate the potentially good balance between reducing the heterogeneity stress and increasing the parameter size. Our code is freely available for reproducing our results.
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
Authors: Chenyu You, Nuo Chen, Fenglin Liu, Dongchao Yang, Yuexian Zou
Date: 2020-10-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Conversational Question Answering, Question Answering, speech-recognition, Speech Recognition

In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. In this task, our main objective is to build a QA system to deal with conversational questions both in spoken and text forms, and to explore the plausibility of providing more cues in spoken documents with systems in information gathering. To this end, instead of adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which directly fuse audio-text features to reduce the misalignment between automatic speech recognition hypotheses and the reference transcriptions. In addition, to evaluate the capacity of QA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 120k question-answer pairs. Experiments demonstrate that our proposed method achieves superior performance in spoken conversational question answering.
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Authors: Vektor Dewanto, George Dunn, Ali Eshragh, Marcus Gallagher, Fred Roosta
Date: 2020-10-18 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Reinforcement learning is important part of artificial intelligence. In this paper, we review model-free reinforcement learning that utilizes the average reward optimality criterion in the infinite horizon setting. Motivated by the solo survey by Mahadevan (1996a), we provide an updated review of work in this area and extend it to cover policy-iteration and function approximation methods (in addition to the value-iteration and tabular counterparts). We present a comprehensive literature mapping. We also identify and discuss opportunities for future work.
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution
Authors: Xiaoyu Xiang, Qian Lin, Jan P. Allebach
Date: 2020-10-18 00:00:00
Tasks: Face Detection, Optical Character Recognition, Optical Character Recognition (OCR), Scene Text Recognition, Super-Resolution

Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% shorter runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).
Disguising Personal Identity Information in EEG Signals
Authors: Shiya Liu, Yue Yao, Chaoyue Xing, Tom Gedeon
Date: 2020-10-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

There is a need to protect the personal identity information in public EEG datasets. However, it is challenging to remove such information that has infinite classes (open set). We propose an approach to disguise the identity information in EEG signals with dummy identities, while preserving the key features. The dummy identities are obtained by applying grand average on EEG spectrums across the subjects within a group that have common attributes. The personal identity information in original EEGs are transformed into disguised ones with a CycleGANbased EEG disguising model. With the constraints added to the model, the features of interest in EEG signals can be preserved. We evaluate the model by performing classification tasks on both the original and the disguised EEG and compare the results. For evaluation, we also experiment with ResNet classifiers, which perform well especially on the identity recognition task with an accuracy of 98.4%. The results show that our EEG disguising model can hide about 90% of personal identity information and can preserve most of the other key features.
Prediction of daily maximum ozone levels using Lasso sparse modeling method
Authors: Jiaqing Lv, Xiaohong Xu
Date: 2020-10-18 00:00:00
Tasks: Dimensionality Reduction, Prediction

This paper applies modern statistical methods in the prediction of the next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone concentration of the next day. The model uses a large number of candidate features, including the present day's hourly concentration level of various pollutants, as well as the meteorological variables of the present day's observation and the future day's forecast values. In order to solve such an ultra-high dimensional problem, the least absolute shrinkage and selection operator (Lasso) was applied. The $L_1$ nature of this methodology enables the automatic feature dimension reduction, and a resultant sparse model. The model trained by 3-years data demonstrates relatively good prediction accuracy, with RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum $O_3$ concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's maximum 8-hour-mean $O_3$ concentration. Our modeling approach is also compared with several other methods recently applied in the field and demonstrates superiority in the prediction accuracy.
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
Authors: Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson
Date: 2020-10-18 00:00:00

We propose a general scheme for solving convex and non-convex optimization problems on manifolds. The central idea is that, by adding a multiple of the squared retraction distance to the objective function in question, we "convexify" the objective function and solve a series of convex sub-problems in the optimization procedure. One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e.g., whether the objective function is convex or non-convex, and the degree of non-convexity. Our proposed algorithm adapts to the level of complexity in the objective function. We show that when the objective function is convex, the algorithm provably converges to the optimum and leads to accelerated convergence. When the objective function is non-convex, the algorithm will converge to a stationary point. Our proposed method unifies insights from Nesterov's original idea for accelerating gradient descent algorithms with recent developments in optimization algorithms in Euclidean space. We demonstrate the utility of our algorithms on several manifold optimization tasks such as estimating intrinsic and extrinsic Fr\'echet means on spheres and low-rank matrix factorization with Grassmann manifolds applied to the Netflix rating data set.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
Authors: Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney
Date: 2020-10-18 00:00:00
Tasks: Recommendation Systems

In this paper, we consider hybrid parallelism -- a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) -- to scale distributed training of large recommendation models. We propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communicated across the network through a simple hard-thresholding function, allowing only the most relevant information to pass through. For communication efficient DP, DCT compresses the parameter gradients sent to the parameter server during model synchronization. The threshold is updated only once every few thousand iterations to reduce the computational overhead of compression. For communication efficient MP, DCT incorporates a novel technique to compress the activations and gradients sent across the network during the forward and backward propagation, respectively. This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data. We evaluate DCT on publicly available natural language processing and recommender models and datasets, as well as recommendation systems used in production at Facebook. DCT reduces communication by at least $100\times$ and $20\times$ during DP and MP, respectively. The algorithm has been deployed in production, and it improves end-to-end training time for a state-of-the-art industrial recommender model by 37\%, without any loss in performance.
Fourier Neural Operator for Parametric Partial Differential Equations
Authors: Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
Date: 2020-10-18 00:00:00
Proceeding: ICLR 2021 1
Tasks: Super-Resolution

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. The Fourier neural operator is the first ML-based method to successfully model turbulent flows with zero-shot super-resolution. It is up to three orders of magnitude faster compared to traditional PDE solvers. Additionally, it achieves superior accuracy compared to previous learning-based solvers under fixed resolution.
Mixed-Lingual Pre-training for Cross-lingual Summarization
Authors: Ruochen Xu, Chenguang Zhu, Yu Shi, Michael Zeng, Xuedong Huang
Date: 2020-10-18 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Translation

Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language. Traditional solutions employ a two-step approach, i.e. translate then summarize or summarize then translate. Recently, end-to-end models have achieved better results, but these approaches are mostly limited by their dependence on large-scale labeled data. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. Thus, our model can leverage the massive monolingual data to enhance its modeling of language. Moreover, the architecture has no task-specific components, which saves memory and increases optimization efficiency. We show in experiments that this pre-training scheme can effectively boost the performance of cross-lingual summarization. In Neural Cross-Lingual Summarization (NCLS) dataset, our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs
Authors: Aayam Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern
Date: 2020-10-18 00:00:00
Proceeding: ICLR 2021 1
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjustment to changing environments and goals. Our main contribution is to introduce the Deep Averagers with Costs MDP (DAC-MDP) and to investigate its solutions for offline RL. DAC-MDPs are a non-parametric model that can leverage deep representations and account for limited data by introducing costs for exploiting under-represented parts of the model. In theory, we show conditions that allow for lower-bounding the performance of DAC-MDP solutions. We also investigate the empirical behavior in a number of environments, including those with image-based observations. Overall, the experiments demonstrate that the framework can work in practice and scale to large complex offline RL problems.
Light Stage Super-Resolution: Continuous High-Frequency Relighting
Authors: Tiancheng Sun, Zexiang Xu, Xiuming Zhang, Sean Fanello, Christoph Rhemann, Paul Debevec, Yun-Ta Tsai, Jonathan T. Barron, Ravi Ramamoorthi
Date: 2020-10-17 00:00:00
Tasks: Super-Resolution, Vocal Bursts Intensity Prediction

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite number of lights in the stage, the light transport matrix only represents a sparse sampling on the entire sphere. As a consequence, relighting the subject with a point light or a directional source that does not coincide exactly with one of the lights in the stage requires interpolation and resampling the images corresponding to nearby lights, and this leads to ghosting shadows, aliased specularities, and other artifacts. To ameliorate these artifacts and produce better results under arbitrary high-frequency lighting, this paper proposes a learning-based solution for the "super-resolution" of scans of human faces taken from a light stage. Given an arbitrary "query" light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a "virtual" light source at the query location. This neural network must circumvent the inherent aliasing and regularity of the light stage data that was used for training, which we accomplish through the use of regularized traditional interpolation methods within our network. Our learned model is able to produce renderings for arbitrary light directions that exhibit realistic shadows and specular highlights, and is able to generalize across a wide variety of subjects.
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Authors: Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
Date: 2020-10-17 00:00:00
Proceeding: ICLR 2021 1
Tasks: Contrastive Learning, Representation Learning

Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective domain-agnostic regularization strategy for improving contrastive representation learning. We cast contrastive learning as training a non-parametric classifier by assigning a unique virtual class to each data in a batch. Then, data instances are mixed in both the input and virtual label spaces, providing more augmented data during training. In experiments, we demonstrate that i-Mix consistently improves the quality of learned representations across domains, including image, speech, and tabular data. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes. The code is available at https://github.com/kibok90/imix.
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Authors: Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora, Xiaoyan Wang, Yucen Lily Li, Nazanin Khosravani Tehrani, Michael Tingley, David Noursi, Narjes Torabi, Sepehr Akhavan Masouleh, Eric Lippert, Erik Meijer
Date: 2020-10-17 00:00:00
Tasks: Probabilistic Programming

We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs. All of the benchmark code and PPL implementations are available on Github. We welcome contributions of new models and PPLs and as well as improvements in existing PPL implementations. The purpose of the benchmark is two-fold. First, we want researchers as well as conference reviewers to be able to evaluate improvements in PPLs in a standardized setting. Second, we want end users to be able to pick the PPL that is most suited for their modeling application. In particular, we are interested in evaluating the accuracy and speed of convergence of the inferred posterior. Each PPL only needs to provide posterior samples given a model and observation data. The framework automatically computes and plots growth in predictive log-likelihood on held out data in addition to reporting other common metrics such as effective sample size and $\hat{r}$.
A Game AI Competition to foster Collaborative AI research and development
Authors: Ana Salta, Rui Prada, Francisco S. Melo
Date: 2020-10-17 00:00:00

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper, we discuss the competition and the challenges it brings, and present an overview of the current solutions.
Question Answering over Knowledge Base using Language Model Embeddings
Authors: Sai Sharath Japa, Rekabdar Banafsheh
Date: 2020-10-17 00:00:00
Tasks: Knowledge Base Question Answering, Language Modeling, Language Modelling, Question Answering

Knowledge Base, represents facts about the world, often in some form of subsumption ontology, rather than implicitly, embedded in procedural code, the way a conventional computer program does. While there is a rapid growth in knowledge bases, it poses a challenge of retrieving information from them. Knowledge Base Question Answering is one of the promising approaches for extracting substantial knowledge from Knowledge Bases. Unlike web search, Question Answering over a knowledge base gives accurate and concise results, provided that natural language questions can be understood and mapped precisely to an answer in the knowledge base. However, some of the existing embedding-based methods for knowledge base question answering systems ignore the subtle correlation between the question and the Knowledge Base (e.g., entity types, relation paths, and context) and suffer from the Out Of Vocabulary problem. In this paper, we focused on using a pre-trained language model for the Knowledge Base Question Answering task. Firstly, we used Bert base uncased for the initial experiments. We further fine-tuned these embeddings with a two-way attention mechanism from the knowledge base to the asked question and from the asked question to the knowledge base answer aspects. Our method is based on a simple Convolutional Neural Network architecture with a Multi-Head Attention mechanism to represent the asked question dynamically in multiple aspects. Our experimental results show the effectiveness and the superiority of the Bert pre-trained language model embeddings for question answering systems on knowledge bases over other well-known embedding methods.
Visualization of Contributions to Open-Source Projects
Authors: Andreas Schreiber
Date: 2020-10-17 00:00:00
Tasks: Visual Relationship Detection

We want to analyze visually, to what extend team members and external developers contribute to open-source projects. This gives a high-level impression about collaboration in that projects. We achieve this by recording provenance of the development process and use graph drawing on the resulting provenance graph. Our graph drawings show, which developers are jointly changed the same files -- and to what extent -- which we show at Germany's COVID-19 exposure notification app 'Corona-Warn-App'.