pwc-archive/papers-with-abstracts huggingface dataset:
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation
Authors: Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta
Date: 2021-11-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adversarial attacks during training can strongly influence the performance of multi-agent reinforcement learning algorithms. It is, thus, highly desirable to augment existing algorithms such that the impact of adversarial attacks on cooperative networks is eliminated, or at least bounded. In this work, we consider a fully decentralized network, where each agent receives a local reward and observes the global state and action. We propose a resilient consensus-based actor-critic algorithm, whereby each agent estimates the team-average reward and value function, and communicates the associated parameter vectors to its immediate neighbors. We show that in the presence of Byzantine agents, whose estimation and communication strategies are completely arbitrary, the estimates of the cooperative agents converge to a bounded consensus value with probability one, provided that there are at most $H$ Byzantine agents in the neighborhood of each cooperative agent and the network is $(2H+1)$-robust. Furthermore, we prove that the policy of the cooperative agents converges with probability one to a bounded neighborhood around a local maximizer of their team-average objective function under the assumption that the policies of the adversarial agents asymptotically become stationary.
Simplifying approach to Node Classification in Graph Neural Networks
Authors: Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata
Date: 2021-11-12 00:00:00
Tasks: Classification, feature selection, Graph Neural Network, Node Classification

Graph Neural Networks have become one of the indispensable tools to learn from graph-structured data, and their usefulness has been shown in wide variety of tasks. In recent years, there have been tremendous improvements in architecture design, resulting in better performance on various prediction tasks. In general, these neural architectures combine node feature aggregation and feature transformation using learnable weight matrix in the same layer. This makes it challenging to analyze the importance of node features aggregated from various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance. We show that not all features generated via aggregation steps are useful, and often using these less informative features can be detrimental to the performance of the GNN model. Through our experiments, we show that learning certain subsets of these features can lead to better performance on wide variety of datasets. We propose to use softmax as a regularizer and "soft-selector" of features aggregated from neighbors at different hop distances; and L2-Normalization over GNN layers. Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model achieves comparable or even higher accuracy than state-of-the-art GNN models in nine benchmark datasets for the node classification task, with remarkable improvements up to 51.1%.
Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation
Authors: Sriram Siva, Maggie Wigness, John G. Rogers, Long Quang, Hao Zhang
Date: 2021-11-12 00:00:00
Tasks: Disaster Response, Navigate

Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.
Neural Motion Planning for Autonomous Parking
Authors: Dongchan Kim, Kunsoo Huh
Date: 2021-11-12 00:00:00
Tasks: Motion Planning

This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. A non-uniform expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.
Mobility prediction Based on Machine Learning Algorithms
Authors: Donglin Wang, Qiuheng Zhou, Sanket Partani, Anjie Qiu, Hans D. Schotten
Date: 2021-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Management, Prediction

Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the complicated traffic network. In the past few years, plenty of researches have been done in this area, including Non-Machine-Learning (Non-ML)- based and Machine-Learning (ML)-based mobility prediction. In this paper, firstly we introduce the state of the art technologies for mobility prediction. Then, we selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical traffic date training. Lastly, we analyse the simulation results for mobility prediction and introduce a future work plan where mobility prediction will be applied for improving mobile communication.
BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
Authors: Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Rui Xu, Yongzhuo Zhang, Bingzhe Li, Lei Yang
Date: 2021-11-12 00:00:00
Tasks: global-optimization

Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more applications require ML on tiny devices with extremely limited resources, like implantable cardioverter defibrillator (ICD), which is known as TinyML. Unlike ML on the edge, TinyML with a limited energy supply has higher demands on low-power execution. Stochastic computing (SC) using bitstreams for data representation is promising for TinyML since it can perform the fundamental ML operations using simple logical gates, instead of the complicated binary adder and multiplier. However, SC commonly suffers from low accuracy for ML tasks due to low data precision and inaccuracy of arithmetic units. Increasing the length of the bitstream in the existing works can mitigate the precision issue but incur higher latency. In this work, we propose a novel SC architecture, namely Block-based Stochastic Computing (BSC). BSC divides inputs into blocks, such that the latency can be reduced by exploiting high data parallelism. Moreover, optimized arithmetic units and output revision (OUR) scheme are proposed to improve accuracy. On top of it, a global optimization approach is devised to determine the number of blocks, which can make a better latency-power trade-off. Experimental results show that BSC can outperform the existing designs in achieving over 10% higher accuracy on ML tasks and over 6 times power reduction.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Authors: Kunal Dahiya, Deepak Saini, Anshul Mittal, Ankush Shaw, Kushal Dave, Akshay Soni, Himanshu Jain, Sumeet Agarwal, Manik Varma
Date: 2021-11-12 00:00:00
Tasks: Multi-Label Learning

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set. This paper develops the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label task into four simpler sub-tasks each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of algorithms with varying trade-offs between accuracy and scalability. In particular, DeepXML yields the Astec algorithm that could be 2-12% more accurate and 5-30x faster to train than leading deep extreme classifiers on publically available short text datasets. Astec could also efficiently train on Bing short text datasets containing up to 62 million labels while making predictions for billions of users and data points per day on commodity hardware. This allowed Astec to be deployed on the Bing search engine for a number of short text applications ranging from matching user queries to advertiser bid phrases to showing personalized ads where it yielded significant gains in click-through-rates, coverage, revenue and other online metrics over state-of-the-art techniques currently in production. DeepXML's code is available at https://github.com/Extreme-classification/deepxml
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
Authors: Steffen Gracla, Edgar Beck, Carsten Bockelmann, Armin Dekorsy
Date: 2021-11-12 00:00:00
Tasks: Model Selection, Scheduling

Greater capabilities of mobile communications technology enable interconnection of on-site medical care at a scale previously unavailable. However, embedding such critical, demanding tasks into the already complex infrastructure of mobile communications proves challenging. This paper explores a resource allocation scenario where a scheduler must balance mixed performance metrics among connected users. To fulfill this resource allocation task, we present a scheduler that adaptively switches between different model-based scheduling algorithms. We make use of a deep Q-Network to learn the benefit of selecting a scheduling paradigm for a given situation, combining advantages from model-driven and data-driven approaches. The resulting ensemble scheduler is able to combine its constituent algorithms to maximize a sum-utility cost function while ensuring performance on designated high-priority users.
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
Authors: Gerhard Wunder, Benedikt Groß, Rick Fritschek, Rafael F. Schaefer
Date: 2021-11-12 00:00:00
Tasks: Mutual Information Estimation

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the H\"older inequality. In a probabilistic setting, the Jensen inequality describes the relationship between a convex function and the expected value. In this work, we want to look at the probabilistic setting from the reverse direction of the inequality. We show that under minimal constraints and with a proper scaling, the Jensen inequality can be reversed. We believe that the resulting tool can be helpful for many applications and provide a variational estimation of mutual information, where the reverse inequality leads to a new estimator with superior training behavior compared to current estimators.
Understanding the Information Needs and Practices of Human Supporters of an Online Mental Health Intervention to Inform Machine Learning Applications
Authors: Anja Thieme
Date: 2021-11-12 00:00:00

In the context of digital therapy interventions, such as internet-delivered Cognitive Behavioral Therapy (iCBT) for the treatment of depression and anxiety, extensive research has shown how the involvement of a human supporter or coach, who assists the person undergoing treatment, improves user engagement in therapy and leads to more effective health outcomes than unsupported interventions. Seeking to maximize the effects and outcomes of this human support, the research investigates how new opportunities provided through recent advances in the field of AI and machine learning (ML) can contribute useful data insights to effectively support the work practices of iCBT supporters. This paper reports detailed findings of an interview study with 15 iCBT supporters that deepens understanding of their existing work practices and information needs with the aim to meaningfully inform the development of useful, implementable ML applications particularly in the context of iCBT treatment for depression and anxiety. The analysis contributes (1) a set of six themes that summarize the strategies and challenges that iCBT supporters encounter in providing effective, personalized feedback to their mental health clients; and in response to these learnings, (2) presents for each theme concrete opportunities for how methods of ML could help support and address identified challenges and information needs. It closes with reflections on potential social, emotional and pragmatic implications of introducing new machine-generated data insights within supporter-led client review practices.
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
Authors: Igor Kulev, Berkay Köprü, Raul Rodriguez-Esteban, Diego Saldana, Yi Huang, Alessandro La Torraca, Elif Ozkirimli
Date: 2021-11-12 00:00:00
Tasks: Data Augmentation

The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines. For our submission to this challenge, we expanded the available training data by using several data augmentation techniques. The augmented data was then used to fine-tune an ensemble of language models that had been pre-trained on general-domain Twitter content. The proposed approach outperformed the prior state-of-the-art algorithm Kusuri and ranked high in the competition for our selected objective function, overlapping F1 score.
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values
Authors: Viola Wenz, Arno Kesper, Gabriele Taentzer
Date: 2021-11-12 00:00:00
Tasks: Clustering, Diversity

Data is of high quality if it is fit for its intended use. The quality of data is influenced by the underlying data model and its quality. One major quality problem is the heterogeneity of data as quality aspects such as understandability and interoperability are impaired. This heterogeneity may be caused by quality problems in the data model. Data heterogeneity can occur in particular when the information given is not structured enough and just captured in data values, often due to missing or non-suitable structure in the underlying data model. We propose a bottom-up approach to detecting quality problems in data models that manifest in heterogeneous data values. It supports an explorative analysis of the existing data and can be configured by domain experts according to their domain knowledge. All values of a selected data field are clustered by syntactic similarity. Thereby an overview of the data values' diversity in syntax is provided. It shall help domain experts to understand how the data model is used in practice and to derive potential quality problems of the data model. We outline a proof-of-concept implementation and evaluate our approach using cultural heritage data.
Collaboration Promotes Group Resilience in Multi-Agent AI
Authors: Sarah Keren, Matthias Gerstgrasser, Ofir Abu, Jeffrey Rosenschein
Date: 2021-11-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning (RL)

AI agents need to be robust to unexpected changes in their environment in order to safely operate in real-world scenarios. While some work has been done on this type of robustness in the single-agent case, in this work we introduce the idea that collaboration with other agents can help agents adapt to environment perturbations in multi-agent reinforcement learning settings. We first formalize this notion of resilience of a group of agents. We then empirically evaluate different collaboration protocols and examine their effect on resilience. We see that all of the collaboration approaches considered lead to greater resilience compared to baseline, in line with our hypothesis. We discuss future direction and the general relevance of the concept of resilience introduced in this work.
Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence
Authors: Yanyi Ding, Zhiyi Kuang, Yuxin Pei, Jeff Tan, Ziyu Zhang, Joseph Konan
Date: 2021-11-12 00:00:00

SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3 million deaths and infecting over 150 million worldwide as of May 2021. With thousands of strains sequenced to date, SARS-CoV-2 mutations pose significant challenges to scientists on keeping pace with vaccine development and public health measures. Therefore, an efficient method of identifying the divergence of lab samples from patients would greatly aid the documentation of SARS-CoV-2 genomics. In this study, we propose a neural network model that leverages recurrent and convolutional units to directly take in amino acid sequences of spike proteins and classify corresponding clades. We also compared our model's performance with Bidirectional Encoder Representations from Transformers (BERT) pre-trained on protein database. Our approach has the potential of providing a more computationally efficient alternative to current homology based intra-species differentiation.
Implicit vs Unfolded Graph Neural Networks
Authors: Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf
Date: 2021-11-12 00:00:00
Tasks: Graph Attention, Node Classification

It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient/scalable modeling of long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations, sensitivity to spurious edges, or inadequate model interpretability. To address these and other issues, two separate strategies have recently been proposed, namely implicit and unfolded GNNs (that we abbreviate to IGNN and UGNN respectively). The former treats node representations as the fixed points of a deep equilibrium model that can efficiently facilitate arbitrary implicit propagation across the graph with a fixed memory footprint. In contrast, the latter involves treating graph propagation as unfolded descent iterations as applied to some graph-regularized energy function. While motivated differently, in this paper we carefully quantify explicit situations where the solutions they produce are equivalent and others where their properties sharply diverge. This includes the analysis of convergence, representational capacity, and interpretability. In support of this analysis, we also provide empirical head-to-head comparisons across multiple synthetic and public real-world node classification benchmarks. These results indicate that while IGNN is substantially more memory-efficient, UGNN models support unique, integrated graph attention mechanisms and propagation rules that can achieve strong node classification accuracy across disparate regimes such as adversarially-perturbed graphs, graphs with heterophily, and graphs involving long-range dependencies.
AnchorGAE: General Data Clustering via $O(n)$ Bipartite Graph Convolution
Authors: Hongyuan Zhang, Jiankun Shi, Rui Zhang, Xuelong Li
Date: 2021-11-12 00:00:00
Tasks: Clustering

Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neural networks (GNNs) can be applied to augment the capacity. The core problems mainly come from two aspects: (1) the graph is unavailable in the most clustering scenes so that how to construct high-quality graphs on the non-graph data is usually the most important part; (2) given n samples, the graph-based clustering methods usually consume at least $\mathcal O(n^2)$ time to build graphs and the graph convolution requires nearly $\mathcal O(n^2)$ for a dense graph and $\mathcal O(|\mathcal{E}|)$ for a sparse one with $|\mathcal{E}|$ edges. Accordingly, both graph-based clustering and GNNs suffer from the severe inefficiency problem. To tackle these problems, we propose a novel clustering method, AnchorGAE, with the self-supervised estimation of graph and efficient graph convolution. We first show how to convert a non-graph dataset into a graph dataset, by introducing the generative graph model and anchors. We then show that the constructed bipartite graph can reduce the computational complexity of graph convolution from $\mathcal O(n^2)$ and $\mathcal O(|\mathcal{E}|)$ to $\mathcal O(n)$. The succeeding steps for clustering can be easily designed as $\mathcal O(n)$ operations. Interestingly, the anchors naturally lead to siamese architecture with the help of the Markov process. Furthermore, the estimated bipartite graph is updated dynamically according to the features extracted by GNN, to promote the quality of the graph. However, we theoretically prove that the self-supervised paradigm frequently results in a collapse that often occurs after 2-3 update iterations in experiments, especially when the model is well-trained. A specific strategy is accordingly designed to prevent the collapse.