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Learning Fair Representations through Uniformly distributed Sensitive Attributes

Learning Fair Representations through Uniformly Distributed ...

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Secure and Trustworthy Machine Learning (SaTML), IEEE conference on

作者： Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep neural networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

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Recognition of grammatical classes of overt speech using electrophysiological signals and machine learning

Recognition of grammatical classes of overt speech using ele...

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BioInspired processing (BIP), IEEE International conference on

作者： Samanta J. Barrientos Rojas Raquel Ramírez-Valencia Denise Alonso-Vázquez Ricardo Caraza Hector R. Martinez Omar Mendoza-Montoya Javier M. Antelis Escuela de Ingeniería y Ciencias Instituto Tecnológico y de Estudios Superiores de Monterrey Zapopan México Escuela de Medicina y Ciencias de la Salud Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico

ISBN: (纸本)9781665470490

In this paper, we propose the use of overt speech in grammatical class classification using electroencephalography (EEG) and electromyography (EMG) signals as a first approach to attempt speech decoding in patients with bulbar amyotrophic lateral sclerosis (ALS). The work was carried out with 4 healthy participants, 32 EEG channels evenly distributed over the scalp and 4 EMG channels located in the zygomaticus major and triangular muscles were recorded during Spanish word production. Different classification algorithms, including a neural network, were evaluated in two groups of grammatical classes: adverbs vs. nouns. In EEG signals, 92.6±2.09% accuracy was achieved, while with EMG, 94.84±4.71%. These results show that it is possible to recognize grammatical classes in overt speech using EEG or EMG signals. The best performance in the EEG and EMG signals was obtained with the linear SVM classifier. Based on the classification accuracy of each class in each of the classification models, it is concluded that it is more convenient to use EMG signals in overt speech concerning EEG; however, if we want to decode the attempted speech in ALS patients using non-invasive methods, it is necessary to use the information provided by the EEG signals.

关键词： Support vector machines Scalp neural networks Speech recognition Machine learning Production Electroencephalography

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distributed distributionally robust optimization with non-convex objectives 22

Distributed distributionally robust optimization with non-co...

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Proceedings of the 36th International conference on neural Information processing Systems

作者： Yang Jiao Kai Yang Dongjin Song Tongji University University of Connecticut

ISBN: (纸本)9781713871088

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the distributed distributionally robust optimization (DDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

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Analysis on Roles of DNNs in End-to-End Acoustic Scene Analysis Framework with distributed Sound-to-Light Conversion Devices

Analysis on Roles of DNNs in End-to-End Acoustic Scene Analy...

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Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Yuma Kinoshita Nobutaka Ono Tokyo Metropolitan University Tokyo Japan

ISBN: (纸本)9781665441629

We conduct an analysis on roles of deep neural networks (DNNs) in an end-to-end acoustic scene analysis frame-work with distributed sound-to-light conversion devices called Blinkies. Blinkies transmit sound information as the intensity of an onboard light-emitting diode (LED). A video camera can then easily collect acoustic information by capturing the LED intensities from multiple Blinkies distributed over a large area. In the end-to-end framework, both sound-to-light conversion and scene analysis processes are performed using two types of DNNs: an encoding network and a scene analysis network. These DNNs are optimized in an end-to-end manner for acoustic scene analysis. Although the efficacy of the end-to-end framework is already confirmed, it is unclear what role each network plays in the framework. In this paper, we examine the role of these networks through a simulation experiment using intermediate signal shuffling. Experimental results suggest that an encoding network does not output scene analysis results, but it achieves node-specific sound-to-light conversion that encodes spatial information of sounds. By using the node-dependent features obtained by encoding networks, the scene analysis network classifies scenes.

关键词： Training Performance evaluation Image analysis neural networks Light emitting diodes Encoding Acoustics

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Dynamic Multichannel Access for 5G and Beyond with Fast Time-Varying Channel

Dynamic Multichannel Access for 5G and Beyond with Fast Time...

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IEEE International conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Wang, Shaoyang Lv, Tiejun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

ISBN: (纸本)9781728150895

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

关键词： Dynamic multichannel access recurrent neural network high-mobility deep reinforcement learning prediction-based deep deterministic policy gradient

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MPANet: Multi-level Progressive Aggregation network for Crowd Counting 28th

MPANet: Multi-level Progressive Aggregation Network for Cro...

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28th International conference on neural Information processing, ICONIP 2021

作者： Meng, Chen Han, Run Pang, Chen Kang, Chunmeng Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030922375

Crowd counting has important applications in many fileds, but it is still a challenging task due to background occlusion, scale variation and uneven distribution of crowd. This paper proposes the Multi-level Progressive Aggregation network (MPANet) to enhance the channel and spatial dependencies of feature maps and effectively integrate the multi-level features. Besides, the Aggregation Refinement (AR) module is designed to integrate low-level spatial information and high-level semantic information. The proposed AR module can effectively utilize the complementary properties between multi-level features to generate high-quality density maps. Moreover, the Multi-scale Aware (MA) module is constructed to capture rich contextual information through convolutional kernels of different sizes. Furthermore, the Semantic Attention (SA) module is designed to enhance spatial and channel response on feature maps, which can reduce false recognition of the background region. Extensive experiments on four challenging datasets demonstrate that our approach outperforms most state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Computer vision

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Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System 40

Tailored Learning-Based Scheduling for <i>Kubernetes</i>-Ori...

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40th IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Han, Yiwen Shen, Shihao Wang, Xiaofei Wang, Shiqiang Leung, Victor C. M. Tianjin Univ Coll Intelligence & Comp TANKLab Tianjin Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY USA Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9780738112817

Kubernetes (k8s) has the potential to merge the distributed edge and the cloud but lacks a scheduling framework specifically for edge-cloud systems. Besides, the hierarchical distribution of heterogeneous resources and the complex dependencies among requests and resources make the modeling and scheduling of k8s-oriented edge-cloud systems particularly sophisticated. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 14.3% while reducing scheduling cost by 34.7% compared to baselines.

关键词： Processor scheduling Heuristic algorithms conferences Clustering algorithms Throughput Dynamic scheduling Graph neural networks

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Deep neural network aided Sparse Bayesian Learning for Wireless Access Channel Estimation in mm-Wave Massive MIMO Cloud Radio Access network Systems

Deep Neural Network aided Sparse Bayesian Learning for Wirel...

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International conference on Futuristic Technologies (INCOFT)

作者： Jayanta Datta David Zabala-Blanco Francisco Ruben Castillo Soria Department of Electrical Engineering University of Chile Santiago Chile Department of Computer Science and Industry Universidad Católica de Maule Talca Chile Telecommmunications Engineering Faculty of Sciences Autonomous University of San Luis Potosi San Luis Potosi Mexico

Cloud Radio Access network systems with mmWave Massive MIMO framework can be considered as a potential candidate for next generation wireless communications due to its promise of increased spectral efficiency and distributed signal processing capability. State-of-the-art compressive sensing algorithms like sparse Bayesian learning can exploit the inherent sparsity of the mm-Wave wireless channels to estimate the channel connecting the remote radio head and the user equipment in the wireless access link. The performance of the sparse Bayesian learning based channel estimation can be adversely affected by impairments due to optical fiber based front-haul channel and quantization noise. As a result, it is necessary to compensate for the performance degradation by applying methods which can combat the effects of the front-haul channel. Contemporary research has demonstrated the capability of deep learning algorithms in signal enhancement under low signal-to-noise ratio conditions, such as hybrid beamforming design, channel estimation as well as feedback of channel state in heterogeneous multi-antenna wireless systems. Motivated by their de-noising and signal prediction capabilities, convolutional long short term memory networks are employed in this work to jointly remove the quantization noise and optical fiber impairment due to the front-haul channel, which can improve the performance of sparse Bayesian learning in estimating the wireless access channel at the base-band processing unit. Computer simulation results show that the proposed methodology performs well under low signal-to-noise ratio conditions.

关键词： Wireless communication Deep learning Radio frequency Wireless sensor networks Quantization (signal) Array signal processing Channel estimation

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An Effective Design to Improve the Efficiency of DPUs on FPGA 26

An Effective Design to Improve the Efficiency of DPUs on FPG...

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26th IEEE International conference on Parallel and distributed Systems (IEEE ICPADS)

作者： Lei, Yutian Deng, Qingyong Long, Saiqin Liu, Shaohui Oh, Sangyoon Xiangtan Univ Sch Comp Sci Key Lab Hunan Prov Internet Things & Informat Sec Sci & Technol Parallel & Distributed Proc Lab Xiangtan Peoples R China PengCheng Lab Artificial Intelligence Ctr Shenzhen Peoples R China Ajou Univ Suwon South Korea

ISBN: (纸本)9781728190747

Convolutional neural networks (CNNs) have been widely used in various complicated problems, such as image classification, objection detection, semantic segmentation. To meet diversified CNN structures, the deep learning processing unit (DPU) is designed as a general accelerator on field programmable gate array (FPGA) to support various CNN layers, such as convolution, pooling, activation, etc. However, low DPU utilization and schedule efficiency appear when DPU used to multitask application completed by CNN models. In this paper, an effective design including multi-core with different size (MCDS) and DPU Plus is proposed to improve the efficiency of DPUs usage from the two dimensions of time and space. Through increasing the number of DPU cores on an FPGA and the utilization of single DPU core, the design of MCDS can effectively improve the overall throughput with restricted on-chip resources. Furthermore, the design of DPU Plus is proposed to improve the schedule efficiency of DPUs through simultaneously implementing DPU with other significant auxiliary modules of the application system on the same FPGA. Finally, a color space conversion module is implemented cooperate to the DPU cores to testify its performance, and the experimen shows that compared with running on the the CPU completely, it achieves16.2x acceleration, and increases the throughput of the entire system by 3.0x.

关键词： Deep learning processor unit (DPU) convolutional neural network (CNN) field programmable gate array (FPGA) efficiency

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DMAP: a distributed morphological attention policy for learning to locomote with a changing body 22

DMAP: a distributed morphological attention policy for learn...

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Proceedings of the 36th International conference on neural Information processing Systems

作者： Alberto Silvio Chiappa Alessandro Marin Vargas Alexander Mathis École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781713871088

Biological and artificial agents need to deal with constant changes in the real world. We study this problem in four classical continuous control environments, augmented with morphological perturbations. Learning to locomote when the length and the thickness of different body parts vary is challenging, as the control policy is required to adapt to the morphology to successfully balance and advance the agent. We show that a control policy based on the proprioceptive state performs poorly with highly variable body configurations, while an (oracle) agent with access to a learned encoding of the perturbation performs significantly better. We introduce DMAP, a biologically-inspired, attention-based policy network architecture. DMAP combines independent proprioceptive processing, a distributed policy with individual controllers for each joint, and an attention mechanism, to dynamically gate sensory information from different body parts to different controllers. Despite not having access to the (hidden) morphology information, DMAP can be trained end-to-end in all the considered environments, overall matching or surpassing the performance of an oracle agent. Thus DMAP, implementing principles from biological motor control, provides a strong inductive bias for learning challenging sensorimotor tasks. Overall, our work corroborates the power of these principles in challenging locomotion tasks.

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