Proceedings of the Second International Conference on AI-ML Systems最新文献_第2页

Malware Analysis and Detection 恶意软件分析与检测

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564809

Hemant Rathore, Mohit Sewak

引用次数: 3

Performance improvement of reinforcement learning algorithms for online 3D bin packing using FPGA 基于FPGA的在线三维装箱强化学习算法的性能改进

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564795

Kavya Borra, Ashwin Krishnan, H. Khadilkar, M. Nambiar, Ansuma Basumatary, Rekha Singhal, A. Mukherjee

{"title":"Performance improvement of reinforcement learning algorithms for online 3D bin packing using FPGA","authors":"Kavya Borra, Ashwin Krishnan, H. Khadilkar, M. Nambiar, Ansuma Basumatary, Rekha Singhal, A. Mukherjee","doi":"10.1145/3564121.3564795","DOIUrl":"https://doi.org/10.1145/3564121.3564795","url":null,"abstract":"Online 3D bin packing is a challenging real-time combinatorial optimisation problem that involves packing of parcels (typically rigid cuboids) arriving on a conveyor into a larger bin for further shipment. Recent automation methods have introduced manipulator robots for packing, which need a processing algorithm to specify the location and orientation in which each parcel must be loaded. Value-based Reinforcement learning (RL) algorithms such as DQN are capable of producing good solutions in the available computation times. However, their deployment on CPU based systems employs rule-based heuristics to reduce the search space which may lead to a sub-optimal solution. In this paper, we use FPGA as a hardware accelerator to reduce inference time of DQN as well as its pre-/post-processing steps. This allows the optimised algorithm to cover the entire search space within the given time constraints. We present various optimizations, such as accelerating DQN model inference and fast checking of constraints. Further, we show that our proposed architecture achieves almost 15x computational speed-ups compared to an equivalent CPU implementation. Additionally, we show that as a result of evaluating the entire search space, the DQN rewards generated for complex data sets has improved by 1%, which can cause a significant reduction in enterprise operating costs.","PeriodicalId":166150,"journal":{"name":"Proceedings of the Second International Conference on AI-ML Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122765541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Causal Dependencies in Multivariate Time Series 多元时间序列中因果关系的识别

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564810

Sujoy Roy Chowdhury, Serene Banerjee, Ranjani H. G., Chaitanya Kapoor

{"title":"Identification of Causal Dependencies in Multivariate Time Series","authors":"Sujoy Roy Chowdhury, Serene Banerjee, Ranjani H. G., Chaitanya Kapoor","doi":"10.1145/3564121.3564810","DOIUrl":"https://doi.org/10.1145/3564121.3564810","url":null,"abstract":"Telecommunications networks operate on enormous amount of time-series data, and often exhibit anomalous trends in their behaviour. This is caused due to increased latency and reduced throughput in the network which inevitably leads to poor customer experience [17]. One of the common problems in machine learning in the telecom domain is to predict anomalous behaviour ahead of time. Whilst this is a well-researched problem, there is far less work done in identifying causal structures from the temporal patterns of various Key Performance Indicators (KPI) in the telecom network. The ability to identify causal structures from anomalous behaviours would allow more effective intervention and generalisation of different environments and networks. The tutorial is focused on discussing existing frameworks for establishing causal discovery for time-series data sets. In this hands-on tutorial, we will be covering at least 3 state-of-the-art (SOTA) methods on causal time series analysis including Granger causality[8],convergent cross-mapping [4, 10, 15], Peter-Clark Momentary Conditional Independence (PC-MCI) [6, 14] and Temporal Causal discovery framework (TCDF)[11]. The need for a causation analysis[7], beyond correlation will also be explained using publicly available datasets, such as, double pendulum dataset [1]. The state-of-art methods are chosen to cover various aspects of the causal time series analysis, such as modelling the non-linearity (non-linear Granger Causality), attempting the problem from chaos and dynamic systems (CCM), information-theoretic approaches (PC-MCI, or having a data-driven approach (TCDF). State-of-the-art survey papers [2, 12] show that none of the methods can be said to be ideal for all the possible time series and there are relative advantages and shortcomings for each of these methods.","PeriodicalId":166150,"journal":{"name":"Proceedings of the Second International Conference on AI-ML Systems","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115742466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RIDEN: Neural-based Uniform Density Histogram for Distribution Shift Detection 基于神经的均匀密度直方图分布偏移检测

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564136

Kei Yonekawa, Kazuhiro Saito, Mori Kurokawa

{"title":"RIDEN: Neural-based Uniform Density Histogram for Distribution Shift Detection","authors":"Kei Yonekawa, Kazuhiro Saito, Mori Kurokawa","doi":"10.1145/3564121.3564136","DOIUrl":"https://doi.org/10.1145/3564121.3564136","url":null,"abstract":"It is required to detect distribution shift in order to prevent a machine learning model from performance degradation, and human-mediated data analysis from erroneous conclusions. For the purpose of comparing between unknown distributions of high-dimensional data, histograms are suitable density estimators due to its computational efficiency. It is important for histograms for distribution shift detection to have uniform density, which has been demonstrated in existing tree-based or cluster-based histograms. However, existing histograms do not consider generalization capability to out-of-sample data, resulting in degraded detection performance at test time. In this paper, we propose a neural-based histogram for distribution shift detection, which generalizes well to out-of-sample data. The bins of histogram are determined by a model trained to discriminate between a handful reference instances, which reflects their underlying distribution. Due to the batch-wise maximum entropy regularizer calculated from a bootstrap sample, the bins as a subset of the feature space partitioned by the decision boundaries of the model generalize, and thus the histogram keeps its density uniform for out-of-sample data. We evaluate our method on distribution shift detection task using multi-domain real-world datasets. The results show that our method outperforms state-of-the-art histogram-based methods.","PeriodicalId":166150,"journal":{"name":"Proceedings of the Second International Conference on AI-ML Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115904571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unposed: Unsupervised Pose Estimation based Product Image Recommendations Unposed:基于产品图像推荐的无监督姿态估计

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564126

Saurabh Sharma, Faizan Ahemad

{"title":"Unposed: Unsupervised Pose Estimation based Product Image Recommendations","authors":"Saurabh Sharma, Faizan Ahemad","doi":"10.1145/3564121.3564126","DOIUrl":"https://doi.org/10.1145/3564121.3564126","url":null,"abstract":"Product images are the most impressing medium of customer interaction on the product detail pages of e-commerce websites. Millions of products are onboarded on to webstore catalogues daily and maintaining a high quality bar for a product’s set of images is a problem at scale. Grouping products by categories, clothing is a very high volume and high velocity category and thus deserves its own attention. Given the scale it is challenging to monitor the completeness of image set, which adequately details the product for the consumers, which in turn often leads to a poor customer experience and thus customer drop off. To supervise the quality and completeness of the images in the product pages for these product types and suggest improvements, we propose a Human Pose Detection based unsupervised method to scan the image set of a product for the missing ones. The unsupervised approach suggests a fair approach to sellers based on product and category irrespective of any biases. We first create a reference image set of popular products with wholesome imageset. Then we create clusters of images to label most desirable poses to form the classes for the reference set from these ideal products set. Further, for all test products we scan the images for all desired pose classes w.r.t. reference set poses, determine the missing ones and sort them in the order of potential impact. These missing poses can further be used by the sellers to add enriched product listing image. We gathered data from popular online webstore and surveyed ~200 products manually, a large fraction of which had at least 1 repeated image or missing variant, and sampled 3K products(~20K images) of which a significant proportion had scope for adding many image variants as compared to high rated products which had more than double image variants, indicating that our model can potentially be used on a large scale.","PeriodicalId":166150,"journal":{"name":"Proceedings of the Second International Conference on AI-ML Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125425807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Health Assurance: AI Model Monitoring Platform 健康保障:AI模型监测平台

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564798

Anirban I Ghosh, Radhika Sharma, Karan Goyal, Balakarthikeyan Rajan, S. Mani

引用次数: 0

DEC-aided SM-OFDM: A Spatial Modulation System with Deep Learning based Error Correction dec辅助SM-OFDM:一种基于深度学习纠错的空间调制系统

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564131

H. Verma, V. Bohara, Anubha Gupta

引用次数: 0

Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices 异构边缘设备的自动深度学习模型划分

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564796

Arijit Mukherjee, Swarnava Dey

引用次数: 1

Hetero-Rec: Optimal Deployment of Embeddings for High-Speed Recommendations Hetero-Rec:高速推荐的嵌入优化部署

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564134

Chinmay Mahajan, Ashwin Krishnan, M. Nambiar, Rekha Singhal

{"title":"Hetero-Rec: Optimal Deployment of Embeddings for High-Speed Recommendations","authors":"Chinmay Mahajan, Ashwin Krishnan, M. Nambiar, Rekha Singhal","doi":"10.1145/3564121.3564134","DOIUrl":"https://doi.org/10.1145/3564121.3564134","url":null,"abstract":"We see two trends emerging due to exponential increase in AI research- rise in adoption of AI based models in enterprise applications and development of different types of hardware accelerators with varying memory and computing architectures for accelerating AI workloads. Accelerators may have different types of memories, varying on access latency and storage capacity. A recommendation model’s inference latency is highly influenced by the time to fetch embeddings from the embedding tables. In this paper, we present Hetero-Rec, a framework for optimal deployment of embeddings for faster inference of recommendation model. The main idea is to cache frequently accessed embeddings on faster memories to reduce average latency during inference. Hetero-Rec uses performance model-based optimization algorithm and use of spline based learned index for determining the optimal reservation of portions of embedding tables across different memory types available for deployment, based on their past access patterns. We validate our approach for heterogeneous memory architectures, such as URAM (Ultra-Random Access Memory), BRAM (Block Random Access Memory), HBM (High-Bandwidth Memory) and DDR (Double Data Rate) on a server platform with an FPGA accelerator. We observe that the presented optimization algorithm for dynamic placement of embedding tables yields a reduction on average latency of up to 1.52x, 1.68x, and 2.91x for the weekly, daily, and hourly access patterns, respectively in the transaction history as compared to the state-of-the-art systems.","PeriodicalId":166150,"journal":{"name":"Proceedings of the Second International Conference on AI-ML Systems","volume":"66 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133488632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TinyML Techniques for running Machine Learning models on Edge Devices 在边缘设备上运行机器学习模型的TinyML技术

Proceedings of the Second International Conference on AI-ML Systems Pub Date : 2022-10-12 DOI: 10.1145/3564121.3564812

Arijit Mukherjee, A. Ukil, Swarnava Dey, Gitesh Kulkarni

引用次数: 0