Bookmarks (122)

  • The ants and the pheromones

    I’m super excited about the new chapter emerging in our research on a programmable cloud. This...

  • An overview of end-to-end entity resolution for big data

    An overview of end-to-end entity resolution for big data, Christophides et al., ACM Computing Surveys, Dec....

  • Bias in word embeddings

    Bias in word embeddings, Papakyriakopoulos et al., FAT*’20 There are no (stochastic) parrots in this paper,...

  • Seeing is believing: a client-centric specification of database isolation

    Seeing is believing: a client-centric specification of database isolation, Crooks et al., PODC’17. Last week we...

  • Elle: inferring isolation anomalies from experimental observations

    Elle: inferring isolation anomalies from experimental observations, Kingsbury & Alvaro, VLDB’20 Is there anything more terrifying,...

  • Achieving 100Gbps intrusion prevention on a single server

    Achieving 100 Gbps intrusion prevention on a single server, Zhao et al., OSDI’20 Papers-we-love is hosting...

  • Virtual consensus in Delos

    Virtual consensus in Delos, Balakrishnan et al. (Facebook, Inc.), OSDI’2020 Before we dive into this paper,...

  • Helios: hyperscale indexing for the cloud & edge (part II)

    Helios: hyperscale indexing for the cloud & edge, Potharaju et al., PVLDB’20 Last time out we...

  • Helios: hyperscale indexing for the cloud & edge – part 1

    Helios: hyperscale indexing for the cloud & edge, Potharaju et al., PVLDB’20 On the surface this...

  • The case for a learned sorting algorithm

    The case for a learned sorting algorithm, Kristo, Vaidya, et al., SIGMOD’20 We’ve watched machine learning...

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    Firecracker: lightweight virtualization for serverless applications

    Firecracker: lightweight virtualisation for serverless applications, Agache et al., NSDI’20 Finally the NSDI’20 papers have opened...

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    STELLA: report from the SNAFU-catchers workshop on coping with complexity

    STELLA: report from the SNAFU-catchers workshop on coping with complexity, Woods 2017, Coping with Complexity workshop...

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    Synthesizing data structure transformations from input-output examples

    Synthesizing data structure transformations from input-output examples, Feser et al., PLDI’15 The Programmatically Interpretable Reinforcement Learning...

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    Programmatically interpretable reinforcement learning

    Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a...

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    Challenges of real-world reinforcement learning

    Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML’19 Last week we looked at some of...

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    Ten challenges for making automation a ‘team player’ in joint human-agent activity

    Ten challenges for making automation a ‘team player’ in joint human-agent activity, Klein et al., IEEE...

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    Ironies of automation

    Ironies of automation, Bainbridge, Automatica, Vol. 19, No. 6, 1983 With thanks to Thomas Depierre for...

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    The Year Ahead

    Welcome to another year of The Morning Paper! Over the holidays I spent some time mapping...

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    Benchmarking spreadsheet systems

    Benchmarking spreadsheet systems Rahman et al., Preprint A recent TwThread drew my attention to this pre-print...

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    Declarative assembly of web applications from pre-defined concepts

    Declarative assembly of web applications from predefined concepts De Rosso et al., Onward! 2019 I chose...

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    Efficient lock-free durable sets

    Efficient lock-free durable sets Zuriel et al., OOPSLA’19 Given non-volatile memory (NVRAM), the naive hope for...