Search Results for author: Peter Robinson

Found 15 papers, 8 papers with code

Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding

no code implementations5 Jan 2021 Giorgio Valentini, Elena Casiraghi, Luca Cappelletti, Vida Ravanmehr, Tommaso Fontana, Justin Reese, Peter Robinson

We introduce a set of algorithms (Het-node2vec) that extend the original node2vec node-neighborhood sampling method to heterogeneous multigraphs, i. e. networks characterized by multiple types of nodes and edges.

General Purpose Atomic Crosschain Transactions

3 code implementations24 Nov 2020 Peter Robinson, Raghavendra Ramesh

The General Purpose Atomic Crosschain Transaction protocol allows composable programming across multiple Ethereum blockchains.

Cryptography and Security

Performance Overhead of Atomic Crosschain Transactions

1 code implementation19 May 2020 Peter Robinson

Atomic Crosschain Transaction technology allows composable programming across permissioned Ethereum blockchains.

Cryptography and Security

Layer 2 Atomic Cross-Blockchain Function Calls

2 code implementations19 May 2020 Peter Robinson, Raghavendra Ramesh

Existing atomic cross-blockchain function call protocols are Blockchain Layer 1 protocols, which require changes to the blockchain platform software to operate.

Cryptography and Security

Atomic Crosschain Transactions White Paper

1 code implementation28 Feb 2020 Peter Robinson, Raghavendra Ramesh, John Brainard, Sandra Johnson

Atomic Crosschain Transaction technology allows composable programming across private Ethereum blockchains.

Cryptography and Security

Enabling Machine Learning-Ready HPC Ensembles with Merlin

no code implementations5 Dec 2019 J. Luc Peterson, Ben Bay, Joe Koning, Peter Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam A. Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steven Langer, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, Jae-Seung Yeom

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data.

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

2 code implementations5 Oct 2019 Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process.

Atomic Crosschain Transactions for Ethereum Private Sidechains

1 code implementation26 Apr 2019 Peter Robinson, David Hyland-Wood, Roberto Saltini, Sandra Johnson, John Brainard

Ethereum Private Sidechains is a private blockchain technology which allows many blockchains to be operated in parallel.

Cryptography and Security

Anonymous State Pinning for Private Blockchains

1 code implementation7 Mar 2019 Peter Robinson, John Brainard

Existing solutions offering pinning to the public blockchain would reveal the transaction rate of the private blockchain, and do not provide a mechanism to contest the validity of a pin.

Cryptography and Security

An Empirical Study of Recent Face Alignment Methods

no code implementations16 Nov 2015 Heng Yang, Xuhui Jia, Chen Change Loy, Peter Robinson

In this paper, we carry out a rigorous evaluation of these methods by making the following contributions: 1) we proposes a new evaluation metric for face alignment on a set of images, i. e., area under error distribution curve within a threshold, AUC$_\alpha$, given the fact that the traditional evaluation measure (mean error) is very sensitive to big alignment error.

Face Alignment Face Detection

Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features

no code implementations16 Sep 2015 Heng Yang, Renqiao Zhang, Peter Robinson

Furthermore, we study the impact of training data imbalance on model performance and propose a training sample augmentation scheme that produces more initialisations for training samples from the minority.

Face Alignment Assisted by Head Pose Estimation

1 code implementation11 Jul 2015 Heng Yang, Wenxuan Mou, Yichi Zhang, Ioannis Patras, Hatice Gunes, Peter Robinson

In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation.

Face Alignment Head Pose Estimation

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