Joshua Robinson

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	Visi	Nuo 2019
Šaltiniai	2024	2022
h-rodyklė	11	11
i10-rodyklė	12	12

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Stefanie JegelkaTUM and MITPatvirtintas el. paštas mit.edu
Suvrit SraTUM and MITPatvirtintas el. paštas mit.edu
Ching-Yao ChuangMeta GenAIPatvirtintas el. paštas meta.com
Derek LimMITPatvirtintas el. paštas mit.edu
Antonio TorralbaProfessor of Computer Science, MITPatvirtintas el. paštas csail.mit.edu
Haggai MaronAssistant Professor at the Technion, Research Scientist at NVIDIA ResearchPatvirtintas el. paštas technion.ac.il
Kayhan BatmanghelichAssistant Professor, Boston UniversityPatvirtintas el. paštas bu.edu
Dilip KrishnanResearch Scientist, GooglePatvirtintas el. paštas google.com
Jure LeskovecProfessor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Robert MacKayProfessor of Mathematics, University of WarwickPatvirtintas el. paštas warwick.ac.uk
Karalias NikolaosMITPatvirtintas el. paštas mit.edu
Sharut GuptaPhD at MIT CSAILPatvirtintas el. paštas mit.edu
Andreas Loukason a break, ex: Prescient Design, EPFL

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Joshua Robinson

Stanford

Patvirtintas el. paštas mit.edu - Pagrindinis puslapis

machine learning geometric deep learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Contrastive learning with hard negative samples J Robinson, CY Chuang, S Sra, S Jegelka ICLR, 2021	869	2021
Debiased contrastive learning CY Chuang, J Robinson, L Yen-Chen, A Torralba, S Jegelka NeurIPS, 2020	680	2020
Sign and basis invariant networks for spectral graph representation learning D Lim, J Robinson, L Zhao, T Smidt, SJ Suvrit Sra, Haggai Maron ICLR, 2023	147	2023
Can contrastive learning avoid shortcut solutions? J Robinson, L Sun, K Yu, K Batmanghelich, S Jegelka, S Sra NeurIPS, 2021	137	2021
Strength from Weakness: Fast Learning Using Weak Supervision J Robinson, S Jegelka, S Sra ICML, 2020	46	2020
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations S Mishra, J Robinson, H Chang, D Jacobs, A Sarna, A Maschinot, ... Technical Report, 2022	30	2022
Aggregation of Markov flows I: theory R MacKay, J Robinson Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2018	25	2018
On the Stability of Expressive Positional Encodings for Graph Neural Networks Y Huang, W Lu, J Robinson, Y Yang, M Zhang, S Jegelka, P Li ICLR, 2024	22	2024
Expressive Sign Equivariant Networks for Spectral Geometric Learning D Lim, J Robinson, S Jegelka, Y Lipman, H Maron NeurIPS, 2023	16	2023
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning S Gupta, J Robinson, D Lim, S Villar, S Jegelka ICLR, 2024	15	2024
Flexible modeling of diversity with strongly log-concave distributions J Robinson, S Sra, S Jegelka NeurIPS, 2019	15	2019
Relational Deep Learning: Graph Representation Learning on Relational Databases M Fey, W Hu, K Huang, JE Lenssen, R Ranjan, J Robinson, R Ying, J You, ... ICML Position Paper, 2023	11*	2023
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions N Karalias, J Robinson, A Loukas, S Jegelka NeurIPS, 2022	8	2022
RelBench: A benchmark for deep learning on relational databases J Robinson, R Ranjan, W Hu, K Huang, J Han, A Dobles, M Fey, ... arXiv preprint arXiv:2407.20060, 2024	3	2024
The First Learning on Graphs Conference: Preface B Rieck, R Pascanu, Y Du, H Stärk, D Lim, CK Joshi, A Deac, I Duta, ... Learning on Graphs Conference, i-xxiii, 2022		2022

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