フォロー
Mehran Kazemi
Mehran Kazemi
Staff Research Scientist, Google DeepMind
確認したメール アドレス: google.com - ホームページ
タイトル
引用先
引用先
年
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
24952023
Simple embedding for link prediction in knowledge graphs
SM Kazemi, D Poole
NeurIPS, 2018
9992018
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
9722024
Representation learning for dynamic graphs: A survey
SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart
The Journal of Machine Learning Research 21 (1), 2648-2720, 2020
5442020
Time2vec: Learning a vector representation of time
SM Kazemi, R Goel, S Eghbali, J Ramanan, J Sahota, S Thakur, S Wu, ...
arXiv preprint arXiv:1907.05321, 2019
4832019
Diachronic embedding for temporal knowledge graph completion
R Goel, SM Kazemi, M Brubaker, P Poupart
AAAI, 2020
4032020
Gemma 2: Improving open language models at a practical size
G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ...
arXiv preprint arXiv:2408.00118, 2024
3102024
Gemini: A family of highly capable multimodal models
R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805 1, 2023
3022023
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
B Fatemi, LE Asri, SM Kazemi
NeurIPS, 2021
1802021
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
SM Kazemi, N Kim, D Bhatia, X Xu, D Ramachandran
ACL, 2023
752023
Relational Logistic Regression
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
in Proc. 14th International Conference on Principles of Knowledge …, 2014
692014
RelNN: A deep neural model for relational learning
SM Kazemi, D Poole
AAAI, 2018
652018
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
A Saparov, RY Pang, V Padmakumar, N Joshi, SM Kazemi, N Kim, H He
NeurIPS, 2023
572023
Generative verifiers: Reward modeling as next-token prediction
L Zhang, A Hosseini, H Bansal, M Kazemi, A Kumar, R Agarwal
arXiv preprint arXiv:2408.15240, 2024
47*2024
New liftable classes for first-order probabilistic inference
SM Kazemi, A Kimmig, GV Broeck, D Poole
NeurIPS, 2016
472016
Let your graph do the talking: Encoding structured data for llms
B Perozzi, B Fatemi, D Zelle, A Tsitsulin, M Kazemi, R Al-Rfou, J Halcrow
arXiv preprint arXiv:2402.05862, 2024
442024
Relational representation learning for dynamic (knowledge) graphs: A survey
SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart
Journal of Machine Learning Research (JMLR) 21 (70), 1-73, 2020
432020
In-context learning with retrieved demonstrations for language models: A survey
M Luo, X Xu, Y Liu, P Pasupat, M Kazemi
arXiv preprint arXiv:2401.11624, 2024
402024
Dr. ICL: Demonstration-Retrieved In-context Learning
M Luo, X Xu, Z Dai, P Pasupat, M Kazemi, C Baral, V Imbrasaite, VY Zhao
FoMo Workshop, 2023
382023
Population size extrapolation in relational probabilistic modelling
D Poole, D Buchman, SM Kazemi, K Kersting, S Natarajan
Scalable Uncertainty Management: 8th International Conference, SUM 2014 …, 2014
382014
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