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Aditya Grover
Researcher at Stanford University
Publications - 85
Citations - 12305
Aditya Grover is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 22, co-authored 62 publications receiving 6774 citations. Previous affiliations of Aditya Grover include Indian Institute of Technology Delhi & University of California, Berkeley.
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Proceedings ArticleDOI
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
TL;DR: Node2vec as mentioned in this paper learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes by using a biased random walk procedure.
Posted Content
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
TL;DR: In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods.
Posted Content
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen,Kevin Lu,Aravind Rajeswaran,Kimin Lee,Aditya Grover,Michael Laskin,Pieter Abbeel,Aravind Srinivas,Igor Mordatch +8 more
TL;DR: Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
Journal ArticleDOI
Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Peter M. Attia,Aditya Grover,Norman Jin,Kristen A. Severson,Todor M. Markov,Yang-Hung Liao,Michael H. Chen,Bryan Cheong,Nicholas Perkins,Zi Yang,Patrick Herring,Muratahan Aykol,Stephen J. Harris,Stephen J. Harris,Richard D. Braatz,Stefano Ermon,William C. Chueh,William C. Chueh +17 more
TL;DR: A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.
Proceedings ArticleDOI
A Deep Hybrid Model for Weather Forecasting
TL;DR: This work studies specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables.