Sharad Vikram
Sharad Vikram
Zweryfikowany adres z google.com - Strona główna
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Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
What are Bayesian neural network posteriors really like?
P Izmailov, S Vikram, MD Hoffman, AGG Wilson
International Conference on Machine Learning, 4629-4640, 2021
SOLAR: Deep structured representations for model-based reinforcement learning
M Zhang, S Vikram, L Smith, P Abbeel, M Johnson, S Levine
International Conference on Machine Learning, 7444-7453, 2019
Handwriting and Gestures in the Air, Recognizing on the Fly
S Vikram, L Li, S Russell
Proceedings of the CHI 13, 1179-1184, 2013
Capturing meaning in product reviews with character-level generative text models
ZC Lipton, S Vikram, J McAuley
arXiv preprint arXiv:1511.03683, 2015
Estimating reactions and recommending products with generative models of reviews
J Ni, ZC Lipton, S Vikram, J McAuley
Proceedings of the Eighth International Joint Conference on Natural Language …, 2017
Interactive bayesian hierarchical clustering
S Vikram, S Dasgupta
International Conference on Machine Learning, 2081-2090, 2016
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Q Vuong, S Vikram, H Su, S Gao, HI Christensen
arXiv preprint arXiv:1903.11774, 2019
Methods of predicting pathogenicity of genetic sequence variants
IS Haque, EA Evans, SM Vikram, MD Rasmussen
US Patent App. 15/189,957, 2016
Automatic structured variational inference
L Ambrogioni, K Lin, E Fertig, S Vikram, M Hinne, D Moore, M van Gerven
International Conference on Artificial Intelligence and Statistics, 676-684, 2021
Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring
S Vikram, A Collier-Oxandale, MH Ostertag, M Menarini, C Chermak, ...
Atmospheric Measurement Techniques 12 (8), 4211-4239, 2019
Automatic Differentiation Variational Inference with Mixtures
W Morningstar, S Vikram, C Ham, A Gallagher, J Dillon
International Conference on Artificial Intelligence and Statistics, 3250-3258, 2021
The LORACs prior for VAEs: Letting the trees speak for the data
S Vikram, MD Hoffman, MJ Johnson
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Evaluating Approximate Inference in Bayesian Deep Learning
AG Wilson, P Izmailov, MD Hoffman, Y Gal, Y Li, MF Pradier, S Vikram, ...
NeurIPS 2021 Competitions and Demonstrations Track, 113-124, 2022
Estimating the changing infection rate of COVID-19 using Bayesian models of mobility
L Liu, S Vikram, J Lao, X Ben, A D’Amour, S O’Banion, M Sandler, ...
medRxiv, 2020.08. 06.20169664, 2020
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
Training Chain-of-Thought via Latent-Variable Inference
D Phan, MD Hoffman, D Dohan, S Douglas, TA Le, A Parisi, P Sountsov, ...
arXiv preprint arXiv:2312.02179, 2023
Interactive comment on “Evaluating and Improving the Reliability of Gas-Phase Sensor System Calibrations Across New Locations for Ambient Measurements and Personal Exposure …
S Vikram
Bayesian Structured Representation Learning
S Vikram
University of California, San Diego, 2019
SSCM: A method to analyze and predict the pathogenicity of sequence variants
S Vikram, MD Rasmussen, EA Evans, IS Haque
bioRxiv, 021527, 2015
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