activity
20152024
most citedGemini 1.5: Unlocking multimodal understanding across millions of tokens of context

297 citations · 480 across the 13 of their papers we have counts for

collaborators

14 papers

cs.CY2024★ 20 cited

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Irina Jurenka, Markus Kunesch, Kevin R. McKee +71

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about…

cs.CL2024★ 297 cited

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…

cs.LG2021★ 3 cited

Task-agnostic Continual Learning with Hybrid Probabilistic Models

Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao +6

Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learnin…

cs.LG2020

Neural Rate Control for Video Encoding using Imitation Learning

Hongzi Mao, Chenjie Gu, Miaosen Wang +9

In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rat…

cs.LG2020★ 6 cited

Balancing Constraints and Rewards with Meta-Gradient D4PG

Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy +4

Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly…

cs.LG2020

A maximum-entropy approach to off-policy evaluation in average-reward MDPs

Nevena Lazic, Dong Yin, Mehrdad Farajtabar +4

This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear…