activity
20212026
most citedGemma: Open Models Based on Gemini Research and Technology

238 citations · 530 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CL2026

CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees

S M Rafiuddin, Atriya Sen

Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…

cs.CV2025

Efficiently Reconstructing Dynamic Scenes One D4RT at a Time

Chuhan Zhang, Guillaume Le Moing, Skanda Koppula +11

Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simpl…

cs.AI2025

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

SIMA team, Adrian Bolton, Alexander Lerchner +63

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…

cs.CL2025★ 65 cited

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…

cs.CL2024★ 145 cited

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Morgane Riviere, Shreya Pathak +195

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…

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★ 238 cited

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Thomas Mesnard, Cassidy Hardin +105

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…

cs.LG2024★ 4 cited

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Aleksandar Botev, Soham De, Samuel L Smith +59

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve ex…

cs.CV2023

Informative Priors Improve the Reliability of Multimodal Clinical Data Classification

L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout

Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…

cs.LG2022★ 38 cited

Plex: Towards Reliability using Pretrained Large Model Extensions

Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23

A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. P…

cs.LG2022★ 2 cited

Pre-training helps Bayesian optimization too

Zi Wang, George E. Dahl, Kevin Swersky +6

Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing…

stat.ML2022★ 2 cited

Neural Diffusion Processes

Vincent Dutordoir, Alan Saul, Zoubin Ghahramani +1

Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on…

cs.LG2021★ 15 cited

Pre-trained Gaussian Processes for Bayesian Optimization

Zi Wang, George E. Dahl, Kevin Swersky +5

Bayesian optimization (BO) has become a popular strategy for global optimization of expensive real-world functions. Contrary to a common expectation that BO is suited to optimizing…

stat.ML2021★ 1 cited

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Vincent Dutordoir, James Hensman, Mark van der Wilk +3

Neural networks and Gaussian processes are complementary in their strengths and weaknesses. Having a better understanding of their relationship comes with the promise to make each…