activity
20162025
most citedSoft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations

355 citations · 1.3k across the 14 of their papers we have counts for

collaborators

20 papers

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

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.CV2023

GIVT: Generative Infinite-Vocabulary Transformers

Michael Tschannen, Cian Eastwood, Fabian Mentzer

We introduce Generative Infinite-Vocabulary Transformers (GIVT) which generate vector sequences with real-valued entries, instead of discrete tokens from a finite vocabulary. To th…

cs.CV2023★ 13 cited

Finite Scalar Quantization: VQ-VAE Made Simple

Fabian Mentzer, David Minnen, Eirikur Agustsson +1

We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE repres…

eess.IV2023★ 12 cited

High-Fidelity Image Compression with Score-based Generative Models

Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer +3

Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this…

eess.IV2023

M2T: Masking Transformers Twice for Faster Decoding

Fabian Mentzer, Eirikur Agustsson, Michael Tschannen

We show how bidirectional transformers trained for masked token prediction can be applied to neural image compression to achieve state-of-the-art results. Such models were previous…