Institution

Google Research

Google's research organization, with foundational work across machine learning, systems, language, and vision.

DeepLab: Atrous Convolution for Semantic Segmentation

DeepLab turns semantic image segmentation into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.

Theorem Proving · Google Research

HOList: Machine Learning for Higher-Order Theorem Proving

HOList turns machine learning for higher-order theorem proving into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.

Small Language Models · Google Research

MobileBERT: Compact BERT for Resource-Limited Devices

MobileBERT turns mobile-friendly BERT compression into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.

Self-Supervised Learning · Google Research

SimCLR: Contrastive Learning for Visual Representations

SimCLR turns contrastive visual representation learning into a concrete research object, with evidence anchors, method tradeoffs, and limits for practical use.

Language Models · Google Research

Language Models Need Sleep: A Consolidate-and-Dream Recipe

Google Research argues LLMs need an offline sleep phase to turn short-term context into stable weights. With sleep, Qwen3-8B hits 79.2% on AIME-24 and a Transformer reaches 80% on ARC few-shot, beating SEAL.

Language Models · Google Research

BERT Explained: Bidirectional Transformer Pretraining for NLP

BERT pretrains a deep bidirectional Transformer encoder with masked language modeling, then fine-tunes with one extra layer — pushing GLUE to 80.5% and topping 11 NLP tasks.

LLM Reasoning · Google Research

Chain-of-Thought Prompting: How Showing the Steps Unlocks LLM Reasoning

Showing a few worked examples with intermediate reasoning steps lets big models solve multi-step problems — a 540B model with 8 chain-of-thought exemplars hits 57% on GSM8K, beating fine-tuned GPT-3 with a verifier.

Text-to-Image · Google Research

Imagen: Why a Frozen Text Encoder Beats a Bigger Image Model

Google's Imagen hit a new COCO FID of 7.27 without training on COCO, and showed that scaling a frozen T5-XXL text encoder lifts fidelity and alignment more than scaling the diffusion model.

Language Models · Google Research

PaLM: Scaling a 540B Dense Language Model with Pathways

PaLM is a 540-billion-parameter dense Transformer trained on 6,144 TPU v4 chips with Pathways. It hit breakthrough few-shot results and beat average human scores on BIG-bench.

Mixture of Experts · Google Research

Switch Transformer: One Expert Per Token, Up to a Trillion Parameters

Switch Transformer simplifies Mixture-of-Experts by routing each token to a single expert, hitting up to 7x faster T5 pretraining at fixed compute and scaling to 1.6 trillion parameters with bfloat16 training.

Language Models · Google Research

T5 Explained: One Text-to-Text Interface for Every NLP Task

T5 reframes every NLP task as text-in, text-out, then runs a systematic sweep over objectives, architectures, data, and scale. The 11B model set state of the art on GLUE, SuperGLUE, and SQuAD.

Vision Foundation Models · Google Research

Vision Transformer (ViT): An Image is Worth 16x16 Words

ViT shows a plain Transformer fed raw 16x16 image patches beats top CNNs once pre-trained on JFT-300M, reaching 88.55% on ImageNet while using far less training compute.

Transformers · Google Research

Attention Is All You Need: The Transformer Architecture Explained

The 2017 Transformer dropped recurrence and convolution for pure attention, hit 28.4 BLEU on WMT14 EN-DE and 41.8 on EN-FR, and trained in 3.5 days on 8 GPUs. Nearly every modern LLM inherits it.