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Google Research

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22

RESEARCH · 1 SOURCE · Google Research

Google Research introduces SEEDS — a diffusion-based generative AI to produce large weather forecast ensembles

Google Research published a paper in Science Advances describing SEEDS (Scalable Ensemble Envelope Diffusion Sampler), a generative AI method based on denoising diffusion probabilistic models that can efficiently produce large ensembles of weather forecasts conditioned on as few as one or two numerical model runs. The authors report that SEEDS can generate realistic, probabilistic forecast ensembles at a small fraction of the computational cost of traditional physics-based ensembles and that the generated ensembles match or exceed physics-based ensembles on some skill metrics.

8.0

RESEARCH · 1 SOURCE · Google Research

AutoBNN — compositional Bayesian neural networks for probabilistic time-series forecasting (open-source, Google Research)

Google Research (post by Urs Köster) presents AutoBNN, an open-source JAX package available within TensorFlow Probability that replaces Gaussian processes with compositional Bayesian neural networks to automate discovery of interpretable time-series forecasting models, produce uncertainty estimates, and scale more efficiently to large datasets. AutoBNN maps compositional GP kernels to BNN architectures and supports operators analogous to GP addition and multiplication while enabling GPU/TPU acceleration and possible hybrid architectures with deep BNN components.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research study on assistive AI for CT lung cancer screening shows increased radiologist specificity

Google Research reports a retrospective multinational study (US and Japan) in Radiology AI evaluating an assistive ML system for lung cancer CT screening that outputs a four-category suspicion rating and localized regions of interest; randomized reader studies found that radiologist specificity increased with model assistance. The team improved prior models (including self-attention), deployed a 13-model system on Google Cloud/GKE, and open-sourced code to generate PACS-compatible CT images to facilitate similar evaluations.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research paper shows ML improves global flood forecasts for ungauged watersheds

Google Research published a Nature paper, “Global prediction of extreme floods in ungauged watersheds,” reporting that machine learning models (including LSTM-based approaches) can materially improve global-scale flood forecasting where local streamflow data are scarce—extending forecast reliability on average from zero to five days and enabling Flood Hub to deliver up to seven-day river forecasts across reaches in over 80 countries; model evaluation was done in collaboration with ECMWF and the team has open-sourced related hydrology datasets.

8.0

MODELS · 1 SOURCE · Google Research

ScreenAI: Google Research’s 5B-parameter vision-language model for UIs and infographics

Google Research describes ScreenAI, a 5B-parameter vision-language model built on the PaLI architecture with pix2struct-style flexible patching, trained on a mixture of autogenerated and human-labeled screen and infographic data (including a novel Screen Annotation task). The team reports state-of-the-art results on UI/infographic tasks such as WebSRC and MoTIF and competitive performance on ChartQA, DocVQA and InfographicVQA, and is releasing three new evaluation datasets: Screen Annotation, ScreenQA Short, and Complex ScreenQA.

8.0

RESEARCH · 1 SOURCE · Google Research

SCIN: open-access, crowd-contributed dermatology image dataset focused on everyday conditions and diverse skin tones

Google Research and Stanford Medicine released the Skin Condition Image Network (SCIN), an open-access dataset of over 10,000 crowd-contributed images of skin, nail, and hair concerns collected in the US under an IRB-approved study. Images include self-reported demographics and tanning propensity, dermatologist labels (1–3 labelers per contribution with confidence scores and aggregated weighted differentials), and estimated skin type/tone annotations (self-reported sFST, dermatologist-estimated eFST, and layperson eMST); the dataset emphasizes common allergic, inflammatory, and infectious conditions and contains many early-stage presentations and a broader distribution of darker Fitzpatrick skin types than several clinical datasets.

7.0

RESEARCH · 1 SOURCE · Google Research

MELON: reconstructing 3D objects from unposed images using a lightweight CNN and modulo loss

Google Research presents MELON (Modulo Equivalent Latent Optimization of NeRF), a method spotlighted at 3DV 2024 that jointly infers object-centric camera poses from scratch and reconstructs a NeRF from as few as 4–6 unposed images. MELON uses a tiny CNN (initialized from noise, no pre-training) to regress poses and a modulo loss that accounts for object pseudo-symmetries, integrating both into standard NeRF training to achieve state-of-the-art accuracy without initial pose estimates or complex GAN/pretraining schemes.

7.0

RESEARCH · 1 SOURCE · Google Research

HEAL: a 4-step framework and metric to assess health equity of ML-based health tools

Google researchers (Schaekermann and Horn) propose HEAL, a four‑step framework and accompanying HEAL metric to quantify whether machine‑learning health technologies prioritize performance for populations with worse pre‑existing health outcomes. The paper—published in The Lancet eClinicalMedicine—defines steps to identify equity‑relevant factors and metrics, quantify pre‑existing disparities, measure model performance across subpopulations, and compute how anticorrelated performance is with health disparities; a dermatology CNN trained on ~29k cases (288 conditions) is presented as an illustrative case study.

8.0

RESEARCH · 1 SOURCE · Google Research

Cappy: 360M-parameter RoBERTa scorer that authors say boosts and adapts large multi-task LLMs without finetuning

Google Research introduces Cappy, a lightweight (≈360M params) scorer based on continual pretraining of RoBERTa that assigns a 0–1 correctness score to an instruction–response pair. The authors report Cappy can operate standalone on classification tasks or be used as an auxiliary component to boost large multi-task LLMs and enable downstream supervision and adaptation without backpropagating through the LLM or needing access to its parameters (e.g., closed-source WebAPI models).

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research presents 'Talk like a Graph' and GraphQA benchmark for encoding graphs to LLMs

Researchers at Google (Bahare Fatemi and Bryan Perozzi) propose methods to translate graph-structured data into text that large language models can reason over, and introduce GraphQA, a benchmark of graph reasoning tasks and graph generators. They report that LLM performance varies with encoding method, task type, and graph structure, and that choosing the right encoding can improve graph-task performance by up to about 60%.

7.0

MODELS · 1 SOURCE · Google Research

Google releases Derm Foundation and Path Foundation embedding tools for dermatology and pathology research

Google Health / Google Research announced two domain-specific embedding tools—Derm Foundation for dermatology images and Path Foundation for digital pathology (H&E whole-slide) images—made available for research use. The tools produce specialized numerical embeddings to accelerate downstream tasks (e.g., linear-probe classifiers) and build on prior CXR Foundation work and pathology-specific self-supervised learning optimizations.

7.0

RESEARCH · 1 SOURCE · Google Research

Social learning: Google Research framework for LLMs to teach each other via natural language

Google Research authors propose a "social learning" framework in which teacher LLMs transfer knowledge to a student LLM using natural-language instructions and synthesized examples rather than sharing raw data. Evaluated with PaLM 2‑S on tasks including spam detection, grade‑school math, and text-based question answering, they find that generated examples (e.g., 8–16 per case) often preserve privacy while yielding comparable student performance for most tasks, though spam detection was a notable exception; the paper also proposes quantitative privacy measures for this setting.

7.0

RESEARCH · 1 SOURCE · Google Research

Croissant 1.0: a schema.org-based metadata format for ML-ready datasets, with support from major dataset hosts and frameworks

Croissant is a new ML-oriented metadata format (v1.0) built on schema.org that standardizes description and organization of datasets without changing underlying file formats. The release includes a spec, example datasets, an open-source Python validator/consumer/generator, a visual editor, and a Responsible AI (RAI) vocabulary extension, and is being adopted by Kaggle, Hugging Face, OpenML, indexed by Dataset Search, and made loadable in major frameworks via TensorFlow Datasets.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Quantum AI hosts booth and 50+ presentations at APS March Meeting 2024

Google’s Quantum AI team announced a prominent presence at the American Physical Society March Meeting 2024 in Minneapolis, including a booth, over 50 talks, and presentations of tools and projects such as the Crumble visualization prototype, the Qualtran resource-estimation library, and an XPRIZE-related Q&A. The schedule covers topics across quantum error correction, calibration, noisy and fault-tolerant hardware, quantum algorithms, and ML applications for materials and chemistry.

6.0

MODELS · 1 SOURCE · Google Research

VideoPrism — Google Research's large foundational visual encoder for diverse video understanding

Google Research introduces VideoPrism, a video foundation model (ViFM) that produces frozen video representations for a wide range of tasks (classification, localization, retrieval, captioning, QA). It is pre-trained on a hybrid corpus of 36 million high-quality video-text pairs plus 582 million additional clips with noisy or machine-generated text and uses a two-stage training scheme (video-text contrastive learning followed by masked video modeling).

8.0

RESEARCH · 1 SOURCE · Google Research

Google Research: instance-conditional timescales of decay to reweight training data under concept drift

Google Research proposes learning an auxiliary, instance-conditional weighting model that assigns importance scores to training examples as a function of their content and age, combining multiple fixed timescales of decay and meta-learning the assignment alongside the primary model. The method aims to blend benefits of offline and continual learning and yields up to ~15% relative accuracy gains on a large nonstationary photo benchmark (~39M images over 10 years) and improvements across other nonstationary learning benchmarks.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research releases DP-Auditorium, a Python library for black-box auditing of differential privacy

Google Research published DP-Auditorium, an open-source Python library that audits differential privacy guarantees using only black-box access to mechanisms. The library provides property testers and dataset finders (including novel HockeyStick, Rényi, and MMD testers) and demonstrates the ability to detect DP guarantee violations across multiple DP definitions (pure, approximate, Rényi).

7.0

CODING · 1 SOURCE · Google Research

Google releases TensorFlow GNN 1.0 for production-scale graph neural networks

Google announces TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building and training graph neural networks in TensorFlow with first-class GraphTensor support, Keras integration, and flexible dynamic or batch subgraph sampling—scalable from interactive Colab use to distributed sampling via Apache Beam for datasets of up to hundreds of millions of nodes and billions of edges.

7.0

MODELS · 1 SOURCE · Google Research

Google Research presents TimesFM, a 200M-parameter decoder-only foundation model for time-series forecasting

Google Research researchers Rajat Sen and Yichen Zhou introduce TimesFM, a decoder-only transformer foundation model pre-trained on a corpus of about 100 billion real-world time-points. At 200 million parameters, TimesFM treats contiguous time-point patches as tokens and achieves strong zero-shot forecasting on diverse unseen datasets—approaching state-of-the-art supervised methods—with plans to make the model available via Google Cloud Vertex AI later this year.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research proposes 'early readouts' and a 'feature sieve' to mitigate spurious features and simplicity bias

Google Research authors Rishabh Tiwari and Pradeep Shenoy describe two interventions—using predictions from early intermediate layers ('early readouts') and inducing 'feature forgetting' via a 'feature sieve'—to detect and reduce reliance on spurious features and the simplicity bias in deep networks. They apply the early-readout signal to reweight teacher contributions during distillation and use a feature-sieving intervention to encourage models to find more predictive features, reporting improved worst-group accuracy across benchmarks (Waterbirds, CelebA, CivilComments, MNLI) and better generalization to unseen domains compared with prior methods.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research outlines software optimizations for mixed-input matmul on NVIDIA Ampere (CUTLASS)

A Google Research blog post by Manish Gupta presents software techniques to implement mixed-input matrix multiplication (e.g., F16 inputs × U8 weights) on NVIDIA Ampere Tensor Cores by handling data-type conversion and layout conformance; the methods are released in the open-source NVIDIA/CUTLASS repository and are reported to add minimal software overhead while approaching hardware peak performance. The work targets memory- and compute-heavy LLM workloads by enabling weight-only quantization patterns that reduce model memory footprint.

7.0

MODELS · 1 SOURCE · Google Research

Exphormer: using expander graphs to scale graph transformers

Exphormer is a sparse-attention framework for graph transformers proposed by Google Research (presented at ICML 2023) that replaces the dense full-attention interaction graph with a sparse d-regular expander graph (using a randomized Friedman near-Ramanujan construction). The approach combines expander edges with the input graph and virtual nodes to achieve linear edges and reports strong empirical results across diverse graph datasets; an implementation is available on GitHub.

7.0