activity
20242026
most citedGemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.DS2026

Near-Linear Time Generalized Sinkhorn Algorithms for Bounded Genus Graphs

Krzysztof Choromanski, Derek Long, Ananya Parashar +1

We present GenusSink, a new class of approximate generalized Sinkhorn algorithms with shortest-path-distance costs for bounded genus (e.g. planar) graphs, providing near-linear tim…

cs.CV2026

TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

Bingyi Cao, Koert Chen, Kevis-Kokitsi Maninis +16

Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation…

cs.LG2026

SLAY: Geometry-Aware Spherical Linearized Attention with Yat-Kernel

Jose Miguel Luna, Taha Bouhsine, Krzysztof Choromanski

We propose a new class of linear-time attention mechanisms based on a relaxed and computationally efficient formulation of the recently introduced E-Product, often referred to as t…

cs.RO20251 cited

Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer

Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169

General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…

cs.LG2025

Graph Random Features for Scalable Gaussian Processes

Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski +3

We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input space…

cs.LG2024

Linear Transformer Topological Masking with Graph Random Features

Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12

When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…