57 citations · 64 across the 13 of their papers we have counts for
13 papers
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…
Computationally-efficient Graph Modeling with Refined Graph Random Features
Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish +1
We propose refined GRFs (GRFs++), a new class of Graph Random Features (GRFs) for efficient and accurate computations involving kernels defined on the nodes of a graph. GRFs++ reso…
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
Connor Schenck, Isaac Reid, Mithun George Jacob +19
We introduce STRING: Separable Translationally Invariant Position Encodings. STRING extends Rotary Position Encodings, a recently proposed and widely used algorithm in large langua…
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…
Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs
Krzysztof Choromanski, Isaac Reid, Arijit Sehanobish +1
We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels (RWKs) for sparse graphs. This…
Variance-Reducing Couplings for Random Features
Isaac Reid, Stratis Markou, Krzysztof Choromanski +2
Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpi…
General Graph Random Features
Isaac Reid, Krzysztof Choromanski, Eli Berger +1
We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This…
Repelling Random Walks
Isaac Reid, Eli Berger, Krzysztof Choromanski +1
We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting e…
Quasi-Monte Carlo Graph Random Features
Isaac Reid, Krzysztof Choromanski, Adrian Weller
We present a novel mechanism to improve the accuracy of the recently-introduced class of graph random features (GRFs). Our method induces negative correlations between the lengths…
Simplex Random Features
Isaac Reid, Krzysztof Choromanski, Valerii Likhosherstov +1
We present Simplex Random Features (SimRFs), a new random feature (RF) mechanism for unbiased approximation of the softmax and Gaussian kernels by geometrical correlation of random…
Karyotype AI for Precision Oncology
Zahra Shamsi, Isaac Reid, Drew Bryant +13
We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cel…
Entanglement Barriers in Dual-Unitary Circuits
Isaac Reid, Bruno Bertini
After quantum quenches in many-body systems, finite subsystems evolve non-trivially in time, eventually approaching a stationary state. In typical situations, the reduced density m…
Why flatness does and does not correlate with generalization for deep neural networks
Shuofeng Zhang, Isaac Reid, Guillermo Valle Pérez +1
The intuition that local flatness of the loss landscape is correlated with better generalization for deep neural networks (DNNs) has been explored for decades, spawning many differ…