9 citations · 14 across the 6 of their papers we have counts for
6 papers
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Peisong Zhang, Manqiang Peng, Yuxuan Wu +21
Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing co…
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production
Alexandre Galashov, Matt Jones, Rosemary Ke +3
Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling -- which involves the insertion of <pause> tokens in the input strea…
Scaling Instructable Agents Across Many Simulated Worlds
SIMA Team, Maria Abi Raad, Arun Ahuja +91
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires lear…
Prequential MDL for Causal Structure Learning with Neural Networks
Jorg Bornschein, Silvia Chiappa, Alan Malek +1
Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology. We show that the prequential min…
Fast and Slow Learning of Recurrent Independent Mechanisms
Kanika Madan, Nan Rosemary Ke, Anirudh Goyal +2
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is l…