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Found 8 papers, 2 papers with code
Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
We find empirically that across multiple language tasks, surprisingly, Voting Inference Systems' performance first increases but then decreases as a function of the number of LLM calls.
Controlling Commercial Cooling Systems Using Reinforcement Learning
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.
Semi-analytical Industrial Cooling System Model for Reinforcement Learning
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation.
Decentralized Training of Foundation Models in Heterogeneous Environments
Our key technical contribution is a scheduling algorithm that allocates different computational "tasklets" in the training of foundation models to a group of decentralized GPU devices connected by a slow heterogeneous network.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Ode to an ODE
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).
An Ode to an ODE
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).
Time Dependence in Non-Autonomous Neural ODEs
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.
