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Found 7 papers, 5 papers with code
Learning Formal Mathematics From Intrinsic Motivation
We propose novel methods for hindsight relabeling on proof search trees to significantly improve the agent's sample efficiency in both tasks.
Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
To make this tractable, we factorize our problem into optimizing the free-form instructions and few-shot demonstrations of every module and introduce several strategies to craft task-grounded instructions and navigate credit assignment across modules.
Scorch: A Library for Sparse Deep Learning
To bridge this gap, we introduce Scorch, a library that seamlessly integrates efficient sparse tensor computation into the PyTorch ecosystem, with an initial focus on inference workloads on CPUs.
Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control
For such solvers, we leverage a previously proposed complexity certification framework to generate a finite set of archetypal optimization problems; we prove that these archetypal problems form an execution-time equivalent cover of all possible problems; that is, that they capture the execution time for solving any possible optimization problem that can be encountered online.
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.
Co-simulation: State of the art
It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures.
Systems and Control 65Y10 I.6.1; I.6.7
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties.
