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Found 7 papers, 1 papers with code
Causal AI-based Root Cause Identification: Research to Practice at Scale
Modern applications are built as large, distributed systems spanning numerous modules, teams, and data centers.
Sparsity May Be All You Need: Sparse Random Parameter Adaptation
We propose simply reducing the number of trainable parameters by randomly selecting a small proportion of the model parameters to train on.
Evaluating the Prompt Steerability of Large Language Models
Achieving this requires first being able to evaluate the degree to which a given model is capable of reflecting various personas.
Contextual Moral Value Alignment Through Context-Based Aggregation
Developing value-aligned AI agents is a complex undertaking and an ongoing challenge in the field of AI.
Fourier Representations for Black-Box Optimization over Categorical Variables
In order to improve the performance and sample efficiency of such algorithms, we propose to use existing methods in conjunction with a surrogate model for the black-box evaluations over purely categorical variables.
Combinatorial Black-Box Optimization with Expert Advice
We consider the problem of black-box function optimization over the boolean hypercube.
Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling
We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers.
