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Found 6 papers, 4 papers with code
Non-separable Non-stationary random fields
We describe a framework for constructing non-separable non-stationary random fields that is based on an infinite mixture of convolved stochastic processes.
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Large language models have shown astonishing performance on a wide range of reasoning tasks.
Explicit Planning Helps Language Models in Logical Reasoning
In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
Trap of Feature Diversity in the Learning of MLPs
In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity in the first phase.
Multi-resolution Multi-task Gaussian Processes
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels.
