The article introduces ForecastPFN, a novel zero-shot forecasting model trained on synthetic data distribution. This approach is designed to overcome the limitations of many forecasting methods that require substantial initial…
Continue readingThis research paper discusses the limitations of autoregressive large language models (LLMs) in compressing knowledge from training data. The authors propose a solution by using amortized Bayesian inference to sample…
Continue readingThis research presents a way to improve the performance of Amortized Bayesian Inference (ABI) by using universal symmetries in the probabilistic joint model of parameters and data. The authors propose…
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