Arguing that the standard situation for studying transitivity prompts correct but nontransitive conclusions, we compared the standard with two other situations which were more likely to require the ...

Abstract: Federated learning (FL) has emerged as an ideal privacy-preserving learning technique which can train a global model in a collaborative way while preserving the private data in the local.

Abstract: Recent improvements in the accuracy of machine learning (ML) models in the language domain have propelled their use in a multitude of products and services, touching millions of lives daily.

Teaching inference to kids is an essential part of learning to read and comprehend texts. Inference is the ability to draw conclusions based on evidence and reasoning, and it is a skill that kids will ...

Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in ...

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...

Bayesian inference is a statistical method of inductive reasoning based on the reassessment of competing hypotheses in the presence of new evidence. Conceptually similar to the scientific method ...

Then you can start exploring with the main_simple.ipynb notebook. For running on datasets instead of individual examples, use main_batch.py as discussed later on. ℹ️ NOTE: OpenAI discontinued support ...

Observational studies aiming to estimate causal effects often rely on conceptual frameworks that are unfamiliar to many researchers and practitioners. We provide a clear, structured overview of key ...

A benchmark framework for evaluating Large Language Model (LLM) logical reasoning capabilities using plant phenotype data. This framework tests LLMs on multi-turn dialogues involving transitive ...