The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...

Machine learning models often perform impressively in the lab but struggle in the real world. The main culprit? Domain shift: the difference between the data a model was trained on and the data it ...

Enterprise tech leaders are done chasing the promise of all-in-one platforms. Right now, the question isn’t, "How do we do more with less?" It’s, "How do we do less but better?" As budget scrutiny ...

Deep neural networks’ seemingly anomalous generalization behaviors, benign overfitting, double descent, and successful overparametrization are neither unique to neural networks nor inherently ...

Ashish Pawar is an experienced software engineer skilled in creating scalable software and AI-enhanced solutions across data-driven and cloud applications, with a proven track record at companies like ...

To consider the relationship between generalization, explanation, and understanding, let us consider an example. Suppose we have two intelligent agents, AG1 and AG2, and suppose we ask both to ...

The delicate balance between discrimination and generalization of responses is crucial for survival in our ever-changing environment. In particular, it is important to understand how stimulus ...

Large language models (LLMs) appear to solve certain novel tasks with appropriately formatted prompts, which is an ability known as out-of-distribution generalization. However, the underlying ...

Abstract: Rule learning is a data analysis task consisting of extracting rules to generalize examples. For a data scientist, some generalizations called here admissible generalizations, make more ...

This is the first attempt to propose a Generalization Assessment Index for super-resolution and restoration networks, namely SRGA. SRGA exploits the statistical characteristics of the internal ...