Systematic compositionality is a critical aspect of cognitive ability, both in humans and, as it's now being discovered , in artificial intelligence (AI) as well. It's a principle that governs how simpler parts can be combined to form more complex expressions and understandings. Recent advancements in AI have brought to light a method known as Meta-Learning for Compositionality (MLC), which seems to play a pivotal role in enabling systematic compositionality in neural netwo

Investing involves a myriad of decisions based on assessments of risks , rewards, and comparisons between different options. An investor might wonder: “ What would have been the outcome had I chosen another investment? ” Answering such questions can be challenging due to confounding factors . This is where Propensity Score Matching (PSM) comes in. PSM is a statistical method used to reduce bias by equating groups based on these confounding factors . What is Propensity Score

TimesNet represents a notable advancement in the domain of time series analysis, which is a fundamental area of study for investors. The...

The quest for causality in Artificial Intelligence is about understanding the 'why' behind patterns, enabling more informed and...

In today's investment landscape, advanced analytics are vital. The ability to extract meaningful insights from vast datasets can offer...

Quantitative bias analysis is a critical tool for investors . It refers to the process of quantifying the bias that may be present in data or analytical results. Every decision made by an investor, no matter how educated or sophisticated, is influenced by the data they utilize. However, if that data contains bias , their decisions might be fundamentally flawed. Understanding Bias Bias is a systematic error that can distort the true relationship or measurement in your data

In the burgeoning field of artificial intelligence, terms and concepts can often become jargon-heavy and complex. For investors looking...

In machine learning and artificial intelligence , Graph Neural Networks (GNNs) have emerged as a powerful tool to process structured data. A particularly interesting concept within GNNs is that of heterophilic graphs. In this article, we aim to provide investors with a comprehensive understanding of heterophilic and homophilic graphs, their significance in GNNs , and how they can reshape industries. Basics: What are Graph Neural Networks (GNNs) ? Graph Neural Networks are