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Found 9 papers, 1 papers with code
From Physics to Sentience: Deciphering the Semantics of the Free-Energy Principle and Evaluating its Claims
The Free-Energy Principle (FEP) [1-3] has been adopted in a variety of ambitious proposals that aim to characterize all adaptive, sentient, and cognitive systems within a unifying framework.
Effective Latent Differential Equation Models via Attention and Multiple Shooting
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques.
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok
In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on.
Attention Schema in Neural Agents
A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents.
Sources of Richness and Ineffability for Phenomenally Conscious States
Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully describe or recall.
The return of (I)DeFiX
We investigate four possible drivers of DeFi returns: exposure to cryptocurrency market, the network effect, the investor's attention, and the valuation ratio.
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
We revise the existing OOD generalization algorithms for time series tasks and evaluate them using our systematic framework.
Social Neuro AI: Social Interaction as the "dark matter" of AI
First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence.
Generative Models of Brain Dynamics -- A review
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.
