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Found 10 papers, 3 papers with code
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions
The LEP-AD model scales favorably in performance with the size of training data.
Ranked #1 on
Protein Language Model
on DAVIS-DTA
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research.
Learning Functions in Large Networks requires Modularity and produces Multi-Agent Dynamics
We ask whether larger dynamical network motifs exist in biological networks, thus contributing to the higher-order organization of a network.
Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
Complex data usually results from the interaction of objects produced by different generating mechanisms.
Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification
We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm.
Data Structures and Algorithms Information Theory Information Theory Physics and Society
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space.
Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results
Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems.
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back.
The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition
We survey concepts at the frontier of research connecting artificial, animal and human cognition to computation and information processing---from the Turing test to Searle's Chinese Room argument, from Integrated Information Theory to computational and algorithmic complexity.
