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Found 10 papers, 2 papers with code
Meta-Learning by the Baldwin Effect
The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan.
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
Slope Gap Distributions of Veech Surfaces
We show that the limiting gap distribution of slopes of saddle connections on a Veech translation surface is always piecewise real-analytic with \emph{finitely} many points of non-analyticity.
Dynamical Systems Geometric Topology 32G15, 37D40, 14H55
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data.
An Artificial Intelligence Dataset for Solar Energy Locations in India
Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure.
Learning to Induce Causal Structure
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
DiscoGen: Learning to Discover Gene Regulatory Networks
To accurately identify GRNs, perturbational data is required.
Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach
The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction.
