103 citations · 527 across the 18 of their papers we have counts for
5 papers · 1 filter
Graph Information Bottleneck
Tailin Wu, Hongyu Ren, Pan Li +1
Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an…
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng +3
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. I…
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
Tailin Wu
How can we enable machines to make sense of the world, and become better at learning? To approach this goal, I believe viewing intelligence in terms of many integral aspects, and a…
Discovering Nonlinear Relations with Minimum Predictive Information Regularization
Tailin Wu, Thomas Breuel, Michael Skuhersky +1
Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications…
Phase Transitions for the Information Bottleneck in Representation Learning
Tailin Wu, Ian Fischer
In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the…