Search Results for author:
Found 6 papers, 3 papers with code
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
Our method provides valuable insights into the qualitative, long-term behavior of a wide range of dynamical systems, and can detect bifurcations or catastrophic transitions in large-scale physical and biological systems.
Phase2vec: Dynamical systems embedding with a physics-informed convolutional network
In an auxiliary training period, embeddings are optimized so that they robustly encode the equations of unseen data over and above the performance of a per-equation fitting method.
KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.
Go with the Flow: Adaptive Control for Neural ODEs
Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations.
Same-different problems strain convolutional neural networks
The robust and efficient recognition of visual relations in images is a hallmark of biological vision.
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks
The robust and efficient recognition of visual relations in images is a hallmark of biological vision.
