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Found 8 papers, 5 papers with code
Unified Long-Term Time-Series Forecasting Benchmark
In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting.
Worrisome Properties of Neural Network Controllers and Their Symbolic Representations
We raise concerns about controllers' robustness in simple reinforcement learning benchmark problems.
Improved Overparametrization Bounds for Global Convergence of Stochastic Gradient Descent for Shallow Neural Networks
Second, we provide a technical result (working also for general approximators) relating solutions of the aforementioned differential inclusion to the (discrete) stochastic gradient descent sequences, hence establishing linear convergence towards zero loss for the stochastic gradient descent iterations.
SPP-RL: State Planning Policy Reinforcement Learning
We introduce an algorithm for reinforcement learning, in which the actor plans for the next state provided the current state.
On The Verification of Neural ODEs with Stochastic Guarantees
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems.
Lagrangian Reachtubes: The Next Generation
Secondly, it computes the next reachset as the intersection of two balls: one based on the Cartesian metric and the other on the new metric.
Mapper Based Classifier
We propose a classifier based on applying the Mapper algorithm to data projected onto a latent space.
Computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model
We present a computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model of diblock copolymers.
Analysis of PDEs Computational Engineering, Finance, and Science Dynamical Systems Numerical Analysis 35B40, 35B41, 35K55, 65C20, 35Q99, 15B99, 68U20
