Here are a number of recent accepted/published papers in machine learning and computer vision:

• Approximation Theory of Convolutional Architectures for Time Series Modelling: In this paper, we develop some approximation theory for convolutional based architectures for time series analysis – with WaveNet as a prime example. This can be seen as a parallel for the approach taken in our previous paper, but this time for convolutional networks instead of recurrent networks. Our key finding here is that convolutional structures exploits certain “effective low rank” structures for efficient approximation, which can be very different from the “exponentially decaying memory structures” that RNN brings. This paper will appear at ICML 2021.
• Adversarial Invariant Learning: In this paper, we develop methods to use adversarially chosen data splits to tackle the out-of-distribution generalization problems. This paper is published at CVPR 2021.

We also have a number of recent papers on the application of machine learning to science and engineering:

• A Data Driven Method for Computing Quasipotentials: In this paper, we develop a efficient method for computing the Freidlin-Wentzell quasipotential for rare event analysis, especially in high dimensions. The idea is based on learning a decomposition of the force field, from which the quasipotential can be identified. This will appear in MSML 2021.
• Two-step machine learning enables optimized nanoparticle synthesis: In this paper, we develop inverse design methodologies for nanoparticles  of desired properties, through a combination of Bayesian optimization and neural network based methods. This paper is published at Npj Computational Materials.
• Machine learning and high-throughput robust design of P3HT-CNT composite thin films for high electrical conductivity: In this paper, we investigate how to design carbon nanotubes with desired conductive properties through machine learning. The key difficulty here is the large amount of experimental noise, and we overcome this issue by developing a graph-based regression approach that takes more experimental information into account. This paper will appear in Advanced Functional Materials.