8 citations · 18 across the 5 of their papers we have counts for
5 papers
Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems
Frithjof Gressmann, Ivan Georgiev Raikov, Seung Hyun Kim +3
Gaussian process surrogates dominate constrained multi-objective optimization because they are effective in data-scarce regimes, but their cubic scaling in training samples limits…
Towards Structured Dynamic Sparse Pre-Training of BERT
Anastasia Dietrich, Frithjof Gressmann, Douglas Orr +3
Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research. In this work, we develop and study a…
GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures
Ivan Chelombiev, Daniel Justus, Douglas Orr +4
Attention based language models have become a critical component in state-of-the-art natural language processing systems. However, these models have significant computational requi…
Improving Neural Network Training in Low Dimensional Random Bases
Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi
Stochastic Gradient Descent (SGD) has proven to be remarkably effective in optimizing deep neural networks that employ ever-larger numbers of parameters. Yet, improving the efficie…
Probabilistic supervised learning
Frithjof Gressmann, Franz J. Király, Bilal Mateen +1
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel me…