6 citations · 11 across the 10 of their papers we have counts for
10 papers
Spectral Batch Normalization: Normalization in the Frequency Domain
Rinor Cakaj, Jens Mehnert, Bin Yang
Regularization is a set of techniques that are used to improve the generalization ability of deep neural networks. In this paper, we introduce spectral batch normalization (SBN), a…
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction
Robert A. Marsden, Mario Döbler, Bin Yang
Since distribution shifts are likely to occur during test-time and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model…
Prompt Tuning for Parameter-efficient Medical Image Segmentation
Marc Fischer, Alexander Bartler, Bin Yang
Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to…
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation
Mario Döbler, Robert A. Marsden, Bin Yang
Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption (TTA) continues to adapt the model after deployment. Recently, the area of continual…
Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning
Yiwen Liao, Raphaël Latty, Bin Yang
Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devi…
Deep Feature Selection Using a Novel Complementary Feature Mask
Yiwen Liao, Jochen Rivoir, Raphaël Latty +1
Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of fe…
Introducing Intermediate Domains for Effective Self-Training during Test-Time
Robert A. Marsden, Mario Döbler, Bin Yang
Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation co…
A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning
Yiwen Liao, Bin Yang, Raphaël Latty +1
In post-silicon validation, tuning is to find the values for the tuning knobs, potentially as a function of process parameters and/or known operating conditions. In this sense, an…
Conditional Variable Selection for Intelligent Test
Yiwen Liao, Tianjie Ge, Raphaël Latty +1
Intelligent test requires efficient and effective analysis of high-dimensional data in a large scale. Traditionally, the analysis is often conducted by human experts, but it is not…
Conditional Seq2Seq model for the time-dependent two-level system
Bin Yang, Mengxi Wu, Winfried Teizer
We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the networ…