4 papers
Tuning Out-of-Distribution (OOD) Detectors Without Given OOD Data
Sudeepta Mondal, Xinyi Mary Xie, Ruxiao Duan +2
Existing out-of-distribution (OOD) detectors are often tuned by a separate dataset deemed OOD with respect to the training distribution of a neural network (NN). OOD detectors proc…
Understanding vision transformer robustness through the lens of out-of-distribution detection
Joey Kuang, Alexander Wong
Vision transformers have shown remarkable performance in vision tasks, but enabling them for accessible and real-time use is still challenging. Quantization reduces memory and infe…
Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification
Timothy Oladunni, Alex Wong
This study proposes a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing how complementary feature domains impact model pe…
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization
Stone Yun, Alexander Wong
Deep neural network (DNN) quantization for fast, efficient inference has been an important tool in limiting the cost of machine learning (ML) model inference. Quantization-specific…