43 citations · 52 across the 6 of their papers we have counts for
8 papers
Label Encoding for Regression Networks
Deval Shah, Zi Yu Xue, Tor M. Aamodt
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regress…
Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization
Zhengqi Gao, Sucheng Ren, Zihui Xue +2
Multimodal fusion emerges as an appealing technique to improve model performances on many tasks. Nevertheless, the robustness of such fusion methods is rarely involved in the prese…
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Zihui Xue, Yuedong Yang, Mengtian Yang +1
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Neve…
Characterizing and Improving the Resilience of Accelerators in Autonomous Robots
Deval Shah, Zi Yu Xue, Karthik Pattabiraman +1
Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…
What Makes Multi-modal Learning Better than Single (Provably)
Yu Huang, Chenzhuang Du, Zihui Xue +3
The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is…