Summary of Revisiting Essential and Nonessential Settings Of Evidential Deep Learning, by Mengyuan Chen et al.
Revisiting Essential and Nonessential Settings of Evidential Deep Learningby Mengyuan Chen, Junyu Gao, Changsheng XuFirst…
Revisiting Essential and Nonessential Settings of Evidential Deep Learningby Mengyuan Chen, Junyu Gao, Changsheng XuFirst…
Scalable Multi-Task Transfer Learning for Molecular Property Predictionby Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko,…
Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integrationby Siyi HeFirst submitted to…
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition…
Characterizing and Efficiently Accelerating Multimodal Generation Model Inferenceby Yejin Lee, Anna Sun, Basil Hosmer, Bilge…
Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flowby Qin Li, Maria Oprea, Li Wang,…
Accelerating Non-Maximum Suppression: A Graph Theory Perspectiveby King-Siong Si, Lu Sun, Weizhan Zhang, Tieliang Gong,…
End-to-End Conformal Calibration for Optimization Under Uncertaintyby Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman,…
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshesby Tianchang Shen, Zhaoshuo Li, Marc Law,…
The Perfect Blend: Redefining RLHF with Mixture of Judgesby Tengyu Xu, Eryk Helenowski, Karthik Abinav…