Our motivation is grounded in a fundamental observation: aligning the gradient and the Hessian of the SDF provides a more efficient mechanism to govern gradient directions.

Neural Information Processing Systems http://nips.cc/

As reviewers noticed, detailed description of the experiments is provided in the3 supplement. Wealso6 switched to a finer discretization of 1 minute on Physionet dataset, instead of 6 minutes. Modeling state-interactions via26 an ODE allows better generalization outside the training interval compared to directly modeling a function of time.27 Using an ODE-RNN as a decoder is a possible extension.

We refer to it as theContinuous Category Discovery(CCD) problem, which is significantly more challenging than the static setting.

Interestingly, although the diffusion model shows great potential in 3D generation tasks, e.g.

Existing MIPL algorithms often overlook the margins for attention scores andpredicted probabilities, leading tosuboptimal generalization performance.

Tofillthisgap,thispaper proposes amulti-lingual finetuning method, dubbed Less-forgetting Multi-lingual Fine-tuning (LF-MLF).

Partial-label learning(PLL) is a multi-class classification problem, where each training example isassociated withasetofcandidate labels.