Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

The structure of protein-protein complexes is critical for understanding binding dynamics,
biological mechanisms, and intervention strategies. Rigid protein docking, a fundamental …

Bi-level optimization, especially the gradient-based category, has been widely used in the
deep learning community including hyperparameter optimization and meta-knowledge …

We present HUME, a simple model-agnostic framework for inferring human labeling of a
given dataset without any external supervision. The key insight behind our approach is that …

Existing meta-learning works assume that each task has available training and testing data.
However, there are many available pre-trained models without accessing their training data …

Offline model-based optimization aims to maximize a black-box objective function with a
static dataset of designs and their scores. In this paper, we focus on biological sequence …

Gradient-based meta-learning algorithms offer promising solutions to the challenge of swift
adaptation to new tasks, especially when faced with limited sample data. One pivotal …

Calibration of neural networks is a topical problem that is becoming more and more
important as neural networks increasingly underpin real-world applications. The problem is …

How to rapidly adapt to new tasks and improve model generalization through few-shot
learning remains a significant challenge in meta-learning. Model-Agnostic Meta-Learning …

Image-driven social sensing (ISS) is emerging as a pervasive sensing paradigm that collects
the status of the physical world by leveraging image data from human sensors. This paper …