9 accepted papers
This letter proposes a mole-inspired scratch-digging robot to investigate four-limb subsurface locomotion in granular media. The robot integrated a conical head, a rigid torso, a hybrid crank-rocker and crank-slider forelimb that reproduces scratch-digging strokes, and a two-degree-of-freedom (DOF)
The rise of Vision Transformers (ViTs) as cornerstone models in safety-critical applications like autonomous driving and medical diagnosis has shifted the focus from pure accuracy to verifiable trustworthiness. However, the very mechanisms used to explain these models, their internal attention maps,
Planetary exploration requires efficient methods for subsurface sampling, especially in extreme energy limitations. Traditional drilling methods are often energy intensive and require large platforms, limiting their applicability. Bio-inspired burrowing techniques, inspired by animals like moles, of
Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and difficulties in model parallelization during training. Recent research has sought to address these issues, with one promising…
Recently, textual information has been proven to positively affect recommendation systems. However, most of the existing methods only focus on representation learning of textual information in ratings, while potential selection bias induced by the textual information is ignored. In this work, we pro…
In this paper we study the (sparse) Generalized Eigenvalue Problem (GEP), which arises in a number of modern statistical learning models, such as principal component analysis (PCA), canonical correlation analysis (CCA), Fisher’s discriminant analysis (FDA) and sliced inverse regression (SIR). We pro…
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology studies on learning from partial labels, there still lacks theoretical understanding…
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and…
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the lig…