6 accepted papers
Permutation invariance is among the most common symmetries that can be exploited to simplify complex problems in machine learning. There has been a tremendous surge of research activities in building permutation invariant machine learning architectures. However, less attention is given to: (1) how t…
Large Language Models (LLMs) excel in many areas but continue to face challenges with complex reasoning tasks, such as Multi-Hop Question Answering (MHQA). MHQA requires integrating evidence from diverse sources while managing intricate logical dependencies, often leads to errors in reasoning. Retri…
Despite significant advances in self-supervised monocular depth estimation methods, achieving temporally consistent and accurate depth maps from frame sequences remains a formidable challenge. Existing approaches often estimate depth maps for individual frames in isolation, neglecting the rich geome
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These app…
Monocular self-supervised depth estimation can be easily applied in many areas since only a single camera is required. However, current methods do not predict well in depth borders. Besides, factors such as occlusion and texture sparsity can lead to the failure of the photometric consistency, affect…
We propose two semiparametric versions of the debiased Lasso procedure for the model $Y_{i}=X_{i}\beta_{0}+g_{0}(Z_{i})+\varepsilon_{i}$, where the parameter vector of interest $\beta_{0}$ is high dimensional but sparse (exactly or approximately) and $g_{0}$ is an unknown nuisance function. Both ver…