23 accepted papers
Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representati…
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights t
To facilitate the large-scale deployment of autonomous driving in real-world scenarios, developing low-cost and high-performance 3D object detection systems has become a critical technical challenge. Although high-beam LiDARs provide denser point cloud data, their prohibitive hardware cost and high
Multimodal Machine Translation (MMT) enhances textual translation through auxiliary inputs such as images, which is particularly effective in resolving linguistic ambiguities. However, visual information often introduces redundancy or noise, potentially impairing translation quality. To address this
Multimodal machine translation (MMT) integrates visual information to address ambiguity and contextual limitations in neural machine translation (NMT). Some empirical studies have revealed that many MMT models underutilize visual data during translation. They attempt to enhance cross-modal interacti…
Multimodal machine translation (MMT) aims to enhance translation quality by integrating visual information. However, existing methods often extract visual features using pre-trained models while learning text features from scratch, leading to representation imbalance. These methods are also prone to
Whole slide image (WSI) classification is a crucial component in automated pathology analysis. Due to the inherent challenges of high-resolution WSIs and the absence of patch-level labels, most of the proposed methods follow the multiple instance learning (MIL) formulation. While MIL has been equipp…
Unsupervised domain adaptation of multivariate time series aims to train a model to adapt its classification ability from a labeled source domain to an unlabeled target domain, where there are differences in the distribution between domains. Existing methods extract domain-invariant features directl…
Multi-view K-means clustering successfully generalizes K-means from single-view to multi-view, and obtains excellent clustering performance. In every view, it makes each data point close to the center of the corresponding cluster. However, multi-view K-means only considers the compactness of each cl…
The conversational recommender system (CRS) aims to provide high-quality recommendations through interactive dialogues. However, previous CRS models have no effective mechanisms for task planning and topic elaboration, and thus they hardly maintain coherence in multi-task recommendation dialogues. I…
Doubly stochastic matrix plays an essential role in several areas such as statistics and machine learning. In this paper we consider the optimal approximation of a square matrix in the set of doubly stochastic matrices. A structured BFGS method is proposed to solve the dual of the primal problem. Th…
This work proposes a novel unsupervised cross-modality adaptive segmentation method for medical images to tackle the performance degradation caused by the severe domain shift when neural networks are being deployed to unseen modalities. The proposed method is an end-2-end framework, which conducts a…
Black-box attacks in deep reinforcement learning usually retrain substitute policies to mimic behaviors of target policies as well as craft adversarial examples, and attack the target policies with these transferable adversarial examples. However, the transferability of adversarial examples is not a…
Temporal knowledge graphs (TKGs) have been widely used in various fields that model the dynamics of facts along the timeline. In the extrapolation setting of TKG reasoning, since facts happening in the future are entirely unknowable, insight into history is the key to predicting future facts. Howeve…
Dysarthria is a manifestation of disruption in the neuromuscular physiology resulting in uneven, slow, slurred, harsh, or quiet speech. Despite the remarkable progress of automatic speech recognition (ASR), it poses great challenges in developing stable ASR for dysarthric individuals due to the high…
Estimating the complete 3D point cloud from an incomplete one lies at the core of many vision and robotics applications. Existing methods typically predict the complete point cloud based on the global shape representation extracted from the incomplete input. Although they could predict the overall s…
Humans learn language speaking and translation with the help of common knowledge of the external world, while standard machine translation depends only on parallel corpora. There-fore, zero-resource translation has been proposed to explore a way to make models learn to translate without any parallel…
Dysarthria is a motor speech disorder caused by damage to the part of the nervous system that controls the physical production of speech. It poses great challenges in building robust dysarthric speech recognition (DSR) due to the high inter- and intra-speaker variability. To this end, we propose a m…
The scarcity of available samples and the high annotation cost of medical data cause a bottleneck in many digital diagnosis tasks based on deep learning. This problem is especially severe in pediatric tumor tasks, due to the small population base of children and high sample diversity caused by the h…
The structured sparsity-inducing $\ell_{1, \infty}$-norm, as a generalization of the classical $\ell_1$-norm, plays an important role in jointly sparse models which select or remove simultaneously all the variables forming a group. However, its resulting problem is more difficult to solve than the c…
PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Th…
In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction. We compare the estimation and approximation error of both underestimate and overestimate models. The result shows that, from the perspective of learning errors, performance…
We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, w…