We propose SG-XDEAT (Sparsity-Guided Cross Dimensional and Cross-Encoding Attention with Target Aware Conditioning), a novel framework designed for supervised learning on tabular data. At its core, SG-XDEAT employs a dual-stream encoder that decomposes each input feature into two parallel representations: a raw value stream and a target-conditioned (label-aware) stream. These dual representations are then propagated through a hierarchical stack of attention-based modules. SG-XDEAT integrates three key components: (i) Cross-Dimensional self-attention, which captures intra-view dependencies among features within each stream; (ii) Cross-Encoding self-attention, which enables bidirectional interaction between raw and target-aware representations; and (iii) an Adaptive Sparse Self-Attention (ASSA) mechanism, which dynamically suppresses low-utility tokens by driving their attention weights toward zero--thereby mitigating the impact of noise. Empirical results on multiple public benchmarks show consistent gains over strong baselines, confirming that jointly modeling raw and target-aware views--while adaptively filtering noise--yields a more robust deep tabular learner.

翻译：我们提出了SG-XDEAT（稀疏性引导的跨维度与跨编码注意力及目标感知条件化），这是一个专为表格数据监督学习设计的新型框架。SG-XDEAT的核心采用双流编码器，将每个输入特征分解为两个并行表示：原始值流和目标条件化（标签感知）流。这些双重表示随后通过一个基于注意力模块的层次化堆栈进行传播。SG-XDEAT集成了三个关键组件：(i) 跨维度自注意力，用于捕获每个流内特征间的视图内依赖关系；(ii) 跨编码自注意力，实现原始表示与目标感知表示之间的双向交互；(iii) 自适应稀疏自注意力机制，通过将低效用标记的注意力权重驱动至零来动态抑制它们，从而减轻噪声的影响。在多个公开基准测试上的实证结果表明，该方法相对于强基线模型取得了持续的性能提升，证实了联合建模原始视图与目标感知视图——同时自适应地过滤噪声——能够产生一个更鲁棒的深度表格学习器。