Reconstruction T arget: Point-MAE's reconstruction task aims to restore the coordinates of the Finally, an MLP with a dropout rate of 0.5 was used Pre-training 3d point cloud transformers with masked point modeling.

Historical documents are invaluable carriers of human cultural heritage, containing important information about human history, culture, and literary arts.

In this paper, we present EV-Eye, a first-of-its-kind large-scale multimodal eye tracking dataset aimed at inspiring research on high-frequency eye/gaze tracking. EV -Eye utilizes the emerging bio-inspired event camera to capture independent pixel-level intensity changes induced by eye movements, achieving sub-microsecond latency.

Inspired by the work by Ishida et al. (2023), we propose an estimator

Besides evaluating the next-item prediction, we estimate the models' capabilities to capture

Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model.

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? In Section 4.1, we briefly introduced how humans annotate the reconstructed images for different Figure 1 displays the GUI, where (A) and (B) were specifically designed for annotating different datasets. To minimize the influence of subjective bias, we use a relatively objective formulation: whether the reconstructed image can be correctly labeled. Figure 2. It can be observed that when We think there are two potential reasons for this observation. Table 1 provides detailed information about these models.

We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresponds to a transport problem regularized by an implicit uniform prior. We introduce Generalized Optimal transport Attention with Trainable priors (GOAT), a new attention mechanism that replaces this naive assumption with a learnable, continuous prior. This prior maintains full compatibility with optimized kernels such as FlashAttention. GOAT also provides an EOT-based explanation of attention sinks and materializes a solution for them, avoiding the representational trade-offs of standard attention. Finally, by absorbing spatial information into the core attention computation, GOAT learns an extrapolatable prior that combines the flexibility of learned positional embeddings with the length generalization of fixed encodings.