the Relational Transformer
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Related Articles from SNS
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
arXiv:2606.03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure. Relational Deep Learning (RDL) addresses this by representing databases as heterogeneous graphs and applying graph neural networks (GNNs) directly. RelBench v2 recently introduced autocomplete tasks -- a practically motivated task...
OpenRFM: Dissecting Relational In-Context Learning
arXiv:2606.04320v1 Announce Type: new Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context learning (ICL). Yet a substantial gap separates open RFMs from their commercial counterparts, and the origin of this gap has not been systematically understood. We dissect a representative framework, the Relational Transformer (RT), from two perspectives.
Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction
Announce Type: replace Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extraction from a 1-shot to a few-shot setting. Specifically, we introduce a novel strategy for example selection, in which new examples are selected based on the similarity of their underlying syntactic-semantic structure to the provided 1-shot example. We show that our strategy results in complementary word choices...
Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition
Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices. Among existing compression approaches, cross-layer parameter sharing remains relatively unexplored for transformer models.
Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
Announce Type: replace Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transformers rely heavily on relative positional bias~(RPB), which prevents them from leveraging hardware-efficient attention kernels such as FlashAttention.
Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models
Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence graphs that encode semantic relations more directly. While transformer-based embeddings achieve strong performance, their induced geometries often display unsatisfactory distributions.
PM reiterates need to 'add more momentum' to reforms at EAC meet
Prime Minister Narendra Modi on Saturday met members of the Economic Advisory Council and for the second straight day underlined the need for further reforms. "Chaired a meeting of the Economic Advisory Council to the Prime Minister. Deliberated on a wide range of issues relating to India's economic transformation and long-term development priorities.
ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias
arXiv:2606.00263v1 Announce Type: cross Abstract: This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations. ReFLEX is a length-generalizable Transformer whose frequency attention uses a relative-frequency position bias (RFPB) generated from subcarrier offsets. A single checkpoint handles unseen RB lengths and can be applied to sparse DM-RS observations in the tested RB5/
Learned Non-Maximum Suppression for 3D Object Detection
arXiv:2606.03568v1 Announce Type: new Abstract: Post-processing is a critical stage in LiDAR-based 3D object detection, where dense and overlapping proposals must be filtered for compact and reliable perception. This work introduces two learned filtering modules that replace heuristic non-maximum suppression (NMS) by leveraging relations among detections. D2D-Rescore employs transformer-based detection-to-detection (D2D) attention, while GossipNet3D adapts the 2D GossipNet concept to 3D...
EXCLUSIVE: Serbian President Vučić says support for US 'surged' under Trump, invites him to visit Belgrade
Serbian President Aleksandar Vučić says relations between Serbia and the United States have undergone a dramatic transformation under President Donald Trump, a shift he says has changed public perceptions in a country where memories of the 1999 NATO bombing campaign remain deeply rooted. In an exclusive interview with Fox News Digital, Vučić praised Trump's approach to the Balkans, arguing that the administration's focus on economic cooperation rather than political pressure resonated with...