The paper introduces SIMPLE, a prior‑fitted multi‑view in‑context learner that learns a reusable, task‑conditioned inference procedure instead of a fixed fusion function. By generating synthetic task priors in embedding space, SIMPLE can handle diverse view configurations, class structures, and missingness patterns. Experiments on multi‑view and multi‑omics benchmarks show that a frozen SIMPLE model performs competitively, and lightweight adapter calibration further improves performance across most datasets.
By Jielong Lu, Zhihao Wu, Jiajun Yu, Zhaoliang Chen, Haishuai Wang
arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.
By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys
arXiv:2602. 24012v2 Announce Type: replace Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) models.
By Roy Betser, Eyal Gofer, Meir Yossef Levi, Guy Gilboa
arXiv:2606. 02221v1 Announce Type: cross Abstract: Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains.
By Chengfeng Wu, Tao Zou, Yanru Wu, Jingge Wang
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.
arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.
By Vasiliki Rizou, Pascal Frossard, Dorina Thanou
arXiv:2609.39019v1 Announce Type: new
Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vi...
By Yongquan Shi, Weijun Huang, Yueyang Pi, Wendi Zhao, Yiqing Shi, Shiping Wang
arXiv:2606. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.
By Angeliki Papathanasiou, Jascha Achterberg, Thomas E. Nichols, Rui Ponte Costa
arXiv:2608. 16245v1 Announce Type: new Abstract: Disentangled representation learning seeks latent representations whose indicidual dimensions each align with a distinct covariate.
By Ma{\l}gorzata {\L}az\k{e}cka, Ewa Szczurek
MVVBench is a new benchmark for multi‑view video reasoning that tests vision‑language models on tasks requiring integration of spatial and temporal evidence across multiple, often non‑overlapping camera streams. The benchmark contains questions that cannot be answered from any single view or single moment, forcing models to jointly reason across views and time. It evaluates six capabilities—including attribute identification, relative distance, camera pose, and compositional counting—and provides human‑authored QA, rigorous verification, and detailed error analysis.
"whyItMatters":"The benchmark offers a rigorous evaluation of 4D multi‑view reasoning and a foundation for future progress toward reliable embodied perception."
By Hyungjin Chung, Byeongjun Park, Joonseok Lee, Hojun Kim, Jaeho Choi, Byung-Hoon Kim
arXiv:2609.40047v1 Announce Type: cross
Abstract: Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing...
By Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
arXiv:2608.30388v1 Announce Type: cross
Abstract: Cross-view video representation learning aims to capture viewpoint-invariant action semantics despite substantial appearance changes across egocentri...
By Youngchae Chee, Hosu Lee, Sungjune Park, Junho Kim, Yong Man Ro