arXiv Machine Learning By Sushovan Majhi, Atish Mitra, \v{Z}iga Virk, Pramita Bagchi

COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules

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COMPLEX is a closed‑form, training‑free embedding for multiparameter persistence modules that provides both an upper and a lower Lipschitz bound, enabling faithful feature representations. By slicing modules along a near‑diagonal net and embedding each slice with the certified PLACE/PALACE landmark map, the method guarantees that separated modules remain separated in the embedding. On Orbit benchmarks and molecular graph tasks, COMPLEX achieves state‑of‑the‑art accuracy, outperforming existing landmark, transformer, and graph‑based approaches.

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arXiv Machine Learning
Jul 8

Geometric Stability: The Missing Axis of Representations

arXiv:2601. 09173v5 Announce Type: replace Abstract: Representational similarity analysis and related methods compare the internal geometries of neural networks, but they measure only alignment between spaces, leaving a blind spot -- whether a representation's structure is reliably recoverable, not merely similar.

By Prashant C. Raju
arXiv Computer Vision
Sep 23

Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition

The paper introduces TFA, a training‑free aggregation technique that calibrates frozen visual foundation models for visual place recognition. TFA uses cross‑codebook agreement, retrieval coverage, and spectral statistics to adjust residual assignment, spectral shaping, and global‑feature fusion without requiring place labels or task‑specific weights. Experiments with a DINOv2‑B backbone show significant Recall@1 gains over existing training‑free methods across multiple benchmarks, demonstrating that reliability‑guided aggregation can unlock additional retrieval performance from frozen representations.

By Xin Li, Zhimin Mao, Shang Wang, Siyuan Duan, Geng Zhang