arXiv AI

MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization

arXiv:2410. 19504v3 Announce Type: replace-cross Abstract: Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential information.

arXiv Machine Learning

Sep 1

Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification

The paper introduces an unsupervised framework that merges manifold learning with rank‑based interpretable graph embeddings to address the Geometric and Interpretability Gaps in visual representation learning. By first analyzing contextual information on the dataset manifold and then producing sparse, self‑explainable embeddings, the method achieves dimensionality reduction while preserving or improving performance in image retrieval and semi‑supervised Graph Convolutional Network classification. Experiments across varied datasets confirm that these context‑aware representations maintain high downstream effectiveness.

By Thiago C\'esar Castilho Almeida, Gustavo Rosseto Let\'icio, Vinicius Atsushi Sato Kawai, Daniel Carlos Guimar\~aes Pedronette

llms rag safety

arXiv AI

Jul 9

SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts

arXiv:2607. 06620v1 Announce Type: cross Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry.

By Haida Feng, Hao Wei, Haolin Wang, Shiwei Li, Chade Li, Yihong Wu

llms multimodal benchmarks

arXiv Machine Learning

Sep 1

Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction

arXiv:2608.29867v1 Announce Type: new Abstract: Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear enc...

By Louen Pottier, Louis Lesueur, Anders Thorin

safety

arXiv Machine Learning

Jul 31

FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction

arXiv:2607. 27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models.

By Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva, Luis Gustavo Nonato

arXiv AI

Sep 10

ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers

arXiv:2609.06155v1 Announce Type: cross Abstract: Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Des...

By Effrosyni Sokli, Isaac Roberts, Alexander Schulz, Barbara Hammer, Gabriella Pasi

rag benchmarks safety

arXiv Machine Learning

Jul 30

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting

arXiv:2605. 23540v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data.

By Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich

rag

arXiv AI

Jul 8

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.

By Franciskus Xaverius Erick, Johanna Paula M\"uller, Bernhard Kainz

llms rag fine-tuning efficiency multimodal safety

arXiv AI

Jun 12

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

arXiv:2601. 13591v2 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning.

By Maojun Sun, Yifei Xie, Yue Wu, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang

llms agents multimodal benchmarks

arXiv AI

2d ago

Geometric Similarity in VLM Low-Level Vision Representations

arXiv:2610.00848v1 Announce Type: cross Abstract: Vision-language models (VLMs) have emerged as powerful candidates for universal vision backbones, with representative architectures including autoreg...

By Shao-Jun Xia, Huixin Zhang, Zhen Lei, Anlan Sun, Yuner Zhang, Xiaoyang Chen

llms diffusion multimodal safety

arXiv Computer Vision

Aug 24

Aristotelian Manifolds: Leveraging Platonic Perceptual Features for Backpropagation Free Rapid Concept Learning

arXiv:2608.20682v1 Announce Type: new Abstract: This paper formalizes and systematically characterizes Aristotelian Manifolds, a generalized structural framework built upon the Platonic Representatio...

By Michael Karnes, Alper Yilmaz

arXiv Machine Learning

Jun 9

MS-COOT: Comparing Morse-Smale Complexes with Co-Optimal Transport

arXiv:2606. 08258v1 Announce Type: cross Abstract: Understanding and comparing structures in scalar fields is a central challenge in scientific visualization, with applications ranging from feature analysis to temporal and structural comparison.

By Guangyu Meng, Mingzhe Li, Erin Wolf Chambers

arXiv Machine Learning

Jul 10

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

arXiv:2607. 08337v1 Announce Type: new Abstract: Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models.

By Siyuan Wen, Jiahao Zeng, Ningning Ding

diffusion benchmarks

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