arXiv:2602.03894v1 Announce Type: new Abstract: Manual labeling of animal images remains a significant bottleneck in ecological research, limiting the scale and efficiency of biodiversity monitoring efforts. This study investigates whether state-of-the-art Vision Transformer (ViT) foundation models can reduce thousands of unlabeled animal images directly to species-level clusters. We present a comprehensive benchmarking framework evaluating five ViT models combined with five dimensionality reduction techniques and four clustering algorithms, two supervised and two unsupervised, across 60 species (30 mammals and […]

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I’ve been working on (yet another…) library for managing ML experiment configs and wanted to share it. This project is intended for production ML research and development, though might be useful elsewhere. The basic idea is that a config is composed of nested dataclasses. Each nesting is defined in the class it configures and doubles as a factory. This keeps params “close” to their point of use and makes for more readable code. from configgle import Fig, Makes […]

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arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing defenses mainly employ gradient perturbation techniques, e.g., noise injection or gradient pruning, to disrupt GIAs’ direct mapping from gradient space to token space. However, these methods often fall short due to the retention of semantics similarity across gradient, […]

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arXiv:2601.17041v1 Announce Type: new Abstract: Arabic Sign Language (ArSL) is an essential communication method for individuals in the Deaf and Hard-of-Hearing community. However, existing recognition systems face significant challenges due to their reliance on single sensor approaches like Leap Motion or RGB cameras. These systems struggle with limitations such as inadequate tracking of complex hand orientations and imprecise recognition of 3D hand movements. This research paper aims to investigate the potential of a multimodal approach that combines Leap […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 4.3. Differentiability & Deep Graph Clustering :::info Authors: (1) Li Sun, North […]

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Author(s): Abdul tayyeb Datarwala Originally published on Towards AI. My journey building operational intelligence — and why most AI initiatives quietly die I’ve built AI-enabled systems that scaled revenue, cut operational cost by multiples, and replaced chaos with clarity. I’ve also watched brilliant AI initiatives fail — not because the models were bad, but because the system was never designed to carry them. That contrast is why I write this. Most AI content today talks about tools, agents, […]

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arXiv:2602.13281v1 Announce Type: new Abstract: This article investigates a family of centrality models for urban networks that incorporate both topological and non-topological factors. Since centrality is inherently recursive, these models can be formulated as fixed-point equations, which we refer to as shifted eigenproblems. Assuming a correlation between node centrality and occupancy, we discuss how experimental data can be used to estimate model parameters via least-squares methods. Furthermore, such data would allow us to infer the intrinsic attraction of […]

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Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different approach. Instead of using satellites only as sensors that relay data to Earth, it turns a nanosatellite constellation into a distributed training system. […]

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