Tootfinder

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Gianlucca Zuin, Adriano Veloso
https://arxiv.org/abs/2509.09073 https:/…

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a challenging task. Related to this topic, the Rashomon Effect refers to cases where multiple models perform similarly well for a given learning problem. This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions.…

Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion
Oh-Tae Jang, Min-Gon Cho, Kyung-Tae Kim
https://arxiv.org/abs/2509.09298 https://

Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion
Synthetic aperture radar (SAR) images contain not only targets of interest but also complex background clutter, including terrain reflections and speckle noise. In many cases, such clutter exhibits intensity and patterns that resemble targets, leading models to extract entangled or spurious features. Such behavior undermines the ability to form clear target representations, regardless of the classifier. To address this challenge, we propose a novel object-centric learning (OCL) framework, named…

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Benjamin Laufer, Hamidah Oderinwale, Jon Kleinberg
https://arxiv.org/abs/2508.06811 https://

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Many have observed that the development and deployment of generative machine learning (ML) and artificial intelligence (AI) models follow a distinctive pattern in which pre-trained models are adapted and fine-tuned for specific downstream tasks. However, there is limited empirical work that examines the structure of these interactions. This paper analyzes 1.86 million models on Hugging Face, a leading peer production platform for model development. Our study of model family trees -- networks th…

Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Roopkatha Banerjee, Sampath Koti, Gyanendra Singh, Anirban Chakraborty, Gurunath Gurrala, Bhushan Jagyasi, Yogesh Simmhan
https://arxiv.org/abs/2508.08022

Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Real-time monitoring of power consumption in cities and micro-grids through the Internet of Things (IoT) can help forecast future demand and optimize grid operations. But moving all consumer-level usage data to the cloud for predictions and analysis at fine time scales can expose activity patterns. Federated Learning~(FL) is a privacy-sensitive collaborative DNN training approach that retains data on edge devices, trains the models on private data locally, and aggregates the local models in the…

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
Nhi Hoai Doan, Tatsuya Hiraoka, Kentaro Inui
https://arxiv.org/abs/2507.07810

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
This paper investigates the relationship between large language models' (LLMs) ability to recognize repetitive input patterns and their performance on in-context learning (ICL). In contrast to prior work that has primarily focused on attention heads, we examine this relationship from the perspective of skill neurons, specifically repetition neurons. Our experiments reveal that the impact of these neurons on ICL performance varies depending on the depth of the layer in which they reside. By comp…

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
Junda Ye, Zhongbao Zhang, Li Sun, Siqiang Luo
https://arxiv.org/abs/2509.09337 https://

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to capture complex, high-order subgraph patterns. leading to insufficient structural expressiveness. Recent efforts have attempted to enhance structural expressiveness by integrating random walk kernels into GNNs. However, these methods are inherently designed for graph-level tasks, which limits their applicability to ot…

The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
Shrabani Ghosh, Erik Saule
https://arxiv.org/abs/2509.09045 https://

The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
In real-world scenarios, large graphs represent relationships among entities in complex systems. Mining these large graphs often containing millions of nodes and edges helps uncover structural patterns and meaningful insights. Dividing a large graph into smaller subgraphs facilitates complex system analysis by revealing local information. Community detection extracts clusters or communities of graphs based on statistical methods and machine learning models using various optimization techniques.…

Collective Communication Profiling of Modern-day Machine Learning Workloads
Jit Gupta, Andrew Li, Tarun Banka, Ariel Cohen, T. Sridhar, Raj Yavatkar
https://arxiv.org/abs/2507.07117

Collective Communication Profiling of Modern-day Machine Learning Workloads
Machine Learning jobs, carried out on large number of distributed high performance systems, involve periodic communication using operations like AllReduce, AllGather, and Broadcast. These operations may create high bandwidth and bursty traffic patterns, leading to network congestion and packet loss, thus impacting the performance of these jobs. Hence it is imperative to analyze these patterns, which can be helpful in provisioning network resources depending on the type of machine learning workl…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
Ananda Prakash Verma
https://arxiv.org/abs/2508.07224 https://arxiv.org/pdf/2508.07…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
We present EDGE, a general-purpose, misconception-aware adaptive learning framework composed of four stages: Evaluate (ability and state estimation), Diagnose (posterior infer-ence of misconceptions), Generate (counterfactual item synthesis), and Exercise (index-based retrieval scheduling). EDGE unifies psychometrics (IRT/Bayesian state space models), cog-nitive diagnostics (misconception discovery from distractor patterns and response latencies), contrastive item generation (minimal perturbati…

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Paul F. R. Wilson, Matteo Ronchetti, R\"udiger G\"obl, Viktoria Markova, Sebastian Rosenzweig, Raphael Prevost, Parvin Mousavi, Oliver Zettinig
https://arxiv.org/abs/2509.09530

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D systems. Sensorless 3D US, which uses deep learning to estimate a 3D probe trajectory from a sequence of 2D US images, is a promising alternative. Local features, such as speckle patterns, can help predict frame-to-frame motion, while global features, such as coarse shapes and anatomical structures, can situate the sc…