Tootfinder

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

This https://arxiv.org/abs/2409.14453 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice
Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can …

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Terry Yi Zhong, Cristian Tejedor-Garcia, Martha Larson, Bastiaan R. Bloem
https://arxiv.org/abs/2507.03594

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Parkinson's Disease (PD) affects over 10 million people globally, with speech impairments often preceding motor symptoms by years, making speech a valuable modality for early, non-invasive detection. While recent deep-learning models achieve high accuracy, they typically lack the explainability required for clinical use. To address this, we propose RECA-PD, a novel, robust, and explainable cross-attention architecture that combines interpretable speech features with self-supervised representati…

AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
https://arxiv.org/abs/2506.23396 https:…

AICO: Feature Significance Tests for Supervised Learning
The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distributio…

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Xintong Hu, Yixuan Chen, Rui Yang, Wenxiang Guo, Changhao Pan
https://arxiv.org/abs/2507.06116

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method buil…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
Cao, Cheng, Li, Zhou, Zhang, Li, Li, Gu, Zhang, Liu, Wu

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

Synthetic Speech Source Tracing using Metric Learning
Dimitrios Koutsianos, Stavros Zacharopoulos, Yannis Panagakis, Themos Stafylakis
https://arxiv.org/abs/2506.02590

Synthetic Speech Source Tracing using Metric Learning
This paper addresses source tracing in synthetic speech-identifying generative systems behind manipulated audio via speaker recognition-inspired pipelines. While prior work focuses on spoofing detection, source tracing lacks robust solutions. We evaluate two approaches: classification-based and metric-learning. We tested our methods on the MLAADv5 benchmark using ResNet and self-supervised learning (SSL) backbones. The results show that ResNet achieves competitive performance with the metric le…

Jet Reconstruction with Mamba Networks in Collider Events
Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
https://arxiv.org/abs/2506.18336 https://

Jet Reconstruction with Mamba Networks in Collider Events
We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning fo…

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
Louis Bradshaw, Honglu Fan, Alexander Spangher, Stella Biderman, Simon Colton
https://arxiv.org/abs/2506.23869

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
We study the capabilities of generative autoregressive transformer models trained on large amounts of symbolic solo-piano transcriptions. After first pretraining on approximately 60,000 hours of music, we use a comparatively smaller, high-quality subset, to finetune models to produce musical continuations, perform symbolic classification tasks, and produce general-purpose contrastive MIDI embeddings by adapting the SimCLR framework to symbolic music. When evaluating piano continuation coherence…

General-purpose audio representation learning for real-world sound scenes
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
https://arxiv.org/abs/2506.00934

General-purpose audio representation learning for real-world sound scenes
While audio foundation models perform well on myriad of tasks from sound classification to speech analysis, these models are trained and tested on dry, non-spatial, single-source audio clips. This limits their success in real-world situations and results in spatially unaware audio embeddings. To address these limitations, we propose a novel self-supervised training approach for General-Purpose, Real-world Audio Models (GRAMs). The GRAM training approach enables robust spatial audio representati…