004.85 — Обучение

Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization Ryoki Hamano, Kento Uchida, Shinichi Shirakawa · 2026

Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design Ondrej Galeta, Lukas Sekanina · 2026

NPSolver: Neural Poisson Solver with Iterative Physics Supervision Bocheng Zeng, Rui Zhang, Runze Mao, Mengtao Yan, Xuan Bai, Yang Liu, Zhi X. Chen, Hao Sun · 2026

On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits Yunlong Hou, Zixin Zhong, Vincent Y. F. Tan · 2026

Looped Diffusion Language Models Sanghyun Lee, Chunsan Hong, Seungryong Kim, Jonghyun Lee, Jongho Park, Dongmin Park · 2026

Prism: A Plug-in Reproducible Infrastructure for Scalable Multimodal Continual Instruction Tuning Jun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie, Da-Wei Zhou · 2026

From Model Scaling to System Scaling: Scaling the Harness in Agentic AI Shangding Gu · 2026

Classification of medical X-ray images using supervised and unsupervised learning approaches Ranjana Battur, Jagadisha Narayana · 2023

Approximating Spectral Clustering via Sampling: A Review Nicolas Tremblay, Andreas Loukas · 2019

Statistical Inference for Stochastic Gradient Descent Beyond Finite Variance Jose Blanchet, Peter Glynn, Wenhao Yang · 2026

Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning Aleksandar Todorov, Matthia Sabatelli · 2026

AdvantageFlow: Advantage-Weighted Least Squares for RL in Flow Models Branislav Kveton, Anup Rao, Subhojyoti Mukherjee, Krishna Kumar Singh, Viet Dac Lai · 2026

Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service Christoffer Loeffler, Tomás Rey Pizarro, Daniel Ignacio Miranda Vásquez, Andrea Martínez Freile · 2026

Merge-Bench: Resolve Merge Conflicts with Large Language Models Benedikt Schesch, Michael D. Ernst · 2026

Predicting Stock Price Direction on Earnings Announcement Days using Multi-modal Deep Learning Manuel Noseda, Nathan Soldati, Marco Paina · 2026

Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing Michał Brzozowski, Zuzanna Dubanowska, Enrico Cassano, Neo Christopher Chung · 2026

Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation Haiyan Zhao, Zirui He, Guanchu Wang, Ali Payani, Yingcong Li, Mengnan Du · 2026

Civil Aviation Passenger Throughput Forecasting Model Based on Machine Learning Luyuan Yang, Diansheng Yang · 2024

Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling Robert E. Lowe, Michael W. Berry · 2019

Information theoretic active learning in unsupervised and supervised problems Jamshid Sourati · 2024

Aerodynamic force reconstruction using physics-informed Gaussian processes Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal · 2026

From Betting to Empirical Bernstein LIL Francesco Orabona · 2026

SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks Yukai Yang, Chenxi Qin, Jungang Li, Xin Zhang, Wenwei Shao, Liqun Chen · 2026

Preisach Attention: A Hysteretic Model of Sequential Memory Piotr Frydrych · 2026

Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models Hongyi Li, Jun Xu, Hong Yan · 2026

Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Mohammad Reza Deylam Salehi · 2026

Onsager-Machlup Posterior Transport for Deep Gaussian Processes Jian Xu, Delu Zeng, John Paisley, Qibin Zhao · 2026

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Snir Hordan, Nadav Dym, Tim Seppelt · 2026

Rmixmod: Classification with Mixture Modelling Florent Langrognet, Remi Lebret, Christian Poli, Serge Iovleff, Benjamin Auder · 2012

225 Supervised, unsupervised, and semi-supervised learning · 2025

Supervised and Unsupervised Machine Learning Approaches for Tree Classification Using Multiwavelength Airborne Polarimetric Lidar Zhong Hu, Songxin Tan · 2024

Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints Chidchanok Lursinsap · 2019

Supervised and Unsupervised Learning for Data Science · 2020

Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification Jiabao Wang, Yang Li, Xiu-Shen Wei, Hang Li, Zhuang Miao, Rui Zhang · 2023

Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification R. Sathya, Annamma Abraham · 2013

Supervised and unsupervised neural networks technique in facies classification and interpretation J. Anggraini · 2024

Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications Amitesh singam · 2023

Types of Machine Learning · 2019

Fidelity-based supervised and unsupervised learning for binary classification of quantum states F. Shahi, A. T. Rezakhani · 2021

Neural Networks Kevin Ashley · 2020

Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions Gabriel Kronberger, Fabricio Olivetti de Franca, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira · 2026

Neural Networks and Deep Learning · 2026

On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents Oliver Mortensen, Mohammad Sadegh Talebi · 2026

MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation Ahanaf Hasan Ariq · 2026

Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network Arisara Pornwattanavichai, Prawpan Brahmasakha na sakolnagara, Pongsakorn Jirachanchaisiri, Janekhwan Kitsupapaisan, Saranya Maneeroj · 2019

Contrastive learning for unsupervised representation and semi-supervised learning for medical image segmentation Krishna Chaitanya, Ender Konukoglu · 2026

Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration Lily Goli, Justin Kerr, Daniele Reda, Alec Jacobson, Andrea Tagliasacchi, Angjoo Kanazawa · 2026

Integrable Elasticity via Neural Demand Potentials Carlos Heredia, Daniel Roncel · 2026

Contradiction Graphs Determine VC Dimension Jesse Campbell, Daniel Ibaibarriaga, Lev Reyzin · 2026

CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel · 2026

Tippett-minimum Fusion of Representation-space Diffusion Models for Multi-Encoder Out-of-Distribution Detection Neelkamal Bhuyan · 2026

On the Stability of Growth in Structural Plasticity Lute Lillo, Nick Cheney · 2026

Perforated Neural Networks for Keyword Spotting Vishy Gopal, Aris Ilias Goutis, Ralph Crewe, Erin Yanacek, Rorry Brenner · 2026

Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search Pranav Somu, Advay Balakrishnan, Stepan Kravtsov, Aaron McDaniel, Jason Zutty · 2026

Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning Adrien Weihs, Hayden Schaeffer · 2026

The Distillation Game: Adaptive Attacks & Efficient Defenses Youssef Allouah, Mahdi Haghifam, Sanmi Koyejo, Reza Shokri · 2026

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation Samson Gourevitch, Yazid Janati, Dario Shariatian, Umut Simsekli, Eric Moulines, Eric P. Xing, Alain Durmus · 2026

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis Stanislav R. Kirpichenko, Andrei V. Konstantinov, Lev V. Utkin · 2026

Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing Alex Fulleda-Garcia, Saray Soldado-Magraner, Josep Maria Margarit-Taulé · 2026

Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons Aaron Spieler, Georg Martius, Anna Levina · 2026

Supervised and unsupervised learning for diagnostic ECG classification N. Silipo, G. Bortolan, C. Marchesi · 2024

Targeted maximum likelihood estimation of vaccine effectiveness and immune correlates in test-negative design studies with missing data Leah I. B. Andrews, Lars van der Laan, Peter B. Gilbert · 2026

Three Costs of Amortizing Gaussian Process Inference with Neural Processes Robin Young · 2026

Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstration Rahul Uma Ramachandran, Serge Massar · 2026

Closed-form predictive coding via hierarchical Gaussian filters Aleksandrs Baskakovs, Sylvain Estebe, Kenneth Enevoldsen, Kristoffer Nielbo, Chris Mathys, Nicolas Legrand · 2026

EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning Tianyun Zhang, Zhen Yang, Haozhao Wang, Ru Zhang, Yongfeng Huang · 2026

Generative Modeling by Value-Driven Transport Pablo Moreno-Muñoz, Adrian Müller, Gergely Neu · 2026

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets Ioannis J. Vourganas, Anna Lito Michala · 2026

Disentanglement Beyond Generative Models with Riemannian ICA Edmond Cunningham · 2026

Task Classification during Visual Search with Deep Learning Neural Networks and Machine Learning Methods Siddartha Thentu · 2024

Decision Tree Taeho Jo · 2020

Joint Supervised and Unsupervised Machine Learning for Spectrum Sensing Bisma Manzoor, Akram Al-Hourani · 2024

RECENT ADVANCES ON OPTIMUM-PATH FOREST FOR DATA CLASSIFICATION: SUPERVISED, SEMI-SUPERVISED, AND UNSUPERVISED LEARNING João Paulo Papa, Willian Paraguassu Amorim, Alexandre Xavier Falcão, João Manuel R. S. Tavares · 2015

Innovative Applications of Supervised Learning in Addressing Missing Data: A Case Study on Social Surveys Simona Cafieri, Francesco Pugliese, Mauro Sodani · 2025

Deep Learning and Neural Networks Overview Orhan Gazi Yalçın · 2020

AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning Yogesh Malhotra · 2018

Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz, Ege Onur Taga, Tara Javidi, Samet Oymak · 2026

Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework Shourov Joarder, Diganta Sikdar, Ahsan Habib Akash, Binod Bhattarai, Prashnna Gyawali · 2026

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection Saif Alzubi, Frederic Stahl · 2026

A note on convergence of Wasserstein policy optimization David Šiška, Yufei Zhang · 2026

Connecting Supervised/Unsupervised Learning to Reinforcement Learning Yannis Flet-Berliac · 2026

Building Surface Crack Detection Based on Deep Convolutional Neural Networks and Ensemble Learning Yichi Li · 2026

Temporal Learning Taeho Jo · 2020

Sampling Technique for Complex Data A. Idarrou, H. Douzi · 2019

Ensemble Learning Taeho Jo · 2020

Evolutionary Approach to Gene Regulatory Networks Hitoshi Iba · 2018

Deep Tobit networks: A novel machine learning approach to microeconometrics Jiaming Zhang, Zhanfeng Li, Xinyuan Song, Hanwen Ning · 2021

Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets Kamil Kashif, Robert Ślepaczuk · 2026

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions Esila Keskin · 2026

Stability and Discretization Error of State Space Model Neural Operators Abderrahim Bendahi, Adrien Fradin, Johan Peralez, Julie Digne, Madiha Nadri · 2026

Adaptive Stochastic Natural Gradient Method for Safe Optimization on Binary Space Kento Uchida, Ryoki Hamano, Masahiro Nomura, Shinichi Shirakawa · 2026

Conclusion Hitoshi Iba · 2018

Automatic microseismic signals classification with Deep Learning using multi-input Convolutional Neural Networks S. Rajeul · 2021

Simultaneously Learning Architectures and Features of Deep Neural Networks Tinghuai Wang, Lixin Fan, Huiling Wang · 2019

A Python‐Based Machine Learning Classification Approach for Healthcare Applications Vishal Sharma · 2024

Learning algorithms for deep spiking neural networks Hong Qu, Xiaoling Luo, Zhang Yi · 2024

Deep Learning Convolutional Neural Networks with Dropout - A Parallel Approach Jingyi Shen, M. Omair Shafiq · 2018

Semi‐Supervised Classification Using Pattern Clustering · 2013

An Introduction to Machine Learning & Deep Neutral Networks · 2020

Cyclic models and recurrent neural networks Thomas P. Trappenberg · 2019

Deep Reinforcement Learning Charu Aggarwal · 2023

When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System Deemah H. Tashman, Soumaya Cherkaoui · 2026

Active Context Selection Improves Simple Regret in Contextual Bandits Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash · 2026

Tail Annealing for Heavy-Tailed Flow Matching Jean Pachebat · 2026

Smooth Partial Lotteries for Stable Randomized Selection Alexander Goldberg, Giulia Fanti, Nihar B. Shah · 2026

Table 1: Review of studies using supervised classification and unsupervised clustering approaches to identify vocal types. · 2024

Natural Computing for Unsupervised Learning · 2019

Using supervised learning successful descriptors to perform protein structural classification through unsupervised learning Cleiton Monteiro, VinIcio Mendes, Giovanni Comarela, Sabrina A. Silveira · 2018

Combined unsupervised and semi-supervised learning for data classification Fabricio Aparecido Breve, Daniel Carlos Guimaraes Pedronette · 2016

Medical Image Classification with Artificial and Deep Convolutional Neural Networks: A Comparative Study Amen Bidani, Mohamed Salah Gouider, Carlos M Travieso-Gonzalez · 2022

Classification of Electrical Treeing Images by Machine Learning of Supervised and Unsupervised Learning Yudai Shiozaki, Hyeon-Gu Jeon, Haruo Ihori · 2023

Document Classification with Unsupervised Nonnegative Matrix Factorization and Supervised Percetron Learning Paresh Chandra Barman, Soo-Young Lee · 2007

Sampling Techniques for Supervised or Unsupervised Tasks · 2020

Deep Learning — MLP Neural Networks Explained Carsten Lange · 2024

StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels Reza M. Asiyabi, Juan Alberto Molina-Valero, The SEOSAW Partnership, Steven Hancock, Casey M. Ryan · 2026

PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents Zhuohan Gu, Qizheng Zhang, Omar Khattab, Samuel Madden · 2026

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation Serhii Zabolotnii · 2026

A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limits Yuyang Zhang, Yifu Zhang, Xuehai Zhou, Xiaoyin Chen · 2026

Semi‐Supervised Classification Using Prior Word Clustering · 2013

Machine Learning, Deep Learning and Neural Networks Evan Shellshear, Michael Tremeer, Cameron Bean · 2022

Machine Learning Foundations Taeho Jo · 2021

Supervised and unsupervised learning in animal classification N. Manohar, Y.H. Sharath Kumar, G. Hemantha Kumar · 2016

Bringing order to the variable star zoo: the effectiveness of semi-supervised and unsupervised learning for classification Ricardo Daniel Pantoja Vásquez · 2024

Machine Learning with Shallow Neural Networks Charu Aggarwal · 2023

TOPIC CLASSIFICATION USING HYBRID OF UNSUPERVISED AND SUPERVISED LEARNING Jayant Shelke · 2024

Evolutionary Approach to Machine Learning and Deep Neural Networks Hitoshi Iba · 2018

Neural Networks and Deep Learning with TensorFlow Pramod Singh, Avinash Manure · 2019

GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization Xingyu Li · 2026

Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstration Rahul Uma Ramachandran, Serge Massar · 2026

Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection Parnian Ghapandar Kashani, Shiqi Chen, Aydogan Ozcan · 2026

optimize_anything: A Universal API for Optimizing any Text Parameter Lakshya A Agrawal, Donghyun Lee, Shangyin Tan, Wenjie Ma, Karim Elmaaroufi, Rohit Sandadi, Sanjit A. Seshia, Koushik Sen, Dan Klein, Ion Stoica, Joseph E. Gonzalez, Omar Khattab, Alexandros G. Dimakis, Matei Zaharia · 2026

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs Pierre Boudart, Pierre Gaillard, Alessandro Rudi · 2026

B-cos GNNs: Faithful Explanations through Dynamic Linearity Joschka Groß, Mohammad Shaique Solanki, Verena Wolf · 2026

Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation Yuxuan Gao, Megan Wang, Yi Ling Yu · 2026

Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization Dmitry Redko, Albert Fazlyev, Konstantin Sozykin, Maria Ivanova, Evgeny Burnaev, Egor Shvetsov · 2026

Calibeating for general proper losses: A Bregman divergence approach Maximilian Fichtl, Cristóbal Guzmán, Nishant A. Mehta · 2026

Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation Hayato Okubo, Yoshifumi Amamoto, Toshimitsu Aritake, Hiroyuki Kumazoe, Shiryu Nakano, Evan Jamison, Satoshi Tanaka, Yoh-ichi Mototake · 2026

Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation Sum Kyun Song, Bong Gyun Shin, Jae Yong Lee · 2026

Broken-symmetry shape discrimination on a driven Duffing ring Kaspar Anton Schindler · 2026

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction Himanshu Udupi, Xiaocong Yang, ChengXiang Zhai · 2026

Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples Anastasis Kratsios · 2026

Stable Causal Discovery via Directed Acyclic Graph Aggregation Yunan Wu, Yue Wang, Chunlin Li, Chenglong Ye · 2026

Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata Mirela-Magdalena Catrina, Ioana Cristina Plajer, Alexandra Băicoianu · 2026

Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing Ningping Li, Hao Zhang, Yi Zhou · 2026

Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders William Lehn-Schiøler, Magnus Ruud Kjær, Rahul Thapa, Magnus Guldberg Pedersen, Anton Mosquera Storgaard, Nick Williams, Radu Gatej, Tue Lehn-Schiøler, Sándor Beniczky, Sadasivan Puthusserypady, James Zou, Lars Kai Hansen · 2026

Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning Taebong Kim, Youngsik Hong, Minsik Kim, Sunyoung Choi, Jaewon Jang, Junghoon Shin, Minseo Kim · 2026

Deep learning and neural networks Kutub Thakur, Al-Sakib Khan Pathan · 2025

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks Lok-Won Kim · 2018

Neural Networks and Deep Learning · 2019

Introduction to Machine Learning Arvind Kumar Tiwari · 2020

Neural Networks for Medical Image Computing V.A. Pravina, P.K. Poonguzhali, A Kishore Kumar · 2022

Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network Yiqun Duan, Chen Feng · 2019

Deep Learning and Neural Networks · 2023

Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels Binyamin Perets, Shie Mannor · 2026

Training Infinitely Deep and Wide Transformers Raphaël Barboni, Maarten V. de Hoop, Takashi Furuya, Gabriel Peyré · 2026

Causal Explanations from the Geometric Properties of ReLU Neural Networks Hector Woods, Philippa Ryan, Rob Alexander · 2026

Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA Pascal Harmeling, Florent De Geeter, Guillaume Drion · 2026

Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees Aditya Tanna, Nassim Bouarour, Mohamed Bouadi, Vinay kumar Sankarapu, Pratinav Seth · 2026

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning Arnab Auddy, Xiangni Peng, Subhadeep Paul · 2026

KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture Luis Balderas, José Alberto Rodríguez, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez · 2026

Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers Rita Adhikari, Shiwei Zeng · 2026

Neural networks and Keras Thomas P. Trappenberg · 2019

Learning Deep Neural Networks for High Dimensional Output Problems Benjamin Labbé, Romain Hérault, Clément Chatelain · 2009

A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks · 2022

Application of English semantic understanding in multimodal machine learning Luo Yun · 2024

Integrative Unsupervised and Supervised Learning Approaches for Breast Cancer Subtype Classification Using Gene Expression Data Ana Beatriz Miranda Valentin, Glaucia Maria Bressan, Elisângela da Silva Lizzi · 2025

Neural Networks and Deep Learning Sergios Theodoridis · 2020

GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems Maximilian Reinhardt, Jonas Scharfenberger, Burkhardt Funk · 2026

scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement Xichen Yan, Zelin Zang, Changxi Chi, Jingbo Zhou, Chang Yu, Jinlin Wu, Shenghui Cheng, Fuji Yang, Jiebo Luo, Zhen Lei, Stan Z. Li · 2026

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Yuhan Wang, Haopeng Zhang, Yibo Ding, Jiaqi Yu, Xinyu Zhao, Yuhang Liu, Ziwei Zhang, Xiao Wang, Ruijie Wang · 2026

When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State Peiying Zhu, Sidi Chang · 2026

SURGE: Approximation-free Training Free Particle Filter for Diffusion Surrogate Lifu Wei, Yinuo Ren, Naichen Shi, Yiping Lu · 2026

ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop Yining Hong, Jiageng Liu, Han Yin, Manling Li, Leonidas Guibas, Li Fei-Fei, Jiajun Wu, Yejin Choi · 2026

A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability Ruitao Liu, Xinyang Tian, Shuo Chen, Tingrui Zhang, Guang Yang, Alan Zhao, Wei Xu · 2026

DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti, Lei Li, Xu Han, Edoardo M. Ponti, André F. T. Martins, Marcos V. Treviso · 2026

R Packages for Neural Networks, Deep Learning, and Naïve Bayes William B. Claster · 2020

Comparative Study of Neural Networks and Decision Trees for Application in Trading Financial Futures Saulius Blaziunas, Aistis Raudys · 2019

Chapter 11: Deep Feedforward Neural Networks · 2024

Quantification learning with deep neural networks Lei Qi · 2024

Bridging Supervised and Unsupervised Learning for Classification of Breast Tissue Virginia Negri, Grazia Iadarola, Alessandro Mingotti, Roberto Tinarelli, Lorenzo Peretto · 2025

Motor imagery signal classification using semi supervised and unsupervised extreme learning machines M. Dharani, M. Sivachitra · 2017

Reinforcement Learning Taeho Jo · 2020

Emerging opportunities in machine learning hardware acceleration Ruizhe Cai · 2024

GEML: Evolutionary Unsupervised and Semi-Supervised Learning of Multi-class Classification with Grammatical Evolution Jeannie M. Fitzgerald, R. Mohammed Atif Azad, Conor Ryan · 2015

Combined Unsupervised and Supervised Learning for Improving Chest X-Ray Classification Anca Ignat, Robert-Adrian Găină · 2023

Dynamic machine learning for supervised and unsupervised classification Adela-Maria Sîrbu · 2024

Hyperspectral Analysis of Apricot Quality Parameters Using Classical Machine Learning and Deep Neural Networks Martin Dejanov · 2025

Efficient event-driven retrieval in high-capacity kernel Hopfield networks Akira Tamamori · 2026

CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models Thomas Bömer, Bastian Amberg, Max Disselnmeyer, Anne Meyer · 2026

The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents Federico Pigozzi, Michael Levin · 2026

A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models Aditya Ranganath, Mukesh Singhal · 2026

FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence Franklin Fuller, Daniele Bertolini, Samantha Liang, Jason Christopher, Aaron M. Smith · 2026

LEVI: Stronger Search Architectures Can Substitute for Larger LLMs in Evolutionary Search Temoor Tanveer · 2026

Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets Jens Egholm Pedersen, Tony Lindeberg, Peter Gerstoft · 2026

EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent Dongxin Guo, Jikun Wu, Siu Ming Yiu · 2026

Classification of lidar measurements using supervised and unsupervised machine learning methods Ghazal Farhani, Robert J. Sica, Mark Joseph Daley · 2021

Simultaneous Supervised and Unsupervised Classification Modeling for Assessing Cluster Analysis and Improving Results Interpretability Mario Fordellone, Maurizio Vichi · 2019

Applications of Unsupervised Techniques for Clustering of Audio Data Surendra Shetty, Sarika Hegde · 2018

Figure 2: Relationship between artificial intelligence, machine learning, artificial neural networks and deep learning. · 2024

Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers Bruno Trentini, Dejan Stancevic, Michael M. Bronstein, Alexander Tong, Luca Ambrogioni · 2026

Navigating Potholes with Geometry-Aware Sharpness Minimization Simon Dufort-Labbé, Mehrab Hamidi, Razvan Pascanu, Ioannis Mitliagkas, Damien Scieur, Aristide Baratin · 2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack) Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte · 2026

Property-Guided LLM Program Synthesis for Planning Augusto B. Corrêa, André G. Pereira, Jendrik Seipp · 2026

Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution Dongxin Guo, Jikun Wu, Siu Ming Yiu · 2026

Layer Equivalence Is Not a Property of Layers Alone: How You Test Redundancy Changes What You Find Gabriel Garcia · 2026

Dynamics-Level Watermarking of Flow Matching Models with Random Codes Shuchan Wang · 2026

AI-Mediated Communication Can Steer Collective Opinion Stratis Tsirtsis, Kai Rawal, Chris Russell, Brent Mittelstadt, Sandra Wachter · 2026

Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks Alexander Gepperth, Florian Wiech · 2019

Other Neural Networks for Deep Learning · 2016

Correction to: Neural Networks and Deep Learning Charu Aggarwal · 2023

Neural Networks and Deep Learning Mutlu Yuksel, Yigit Aydede · 2025

Decomposing Evolutionary Mixture-of-LoRA Architectures: The Routing Lever, the Lifecycle Penalty, and a Substrate-Conditional Boundary Ramchand Kumaresan · 2026

On the Impact of Crossover in Many-Objective Optimization: A Runtime Analysis of NSGA-III Andre Opris · 2026

Leveraging Non-Equilibrium ECRAM Dynamics for Short-Term Plasticity in Neuromorphic Circuits Alex Currie, Sean Borkholder, Nithil Harris Manimaran, Huayuan Han, Cory Merkel, Ke Xu, Tejasvi Das · 2026

Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention Lingdong Li, Hangming Zhang, Qiang Yu · 2026

NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework Alessio Caviglia, Filippo Marostica, Roberta Bardini, Alessandro Savino, Stefano Di Carlo · 2026

Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing Ellwil Sharma, Arastu Sharma · 2026

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability ML Nissen Gonzalez, Melwina Albuquerque, Laurence Wroe, Jacob Meyer Cohen, Logan Riggs Smith, Thomas Dooms · 2026

RefDecoder: Enhancing Visual Generation with Conditional Video Decoding Xiang Fan, Yuheng Wang, Bohan Fang, Zhongzheng Ren, Ranjay Krishna · 2026

The Shallow and the Deep: A biased introduction to neural networks and old school machine learning Michael Biehl · 2023

Neural networks and deep learning Ankur Awadhiya · 2026

Recurrent Neural Networks (RNNs) Ekaba Bisong · 2019

Deep Neural Networks for Supervised Learning: Classification Jojo Moolayil · 2018

Neural Networks and Deep Learning · 2021

Meta-heuristics, Machine Learning, and Deep Learning Methods Hitoshi Iba · 2018

Neural Networks and Deep Learning Sergios Theodoridis · 2015

Machine Learning with Shallow Neural Networks Charu C. Aggarwal · 2018

Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Rafi Al Attrach, Rajna Fani, Sebastian Lobentanzer, Joan Giner-Miguelez, Debanshu Das, Varuni H. K., Nobin Sarwar, Rajat Ghosh, Anwai Archit, Surbhi Motghare, Christina Conrad Parry, Luis Oala, Lara Grosso, Joaquin Vanschoren, Steffen Vogler, Sujata Goswami, Eric S. Rosenthal, Marzyeh Ghassemi, Matthew McDermott, Tom Pollard · 2026

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