惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
V
V2EX
Martin Fowler
Martin Fowler
博客园 - Franky
P
Proofpoint News Feed
P
Palo Alto Networks Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog
The Register - Security
The Register - Security
Latest news
Latest news
S
Security @ Cisco Blogs
Simon Willison's Weblog
Simon Willison's Weblog
Recorded Future
Recorded Future
大猫的无限游戏
大猫的无限游戏
M
Microsoft Research Blog - Microsoft Research
Scott Helme
Scott Helme
T
Tailwind CSS Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
True Tiger Recordings
有赞技术团队
有赞技术团队
I
Intezer
Cisco Talos Blog
Cisco Talos Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
The GitHub Blog
The GitHub Blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
T
Tenable Blog
博客园 - 叶小钗
Hugging Face - Blog
Hugging Face - Blog
Hacker News: Ask HN
Hacker News: Ask HN
S
Security Archives - TechRepublic
F
Future of Privacy Forum
爱范儿
爱范儿
PCI Perspectives
PCI Perspectives
H
Help Net Security
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
MongoDB | Blog
MongoDB | Blog
Security Latest
Security Latest
美团技术团队
博客园 - 三生石上(FineUI控件)
S
Schneier on Security
量子位
C
CERT Recently Published Vulnerability Notes
SecWiki News
SecWiki News

cs.LG updates on arXiv.org

Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective What are the Right Symmetries for Formal Theorem Proving? Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning Provable Joint Decontamination for Benchmarking Multiple Large Language Models On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation Holomorphic Neural ODEs with Kolmogorov-Arnold Networks for Interpretable Discovery of Complex Dynamics When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning Long-term Fairness with Selective Labels Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models Learning Causal Orderings for In-Context Tabular Prediction Toward Understanding Adversarial Distillation: Why Robust Teachers Fail Tabular foundation models for robust calibration of near-infrared chemical sensing data Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents CausalGuard: Conformal Inference under Graph Uncertainty Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification Double descent for least-squares interpolation on contaminated data: A simulation study Detecting Atypical Clients in Federated Learning via Representation-Level Divergence Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics How Many Different Outputs Can a Transformer Generate? Can Transformers Learn to Verify During Backtracking Search? stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation Symbolic Density Estimation for Discrete Distributions On-Policy Consistency Training Improves LLM Safety with Minimal Capability Degradation ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks Aerodynamic force reconstruction using physics-informed Gaussian processes AgForce Enables Antigen-conditioned Generative Antibody Design Harnesses for Inference-Time Alignment over Execution Trajectories A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning Bandit Convex Optimization with Gradient Prediction Adaptivity No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding IKNO: Infinite-order Kernel Neural Operators Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning One LR Doesn't Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization Three Costs of Amortizing Gaussian Process Inference with Neural Processes Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws Manifold-Guided Attention Steering Noise Schedule Design for Diffusion Models: An Optimal Control Perspective MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation When to Switch, Not Just What: Transition Quality Prediction in Clash Royale Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification ASAP: Attention Sink Anchored Pruning Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction Dynamic Mixture of Latent Memories for Self-Evolving Agents From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans Towards Explainability of SLMs by investigating Token Level Activation Predicting Performance of Symbolic and Prompt Programs with Examples Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching One-Way Policy Optimization for Self-Evolving LLMs DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification $\textit{BlockFormer}$ : Transformer-based inference from interaction maps ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising AMUSE: Anytime Muon with Stable Gradient Evaluation
Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
Nils Leutene · 2026-05-23 · via cs.LG updates on arXiv.org

View PDF HTML (experimental)

Abstract:Does the relationship between learning rules and brain alignment generalize across species? We extend our prior finding that untrained CNNs match backpropagation at human V1 by testing the same five learning rules against macaque electrophysiology. The rules are backpropagation (BP), feedback alignment (FA), predictive coding (PC), spike-timing-dependent plasticity (STDP), and an untrained random-weights baseline. The macaque data come from two datasets: MajajHong2015 (V4/IT, 3,200 stimulus presentations, 88/168 neurons) and FreemanZiemba2013 (V1/V2, 135 stimuli, 102/103 neurons). Using RSA with identical model weights from our human study, we find: (1) all models achieve higher alignment with macaque early visual cortex (rho = 0.15-0.30 at V1/V2) than with human fMRI (rho = 0.01-0.08), consistent with the higher signal-to-noise ratio of electrophysiology; (2) STDP and PC produce the highest macaque V1/V2 alignment (rho ~ 0.30 and 0.28), consistent with their leading position among trained rules in human V1; (3) at IT, learning rule rankings show no detectable correlation across species (Kendall's tau = 0.00, p = 1.00), though this null result is expected given that n = 5 provides power only at tau = +/-1.0, and is further confounded by stimulus set differences; (4) a pretrained ResNet-50 (ImageNet) achieves rho = 0.25 at macaque IT, substantially above all custom CNN conditions (rho = 0.07-0.14), suggesting IT alignment is limited by model capacity and training data rather than by the learning rule. Noise ceilings, multi-seed variability (5 seeds), and a stimulus-control analysis are reported. These results demonstrate that early visual alignment is robust across species, while higher-area alignment is modulated by model capacity and stimulus domain.
Comments: 9 pages, 6 figures
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
Cite as: arXiv:2605.22401 [cs.LG]
  (or arXiv:2605.22401v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.22401

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nils Leutenegger [view email]
[v1] Thu, 21 May 2026 12:31:18 UTC (121 KB)