#surrogate model
17 related articles
·3 min
How Theoretical Physicists Can Efficiently Get Started with Machine Learning: Optimal Paths and Resource Guide
A systematic guide for theoretical physicists transitioning to ML, covering math advantages, a three-stage learning path, classic textbooks, and physics-ML cross-disciplinary research directions.
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·2 min
AirProof AI: Using AI to Find the Optimal Placement for Your Air Purifier
AirProof AI simulates indoor airflow with AI to help users find the optimal air purifier placement in seconds, featuring airflow efficiency visualization and recirculation risk detection.
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·4 min
The Evolution of Policy Gradient Algorithms: The Complete Lineage from REINFORCE to PPO to GRPO
Trace the evolution of policy gradient algorithms: from REINFORCE's high variance, through Actor-Critic baselines, TRPO's trust regions, PPO's clipping, to GRPO's group baselines for reasoning models.
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·3 min
The Fundamental Conflict Between Backpropagation and Continual Learning: Can Catastrophic Forgetting Ever Be Truly Solved?
Exploring the fundamental conflict between backpropagation and continual learning, analyzing the roots of catastrophic forgetting, limitations of current solutions, and whether local learning or neuromorphic computing can offer true breakthroughs.
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·6 min
Using RL to Please the Reward Model: The Reward Hacking Concern Behind Soaring Elo Scores
When RL continuously optimizes models to please reward models, do soaring Elo scores truly represent capability gains? A deep dive into Reward Hacking in RLHF, Goodhart's Law in AI, and industry countermeasures.
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·4 min
Statistical Foundations of Machine Learning: From MLE to EWMA and Deep Learning Optimizer Principles
Deep dive into core ML statistics: MLE derivations, multivariate Gaussian, linear regression and least squares equivalence, empirical risk minimization, method of moments, and how EWMA connects to Adam optimizer.
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·4 min
Statistical Foundations of Machine Learning: From MLE to EWMA and Deep Learning Optimizer Principles
Deep analysis of core ML statistics concepts covering MLE derivation, multivariate Gaussian, linear regression and least squares equivalence, empirical risk minimization, method of moments, and EWMA's connection to Adam optimizer.
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·2 min
Pheno4D: A Deep Dive into the Sub-Millimeter 4D Plant Point Cloud Dataset
Pheno4D is a 4D plant point cloud dataset built with laser scanning, covering 20 days of daily scans across 14 maize and tomato plants at 0.012mm precision with per-leaf instance tracking.
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·7 min
Training Machine Learning Models from Scratch: A Complete Beginner's Guide
A complete walkthrough of training machine learning models from scratch—covering problem definition, data preprocessing, algorithm selection, hyperparameter tuning, and evaluation, with tool recommendations for beginners.
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AI-Generated Videos That Activate the …
·3 min
AI-Generated Videos That Activate the Brain: Breakthroughs and Concerns in Neuroscience Closed-Loop Optimization
Scientists use AI closed-loop optimization to auto-generate videos that maximally activate specific brain regions. Explore the science, medical potential, and ethical concerns around neural manipulation.
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·4 min
Reinforcement Learning for AI Agents: A Deep Dive into RLHF and Agentic RL
A deep dive into RL for AI agents: from RLHF to Agentic RL, covering PPO vs. GRPO, sparse rewards, tool-calling optimization, and verifiable rewards.
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·5 min
How the Brain Sees and Thinks Simultaneously: A Complete Guide to Predictive Coding Circuits in Visual Cortex
Why can humans "see" the world even under blur and occlusion? This article analyzes bidirectional feedforward-feedback circuits in visual cortex, revealing how predictive coding fuses perception with cognition and its implications for AI.
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·6 min
Benchmarking as Data Activation: Giving Domain Data a Direction to Climb
How benchmarking transforms dormant domain data into an AI optimization engine. From healthcare to law to manufacturing, building vertical benchmarks activates proprietary data and builds a strategic moat.
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·7 min
How Quantum Computing + AI Supercomputing Could Crack Fusion's Fuel Dilemma
One of the biggest bottlenecks to fusion commercialization is the tritium fuel breeding and cycling problem. This article explores how quantum computing and AI supercomputers can jointly tackle fusion's fuel challenge.
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Neural Render Proxies: A New Paradigm …
·3 min
Neural Render Proxies: A New Paradigm for Real-Time Interaction and Differentiable Lighting
A deep dive into Neural Render Proxies: how neural networks replace costly lighting computation to enable real-time interaction, inverse rendering, and end-to-end differentiable optimization across games, digital twins, and NeRF.
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·3 min
Biological Neurons vs. Artificial Neurons: How Big Is the Computational Gap?
Research shows biological neurons far outperform classical artificial neurons. Explore dendritic computing, temporal coding, and what this means for next-gen AI architecture design.
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·4 min
PAO Project in Practice: Bayesian Optimization-Driven Aspen Plus Automated Simulation Tutorial
Learn how the PAO project integrates Bayesian optimization with Aspen Plus via YAML configuration for automated multi-objective chemical process optimization.
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