Standalone Articles
Solving Constrained Mean-Variance Portfolio Optimization Using Spiral Optimization
Apply Spiral Optimization Algorithm (SOA) to mean-variance portfolio problems with buy-in thresholds and cardinality constraints. Covers MINLP formulation, penalty methods, and performance comparison.
AI Agents Complete Guide: From Theory to Industrial Practice
A practitioner-grade guide to building AI agents: planning (CoT/ReAct/ToT), memory architectures, tool use, reflection, multi-agent patterns, frameworks (LangChain, LangGraph, AutoGen, CrewAI), evaluation, and production …
Prompt Engineering Complete Guide: From Zero to Advanced Optimization
Master prompt engineering from zero-shot basics to Tree of Thoughts, DSPy, and automated optimization. Includes benchmarks, code, and a debugging toolkit.
Low-Rank Matrix Approximation and the Pseudoinverse: From SVD to Regularization
From the least-squares view to the Moore-Penrose pseudoinverse, the four Penrose conditions, computation via SVD, truncated SVD, Tikhonov regularization, and modern applications from PCA to LoRA.
Reparameterization Trick & Gumbel-Softmax: A Deep Dive
Make sense of the reparameterization trick and Gumbel-Softmax: why gradients can flow through sampling, how temperature trades bias for variance, and the practical pitfalls of training discrete latent variables …
Symplectic Geometry and Structure-Preserving Neural Networks
Learn physics-informed neural networks that preserve energy and symplectic structure. Covers HNN, LNN, SympNet, symplectic integrators, and four classical experiments.
LLM Workflows and Application Architecture: Enterprise Implementation Guide
From a single API call to a production LLM platform — workflow patterns, RAG, model routing, deployment, cost levers, observability, and enterprise integration, with the trade-offs that actually matter.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Prefix-Tuning adapts frozen LLMs by learning continuous key/value vectors injected into attention. Covers the method, reparameterization, KV-cache mechanics, and comparisons with prompt tuning, adapters, and LoRA.
Vim Essentials: Modal Editing, Motions, and a Repeatable Workflow
Learn Vim by understanding its grammar -- modes, operators + motions, text objects -- not by memorizing shortcuts. A practical, beginner-friendly guide with a one-week practice plan.
MoSLoRA: Mixture-of-Subspaces in Low-Rank Adaptation
MoSLoRA boosts LoRA expressivity by mixing multiple low-rank subspaces with a lightweight mixer. Covers when vanilla LoRA fails, mixer design choices, and tuning tips.
Tennis-Scene Computer Vision: From Paper Survey to Production
A complete CV system for tennis: small high-speed object detection, multi-camera 3D reconstruction, physics-based trajectory prediction, and pose-based action recognition. From the literature down to a 16.7 ms-per-frame …
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
HCGR embeds session graphs in the Lorentz model of hyperbolic space and trains them with InfoNCE-style contrastive learning. This review unpacks why hierarchical session intent fits hyperbolic geometry, how Lorentz …
Kernel Methods: From Theory to Practice (RKHS, Common Kernels, and Hyperparameter Tuning)
Understand the kernel trick, RKHS theory, and practical kernel selection. Covers RBF, polynomial, Matern, and periodic kernels with sklearn code and a tuning flowchart.
Position Encoding Brief: From Sinusoidal to RoPE and ALiBi
A practitioner's tour of Transformer position encoding: why attention needs it at all, how sinusoidal/learned/relative/RoPE/ALiBi schemes differ, and which one to pick when long-context extrapolation matters.
LAMP Stack on Alibaba Cloud ECS: From Fresh Instance to Production-Ready Web Server
Set up a LAMP stack (Linux, Apache, MySQL, PHP) on Alibaba Cloud ECS. Covers security groups, service installation, Discuz deployment, source compilation, hardening and three-tier scale-out.
Variational Autoencoder (VAE): From Intuition to Implementation and Troubleshooting
Build a VAE from scratch in PyTorch. Covers the ELBO objective, reparameterization trick, posterior collapse fixes, beta-VAE, and a complete training pipeline.
paper2repo: GitHub Repository Recommendation for Academic Papers
paper2repo aligns academic papers with GitHub repositories in a shared embedding space using a constrained GCN. Covers the joint heterogeneous graph, the WARP ranking loss, the cosine alignment constraint, and the full …
Session-based Recommendation with Graph Neural Networks (SR-GNN)
SR-GNN turns a click session into a directed weighted graph and runs a gated GNN to predict the next item. Covers session-graph construction, GGNN updates, attention-based session pooling, training, benchmarks, and the …
Learning Rate: From Basics to Large-Scale Training
A practitioner's guide to the single most important hyperparameter: why too-large LR explodes, how warmup and schedules really work, the LR range test, the LR-batch-size-weight-decay coupling, and recent ideas like WSD, …
Graph Contextualized Self-Attention Network (GC-SAN) for Session-based Recommendation
GC-SAN combines a session-graph GGNN (local transitions) with multi-layer self-attention (global dependencies) for session-based recommendation. Covers graph construction, message passing, attention fusion, and where the …
Lipschitz Continuity, Strong Convexity & Nesterov Acceleration
Three concepts that demystify most of optimization: Lipschitz smoothness fixes the maximum step size, strong convexity sets the convergence rate and uniqueness of the minimizer, and Nesterov acceleration replaces kappa …
Optimizer Evolution: From Gradient Descent to Adam (and Beyond, 2025)
One article that traces the full lineage GD -> SGD -> Momentum -> NAG -> AdaGrad -> RMSProp -> Adam -> AdamW, then onwards to Lion / Sophia / Schedule-Free. Each step is framed by the specific failure of the previous …
LLMGR: Integrating Large Language Models with Graphical Session-Based Recommendation
LLMGR uses an LLM as the semantic engine for session-based recommendation and a GNN as the ranker. Covers the hybrid encoding layer, two-stage prompt tuning, ~8.68% HR@20 lift, and how to deploy without running an LLM …
Multimodal LLMs and Downstream Tasks: A Practitioner's Guide
End-to-end map of multimodal LLMs: vision-language alignment, cross-modal fusion, the CLIP/BLIP/LLaVA families, downstream tasks (VQA, captioning, grounding, OCR), fine-tuning trade-offs, benchmarks, and what it takes to …
Operating System Fundamentals: A Deep Dive
Walk through processes, virtual memory, file systems, the I/O stack, system calls and schedulers, with the actual numbers and the commands you can verify each claim with on a Linux box.
Proximal Operator: From Moreau Envelope to ISTA/FISTA and ADMM
A systematic walk through the proximal operator: convex-analysis basics, the Moreau envelope, closed-form proxes, and how they power ISTA, FISTA, ADMM, LASSO, and SVM in practice.
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Represent a neural network as a directed graph (neurons as nodes, weights as edges) and use a GNN to produce permutation-equivariant embeddings. The right symmetry unlocks generalisation prediction, network …