· ai
WTF is Causal Machine Learning Engineering?
What is Causal Machine Learning Engineering? Causal Machine Learning Engineering is a way of building machine learning models that can understand cause‑and‑eff...
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[Paper] Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism
Microservice systems have become the backbone of cloud-native enterprise applications due to their resource elasticity, loosely coupled architecture, and lightw...
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[Paper] Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices
As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsyst...
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Decoupling Expectations: Mastering Covariance Shift in Machine Learning Models
Stop Blaming the Data: A Better Way to Handle Covariance Shift As developers working with machine learning models, we've all been there—staring at a perplexing...
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When One Track Becomes Four: How AI Stem Splitting Gave Me Back My Creative Time
I make music for videos. Not chart‑toppers—just honest tracks for reels, tutorials, and the occasional client brief. For years, my workflow was simple and slow:...
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Automating machine learning with AI agents
Overview When solving competitions on Kaggle, you quickly notice a pattern: Baseline – upload the data, run CatBoost or LightGBM, and get a baseline metric ≈ ½...
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[Paper] TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs
Execution traces are a critical source of information for understanding, debugging, and optimizing complex software systems. However, traces from OS kernels or ...
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[Paper] Hierarchical temporal receptive windows and zero-shot timescale generalization in biologically constrained scale-invariant deep networks
Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often ...
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Data Analyst Guide: Mastering Neural Networks: When Analysts Should Use Deep Learning
Data Analyst Guide: Mastering Neural Networks – When Analysts Should Use Deep Learning As a data analyst, you're likely familiar with the buzz surrounding neur...
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[Paper] Chronicals: A High-Performance Framework for LLM Fine-Tuning with 3.51x Speedup over Unsloth
Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB--14GB for weights, 14GB for gradients, and 56GB for FP32 optimize...
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🧠 LLMs Explained Like You're 5
The Librarian Analogy Imagine a librarian who has: - Read every book in the library - Memorized patterns of how language works - Can predict what word comes ne...
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[Paper] First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data
Artificial intelligence has advanced rapidly through large neural networks trained on massive datasets using thousands of GPUs or TPUs. Such training can occupy...