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PaperAgent
PaperAgent
Apr 23, 2026 · Artificial Intelligence

Stop RAG, Navigate Enterprise Knowledge Directly with CORPUS2SKILL

The article critiques traditional RAG’s blind spots, introduces CORPUS2SKILL’s offline‑compile, online‑navigate two‑stage architecture that builds a hierarchical topic tree and progressive‑disclosure skill files, and shows through WixQA benchmarks that this approach outperforms dense retrieval and Agentic RAG on F1, factuality and recall while highlighting cost and hierarchy quality trade‑offs.

Agentic AIBenchmarkHierarchical Clustering
0 likes · 7 min read
Stop RAG, Navigate Enterprise Knowledge Directly with CORPUS2SKILL
AI Agent Research Hub
AI Agent Research Hub
Mar 10, 2026 · Artificial Intelligence

How Knowledge Distillation Lets Neural Networks Grow Physical Symmetry Without Hard PINN Constraints

The paper introduces Ψ‑NN, a knowledge‑distillation framework that automatically discovers physics‑consistent network structures for PINNs, eliminating the need for manually imposed loss‑function constraints and achieving faster convergence, higher accuracy, and transferable architectures across PDE problems.

Hierarchical ClusteringNetwork Structure Discoveryknowledge distillation
0 likes · 26 min read
How Knowledge Distillation Lets Neural Networks Grow Physical Symmetry Without Hard PINN Constraints
Volcano Engine Developer Services
Volcano Engine Developer Services
May 8, 2025 · Operations

How ByteBrain-LogParser Achieves 1‑2 Orders Faster Log Parsing in Cloud Services

ByteBrain-LogParser is a cloud‑native log‑parsing framework that transforms unstructured logs into dynamic templates with real‑time precision control, delivering parsing speeds up to two orders of magnitude faster than state‑of‑the‑art methods while maintaining near‑SOTA accuracy and low storage overhead.

Cloud ServicesHierarchical ClusteringReal-time analytics
0 likes · 27 min read
How ByteBrain-LogParser Achieves 1‑2 Orders Faster Log Parsing in Cloud Services
Python Programming Learning Circle
Python Programming Learning Circle
Sep 9, 2022 · Big Data

Four Advanced Data Visualization Techniques in Python: Heat Map, 2D Density Plot, Spider Plot, and Tree Diagram

This article introduces four advanced Python data‑visualization methods—heat map, 2D density plot, spider (radar) plot, and hierarchical tree diagram—explaining their concepts, practical use cases, and providing complete seaborn, matplotlib, and SciPy code examples for each.

Data visualizationHierarchical ClusteringMatplotlib
0 likes · 10 min read
Four Advanced Data Visualization Techniques in Python: Heat Map, 2D Density Plot, Spider Plot, and Tree Diagram
Model Perspective
Model Perspective
Aug 18, 2022 · Artificial Intelligence

Master SciPy Clustering: K‑Means and Hierarchical Methods with Python

This guide introduces SciPy's clustering modules, explaining the vector quantization and k‑means algorithm in scipy.cluster.vq, and demonstrates hierarchical clustering with scipy.cluster.hierarchy, accompanied by complete Python code examples and visualizations to help you apply these techniques to real data.

Hierarchical ClusteringK-Meansclustering
0 likes · 4 min read
Master SciPy Clustering: K‑Means and Hierarchical Methods with Python
Model Perspective
Model Perspective
Jun 4, 2022 · Artificial Intelligence

Master Systematic Clustering: From Distance Matrix to Multi-Level Groupings

Systematic clustering, a widely used hierarchical clustering technique, builds a dendrogram by iteratively merging the closest sample points based on a distance matrix, allowing analysts to visualize and select groupings at various distance thresholds, from a single cluster to each point as its own class.

Hierarchical Clusteringclusteringdistance matrix
0 likes · 3 min read
Master Systematic Clustering: From Distance Matrix to Multi-Level Groupings
Xianyu Technology
Xianyu Technology
Jun 4, 2020 · Artificial Intelligence

NBDT: Neural-Backed Decision Trees for Interpretable Image Classification

NBDT (Neural‑Backed Decision Trees) merges a pretrained CNN with a WordNet‑derived hierarchical tree, using the network’s final‑layer weights as class embeddings and a combined classification loss, to deliver state‑of‑the‑art image classification that remains interpretable through explicit hierarchical reasoning.

CNNDecision TreesExplainable Machine Learning
0 likes · 11 min read
NBDT: Neural-Backed Decision Trees for Interpretable Image Classification
NetEase Media Technology Team
NetEase Media Technology Team
Apr 4, 2019 · Artificial Intelligence

Video Recommendation System: Framework, Topic Clustering, and Related Video Retrieval

The paper proposes a video recommendation framework that combines recall and ranking modules, using a multi‑modal topic clustering approach—integrating audio, visual, and textual features via NeXtVLAD, PCA, and K‑Means—to generate unified video representations, improve candidate selection, and boost click‑through and viewing time, while addressing cold‑start and semantic relevance challenges.

A/B testingHierarchical ClusteringNeXtVLAD
0 likes · 7 min read
Video Recommendation System: Framework, Topic Clustering, and Related Video Retrieval