Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth fa...
Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (Detectio...
We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core...
Neural networks are hypothesized to implement interpretable causal mechanisms, yet verifying this requires finding a causal abstraction -- a simpler, high-level...
Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern models a...
In this article, bipartite ranking, a statistical learning problem involved in many applications and widely studied in the passive context, is approached in a m...
Identifying the full landscape of small and medium-sized enterprises (SMEs) in specialized industry sectors is critical for supply-chain resilience, yet existin...
Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resour...
Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge for comput...
Background I’m writing this based on a Udemy course on Agentic AI, and I wanted to reflect on how AI evolved into what we now call agentic systems. Rule‑Based...
Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based methods...
Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader tissue or...
