In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual…
Tag: AI
Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers
arXiv:2608.19124v1 Announce Type: cross Abstract: Astrolinguistics — communication with minds that categorize reality differently from ours — has been…
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of…
Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles
arXiv:2608.19127v1 Announce Type: cross Abstract: A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as…
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight
In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick…
Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation
arXiv:2608.19098v1 Announce Type: cross Abstract: Multi-teacher on-policy distillation (M-OPD) has emerged as a promising paradigm for consolidating…
GS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting
arXiv:2608.19066v1 Announce Type: cross Abstract: This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts…
Bernstein-Vazirani Networks: Quantum Machine Learning by Interference
arXiv:2608.19043v1 Announce Type: cross Abstract: We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework…
DA-WAM: Decision-Aligned Future Latents for Driving World Models
arXiv:2608.19085v1 Announce Type: cross Abstract: Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full…
ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models
arXiv:2608.19075v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not…