Experiment 26
This post analyzes urban mobility patterns to identify critical mobility hubs, network resilience, and neighborhood structures within cities by applying cent...
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Experiment 25
This post demonstrates that reinforcement learning agents can be trained with sparse reward signals as effectively as carefully tuned dense rewards.
Experiment 24
The blog post demonstrates that few-shot LLMs can match 96% of fine-tuned model accuracy on scene graph extraction with 103x faster inference.
Experiment 23
The blog post describes a fork of the DE-ViT algorithm that adapts it for few-shot object detection on satellite imagery by using DINOv3 vision transformers ...
Experiment 22
The blog post introduces CoBAD (Collective Behavior Anomaly Detection), a deep learning approach that detects anomalous group behaviors in human mobility dat...
Experiment 21
This post converts GPS trajectory data into learned embedded features to reveal human mobility patterns, enabling applications in urban planning, personalize...