On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,786 papers · 148 categories

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4 results for “hide-and-seek”

AutoDIME: Automatic Design of Interesting Multi-Agent Environmentscs.LG

AutoDIME automates design of multi-agent environments for RL.

problem Designing multi-agent environments for reinforcement learning is challenging.

method Developed intrinsic teacher rewards for multi-agent settings and evaluated them in various tasks.

result Value disagreement was found to be most consistent and effective across tasks.

reinforcement learning multi-agent systems environment design intrinsic rewards value disagreement

2022-03-04abs ↗pdf ↗

Emergent Tool Use From Multi-Agent Autocurriculacs.LG

Agents learn sophisticated tool use and coordination in hide-and-seek.

problem Training agents to perform complex tasks in multi-agent environments.

method Multi-agent competition with hide-and-seek as a simple objective.

result Agents develop emergent strategies requiring sophisticated tool use and coordination.

multi-agent reinforcement learning emergent strategies tool use hide-and-seek

2019-09-17abs ↗pdf ↗

Hide-and-Seek: A Template for Explainable AIcs.LG

Hides the complexity of neural networks, making them more transparent.

problem Lack of transparency in Neural Networks hinders their adoption.

method Proposes Hide-and-Seek (HnS) framework for training interpretable neural networks.

result Interpretable neural networks can be trained without sacrificing predictive power.

explainable AI neural networks transparency interpretability

2020-04-30abs ↗pdf ↗

Hide-and-Seek Privacy Challengecs.LG

Challenge hides and seeks privacy in clinical time-series data.

problem De-identifying clinical time-series data while preserving utility and privacy.

method Synthetic data generation to preserve temporal dynamics and limit re-identification risk.

result A novel competition tracks synthetic data generation and patient re-identification.

clinical time-series privacy de-identification synthetic data membership inference

2020-07-23abs ↗pdf ↗