Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
Challenges in Representation Learning: A report on three machine learning contests
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.
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2026 2verdicts
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MindMirror combines camera-based emotion detection, structured reflection prompts, and a local LLM into a closed workflow that helps digital workers notice and address fatigue or task blockage while keeping all processing on the user's machine.
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Bayesian Model Merging
Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
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MindMirror: A Local-First Multimodal State-Aware Support System for Digital Workers
MindMirror combines camera-based emotion detection, structured reflection prompts, and a local LLM into a closed workflow that helps digital workers notice and address fatigue or task blockage while keeping all processing on the user's machine.