Dolph2Vec is the first species-specific self-supervised model for dolphin vocalizations, trained on longitudinal recordings from five dolphins, that outperforms general baselines on signature whistle classification and detection while producing embeddings aligned with known whistle categories.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean
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A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.
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Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations
Dolph2Vec is the first species-specific self-supervised model for dolphin vocalizations, trained on longitudinal recordings from five dolphins, that outperforms general baselines on signature whistle classification and detection while producing embeddings aligned with known whistle categories.
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
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Multilingual Vision-Language Models, A Survey
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.