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Found 5 papers, 1 papers with code
Language Grounded QFormer for Efficient Vision Language Understanding
Large-scale pretraining and instruction tuning have been successful for training general-purpose language models with broad competencies.
Transformers are Universal Predictors
We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense.
Learning Optimal Features via Partial Invariance
Learning models that are robust to distribution shifts is a key concern in the context of their real-life applicability.
The Spectral Bias of Polynomial Neural Networks
Inspired by such studies, we conduct a spectral analysis of the Neural Tangent Kernel (NTK) of PNNs.
Design of false data injection attack on distributed process estimation
Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering.
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