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This paper introduces a new parameter pruning scheme for neural networks based on Fisher information distances, which outperforms existing methods on MNIST and CIFAR-10 datasets across fully-connected networks and vision transformers.
This paper critiques optimization-based balancing strategies for multimodal sentiment analysis, showing they fail due to conflating fitting speed with discriminative importance, and proposes a new research agenda focusing on held-out modality valuation.
The paper introduces SURE-Ridge, a non-iterative, closed-form method for causal discovery in equal variance linear Gaussian SEMs, achieving superior performance in small-sample and compute-limited regimes compared to existing baselines.