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Learning Where and When to Reason in Neuro-Symbolic Inference.

, , , and . NeSy, volume 3432 of CEUR Workshop Proceedings, page 411-412. CEUR-WS.org, (2023)

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Tree-Cut for Probabilistic Image Segmentation., , and . CoRR, (2015)Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning., , , and . AAAI, page 11024-11031. AAAI Press, (2024)Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference., , , , and . CVPR, page 9058-9067. IEEE, (2022)Empirical Bayes Transductive Meta-Learning with Synthetic Gradients., , , , , , and . ICLR, OpenReview.net, (2020)Federated Learning for Inference at Anytime and Anywhere., , , , , , , , and . CoRR, (2022)Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention., , , and . CoRR, (2022)Strong Baselines for Parameter Efficient Few-Shot Fine-tuning., , , and . CoRR, (2023)Learning Where and When to Reason in Neuro-Symbolic Inference., , , and . NeSy, volume 3432 of CEUR Workshop Proceedings, page 411-412. CEUR-WS.org, (2023)AvaTr: One-Shot Speaker Extraction with Transformers., , , , , and . Interspeech, page 3510-3514. ISCA, (2021)Re-ranking for image retrieval and transductive few-shot classification., , , , and . NeurIPS, page 25932-25943. (2021)