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Found 5 papers, 2 papers with code
Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
Recent approaches have explored language-guided classifiers capable of classifying examples from novel tasks when provided with task-specific natural language explanations, instructions or prompts (Sanh et al., 2022; R. Menon et al., 2022).
Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
Generalized quantifiers (e. g., few, most) are used to indicate the proportions predicates are satisfied (for example, some apples are red).
MaNtLE: Model-agnostic Natural Language Explainer
Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance.
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation
Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding.
Shared Learning : Enhancing Reinforcement in $Q$-Ensembles
Deep Reinforcement Learning has been able to achieve amazing successes in a variety of domains from video games to continuous control by trying to maximize the cumulative reward.
