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Found 6 papers, 1 papers with code
Can language models handle recursively nested grammatical structures? A case study on comparing models and humans
Prior work suggests that LMs cannot handle these structures as reliably as humans can.
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning
Taking inspiration from cognitive science, we term representations for reoccurring segments of an agent's experience, "perceptual schemas".
Tell me why!—Explanations support learning relational and causal structure
Explanations play a considerable role in human learning, especially in areas that remain major challenges for AI—forming abstractions, and learning about the relational and causal structure of the world.
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
One-shot and few-shot learning of word embeddings
Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily.
Improving image generative models with human interactions
GANs provide a framework for training generative models which mimic a data distribution.
