few-shot-learning

This paper proposes ACSESS, a method for automatically combining multiple sample selection strategies to improve few-shot learning across both in-context learning and gradient-based approaches. The work demonstrates that combining strategies consistently outperforms individual selection methods across 14 datasets with both text and image modalities.

SCHK-HTC is a novel method for few-shot hierarchical text classification that combines sibling contrastive learning with hierarchical knowledge-aware prompt tuning to better distinguish semantically similar classes at deeper hierarchy levels. The approach achieves state-of-the-art performance across three benchmark datasets by enhancing model perception of subtle differences between sibling classes.

OpenAI introduces GPT-3, a 175-billion parameter autoregressive language model that demonstrates strong few-shot learning capabilities across diverse NLP tasks without gradient updates or fine-tuning, representing a paradigm shift in how language models can be applied to new tasks through text interactions alone.

OpenAI presents a new reinforcement learning benchmark based on Sonic the Hedgehog to measure transfer learning and few-shot learning performance in RL agents, along with baseline algorithm evaluations.

This paper analyzes first-order meta-learning algorithms for few-shot learning, introducing Reptile and providing theoretical insights into why these computationally efficient methods work well on established benchmarks.

OpenAI introduces Reptile, a scalable meta-learning algorithm for few-shot classification that achieves comparable performance to MAML while converging faster with lower variance. The paper provides theoretical analysis showing Reptile maximizes inner product between task gradients for improved generalization.

RL² proposes encoding a fast reinforcement learning algorithm as the weights of a recurrent neural network, learned through slow general-purpose RL, enabling agents to adapt to new tasks with few trials similar to biological learning. The method demonstrates strong performance on both small-scale bandit problems and large-scale vision-based navigation tasks.