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Found 4 papers, 1 papers with code
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users
Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-technical users.
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.
Neural Program Synthesis from Diverse Demonstration Videos
To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof.
