3 accepted papers
While imitation learning is a powerful paradigm for teaching robots complex manipulation skills, its effectiveness is often bottlenecked by the need for large-scale, human-collected datasets. This paper presents AutoTrialGen, an automated framework designed to generate large and diverse datasets of
Architectures such as Linformer and Mamba have recently emerged as competitive linear time replacements for transformers. However, corresponding large pretrained models are often unavailable, especially in non-text domains. To remedy this, we present a Cross-Architecture Layerwise Distillation (CALD…
End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerful pretraining objective on text as it enables the model to capture high-level s…