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Found 6 papers, 0 papers with code
EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques
This highlights the gap in reliability of the concept erasure techniques.
Understanding the Local Geometry of Generative Model Manifolds
Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training.
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data.
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring
Automatic Speech Recognition (ASR) has witnessed a profound research interest.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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BD-KD: Balancing the Divergences for Online Knowledge Distillation
We address the challenge of producing trustworthy and accurate compact models for edge devices.
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices.
