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Found 9 papers, 2 papers with code
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one.
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Our resulting CADS imaging system demonstrates improvement of >1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
Achieving RGB-D level Segmentation Performance from a Single ToF Camera
Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras.
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher
A case in point is that the best performing checkpoint of the teacher might not necessarily be the best teacher for training the student in KD.
Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements
The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable.
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge of a large neural network into a smaller one.
Annealing Knowledge Distillation
Knowledge distillation (KD) is a prominent model compression technique for deep neural networks in which the knowledge of a trained large teacher model is transferred to a smaller student model.
LPar -- A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces
While this approach is one of the fastest to market, the monolithic design makes it very hard to scale beyond a point.
Information Based Data-Driven Characterization of Stability and Influence in Power Systems
In this paper, we provide a purely data-driven technique for small-signal stability classification (voltage or angle stability) and influence characterization for a power network.
