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Found 9 papers, 2 papers with code
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation
We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks.
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation.
BD-KD: Balancing the Divergences for Online Knowledge Distillation
We show that adaptively balancing between the reverse and forward divergences shifts the focus of the training strategy to the compact student network without limiting the teacher network's learning process.
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.
Efficient Fine-Tuning of Compressed Language Models with Learners
We introduce Learner modules and priming, novel methods for fine-tuning that exploit the overparameterization of pre-trained language models to gain benefits in convergence speed and resource utilization.
Efficient Fine-Tuning of BERT Models on the Edge
FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset by 30%, and time spent on memory operations by 47%.
Surprisal-Triggered Conditional Computation with Neural Networks
The surprisal of the input, measured as the negative log-likelihood of the current observation according to the autoregressive model, is used as a measure of input difficulty.
Learning Recurrent Binary/Ternary Weights
On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling.
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization
The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity.
