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Found 6 papers, 5 papers with code
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical.
Confounding Tradeoffs for Neural Network Quantization
Many neural network quantization techniques have been developed to decrease the computational and memory footprint of deep learning.
Dynamic Precision Analog Computing for Neural Networks
We propose extending analog computing architectures to support varying levels of precision by repeating operations and averaging the result, decreasing the impact of noise.
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
Ranked #6 on
Cloud Removal
on SEN12MS-CR-TS
VizSeq: A Visual Analysis Toolkit for Text Generation Tasks
Automatic evaluation of text generation tasks (e. g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE.
Practical Deep Learning with Bayesian Principles
Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted.
