[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
[ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective
[ICLR 2025] Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
InvarDiff: Cross-Scale Invariance Caching for Accelerated Diffusion Models
[Journal of Turbulence, DCC 2022] Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers
[DCC 2020] DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
A collection of dataset distillation papers.
[arXiv] ColA: Collaborative Adaptation with Gradient Learning
[CVPR 2025] "Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training" by Lexington Whalen, Zhenbang Du, Haoran You, Chaojian Li, Sixu Li, and Yingyan (Celine) Lin.
On-Statistical-Efficiency-in-Learning
Repository for the SS24 Efficient Machine Learning class at FSU Jena
This project investigates the efficacy of integrating context distillation techniques with parameter-efficient tuning methods such as LoRA, QLoRA, and traditional fine-tuning approaches, utilizing Facebook’s pre-trained OPT 125M model.
[IEEE BigData 2019] Restricted Recurrent Neural Networks
[DCC 2020] Deep Clustering of Compressed Variational Embeddings
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