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Found 7 papers, 3 papers with code
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
However, our scaling laws also predict robustness slowly grows then plateaus at $90$%: dwarfing our new SOTA by scaling is impractical, and perfect robustness is impossible.
NEFTune: Noisy Embeddings Improve Instruction Finetuning
We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation.
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program.
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
However, there still is no clear understanding of the conditions on OOD data and model properties that are required to observe effective robustness.
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts.
The Generalization-Stability Tradeoff In Neural Network Pruning
Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting.
