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Found 9 papers, 4 papers with code
Fair Sampling in Diffusion Models through Switching Mechanism
Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution.
Differentially Private Sharpness-Aware Training
Training deep learning models with differential privacy (DP) results in a degradation of performance.
Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization
Recently, Sharpness-Aware Minimization (SAM) has shown state-of-the-art performance by seeking flat minima.
Stability Analysis of Sharpness-Aware Minimization
Utilizing the qualitative theory of dynamical systems, we explain how SAM becomes stuck in the saddle point and then theoretically prove that the saddle point can become an attractor under SAM dynamics.
Comment on Transferability and Input Transformation with Additive Noise
Adversarial attacks have verified the existence of the vulnerability of neural networks.
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples
We identify another key factor that influences the performance of certifiable training: \textit{smoothness of the loss landscape}.
Implicit Jacobian regularization weighted with impurity of probability output
The eigendecomposition provides a simple relation between the eigenvalues of the low-dimensional matrix and the impurity of the probability output.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
Loss Landscape Matters: Training Certifiably Robust Models with Favorable Loss Landscape
Certifiable training minimizes an upper bound on the worst-case loss over the allowed perturbation, and thus the tightness of the upper bound is an important factor in building certifiably robust models.
