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Found 13 papers, 2 papers with code
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
Despite the merits of being versatile in solving various inverse problems without re-training, the performance of DPS is hindered by the fact that this posterior approximation can be inaccurate especially for high noise levels.
From optimal score matching to optimal sampling
samples from an unknown \(\alpha\)-H\"{o}lder density \(f\) supported on \([-1, 1]\), we prove the minimax rate of estimating the score function of the diffused distribution \(f * \mathcal{N}(0, t)\) with respect to the score matching loss is \(\frac{1}{nt^2} \wedge \frac{1}{nt^{3/2}} \wedge (t^{\alpha-1} + n^{-2(\alpha-1)/(2\alpha+1)})\) for all \(\alpha > 0\) and \(t \ge 0\).
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
Diffusion Transformer, the backbone of Sora for video generation, successfully scales the capacity of diffusion models, pioneering new avenues for high-fidelity sequential data generation.
Provable Statistical Rates for Consistency Diffusion Models
Diffusion models have revolutionized various application domains, including computer vision and audio generation.
FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.
Learning Narrow One-Hidden-Layer ReLU Networks
We consider the well-studied problem of learning a linear combination of $k$ ReLU activations with respect to a Gaussian distribution on inputs in $d$ dimensions.
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization
To the best of our knowledge, this is the very first result which provides an empirical observation and a strict theoretical guarantee on the one-sided convergence of Adam-type algorithms in min-max optimization.
Gap-Dependent Bounds for Two-Player Markov Games
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention.
Diffusion Mechanism in Residual Neural Network: Theory and Applications
In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy.
Making Method of Moments Great Again? -- How can GANs learn distributions
In GANs, the training of the generator usually stops when the discriminator can no longer distinguish the generator's output from the set of training examples.
Mathematical Analysis of Adversarial Attacks
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack.
Hierarchical Attention: What Really Counts in Various NLP Tasks
Ham achieves a state-of-the-art BLEU score of 0. 26 on Chinese poem generation task and a nearly 6. 5% averaged improvement compared with the existing machine reading comprehension models such as BIDAF and Match-LSTM.
