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Found 13 papers, 3 papers with code
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties.
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
We analyze the statistical properties of generalized cross-validation (GCV) and leave-one-out cross-validation (LOOCV) applied to early-stopped gradient descent (GD) in high-dimensional least squares regression.
Sharp Analysis of Power Iteration for Tensor PCA
We investigate the power iteration algorithm for the tensor PCA model introduced in Richard and Montanari (2014).
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
We investigate the approximation efficiency of score functions by deep neural networks in diffusion-based generative modeling.
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
We present a comprehensive solution to learn and improve text-to-image models from human preference feedback.
Gestalt-Guided Image Understanding for Few-Shot Learning
Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks.
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task.
Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
In this paper, we analyze the dynamics of tensor power iteration from random initialization in the overcomplete regime.
Adversarial Examples in Random Neural Networks with General Activations
A substantial body of empirical work documents the lack of robustness in deep learning models to adversarial examples.
Streaming Belief Propagation for Community Detection
The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities".
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models
Unlike the majority of existing methods that assume optimal demonstrations and incorporate the demonstration data as hard constraints on policy optimization, we instead incorporate demonstration data as advice in the form of a reward shaping potential trained as a generative model of states and actions.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
The estimation error of general first order methods
These lower bounds are optimal in the sense that there exist algorithms whose estimation error matches the lower bounds up to asymptotically negligible terms.
