Search Results for author:
Found 11 papers, 2 papers with code
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense
We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF).
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation.
Ranked #1 on
Image Generation
on CelebA-HQ 256x256
Robust Boosting Forests with Richer Deep Feature Hierarchy
We propose a robust variant of boosting forest to the various adversarial defense methods, and apply it to enhance the robustness of the deep neural network.
Quantized Adaptive Subgradient Algorithms and Their Applications
Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model.
Normalized Diversification
We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs.
Conditional Image Generation
Generative Adversarial Network
+2
Trajectory Normalized Gradients for Distributed Optimization
Recently, researchers proposed various low-precision gradient compression, for efficient communication in large-scale distributed optimization.
Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis
For recovering 3D object poses from 2D images, a prevalent method is to pre-train an over-complete dictionary $\mathcal D=\{B_i\}_i^D$ of 3D basis poses.
Learning Random Fourier Features by Hybrid Constrained Optimization
Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data.
Learning Sparse Visual Representations with Leaky Capped Norm Regularizers
To the best of our knowledge, this is the first convergence analysis of the 3D recovery problem.
Gradient Sparsification for Communication-Efficient Distributed Optimization
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures.
Training L1-Regularized Models with Orthant-Wise Passive Descent Algorithms
The $L_1$-regularized models are widely used for sparse regression or classification tasks.
