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07/17/2023
Complexity Matters: Rethinking the Latent Space for Generative Modeling
In generative modeling, numerous successful approaches leverage a low-di...
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07/04/2023
Training Energy-Based Models with Diffusion Contrastive Divergences
Energy-Based Models (EBMs) have been widely used for generative modeling...
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06/15/2023
Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification
Classic learning theory suggests that proper regularization is the key t...
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06/05/2023
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
The proliferation of pretrained models, as a result of advancements in p...
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05/29/2023
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Due to the ease of training, ability to scale, and high sample quality, ...
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05/18/2023
ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis
Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruct...
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05/09/2023
Boosting Visual-Language Models by Exploiting Hard Samples
Large vision and language models, such as Contrastive Language-Image Pre...
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05/05/2023
Random Smoothing Regularization in Kernel Gradient Descent Learning
Random smoothing data augmentation is a unique form of regularization th...
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03/20/2023
ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning
Although many recent works have investigated generalizable NeRF-based no...
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02/24/2023
Inducing Neural Collapse in Deep Long-tailed Learning
Although deep neural networks achieve tremendous success on various clas...
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01/28/2023
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
Self-supervised learning (SSL) aims to learn intrinsic features without ...
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10/17/2022
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Recent advances on large-scale pre-training have shown great potentials ...
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10/11/2022
Continual Learning by Modeling Intra-Class Variation
It has been observed that neural networks perform poorly when the data o...
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07/04/2022
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Deep learning has gained huge empirical successes in large-scale classif...
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12/07/2021
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Deep learning has achieved many breakthroughs in modern classification t...
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07/06/2020
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Overparametrized neural networks trained by gradient descent (GD) can pr...
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01/19/2020
Optimal Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting
Classifiers built with neural networks handle large-scale high-dimension...
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10/08/2018