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05/04/2023
AutoML-GPT: Automatic Machine Learning with GPT
AI tasks encompass a wide range of domains and fields. While numerous AI...
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12/12/2022
PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion
Fusing camera with LiDAR is a promising technique to improve the accurac...
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12/04/2022
Fast Point Cloud Generation with Straight Flows
Diffusion models have emerged as a powerful tool for point cloud generat...
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10/06/2022
Neural Volumetric Mesh Generator
Deep generative models have shown success in generating 3D shapes with d...
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09/02/2022
First Hitting Diffusion Models
We propose a family of First Hitting Diffusion Models (FHDM), deep gener...
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09/02/2022
Diffusion-based Molecule Generation with Informative Prior Bridges
AI-based molecule generation provides a promising approach to a large ar...
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08/31/2022
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Diffusion-based generative models have achieved promising results recent...
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04/20/2022
Residual Mixture of Experts
Mixture of Experts (MoE) is able to scale up vision transformers effecti...
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02/16/2022
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
Although traditional optimization methods focus on finding a single opti...
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12/02/2021
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
Generating images from natural language instructions is an intriguing ye...
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02/17/2021
Centroid Transformers: Learning to Abstract with Attention
Self-attention, as the key block of transformers, is a powerful mechanis...
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02/17/2021
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
We propose firefly neural architecture descent, a general framework for ...
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10/29/2020
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough
Despite the great success of deep learning, recent works show that large...
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03/23/2020
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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10/07/2019
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent
Designing energy-efficient networks is of critical importance for enabli...
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10/06/2019
Splitting Steepest Descent for Growing Neural Architectures
We develop a progressive training approach for neural networks which ada...
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12/31/2018
