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Found 21 papers, 11 papers with code
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
Equivariant Flow Matching with Hybrid Probability Transport
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Our key innovation is to develop a multi-stage diffusion process.
Camouflaged Object Detection with Feature Grafting and Distractor Aware
In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module.
Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point
Exploratory data analysis (EDA) is a vital procedure for data science projects.
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery.
Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression
This paper proposes a novel framework for accelerating support vector clustering.
Particle Based Stochastic Policy Optimization
Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification.
Ranked #1 on
MuJoCo Games
on Ant-v3
Follow Your Path: a Progressive Method for Knowledge Distillation
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
Learning from deep model via exploring local targets
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data.
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data.
Ranked #7 on
Image Generation
on STL-10
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).
Lipschitz Generative Adversarial Nets
By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem.
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs.
Activation Maximization Generative Adversarial Nets
Our proposed model also outperforms the baseline methods in the new metric.
