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Found 11 papers, 7 papers with code
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
We propose denoising diffusion variational inference (DDVI), an approximate inference algorithm for latent variable models which relies on diffusion models as flexible variational posteriors.
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 2/3/4-bit precision on as little as one 24GB GPU.
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning.
Time Series Contrastive Learning with Information-Aware Augmentations
A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks.
From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
Here, an MD dataset containing 3, 218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories.
Going Deeper into Permutation-Sensitive Graph Neural Networks
We prove that our approach is strictly more powerful than the 2-dimensional Weisfeiler-Lehman (2-WL) graph isomorphism test and not less powerful than the 3-WL test.
Graph Data Augmentation for Graph Machine Learning: A Survey
Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.
Information-Aware Time Series Meta-Contrastive Learning
How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.
One-shot Transfer Learning for Population Mapping
Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem.
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community.
