Search Results for author:
Found 15 papers, 6 papers with code
Understanding Cellular Noise with Optical Perturbation and Deep Learning
This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
A natural question is "could alignment really prevent those open-sourced large language models from being misused to generate undesired content?''.
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks.
Globally Interpretable Graph Learning via Distribution Matching
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns.
Spectral Augmentation for Self-Supervised Learning on Graphs
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
How Powerful is Implicit Denoising in Graph Neural Networks
However, the analysis of implicit denoising effect in graph neural networks remains open.
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox
A basic condition for efficient transfer learning is the similarity between a target model and source models.
Communication-Efficient Adaptive Federated Learning
We show that in the nonconvex stochastic optimization setting, our proposed FedCAMS achieves the same convergence rate of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts.
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
We prove that the proposed communication-efficient distributed adaptive gradient method converges to the first-order stationary point with the same iteration complexity as uncompressed vanilla AMSGrad in the stochastic nonconvex optimization setting.
Graph Structural Attack by Perturbing Spectral Distance
Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks.
Graph Embedding with Hierarchical Attentive Membership
The exploitation of graph structures is the key to effectively learning representations of nodes that preserve useful information in graphs.
Unbiased Graph Embedding with Biased Graph Observations
In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes.
A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets
The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets.
Variable Selection
Methodology
JNET: Learning User Representations via Joint Network Embedding and Topic Embedding
Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data.
