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Found 17 papers, 10 papers with code
Random-coupled Neural Network
This process randomly closes some neural connections in the RCNN model, realized by the random inactivation weight matrix of link input.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities.
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services.
Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU
This study introduces the Tempotron, a powerful classifier based on a third-generation neural network model, for pulse shape discrimination.
Dataset for neutron and gamma-ray pulse shape discrimination
In addition to the pulse signals, this dataset includes the source code for all the aforementioned pulse shape discrimination methods.
Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
However, use of FT is problematic due to Gibbs phenomenon.
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
the discrete data space to approximately construct the provably optimal proposal distribution, which is subsequently used by importance sampling to efficiently estimate the original ratio matching objective.
Learning Hierarchical Protein Representations via Complete 3D Graph Networks
In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations.
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood.
Ranked #4 on
Drug Discovery
on QM9
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models
In this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS) to alleviate the above limitations.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Empirical Studies on the Convergence of Feature Spaces in Deep Learning
While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.
Neural Diffusion Model for Microscopic Cascade Prediction
The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data.
Social and Information Networks Physics and Society
A Blended Deep Learning Approach for Predicting User Intended Actions
In this work, we focus on pre- dicting attrition, which is one of typical user intended actions.
