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Found 24 papers, 12 papers with code
NLS: Natural-Level Synthesis for Hardware Implementation Through GenAI
This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level.
A Method for Enhancing Generalization of Adam by Multiple Integrations
The insufficient generalization of adaptive moment estimation (Adam) has hindered its broader application.
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
In this paper, we propose a Simple Training-free Approach for Recommendation (STAR), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning, while maintaining high quality recommendation performance.
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting.
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data.
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue.
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems.
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Specifically, for each conference, we first organize academic conference data in a tree-structured format through a semi-automated method.
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.
Long short-term memory with activation on gradient
As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM.
Zero Stability Well Predicts Performance of Convolutional Neural Networks
We first give the interpretation of zero stability in the context of deep learning and then investigate the performance of existing first- and second-order CNNs under different zero-stable circumstances.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
Activated Gradients for Deep Neural Networks
The experimental results also demonstrate that the proposed method is able to be adopted in various deep neural networks to improve their performance.
Modified Newton Integration Algorithm With Noise Tolerance Applied to Robotics
Currently, the Newton-Raphson iterative algorithm has been extensively employed in the fields of basic research and engineering.
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link).
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years.
Deforming the Loss Surface to Affect the Behaviour of the Optimizer
With visualizations of loss landscapes, we evaluate the flatnesses of minima obtained by both the original optimizer and optimizers enhanced by VDMs on CIFAR-100.
Deforming the Loss Surface
Furthermore, the flatter minima could be obtained by exploiting the proposed deformation functions, which is verified on CIFAR-100, with visualizations of loss landscapes near the critical points obtained by both the original optimizer and optimizer enhanced by deformation functions.
Introspective Neural Networks for Generative Modeling
We study unsupervised learning by developing a generative model built from progressively learned deep convolutional neural networks.
Introspective Classification with Convolutional Nets
We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities.
Introspective Generative Modeling: Decide Discriminatively
We study unsupervised learning by developing introspective generative modeling (IGM) that attains a generator using progressively learned deep convolutional neural networks.
Object Detection Free Instance Segmentation With Labeling Transformations
Instance segmentation has attracted recent attention in computer vision and existing methods in this domain mostly have an object detection stage.
