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Found 26 papers, 14 papers with code
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
Cyclic Neural Network
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.
MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes.
A Hardware Evaluation Framework for Large Language Model Inference
With the large hardware needed to simply run LLM inference, evaluating different hardware designs becomes a new bottleneck.
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Localized Contrastive Learning on Graphs
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
Handling Distribution Shifts on Graphs: An Invariance Perspective
There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.
Conservative Distributional Reinforcement Learning with Safety Constraints
Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.
Distributional Reinforcement Learning
reinforcement-learning
+1
Align Representations With Base: A New Approach to Self-Supervised Learning
Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives.
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
Inductive Collaborative Filtering via Relation Graph Learning
In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods.
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