Search Results for author:
Found 18 papers, 8 papers with code
Preference Discerning with LLM-Enhanced Generative Retrieval
We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences.
Unifying Generative and Dense Retrieval for Sequential Recommendation
This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.
Learning Graph Quantized Tokenizers
Graph Transformers (GTs) have recently emerged as leading models in geometric deep learning, outperforming Graph Neural Networks (GNNs) in various graph learning tasks.
How to Make LLMs Strong Node Classifiers?
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks.
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes
Deep learning provides an excellent avenue for optimizing diagnosis and patient monitoring for clinical-based applications, which can critically enhance the response time to the onset of various conditions.
Material Prediction for Design Automation Using Graph Representation Learning
Successful material selection is critical in designing and manufacturing products for design automation.
Evaluating Graph Generative Models with Contrastively Learned Features
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality.
Learning Graph Augmentations to Learn Graph Representations
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets.
Cross-Domain Few-Shot Graph Classification
We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets.
Classifying Component Function in Product Assemblies with Graph Neural Networks
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose.
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.
Contrastive Multi-View Representation Learning on Graphs
We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol.
Memory-Based Graph Networks
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs.
Relational Graph Representation Learning for Open-Domain Question Answering
We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task.
Unsupervised Multi-Task Feature Learning on Point Clouds
We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds.
Multi-Objective Design of State Feedback Controllers Using Reinforced Quantum-Behaved Particle Swarm Optimization
In this paper, a novel and generic multi-objective design paradigm is proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal configuration of the LQR controller for a given problem considering a set of competing objectives.
Visualizing Natural Language Descriptions: A Survey
A natural language interface exploits the conceptual simplicity and naturalness of the language to create a high-level user-friendly communication channel between humans and machines.
