Search Results for author:
Found 26 papers, 18 papers with code
DeFT: Flash Tree-attention with IO-Awareness for Efficient Tree-search-based LLM Inference
Decoding using tree search can greatly enhance the inference quality for transformer-based Large Language Models (LLMs).
Nemotron-4 15B Technical Report
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens.
Representation Learning for Frequent Subgraph Mining
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
Relational Deep Learning: Graph Representation Learning on Relational Databases
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
RDBench: ML Benchmark for Relational Databases
Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications.
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task.
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks
Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude.
Efficient Automatic Machine Learning via Design Graphs
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.
ROLAND: Graph Learning Framework for Dynamic Graphs
Finally, we propose a scalable and efficient training approach for dynamic GNNs via incremental training and meta-learning.
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Identity-aware Graph Neural Networks
However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.
Design Space for Graph Neural Networks
However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function.
Handling Missing Data with Graph Representation Learning
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
Inductive Learning on Commonsense Knowledge Graph Completion
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between neurons.
Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph.
G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms.
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-12K
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
