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Found 23 papers, 12 papers with code
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future.
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
For example, can we query a LLM to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database?
Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs
The relentless advancement of artificial intelligence (AI) and machine learning (ML) applications necessitates the development of specialized hardware accelerators capable of handling the increasing complexity and computational demands.
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design
Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space.
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks.
BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs
It fills the gap by proposing a series of abstractions and techniques to map binary GNNs and their computations best to fit the nature of bit manipulations on GPUs.
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators.
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Molecular property calculations are the bedrock of chemical physics.
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task.
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Such an approach enables us to propose a hyperbolic normalization layer and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer.
TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models
We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework.
Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction
In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures.
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes
On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.
Knowledge Base Question Answering
Machine Reading Comprehension
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins.
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Design of new drug compounds with target properties is a key area of research in generative modeling.
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water.
Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
Representation learning methods for heterogeneous networks produce a low-dimensional vector embedding for each node that is typically fixed for all tasks involving the node.
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
A Chronological Edge-Driven Approach to Temporal Subgraph Isomorphism
Many real world networks are considered temporal networks, in which the chronological ordering of the edges has importance to the meaning of the data.
Data Structures and Algorithms
When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes
In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner.
Social and Information Networks
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful.
Benchmarking Named Entity Disambiguation approaches for Streaming Graphs
Given data sources such as social media streams and web pages Entity Linking is the task of mapping named entities that are extracted from the data to those present in the Knowledge Graph.
