Search Results for author:
Found 16 papers, 14 papers with code
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis shows that the need for features to evolve across layers results in temporally misaligned features in skip connections, which limits the model's performance.
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
SoftTiger: A Clinical Foundation Model for Healthcare Workflows
We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows.
TigerBot: An Open Multilingual Multitask LLM
We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Group Equivariant Fourier Neural Operators for Partial Differential Equations
We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain.
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems
Based on the proposed lattice convolutions, we design lattice convolutional networks (LCN) that use self-gating and attention mechanisms.
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Cross-Domain Recommendation via Preference Propagation GraphNet
We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure.
Collaborative Policy Learning for Open Knowledge Graph Reasoning
We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.
High Dimensional Similarity Search with Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility
However, we find there are several limitations with NSG: 1) NSG has no theoretical guarantee on nearest neighbor search when the query is not indexed in the database; 2) NSG is too sparse which harms the search performance.
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning
2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer.
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph
In this paper, to further improve the search-efficiency and scalability of graph-based methods, we start by introducing four aspects: (1) ensuring the connectivity of the graph; (2) lowering the average out-degree of the graph for fast traversal; (3) shortening the search path; and (4) reducing the index size.
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
