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Found 41 papers, 21 papers with code
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory
Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
In this paper, we identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the selected tasks based on their importance.
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
Augmentation is an effective alternative to utilize the small amount of labeled protein data.
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
In addition, microenvironments defined in previous work are largely based on experimentally assayed physicochemical properties, for which the "vocabulary" is usually extremely small.
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging.
PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model
The target instruction is then forwarded to a visual grounding system for object pose and size estimation, following which the robot grasps the object accordingly.
CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization
Therefore, we propose a novel Coarse-to-Fine contribution network for multimodal Summarization (CFSum) to consider different contributions of images for summarization.
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained teacher GNN into a student MLP.
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
In this way, however, it is hard to generate realistic fragments with complicated structures.
GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation
Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input.
Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework
Furthermore, we identified a potential information drowning problem for existing GNN-to-MLP distillation, i. e., the high-frequency knowledge of the pre-trained GNNs may be overwhelmed by the low-frequency knowledge during distillation; we have described in detail what it represents, how it arises, what impact it has, and how to deal with it.
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.
A Survey on Protein Representation Learning: Retrospect and Prospect
To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications.
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one.
MogaNet: Multi-order Gated Aggregation Network
Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.
Ranked #1 on
Pose Estimation
on COCO val2017
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning.
Exploring Generative Neural Temporal Point Process
While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization.
I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches
The physical robot experiments confirm the utility of our method in object-cluttered scenes.
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions
This paper studies the task of any objects grasping from the known categories by free-form language instructions.
STONet: A Neural-Operator-Driven Spatio-temporal Network
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks.
An Empirical Study: Extensive Deep Temporal Point Process
In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization.
Git: Clustering Based on Graph of Intensity Topology
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm.
Ranked #1 on
Clustering Algorithms Evaluation
on Fashion-MNIST
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
We propose two strategies for finetuning process: value-based and context-based augmentation.
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation
The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation.
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs).
Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive
In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data.
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.
Towards Robust Graph Neural Networks against Label Noise
A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.
LookHops: light multi-order convolution and pooling for graph classification
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications.
Invertible Manifold Learning for Dimension Reduction
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information.
Clustering Based on Graph of Density Topology
GDT jointly considers the local and global structures of data samples: firstly forming local clusters based on a density growing process with a strategy for properly noise handling as well as cluster boundary detection; and then estimating a GDT from relationship between local clusters in terms of a connectivity measure, givingglobal topological graph.
Neural Pose Transfer by Spatially Adaptive Instance Normalization
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.
Pulsars Detection by Machine Learning with Very Few Features
In this work, two feature selection algorithms ----\textit{Grid Search} (GS) and \textit{Recursive Feature Elimination} (RFE)---- are proposed to improve the detection performance by removing the redundant and irrelevant features.
