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Found 15 papers, 3 papers with code
GenColor: Generative Color-Concept Association in Visual Design
Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references.
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates
Two primary obstacles emerge: (1) the difficulty in designing a 3D line notation that ensures SE(3)-invariant atomic coordinates, and (2) the non-trivial task of tokenizing continuous coordinates for use in LMs, which inherently require discrete inputs.
Brain-inspired continual pre-trained learner via silent synaptic consolidation
In this study, we introduce the Artsy, inspired by the activation mechanisms of silent synapses via spike-timing-dependent plasticity observed in mature brains, to enhance the continual learning capabilities of pre-trained models.
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry.
Improving AlphaFlow for Efficient Protein Ensembles Generation
Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties.
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
In this setting, a CL learner is required to incrementally learn from multiple domains and classify test images from both seen and unseen domains without any domain-identity hint.
VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems
In this article, we propose a novel Variational Born Iterative Network, namely, VBIM-Net, to solve the full-wave ISPs with significantly improved structural rationality and inversion quality.
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties.
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
To address these issues, we propose an active learning (AL)-based fine-tuning framework for SER, called \textsc{After}, that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency.
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.
Ranked #1 on
Face Swapping
on HOD
(using extra training data)
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition
However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance.
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task.
Direct Molecular Conformation Generation
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.
Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs).
