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Found 7 papers, 1 papers with code
Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding
Imagand is a promising solution for data overlap sparsity and allows researchers to efficiently generate ligand PK data for drug discovery research.
Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks
Recent research in Needle-in-a-Haystack (NIAH) benchmarks has explored the capabilities of Large Language Models (LLMs) in retrieving contextual information from large text documents.
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge
This misalignment hinders the zero-shot performance of VLM and the application of fine-tuning methods based on pseudo-labels.
Synthetic Data from Diffusion Models Improve Drug Discovery Prediction
Continuing breakthroughs in AI-based methods for drug discovery require the creation, improvement, and refinement of drug discovery data.
https://paperswithcode.com/paper/negatives-make-a-positive-an-embarrassingly
Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information.
Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures
Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information.
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras
Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images.
