Search Results for author:
Found 9 papers, 6 papers with code
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension
Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data.
Ranked #20 on
Molecule Captioning
on ChEBI-20
TOSS:High-quality Text-guided Novel View Synthesis from a Single Image
In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image.
ChatCounselor: A Large Language Models for Mental Health Support
This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support.
Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches
Examples of such side information include the chemical and geometric features of the target protein, ligand-target compound interactions, and ligand chemical properties.
detrex: Benchmarking Detection Transformers
To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.
Inducing Neural Collapse in Deep Long-tailed Learning
One of the reasons is that the learned representations (i. e. features) from the imbalanced datasets are less effective than those from balanced datasets.
Exploring Vision Transformers as Diffusion Learners
We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND).
