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Found 11 papers, 4 papers with code
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model
Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks.
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations.
Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
Recently, with the development of geometric neural networks, the data-driven schemes have been successfully applied in this field, both for molecular conformation generation (in vacuum) and binding pose generation (in protein pocket).
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out.
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.
Ranked #6 on
Link Property Prediction
on ogbl-wikikg2
CCMB: A Large-scale Chinese Cross-modal Benchmark
In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks.
Ranked #3 on
Image Retrieval
on Flickr30k-CN
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment
Basically, there are two main methods for SLU tasks: (1) Two-stage method, which uses a speech model to transfer speech to text, then uses a language model to get the results of downstream tasks; (2) One-stage method, which just fine-tunes a pre-trained speech model to fit in the downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
DenseBox: Unifying Landmark Localization with End to End Object Detection
How can a single fully convolutional neural network (FCN) perform on object detection?
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Face Recognition has been studied for many decades.
