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Found 9 papers, 5 papers with code
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases.
HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025
In the dynamic field of video retrieval, precise and effective search methods are crucial for managing complex datasets.
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model
State-of-the-art medical multi-modal large language models (med-MLLM), like LLaVA-Med or BioMedGPT, leverage instruction-following data in pre-training.
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data.
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Inspired by recent work on using ensembles of conformers in conjunction with 2D graph representations, we propose $\mathrm{E}$(3)-invariant molecular conformer aggregation networks.
Software Entity Recognition with Noise-Robust Learning
Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation.
Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.
In-context Example Selection with Influences
In-context learning (ICL) is a powerful paradigm emerged from large language models (LLMs).
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms.
