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Found 24 papers, 6 papers with code
Reducing Tool Hallucination via Reliability Alignment
Large Language Models (LLMs) have extended their capabilities beyond language generation to interact with external systems through tool calling, offering powerful potential for real-world applications.
Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity
The increasing context window size in Large Language Models (LLMs), such as the GPT and LLaMA series, has improved their ability to tackle complex, long-text tasks, but at the cost of inference efficiency, particularly regarding memory and computational complexity.
SciDFM: A Large Language Model with Mixture-of-Experts for Science
We further analyze the expert layers and show that the results of expert selection vary with data from different disciplines.
Evolving Subnetwork Training for Large Language Models
In this paper, inspired by the redundancy in the parameters of large language models, we propose a novel training paradigm: Evolving Subnetwork Training (EST).
Sparsity-Accelerated Training for Large Language Models
Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-training and supervised fine-tuning.
Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedback
Large Language Models (LLMs) often generate erroneous outputs, known as hallucinations, due to their limitations in discerning questions beyond their knowledge scope.
Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding
Additionally, we propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively.
ChemDFM: A Large Language Foundation Model for Chemistry
In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence.
ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL
Text-to-SQL aims to generate an executable SQL program given the user utterance and the corresponding database schema.
SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
This design suffers from data leakage problem and lacks the evaluation of subjective Q/A ability.
Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning
Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases.
Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions.
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks.
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features.
Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease
Using a pre-defined 0. 5 threshold on DAT scores, we predicted whether or not a subject would develop DAT in the future.
Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellum of the Tc1 mouse model of Down Syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI
Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules.
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network
Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms.
Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography
In this study, we investigated a learning-based approach of adapting the domain of a publicly available dataset, UK Biobank dataset (UKB).
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL
A dual learning approach is also proposed for the utterance rewrite model to address the data sparsity problem.
Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition
The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment.
Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior
Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Grey matter sublayer thickness estimation in themouse cerebellum
In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.
Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion
The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy.
