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Found 13 papers, 6 papers with code
Take Your Steps: Hierarchically Efficient Pulmonary Disease Screening via CT Volume Compression
Deep learning models are widely used to process Computed Tomography (CT) data in the automated screening of pulmonary diseases, significantly reducing the workload of physicians.
The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs.
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
The sample selection task in SSL has been under-explored for a long time.
Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
To assess the capability of our proposed ARA model in reducing hallucination, we employ three widely used LVLM models (LLaVA-1. 5, Qwen-VL, and mPLUG-Owl2) across four benchmarks.
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing.
Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data
Yet, when applied to tabular data prediction, this paradigm faces challenges due to the limited reusable patterns among diverse tabular datasets (tables) and the general scarcity of tabular data available for fine-tuning.
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs.
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs.
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs) have achieved great success in general domains of natural language processing.
ExcelFormer: A neural network surpassing GBDTs on tabular data
In this paper, we delve into this question: Can we develop a deep learning model that serves as a "sure bet" solution for a wide range of tabular prediction tasks, while also being user-friendly for casual users?
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
This necessitates rethinking cost-sensitive classification in DNNs.
Reprint: a randomized extrapolation based on principal components for data augmentation
This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification.
