逐次推薦のための思考グラフ：ユーザーの短期、長期、協調的な嗜好を統合
大規模言語モデル（LLM）の推論能力を活用し、思考グラフ（GoT）フレームワークを用いてユーザーの短期、長期、協調的な嗜好情報を統合することで、逐次推薦の精度を向上させることができる。
異常スコアは全てを物語るのか？：多段階異常検知のためのベンチマーク
従来の異常検知モデルは、異常の重大度を反映した異常スコアを生成することに効果的ではなく、実用的な異常検知における重要な課題となっている。
Med-2E3: Enhancing 3D Medical Image Analysis by Integrating 2D and 3D Encoders in a Multimodal Large Language Model
Med-2E3, a novel multimodal large language model (MLLM), improves 3D medical image analysis by combining 2D and 3D encoder insights, mirroring the dual perspective used by radiologists.
ORLM: Training Open-Source Large Language Models for Automated Optimization Modeling Using a Customizable Synthetic Data Framework
This paper introduces OR-Instruct, a novel framework for training open-source large language models (ORLMs) to automate optimization modeling, addressing the limitations of existing methods reliant on closed-source LLMs and limited datasets.
Enhancing Multimodal Large Language Models' Reasoning Ability Using Mixed Preference Optimization
This research introduces Mixed Preference Optimization (MPO), a novel approach to significantly improve the reasoning capabilities of Multimodal Large Language Models (MLLMs) by training them on preference data and combining various optimization techniques.
A Comprehensive Review of Generative Adversarial Networks (GANs) for Denoising Low-Dose Computed Tomography (LDCT) Images
Generative Adversarial Networks (GANs) offer a promising approach to reduce noise in Low-Dose Computed Tomography (LDCT) images, enhancing image quality while minimizing radiation exposure for patients.
검색 기반 증강을 활용한 시계열 예측 향상 방법
본 논문에서는 대규모 언어 모델(LLM)에서 사용되는 검색 기반 증강(Retrieval Augmentation) 기술을 시계열 예측 모델에 적용하여 예측 정확도를 향상시키는 RAF(Retrieval Augmented Forecasting) 프레임워크를 제안합니다.
BudgetMLAgent: A Multi-Agent System Using Primarily Free LLMs for Automating Machine Learning Tasks
Combining multiple free and low-cost LLMs in a multi-agent system with strategic calls to more expensive LLMs for planning can achieve comparable or even better performance than single-agent systems using only expensive LLMs, offering a cost-effective solution for automating machine learning tasks.
대규모 언어 모델을 활용한 하이퍼파라미터 최적화: 전통적 방법과 비교 분석
대규모 언어 모델(LLM)을 활용하여 제한된 탐색 예산 내에서 하이퍼파라미터 최적화를 효과적으로 수행할 수 있으며, 그 결과는 기존의 베이지안 최적화와 같은 방법보다 우수하거나 동등한 수준을 보인다.
マルチモーダル大規模言語モデルにおける階層的な分子グラフ表現の探求と限界：包括的な理解と動的な特徴処理の必要性
大規模言語モデル (LLM) に分子グラフの階層的な特徴を効果的に理解させるためには、マルチレベルな情報を統合するだけでなく、LLM自体がグラフ構造を深く理解し、タスクや分子構造に応じて各レベルの特徴を動的に処理できる仕組みが必要である。
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