Trending Research
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
microsoft/recai
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This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs).
FinLangNet: A Novel Deep Learning Framework for Credit Risk Prediction Using Linguistic Analogy in Financial Data
Our research demonstrates that FinLangNet surpasses traditional statistical methods in predicting credit risk and that its integration with these methods enhances credit card fraud prediction models, achieving a significant improvement of over 1. 5 points in the Kolmogorov-Smirnov metric.
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
openbmb/omnilmm
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To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution.
MAexp: A Generic Platform for RL-based Multi-Agent Exploration
duangzhu/maexp
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The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization.
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Leeroo-AI/mergoo
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We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
Ranked #30 on
Question Answering
on TriviaQA
Code Llama: Open Foundation Models for Code
facebookresearch/codellama
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We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #27 on
Code Generation
on MBPP
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
bytedance/MoMA
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This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability.
LOHO: Latent Optimization of Hairstyles via Orthogonalization
dukebw/LOHO
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CVPR 2021
Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer.
