Full Model

"Full Model" research encompasses the development and improvement of large-scale machine learning models across diverse applications, aiming to enhance performance, efficiency, and robustness. Current research focuses on addressing model vulnerabilities (e.g., adversarial attacks, hallucinations), improving efficiency for resource-constrained devices, and developing specialized models for specific domains (e.g., finance, astronomy, medical imaging). This work is significant for advancing AI capabilities in various fields and for mitigating potential risks associated with deploying complex models in real-world settings.

Papers

November 21, 2024

November 20, 2024

November 19, 2024

November 18, 2024

November 16, 2024

ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models
JooHyoung Cha, Munyoung Lee, Jinse Kwon, Jubin Lee, Jemin Lee, Yongin Kwon
Deep Learning Deep Learning Model Full Model

November 15, 2024

Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models
Maryam Shoaeinaeini, Brent Harrison
Reinforcement Learning Full Model Robust Reinforcement Learning Fine Tuned LLM Overconfidence Problem

Full Model

Papers

Evaluating the Robustness of Analogical Reasoning in Large Language Models

On the Fairness, Diversity and Reliability of Text-to-Image Generative Models

Interactive and Expressive Code-Augmented Planning with Large Language Models

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge

Benchmarking a wide range of optimisers for solving the Fermi-Hubbard model using the variational quantum eigensolver

VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation

Puppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation

Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification

ACING: Actor-Critic for Instruction Learning in Black-Box Large Language Models

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models

Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models

JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical Semantics Disagreements

Benchmarking pre-trained text embedding models in aligning built asset information

CNMBert: A Model for Hanyu Pinyin Abbreviation to Character Conversion Task

Re-examining learning linear functions in context

Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics

ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models

Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models