Search Results for author:
Found 10 papers, 5 papers with code
Towards Foundation Models for Mixed Integer Linear Programming
To address this shortcoming, we take a foundation model training approach, where we train a single deep learning model on a diverse set of MILP problems to generalize across problem classes.
Small Language Models for Application Interactions: A Case Study
We study the efficacy of Small Language Models (SLMs) in facilitating application usage through natural language interactions.
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
We propose Reflect-RL, a two-player system to fine-tune an LM using SFT and online RL, where a frozen reflection model (player) assists the policy model (player).
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
Large Language Models for Supply Chain Optimization
Supply chain operations traditionally involve a variety of complex decision making problems.
Learning Oculomotor Behaviors from Scanpath
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task.
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification.
Sparsely Grouped Input Variables for Neural Networks
In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category.
A Facial Affect Analysis System for Autism Spectrum Disorder
In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence.
