We study the connection of two problems within the planning and verification community: Conformant planning and model-checking of hyperproperties. Conformant pl...
Deep neural networks have accelerated inverse-kinematics (IK) inference to the point where low cost manipulators can execute complex trajectories in real time, ...
Deploying large language models (LLMs) on edge devices is challenging due to their limited memory and power resources. Cloud-only inference reduces device burde...
Multimodal Large Language Models (LLMs) introduce an emerging paradigm for medical imaging by interpreting scans through the lens of extensive clinical knowledg...
Parameter-efficient fine-tuning has become the dominant paradigm for adapting large language models to downstream tasks. Low-rank adaptation methods such as LoR...
AI systems are getting faster and more capable, but they are still far from perfect—especially when data is messy, contextual, or high‑stakes. That’s where huma...
Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. How...
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, yet they exhibit systematic errors on complex, multi-step programming...
We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harne...
Video Recap Watch the recap videohttps://youtu.be/-rzMxb1JhuU Thank You I can’t believe we’ve made it to the end together. A massive thank you to everyone who...
Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden r...
Data Leakage pada Machine Learning Sering kali mentee melakukan kesalahan dasar dalam alur kerja Machine Learning: Exploratory Data Analysis EDA → preprocessin...
