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Found 10 papers, 1 papers with code
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains.
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression
These enzymes correspond to the parameters of the DEs.
RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images
Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US.
Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards
In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influence on the AV's safety.
Watch out for the risky actors: Assessing risk in dynamic environments for safe driving
Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan.
Reinforcement Learning based Disease Progression Model for Alzheimer's Disease
We model Alzheimer's disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge.
BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics
Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system.
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
The problem of scheduling of workloads onto heterogeneous processors (e. g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers.
Live Forensics for Distributed Storage Systems
We present Kaleidoscope an innovative system that supports live forensics for application performance problems caused by either individual component failures or resource contention issues in large-scale distributed storage systems.
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents.
