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Found 20 papers, 3 papers with code
Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveillance, is challenging, especially under partial observability, limited communication range, and uncertain environments.
VecTrans: LLM Transformation Framework for Better Auto-vectorization on High-performance CPU
Large language models (LLMs) have demonstrated great capabilities in code generation, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-specific reasoning.
Online Pseudo-average Shifting Attention(PASA) for Robust Low-precision LLM Inference: Algorithms and Numerical Analysis
To accelerate this process, we developed a low-precision, mathematically-equivalent algorithm called PASA, based on Flash Attention.
Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications.
COMET: Towards Partical W4A4KV4 LLMs Serving
We integrate the optimized W4Ax kernel into our inference framework, COMET, and provide efficient management to support popular LLMs such as LLaMA-3-70B.
Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments
Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and transmit real-time imagery to processing servers.
Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics
To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables.
MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs
Nevertheless, there is still a lack of sub-byte and mixed-precision SIMD operations in MCU-class ISA and the limited computing capability of MCUs remains underutilized, which further aggravates the computing bound encountered in neural network processing.
IICPilot: An Intelligent Integrated Circuit Backend Design Framework Using Open EDA
Open-source EDA tools are rapidly advancing, fostering collaboration, innovation, and knowledge sharing within the EDA community.
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection
Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale.
AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab
While capstone projects are an excellent example of experiential learning, given the interdisciplinary nature of this emerging social cybersecurity problem, it can be challenging to use them to engage non-computing students without prior knowledge of AI.
Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
HATEGUARD further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate.
Learning Variable Impedance Skills from Demonstrations with Passivity Guarantee
The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method.
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model
In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for improving multilingual language models.
Statistical Modeling of Soft Error Influence on Neural Networks
Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing.
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data
Integrating the trip data with the COPERT model, this study calculates the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) to estimate the CO2 emission reduction of each ridesplitting trip.
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning
Therefore, our CoTV can well balance the achievement of the reduction of travel time, fuel, and emissions.
Understanding and Measuring Robustness of Multimodal Learning
We then introduce a new type of multimodal adversarial attacks called decoupling attack in MUROAN that aims to compromise multimodal models by decoupling their fused modalities.
R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors
It takes the remote AIoT processor with soft errors in the training loop such that the on-site computing errors can be learned with the application data on the server and the retrained models can be resilient to the soft errors.
3D Object Detection and Tracking Based on Streaming Data
Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning.
