Search Results for author:
Found 9 papers, 2 papers with code
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge.
Skill Transformer: A Monolithic Policy for Mobile Manipulation
We present Skill Transformer, an approach for solving long-horizon robotic tasks by combining conditional sequence modeling and skill modularity.
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation.
Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations
The Meta-LSTM is to construct a framework that increases the generalization ability of long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization.
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning
We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world.
DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators
Dataflow/mapping decides the compute and energy efficiency of DNN accelerators.
An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification
This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data.
Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA
this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification.
Event-based Signal Processing for Radioisotope Identification
This paper identifies the problem of unnecessary high power overhead of the conventional frame-based radioisotope identification process and proposes an event-based signal processing process to address the problem established.
