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Found 10 papers, 4 papers with code
LITE: Intent-based Task Representation Learning Using Weak Supervision
Users write to-dos as personal notes to themselves, about things they need to complete, remember or organize.
Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network
This letter proposes a few-shot physics-guided spatial temporal graph convolutional network (FPG-STGCN) to fast solve unit commitment (UC).
Detection of Problem Gambling with Less Features Using Machine Learning Methods
Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions.
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever.
Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations
Through simulations, we have also shown the effectiveness of the proposed algorithms to fool the state-of-the-art (SOTA) MIL methods.
Fault-Aware Neural Code Rankers
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks.
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems
Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field.
Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration
In this paper, we propose a modified mesh architecture with a one-way/two-way streaming bus to speedup one-to-many (multicast) traffic, and the use of gather packets to support many-to-one (gather) traffic.
Improving the Performance of a NoC-based CNN Accelerator with Gather Support
For many-to-one traffic, repetitive unicast is employed which is not an efficient way.
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps
In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process.
