Search Results for author:
Found 10 papers, 5 papers with code
Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data
Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably.
Orthogonal Calibration for Asynchronous Federated Learning
In this paper, we propose OrthoFL, an orthogonal calibration framework that decouples global and local learning progress and adjusts global shifts to minimize interference before merging them into local models.
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits.
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i. e. proof steps) to search through proof states.
Physics-Informed Data Denoising for Real-Life Sensing Systems
By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications.
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set.
Unleashing the Power of Shared Label Structures for Human Activity Recognition
In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities.
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns.
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
A System-Level Solution for Low-Power Object Detection
As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6. 9W (with display) with an mAP of 66. 4 verified on the PASCAL VOC 2012 dataset.
