Search Results for author:
Found 12 papers, 7 papers with code
Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach
In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency.
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning.
Large Language Models for Time Series: A Survey
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
Misusing Tools in Large Language Models With Visual Adversarial Examples
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities.
PrimeNet: Pre-Training for Irregular Multivariate Time Series
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
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.
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning
We propose using reinforcement learning to optimize the operation of energy harvesting sensors to maximize sensing quality with available energy.
Associative Convolutional Layers
We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space.
Local Binary Pattern Networks for Character Recognition
Memory and computation efficient deep learning architectures are crucial to the continued proliferation of machine learning capabilities to new platforms and systems.
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.
