Search Results for author:
Found 18 papers, 5 papers with code
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days.
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly.
Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering
GCG learns multiple Gaussian functions to characterize the temporal structure of the video, and sample question-critical frames as positive moments to be the visual inputs of LMMs.
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean.
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives
Machine learning (ML) methods offer a wide range of configurable hyperparameters that have a significant influence on their performance.
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be computationally expensive.
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement
Following the spirit of multiple instance learning (MIL), we decompose the weakly supervised correspondence learning problem into three stages: image-level matching, region-level matching, and pixel-level matching.
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
We use one fold as the in-distribution dataset and the others as out-of-distribution datasets to evaluate the proposed method.
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence
Semantic correspondence have built up a new way for object recognition.
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution
The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts.
DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos
To obtain consistent prediction probabilities from the dual path, we further propose a dual path regularization loss, aiming to minimize the divergence between the distributions of two-path embeddings.
Ranked #13 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Representation Learning
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e. g. the power grid or traffic) with an underlying sub-graph structure.
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos.
A data-centric weak supervised learning for highway traffic incident detection
To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways.
A Survey of Human-in-the-loop for Machine Learning
Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention.
Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive.
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction
Tailings ponds are places for storing industrial waste.
