Search Results for author:
Found 10 papers, 5 papers with code
On the Theory of Cross-Modality Distillation with Contrastive Learning
For instance, one typically aims to minimize the L2 distance or contrastive loss between the learned features of pairs of samples in the source (e. g. image) and the target (e. g. sketch) modalities.
KirchhoffNet: A Scalable Ultra Fast Analog Neural Network
Essentially, KirchhoffNet is an analog circuit that can function as a neural network, utilizing its initial node voltages as the neural network input and the node voltages at a specific time point as the output.
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
A Simple Data Mixing Prior for Improving Self-Supervised Learning
More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of Vision Transformers in the self-supervised setting.
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications.
Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization
Multimodal fusion emerges as an appealing technique to improve model performances on many tasks.
Co-advise: Cross Inductive Bias Distillation
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks.
Multimodal Knowledge Expansion
The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data.
Ranked #63 on
Semantic Segmentation
on NYU Depth v2
Projection based Active Gaussian Process Regression for Pareto Front Modeling
A novel projection based active Gaussian process regression (P- aGPR) method is proposed for efficient PF modeling.
