Search Results for author:
Found 6 papers, 3 papers with code
3PointTM: Faster Measurement of High-Dimensional Transmission Matrices
A transmission matrix (TM) describes the linear relationship between input and output phasor fields when a coherent wave passes through a scattering medium.
Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation
Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection.
Learning to Detect Noisy Labels Using Model-Based Features
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation.
GPS: Genetic Prompt Search for Efficient Few-shot Learning
Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models.
ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization
We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting.
Distribution Matching for Rationalization
The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks.
