Search Results for author:
Found 6 papers, 1 papers with code
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit their capabilities for out-of-distribution (OOD) generalization.
Learning Low-Rank Feature for Thorax Disease Classification
To address this challenge, we propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is universally applicable to the training of all neural networks.
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation
In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation.
Better Together: Resnet-50 accuracy with $13 \times $ fewer parameters and at $3 \times $ speed
Our one-shot learning paradigm trains both the original and the smaller networks together.
Similarity-based Distance for Categorical Clustering using Space Structure
To group categorical data many clustering algorithms are used, among which k- modes algorithm has so far given the most significant results.
Adjoined Networks: A Training Paradigm with Applications to Network Compression
In this paper, we introduce Adjoined Networks, or AN, a learning paradigm that trains both the original base network and the smaller compressed network together.
