Search Results for author:
Found 14 papers, 12 papers with code
A Risk Communication Event Detection Model via Contrastive Learning
This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries.
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
Unsupervised image classification is a challenging computer vision task.
Ranked #1 on
Image Clustering
on STL-10
(Backbone metric, using extra
training data)
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time.
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other.
FedDefender: Client-Side Attack-Tolerant Federated Learning
Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework.
Elsa: Energy-based learning for semi-supervised anomaly detection
Anomaly detection aims at identifying deviant instances from the normal data distribution.
Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
Ranked #1 on
Image Clustering
on CIFAR-100
(Train Set metric, using extra
training data)
Active Learning for Human-in-the-Loop Customs Inspection
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected.
Risk Communication in Asian Countries: COVID-19 Discourse on Twitter
This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics.
Social and Information Networks
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.
Lightweight and Robust Representation of Economic Scales from Satellite Imagery
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas.
