research
∙
09/09/2023
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Data augmentation (DA) is widely used to improve the generalization of n...
share
research
∙
08/16/2023
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
This technical report presents AutoGen, a new framework that enables dev...
research
∙
07/20/2023
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Recent advances in large language models (LLMs) have demonstrated notabl...
research
∙
06/28/2023
Subclass-balancing Contrastive Learning for Long-tailed Recognition
Long-tailed recognition with imbalanced class distribution naturally eme...
research
∙
06/26/2023
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
In the last year alone, a surge of new benchmarks to measure composition...
research
∙
06/19/2023
Taming Small-sample Bias in Low-budget Active Learning
Active learning (AL) aims to minimize the annotation cost by only queryi...
research
∙
05/03/2023
MaskSearch: Querying Image Masks at Scale
Machine learning tasks over image databases often generate masks that an...
research
∙
04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
research
∙
12/30/2022
Label-Efficient Interactive Time-Series Anomaly Detection
Time-series anomaly detection is an important task and has been widely a...
research
∙
11/20/2022
Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?
Existing graph contrastive learning (GCL) typically requires two forward...
research
∙
10/06/2022
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
To obtain a large amount of training labels inexpensively, researchers h...
research
∙
10/06/2022
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm...
research
∙
08/02/2022
Binary Classification with Positive Labeling Sources
To create a large amount of training labels for machine learning models ...
research
∙
07/27/2022
Learning Hyper Label Model for Programmatic Weak Supervision
To reduce the human annotation efforts, the programmatic weak supervisio...
research
∙
05/29/2022
Representation Gap in Deep Reinforcement Learning
Deep reinforcement learning gives the promise that an agent learns good ...
research
∙
05/25/2022
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Programmatic Weak Supervision (PWS) aggregates the source votes of multi...
research
∙
04/11/2022
Augmentation-Free Graph Contrastive Learning
Graph contrastive learning (GCL) is the most representative and prevalen...
research
∙
03/13/2022
A Survey on Deep Graph Generation: Methods and Applications
Graphs are ubiquitous in encoding relational information of real-world o...
research
∙
03/02/2022
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Weak Supervision (WS) techniques allow users to efficiently create large...
research
∙
02/11/2022
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using m...
research
∙
02/10/2022
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations
Taxonomies are fundamental to many real-world applications in various do...
research
∙
12/16/2021
ATM: An Uncertainty-aware Active Self-training Framework for Label-efficient Text Classification
Despite the great success of pre-trained language models (LMs) in many n...
research
∙
10/07/2021
Creating Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in...
research
∙
09/23/2021
WRENCH: A Comprehensive Benchmark for Weak Supervision
Recent Weak Supervision (WS) approaches have had widespread success in e...
research
∙
09/29/2019