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Found 10 papers, 3 papers with code
Neural Active Learning Beyond Bandits
We study both stream-based and pool-based active learning with neural network approximations.
Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support
Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support.
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
We propose a new paradigm for zero-shot learners that is format agnostic, i. e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Fairness-aware Model-agnostic Positive and Unlabeled Learning
Our framework is proven to be statistically consistent in terms of both the classification error and the fairness metric.
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application.
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
