Improving Natural Language Understanding Through Adversarial Testing
Explore adversarial testing in natural language understanding to challenge and improve AI systems. Learn to identify weaknesses and develop robust models for real-world applications.
Tradeoffs Between Robustness and Accuracy - Percy Liang
Explore tradeoffs between robustness and accuracy in machine learning, covering topics like spurious correlations, regularization, and model complexity with Percy Liang.
Transformer Models and BERT Model: Overview
Explore Transformer architecture and BERT model, understanding key components and applications in text classification, question answering, and natural language inference.
Today Unsupervised Sentence Transformers, Tomorrow Skynet - How TSDAE Works
Explore unsupervised fine-tuning for sentence transformers using TSDAE. Learn to train models without labeled data, ideal for domains and languages with limited resources.
Towards Large-Scale Natural Language Inference with Distributional Semantics
Explore natural language inference using distributional semantics, covering summarization, semantic models, and knowledge induction for advanced text analysis.
Knowledge-Adapted Fine-Tuning for Medical Vision Language Models
Explore how Knowledge-Adapted Fine-Tuning (KnowAda) improves vision-language models in medical AI, reducing hallucinations while maintaining accurate image descriptions and diagnostic capabilities.