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Found 16 papers, 9 papers with code
Transformer Explainer: Interactive Learning of Text-Generative Models
Transformers have revolutionized machine learning, yet their inner workings remain opaque to many.
Interactive Visual Learning for Stable Diffusion
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention.
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models
The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks.
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex structure with explanations of the underlying operations.
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification.
Energy Transformer
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language.
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training.
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
LMdiff: A Visual Diff Tool to Compare Language Models
While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other.
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior.
FairyTailor: A Multimodal Generative Framework for Storytelling
Natural language generation (NLG) for storytelling is especially challenging because it requires the generated text to follow an overall theme while remaining creative and diverse to engage the reader.
Can a Fruit Fly Learn Word Embeddings?
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Large Transformer-based language models can route and reshape complex information via their multi-headed attention mechanism.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
We present exBERT, an interactive tool named after the popular BERT language model, that provides insights into the meaning of the contextual representations by matching a human-specified input to similar contexts in a large annotated dataset.
