Search Results for author:
Found 7 papers, 5 papers with code
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
In this work, we introduce $\textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-likelihood difference between small tuned and untuned models while sampling from the frozen large model.
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
Accurate representation in media is known to improve the well-being of the people who consume it.
Towards Equitable Representation in Text-to-Image Synthesis Models with the Cross-Cultural Understanding Benchmark (CCUB) Dataset
It has been shown that accurate representation in media improves the well-being of the people who consume it.
Cultural Vocal Bursts Intensity Prediction
Image Generation
+3
Towards Real-Time Text2Video via CLIP-Guided, Pixel-Level Optimization
We introduce an approach to generating videos based on a series of given language descriptions.
A Neural-Symbolic Approach to Natural Language Understanding
Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation
Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis
Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated.
