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Found 7 papers, 2 papers with code
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models
Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data.
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis.
Physically Plausible Animation of Human Upper Body from a Single Image
We present a new method for generating controllable, dynamically responsive, and photorealistic human animations.
Enhancing Transformer with Sememe Knowledge
While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models.
What and Where: A Context-based Recommendation System for Object Insertion
In this work, we propose a novel topic consisting of two dual tasks: 1) given a scene, recommend objects to insert, 2) given an object category, retrieve suitable background scenes.
