{\sigma}-GPTs: A New Approach to Autoregressive Models
Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences. However, this is not a necessity. In this paper, we challenge this assumption and show that by simply adding a positional encoding for the output, this order can be modulated on-the-fly per-sample which offers key advantageous properties. It allows for the sampling of and conditioning on
The 30th Symposium on Sensing via Image Information/Home page
SSII2024 Symposium Announcement The 30th Symposium on Sensing via Image Information will take place as shown below. We look forward to your participation! Date: Wednesday, June 12th, 2024 - Friday, June 14th, 2024 Venue: Pacifico Yokohama Annex Hall Details are posted on the SSII website. Note, that only the front page is available in English. Program is now available! Symposium Registration is op
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the right training setup as well as future generations of architectures. In this work, we argue that scale and training research has been needlessly complex due to reliance on the cosine schedule, which prevents training across
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model's capability via loss or benchmarks, we estimate the number of knowledge bits a model stores. We focus on factual knowledge represented as tuples, such as (USA, capital, Washington D.C.) from a Wikipedia page. Through multiple controlled datasets, we establi
2D Gaussian Splatting
2DGS adopts (a) 2D oriented disks as surface elements and allows (b) high-quality rendering with gaussian splatting. Use the slider as the "splatter" Abstract 3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. However, 3DGS fails to accurately represent surfaces due to the mult
Tips For Faster Rust Compile Times | corrode Rust Consulting
Slow Rust Builds? Here are some tips to speed up your compile times. This list was originally released on my private blog, but I decided to update it for 2024 and move it here. Table of Contents Click here to expand the table of contents. General Tips Update The Rust Compiler And Toolchain Use cargo check Instead Of cargo build Switch To The New Parallel Compiler Frontend Remove Unused Dependencie
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GitHub - steinwurf/wurfapi
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
Birth of a Transformer: A Memory Viewpoint
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
An Empirical Study of Mamba-based Language Models
大規模言語モデルの開発
Batch size invariant Adam
The N Implementation Details of RLHF with PPO | ICLR Blogposts 2024
LCM-Lookahead for Encoder-based Text-to-Image Personalization
2D Gaussian Splatting for Geometrically Accurate Radiance Fields
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Simple and Scalable Strategies to Continually Pre-train Large Language Models
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS