Trending Research
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
Ranked #8 on
Visual Question Answering
on MM-Vet
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability.
LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models
facebookresearch/llm-transparency-tool
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We present the LM Transparency Tool (LM-TT), an open-source interactive toolkit for analyzing the internal workings of Transformer-based language models.
Magic Clothing: Controllable Garment-Driven Image Synthesis
shinechen1024/magicclothing
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We propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task.
Solving Data Quality Problems with Desbordante: a Demo
However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists.
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy.
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
facebookresearch/generative-recommenders
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Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(HR@10 (full corpus) metric)
MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion
zjukg/mygo
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To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities.
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
Ranked #7 on
Image Generation
on ImageNet 256x256
