Trending Research
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM
In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
Let's Verify Step by Step
We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset.
Ranked #1 on
Math Word Problem Solving
on MATH minival
(using extra training data)
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth).
Gorilla: Large Language Model Connected with Massive APIs
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.
Large Language Models as Tool Makers
Our approach consists of two key phases: 1) tool making: an LLM acts as the tool maker that crafts tools for given tasks, where a tool is implemented as a Python utility function.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
threestudio-project/threestudio
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In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation.
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models.
Humans in 4D: Reconstructing and Tracking Humans with Transformers
To analyze video, we use 3D reconstructions from HMR 2. 0 as input to a tracking system that operates in 3D.
Ranked #3 on
Pose Tracking
on PoseTrack2018
Generating Sequences With Recurrent Neural Networks
sjvasquez/handwriting-synthesis
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This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.
Ranked #39 on
Language Modelling
on enwik8
