Trending Research
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data.
Parsel: A (De-)compositional Framework for Algorithmic Reasoning with Language Models
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
Ranked #1 on
Code Generation
on APPS
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation.
Muse: Text-To-Image Generation via Masked Generative Transformers
Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding.
Ranked #16 on
Text-to-Image Generation
on COCO
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
hazyresearch/h3
•
•
First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs.
LAION-5B: An open large-scale dataset for training next generation image-text models
mlfoundations/open_clip
•
•
NeurIPS 2022 Datasets and Benchmarks 2022
We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale.
