#ICLR 2024

FSL-Mate provides a comprehensive set of resources for enhancing the research of few-shot learning (FSL). It includes FewShotPapers, a detailed list of cutting-edge research papers, and PaddleFSL, a Python library based on PaddlePaddle tailored for FSL. The platform is regularly updated with publications from key conferences such as ICLR, AAAI, EMNLP, ICCV, and NeurIPS, ensuring access to the latest developments. FSL-Mate is an essential tool for academic and practical advancements in FSL, offering insights and resources for both research and AI innovation without bias or exaggerated claims.

SWE-bench is a benchmark for testing language models' abilities to solve real-world GitHub software issues. It provides a containerized evaluation environment using Docker, ensuring repeatable assessments. Recent updates feature SWE-bench Verified, a collection of 500 engineer-confirmed solvable problems. Developed in collaboration with OpenAI, SWE-bench supports reproducible evaluations across different systems. Its resources are designed to help with model training, inference, and task creation, supporting NLP and machine learning applications in software engineering.

Explore 22 scalable reinforcement learning environments crafted with JAX to boost research efficiency. These environments, ranging from basic games to intricate NP-complete challenges, support research applications in both academia and industry. Seamlessly integrates with popular frameworks such as OpenAI Gym and DeepMind Env, providing practical examples for easy implementation. Suitable for novice and experienced users alike.

Consistency Trajectory Model (CTM) delivers state-of-the-art results on CIFAR-10 and ImageNet 64x64, as presented at ICLR 2024. CTM effectively manages computational demands while maintaining sample fidelity, offering various sampling options via probability flow learning of ODE trajectories. The PyTorch implementation, along with codes, checkpoints, and evaluation scripts, is accessible in the official repository for developers interested in efficient and high-quality image processing.

This project employs ray diffusion for pose estimation, providing a comprehensive guide on setup, demos, and training. Presented at ICLR 2024, it introduces using cameras as rays for enhanced pose accuracy, offering instructions on configuring the environment with Pytorch and Pytorch3D, acquiring model weights, and demo execution with both known and automatically detected bounding boxes. It also details ray regression and training processes, with evaluation guides, suitable for researchers and developers exploring advanced pose estimation methodologies.

The Think-on-Graph project, presented at ICLR 2024, describes a method for integrating deep reasoning in large language models using knowledge graphs. Now available in a new repository, this project includes resources like datasets, evaluation scripts, and setup guides for Freebase and Wikidata. Users should install Freebase or Wikidata for best results, with complete guidance in the README file. Discover its applications and experiment outcomes through visual demonstrations to maximize utilization of ToG.

FeatUp provides a framework that enhances model feature resolution up to 32 times without changing semantics. Utilizing pretrained upsamplers compatible with backbones such as DINO, CLIP, and ResNet50, it fits seamlessly into current workflows. FeatUp can be installed easily via pip or used in collaborative platforms like Colab. It also enables training an implicit upsampler for specific images. Explore detailed documentation and interactive demo on HuggingFace Spaces to leverage its capabilities.