Massively Parallel Deep Reinforcement Learning. 🔥
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Updated
Jul 5, 2025 - Python
Massively Parallel Deep Reinforcement Learning. 🔥
Implementation of EfficientNet model. Keras and TensorFlow Keras.
Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"
LLaVA-Mini is a unified large multimodal model (LMM) that can support the understanding of images, high-resolution images, and videos in an efficient manner.
A light-weight, power efficient, and general purpose convolutional neural network
Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On i…
Papers and Book to look at when starting AGI 📚
Fast OpenGL Mathematics (GLM) for Python
ICLR 2018 Quick-Thought vectors
[EMNLP 2024 Findings] OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs.
Combining Faster R-CNN and U-net for efficient medical image segmentation
Efficient distortion loss with O(n) realization.
An efficient pytorch implementation of selective scan in one file, works with both cpu and gpu, with corresponding mathematical derivation. It is probably the code which is the most close to selective_scan_cuda in mamba.
2D discrete Wavelet Transform for Image Classification and Segmentation
D^2-MoE: Delta Decompression for MoE-based LLMs Compression
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