Long-RL: Scaling RL to Long Sequences (NeurIPS 2025)
EfficientSAM3 compresses SAM3 into lightweight, edge-friendly models via progressive knowledge distillation for fast promptable concept segmentation and tracking.
[TMLR 2026] Survey: https://arxiv.org/pdf/2507.20198
[ICML 2026] GRACE-VLM: deployable INT4 Qwen3-VL via quantization-aware distillation.
A High-Efficiency System of Large Language Model Based Search Agents
Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)
Official PyTorch implementation of the paper "Dataset Distillation via the Wasserstein Metric" (ICCV 2025).
TinyML and Efficient Deep Learning Computing | MIT 6.S965/6.5940
CVPR 2026 (Main) | MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
Code for paper "Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters"
This is a repository accompanying the survey Edge AI Meets LLM (coming soon), containing a comprehensive list of papers, codebases, toolchains, and open-source frameworks. It is intended to serve as a handbook for researchers and developers interested in Edge/Mobile LLMs.
Dynamic Attention Mask (DAM) generate adaptive sparse attention masks per layer and head for Transformer models, enabling long-context inference with lower compute and memory overhead without fine-tuning.
Code for paper "EdgeKE: An On-Demand Deep Learning IoT System for Cognitive Big Data on Industrial Edge Devices"
Official PyTorch implementation of the paper "Towards Adversarially Robust Dataset Distillation by Curvature Regularization" (AAAI 2025).
A deep learning framework that implements Early Exit strategies in Convolutional Neural Networks (CNNs) using Deep Q-Learning (DQN). This project enhances computational efficiency by dynamically determining the optimal exit point in a neural network for image classification tasks on CIFAR-10.
Research-ready and production-friendly neural network pruning for PyTorch—transparent methods, reproducible baselines, and deployment metrics to compress models for real-world use.
⚡ Fast, concise, LLM-first Generative UI language
Transformer (GPT) implemented from scratch in C++. Runs on modest hardware with complete mathematical derivations and optimized tensor operations.
[LPCVC2025] Official PyTorch implementation of the 2025 IEEE Low-Power Computer Vision Challenge Track1 Winner at the CVPR 2025 Workshop.
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