Train, evaluate, and run machine learning models.
This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.
zchuz
[ACL 2024] A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
ZeamoxWang
Official Training and Benchmarking Implementation of PDMD: Projected Distribution Matching Distillation for Video Diffusion Models
zenteiq-research
JAX/Flax neural operators for scientific machine learning, with benchmarks and pretrained weights.
PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation, distributed training, Optuna etc.
zhaohaojie1998
深度强化学习路径规划, SAC路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真，Adaptive-SAC
zhaozhen2333
Official implementation of “A Turbo-Inference Strategy for Object Detection and Instance Segmentation” (CVIU 2026). A training-free framework for iterative refinement between object detection and instance segmentation.
ZhendongWang6
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
zhengyima
2019百度的关系抽取比赛，使用Pytorch实现苏神的模型，F1在dev集可达到0.75，联合关系抽取，Joint Relation Extraction.
zhgqcn
A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.
zhihu
Fast implementation of BERT inference directly on NVIDIA (CUDA, CUBLAS) and Intel MKL
ZhixiuYe
ACL 2018: Hybrid semi-Markov CRF for Neural Sequence Labeling (http://aclweb.org/anthology/P18-2038)
zhongkaifu
RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.
zhou13
LCNN: End-to-End Wireframe Parsing
machine learning course programming exercise
zhusleep
pytorch中文语言模型预训练
zhuyu-cs
Pytorch implementation of MeanFlow on ImageNet and CIFAR10
Zielon
INSTA - Instant Volumetric Head Avatars [CVPR2023]
zimingttkx
NIPS — Machine-Learning-Based Network Intrusion Prevention System | Kitsune/LUCID integrated | Online unsupervised anomaly detection | Kernel-level real-time traffic interception | FastAPI + CLI management
ZitongYu
🔥Deep Learning for Face Anti-Spoofing
ZjjConan
The official pytorch implemention of our ICML-2021 paper "SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks".
zju3dv
INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models.
zjunlp
[EMNLP 2020] OpenUE: An Open Toolkit of Universal Extraction from Text
zjunlp
PromptKG Family: a Gallery of Prompt Learning & KG-related research works, toolkits, and paper-list.
