paolomanchisi

paolomanchisi's Stars

• TheAlgorithms/Python

  All Algorithms implemented in Python

  Language:Python196k5.9k1.5k46.1k

• chihming/awesome-network-embedding

  A curated list of network embedding techniques.

  2.6k19615504

• steaklive/EveryRay-Rendering-Engine

  Robust real-time rendering engine on DX11, DX12 with many advanced graphical features for quick prototyping

  Language:C++65559425

• atenpas/gpd

  Detect 6-DOF grasp poses in point clouds

  Language:C++64819124236

• dougsm/ggcnn

  Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)

  Language:Python5291260142

• skumra/robotic-grasping

  Antipodal Robotic Grasping using GR-ConvNet. IROS 2020.

  Language:Python476560121

• PaulDanielML/MuJoCo_RL_UR5

  A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

  Language:Python46852555

• lianghongzhuo/PointNetGPD

  PointNetGPD is an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.

  Language:Python348116974

• mmaaz60/mvits_for_class_agnostic_od

  [ECCV'22] Official repository of paper titled "Class-agnostic Object Detection with Multi-modal Transformer".

  Language:Python30383225

• rhett-chen/Robotic-grasping-papers

  paper list of robotic grasping and some related works

  2837019

• ethz-asl/vgn

  Real-time 6 DOF grasp detection in clutter.

  Language:Python26093655

• ElectronicElephant/pybullet_ur5_robotiq

  Gym-Styled UR5 arm with Robotiq-85 / 140 gripper in Bullet simulator

  Language:Python21451039

• shreyas-bk/u2netdemo

  Demonstration using Google Colab to show how U-2-NET can be used for Background Removal, Changing Backgrounds, Bounding Box Creation, Salient Feature Highlighting and Salient Object Cropping.

  Language:Jupyter Notebook1987835

• atenpas/gpg

  Generate grasp pose candidates in point clouds

  Language:C++10461260

• YuelangX/AvatarMAV

  A PyTorch implementation of "AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels"

  Language:Python9515912

• GuangxingHan/Meta-Faster-R-CNN

  Code for AAAI 2022 Oral paper: 'Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment'

  Language:Python8444910

• mahyaret/gym-panda

  An OpenAI Gym Env for Panda

  Language:Python764517

• encounter1997/FP-DETR

  Official Implementation of "FP-DETR: Detection Transformer Advanced by Fully Pre-training"

  Language:Python61542

• ArsenalCheng/Meta-Adapter

  [NeurIPS 2023] Meta-Adapter

  Language:Python41231

• jqtangust/FilmRemoval

  [CVPR 2024] Official Implementation of Learning to Remove Wrinkled Transparent Film with Polarized Prior

  Language:Python34430

• lxn96/ICPE

  The offical code for paper "Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection"

  Language:Python311102

• ZXP-S-works/SE2-equivariant-grasp-learning

  Code for the paper Sample Efficient Grasp Learning Using Equivariant Models

  Language:Python31418

• huetufemchopf/roboticgrasper

  This repo contains the code for an implementation for a tm-robotics robotic grasper in pybullet. Furthermore, there is a gym environment as well as implementaitons of a DQN to train the robotic arm to grasp objects with reinforcement learning

  Language:Python12411

• WNJXYK/DeCoOp

  Language:Python11113

• priscilla100/ensemble_IDS

  The exponential increase in the number of connected "things" and the proliferation in the usage of Internet of Things (IoT) devices has raised numerous challenges in terms of security, privacy, and interoperability. IoT devices are resource constrained in terms of computational power, onboard memory, network bandwidth, and energy availability which limits the implementation of cryptographic solutions. The heterogeneous nature of IoT devices makes them avenue for an attacker to exploit threats like spoofing, routing, MITM, and DoS attacks. With the current sophistication of threats IoT devices are subjected to, an Intrusion Detection System (IDS) is the preferred solution for IoT devices. An IDS continuously monitors incoming traffic, and analyzes it to detect possible signs of cyber threats. This research proposes a novel intelligent ensemble-based IDS that will reside in the IoT gateway. The uniqueness of our approach is to use an ensemble learning technique which combines multiple machine learning techniques in order to the improve the predictive performance and detection accuracy. Ensemble learning have been studied to increase the detection rate while obtaining better generalization performance due to the combination of several machine learning model also known as base learners. Three popularly known ensemble models (i.e. boosting, stacking, and voting) are used in evaluating the performance of our proposed IDS using three machine learning techniques: Decision Tree, Naive Bayes (NB), and k-Nearest Neighbor (KNN). Lastly, the proposed approach will be evaluated on two publicly available dataset; Intrusion Detection Evaluation Dataset (CIC-IDS2017) and N-BaIoT.

  Language:Jupyter Notebook7202

• VFWm614/TA-NET

  [IEEE TITS 2024] TA-NET: Empowering Highly Efficient Traffic Anomaly Detection through Multi-Head Local Self-Attention and Adaptive Hierarchical Feature Reconstruction

  Language:Python3211

• Lelihu/setsuna_nids

  A network intrusion detection system based on incremental statistics (AfterImage), an ensemble of autoencoders (KitNET) and Flask framework.

  Language:Python2001

• Arunachalam4505/Intrusion-Detection-Ensemble

  Artificial Intelligence in Cyber Security

  Language:Jupyter Notebook1100

• Mona-2001/Ensemble-Based-NIDS

  Language:Jupyter Notebook110

• Standby-Coder/NIDS

  Network Intrusion Detection System using an Ensemble of Standard ML Models

  Language:Jupyter Notebook10