This is the code base for our ACM CSCS 2019 paper: "RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs". This codebase contains implementations for several trajectory prediction methods including Social-GAN and TraPHic.

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

BioAgents: An AI scientist framework for autonomous deep research in biological sciences. Multi-agent system combining literature analysis agents with data scientist agents to enable deep research automation through user feedback integration and iterative scientific discovery. Inspired by advances in Edison Kosmos, Sakana AI, K-Dense and others.

RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).

An intelligent task offloading method based on multi-agent deep reinforcement learning in ultra-dense heterogeneous network with mobile edge computing

Multi Agent Reinforcement Learning for Dense Path Planning

Compiling strategy guides into reward functions for reinforcement learning. Uses Claude Vision to extract unit tests from game guides, then trains agents with dense, interpretable rewards.

Graph Attention-Guided Search for Dense Multi-Agent Pathfinding (AAAI-26)

A Collaborative Localization and Dense Mapping Framework for Multi-agent Based Visual-inertial SLAM

Risk-aware multi-agent deep reinforcement learning for packet routing in ultra-dense LEO satellite networks

Explainability toolkit for retrieval models. Explain prediction of vector search models (embeddings similarity models, siamese encoders, bi-encoders, dense retrieval models). Debug your vector search models for RAG or agentic AI system.

SimCAI implementation for our paper: RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments,

PaperPal is an AI-powered agent skill designed to streamline your academic workflow. It transforms dense scientific literature into actionable insights by providing automated summarization, key finding extraction, and context-aware Q&A, allowing researchers to focus on innovation rather than information overload.

A Telegram bot with Claude Agent SDK for knowledge-dense lives

Market data for AI agents. Bloomberg Terminal-style screens with formatted output. Not raw JSON - dense, scannable tables ready to interpret. Smart defaults, progressive disclosure.

A multi-agent system built on MCP (Model Context Protocol) architecture providing specialized AI assistants with database analytics, weather information, and meeting scheduling capabilities.

Curated technical knowledge base. 683+ articles across 27 domains - dense references for AI agents and engineers. Site: happyin.space

Clarus is a RAG system for maximum retrieval accuracy on religious texts. It combines dense semantic search with multi-agent LLM synthesis to provide scholarly-quality comparative analysis across the Quran (8 Turkish translations) and Bible (English KJVA + Turkish).

Autonomous SOC platform that deploys 9 AI agents to detect, investigate, respond to, and verify resolution of security and operational incidents in under 3 minutes. Built on Elasticsearch Agent Builder with ES|QL tools, dense vector search, Elastic Workflows, and A2A inter-agent coordination.

Polymathera's no-RAG, multi-agent framework for extremely long, dense contexts (1B+ tokens).

Persistent long‑term memory for AI agents with dense facts and graph relations.

A DQN Agent which learns to maximize reward in the MineRL Navigate Dense Environment

Video Memory CLI: index once, search/ask forever (dense captions + transcripts + timestamps). Built for agents.

Enhancing Multi-Hop Question Answering with Agentic ReAct, Dense Retrieval, Query Rewriting, and Self-Verification

Dense Sourced Index — Zero-DB Agent Memory for LLMs. 97% context recall without vector databases.

Agentic Insurance Claims Analyzer — Endee Hybrid Search (Dense + BM25) + 3-step RAG pipeline + Groq LLaMA3 + Streamlit UI

Reinforcement Learning–based autonomous driving agent trained with DQN in a dense highway traffic environment using highway-env.

MCP server that compresses OCR-heavy PDFs into dense packed images so AI agents can handle long visual documents

KYBER — Beer's full Viable System Model as a working agent runtime. 777 lines of dense Python, theoretically grounded code..

A full-stack RAG agent that solves a common business problem: getting instant, accurate answers from dense HR policy documents.