Popular GitHub Repositories for Heavy Tailed Distributions

Intelligent (edge and LLM) proxy for agents. Move faster by letting Arch handle the pesky heavy lifting in building agentic apps. Fast ?? task clarification, agent routing, seamless integration of prompts with tools for common tasks, and unified access and observability of LLMs.

the open-source virtual assistant for Ubuntu based Linux distributions

Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

Efficient ML solution for long-tailed demands.

? MassGen: A Multi-Agent Scaling System Inspired by Grok Heavy and Gemini Deep Think. Discord channel: https://discord.com/invite/VVrT2rQaz5

This is a repository with the code for the ACL 2019 paper "Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned" and the ACL 2021 paper "Analyzing Source and Target Contributions to NMT Predictions".

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.

An MDN Layer for Keras using TensorFlow's distributions module

The official code for our ECCV22 oral paper: tracking objects as pixel-wise distributions.

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.

[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model

Lesson material on data science and machine learning topics/concepts

[IJCV 2024] MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs (CVPR 2019)

Balanced Classification: A Unified Framework for Long-Tailed Object Detection (TMM 2023)

[NeurIPS 2023] Official Implementation: "Ambient Diffusion: Learning Clean Distributions from Corrupted Data"

smart-llm-loader is a lightweight yet powerful Python package that transforms any document into LLM-ready chunks. Spend less time on preprocessing headaches and more time building what matters. From RAG systems to chatbots to document Q&A, SmartLLMLoader handles the heavy lifting so you can focus on creating exceptional AI applications.