Research Tools: code, datasets, & models

We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model.

SeeDot is an automatic quantization tool that generates efficient machine learning (ML) inference code for IoT devices. ML models are usually expressed in floating-point, and IoT devices typically lack hardware support for floating-point arithmetic. Hence,…

This data set contains 1.2M sequences of camera trap images, totaling 3.2M images. Species-level labels are provided for 48 species. We have also added approximately 100,000 bounding box annotations to approximately 38,000 images. The images…

This data set contains 3.7M camera trap images from five locations across the United States, with species-level labels for 28 species. More information about this data set is available in the associated manuscript: Tabak, M.…

Microsoft Speech Corpus (Indian languages) release contains conversational and phrasal speech training and test data for Telugu, Tamil and Gujarati languages. The data package includes audio and corresponding transcripts. Data provided in this dataset shall…

This data set contains 244,497 images from 140 camera locations in the Southwestern United States, with species-level labels for 22 species, and approximately 66,000 bounding box annotations.

TensorWatch is a comprehensive library of tools to debug and monitor training phase for Deep Learning and Reinforcement Learning models as well as perform analysis on trained models. TensorWatch is a debugging and visualization tool…

This download contains the data used in the WWW’19 paper NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

This project can be used to reproduce the DQN implementation presented in the ICML2019 paper: Safe Policy Improvement with Baseline Bootstrapping, by Romain Laroche, Paul Trichelair, and Rémi Tachet des Combes. For the finite MDPs…

This project can be used to reproduce the finite MDPs experiments presented in the ICML2019 paper: Safe Policy Improvement with Baseline Bootstrapping, by Romain Laroche, Paul Trichelair, and Rémi Tachet des Combes. For the DQN…