Decision Making

The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches. Source: https://arxiv.org/abs/1701.01480

GolfDB is a high-quality video dataset created for general recognition applications in the sport of golf, and specifically for the task of golf swing sequencing. Source: GolfDB: A Video Database for Golf Swing Sequencing

Simitate is a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. It consists on a dataset containing 1938 sequences where humans perform daily activities in a realistic environment. The dataset is strongly coupled with an integration into a simulator. RGB and depth streams with a resolution of 960×540 at 30Hz and accurate ground truth poses for the demonstrator's hand, as well as the object in 6 DOF at 120Hz are provided. Along with the dataset the 3D model of the used environment and labelled object images are also provided. Source: https://arxiv.org/abs/1905.06002 Image Source: https://github.com/raphaelmemmesheimer/simitate

CoWeSe is a Spanish biomedical corpus consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result of a massive crawler on 3000 Spanish domains executed in 2020.

CUHK Image Cropping is a dataset for image cropping. The photos are of varying aesthetic quality and span a variety of image categories, including animal, architecture, human, landscape, night, plant and man-made objects. Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset.

MineRLis an imitation learning dataset with over 60 million frames of recorded human player data. The dataset includes a set of tasks which highlights many of the hardest problems in modern-day Reinforcement Learning: sparse rewards and hierarchical policies. Source: MineRL

This dataset contains a large set (~3.2 Million) of high quality expert trajectories generated from a geometrically consist hybrid planner in a wide variety of environment (~575,000 environments). We created this dataset to explore the capabilities of neural networks to learn complex robotic motion, mimicking a traditional planner. For more information on how to use this data, please refer to the Github for this project: https://github.com/nvlabs/motion-policy-networks

The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. This ability is referred to as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. PackIt is a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space.

Covid-HeRA is a dataset for health risk assessment and severity-informed decision making in the presence of COVID19 misinformation. It is a benchmark dataset for risk-aware health misinformation detection, related to the 2019 coronavirus pandemic. Social media posts (Twitter) are annotated based on the perceived likelihood of health behavioural changes and the perceived corresponding risks from following unreliable advice found online. Source: https://github.com/TIMAN-group/covid19_misinformation

Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a cumbersome process requiring researchers to train expert agents from scratch, record their interactions and test each benchmark method with newly created data. Moreover, creating new datasets for each new technique results in a lack of consistency in the evaluation process since each dataset can drastically vary in state and action distribution. In response, this work aims to address these issues by creating Imitation Learning Datasets, a toolkit that allows for: (i) curated expert policies with multithreaded support for faster dataset creation; (ii) readily available datasets and techniques with precise measurements; and (iii) sharing implementations of common imitation learning techniques.

This dataset consists of 5808 dialogues, based on 2236 unique scenarios. Each dialogue is converted into two training examples in the dataset, showing the complete conversation from the perspective of each agent. The perspectives differ on their input goals, output choice, and in special tokens marking whether a statement was read or written. Source: Negotiation Dialogues Dataset

PICO is a framework to formulate a well-defined focused clinical question. This framework identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). The PubMed PICO Element Detection dataset is a dataset for evaluating models that automatically detect PICO elements. Source: https://github.com/jind11/PubMed-PICO-Detection

StarData is a StarCraft: Brood War replay dataset, with 65,646 games. The full dataset after compression is 365 GB, 1535 million frames, and 496 million player actions. The entire frame data was dumped out at 8 frames per second. Source: https://github.com/TorchCraft/StarData Image Source: https://www.youtube.com/watch?app=desktop&v=vBjgww8jDgw

A View From Somewhere (AVFS)—a dataset of 638,180 face similarity judgments over 4,921 faces. Each judgment corresponds to the odd-one-out (i.e., least similar) face in a triplet of faces and is accompanied by both the identifier and demographic attributes of the annotator who made the judgment.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset An agent benchmark for adaptive decision-making in dialog measuring agent accuracy and dialog turn efficiency in helping users determine eligibility for public, real-world opportunities. This dataset containss the following: * Natural language descriptions of 82 NYC public benefits programs (e.g., tax credits, childcare, subsidized AC) Two simulated user datasets containing all features relevant to that household's eligibility: * Representative: 25 households with features drawn from real NYC demographic data * Diverse: 56 households which, collectively, satisfy nearly every possible acceptance/rejection criteria in all 82 public benefits opportunities

In this dataset we added [Company Name, Car Model, Car Type, Fuel Type, Transmission, Engine (cc), Mileage, Kms_driven, Buyers, Horsepower (kw), Year Price (Lakhs)]

A large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. D2-City contains more than 10000 video clips which deeply reflect the diversity and complexity of real-world traffic scenarios in China. Source: D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

Two versions of the dataset are offered: one is the full dataset used to train the models in DeformPAM, and the other is a mini dataset for easier examination. Both datasets include data for the supervised and finetuning stages of granular pile shaping, rope shaping, and T-shirt unfolding.

This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years. The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges are the optimal fusion of enterprise and log-based features for the prediction task. Thereby, the interpretability of designed prediction models should be ensured in order to have practical relevancy. Supporting software Kindly use the corresponding GitHub repository (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. Citation and Contact If you use this dataset please cite the following publication: @ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}} If you would like to get in touch, please contact an.nguyen@fau.de.

Multi-agent pursuit in matrix world (pursuitMW) is a partially observable Markov game (POMG) between a swarm of pursuers and a swarm of evaders. Algorithms can be developed for the pursuers, evaders, or both of them.

Two versions of the dataset are offered: one is the full dataset used to train the models in our paper, and the other is a mini dataset for easier examination. Both versions include raw and postprocessed subsets of peeling, wiping and lifting. The raw videos of the tactile dataset used for generate the PCA embedding are also provided.

A benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. Source: Benchmarking Deep Reinforcement Learning for Continuous Control

The Spaceship dataset is a dataset for evaluating agents’ ability to learn to solve a class of physics-based tasks. The tasks consist on a spaceship that has to reach a the mothership in 11 steps, in an environment where static planets exert gravitational forces on the spaceship, which induce complex non-linear dynamics on the motion over the 11 steps. Source: https://arxiv.org/pdf/1705.02670.pdf

Texygen is a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation. Source: Texygen Platform