text stringlengths 307 2.02k | inputs dict | prediction null | prediction_agent null | annotation stringclasses 2
values | annotation_agent stringclasses 1
value | vectors null | multi_label bool 1
class | explanation null | id stringlengths 36 36 | metadata null | status stringclasses 2
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TITLE: SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
ABSTRACT: Machine learning potentials are an important tool for molecular simulation,
but their development is held back by a shortage of high quality datasets to
train them on. We describe the SPICE dataset, a new qu... | {
"abstract": "Machine learning potentials are an important tool for molecular simulation,\nbut their development is held back by a shortage of high quality datasets to\ntrain them on. We describe the SPICE dataset, a new quantum chemistry dataset\nfor training potentials relevant to simulating drug-like small molecu... | null | null | new_dataset | admin | null | false | null | 1218fc36-d914-47a0-b45d-25c4d61b317d | null | Validated | {
"text_length": 1171
} | 0new_dataset |
TITLE: LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities
ABSTRACT: Understanding and interpreting human actions is a long-standing challenge and
a critical indicator of perception in artificial intelligence. However, a few
imperative components of daily human activities are largely missed in ... | {
"abstract": "Understanding and interpreting human actions is a long-standing challenge and\na critical indicator of perception in artificial intelligence. However, a few\nimperative components of daily human activities are largely missed in prior\nliterature, including the goal-directed actions, concurrent multi-ta... | null | null | new_dataset | admin | null | false | null | 238beaef-fde6-4639-afd6-26f57f4322dd | null | Validated | {
"text_length": 1226
} | 0new_dataset |
TITLE: A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters
ABSTRACT: Synthetic datasets are beneficial for machine learning researchers due to the
possibility of experimenting with new strategies and algorithms in the training
and testing phases. These datasets can easily include more scenar... | {
"abstract": "Synthetic datasets are beneficial for machine learning researchers due to the\npossibility of experimenting with new strategies and algorithms in the training\nand testing phases. These datasets can easily include more scenarios that might\nbe costly to research with real data or can complement and, in... | null | null | new_dataset | admin | null | false | null | a407ec96-3a6e-432c-88ce-b3caf3cd1e90 | null | Validated | {
"text_length": 1732
} | 0new_dataset |
TITLE: A Wideband Signal Recognition Dataset
ABSTRACT: Signal recognition is a spectrum sensing problem that jointly requires
detection, localization in time and frequency, and classification. This is a
step beyond most spectrum sensing work which involves signal detection to
estimate "present" or "not present" detec... | {
"abstract": "Signal recognition is a spectrum sensing problem that jointly requires\ndetection, localization in time and frequency, and classification. This is a\nstep beyond most spectrum sensing work which involves signal detection to\nestimate \"present\" or \"not present\" detections for either a single channel... | null | null | new_dataset | admin | null | false | null | 0880825e-0337-4018-b95d-6e5209e389dc | null | Validated | {
"text_length": 777
} | 0new_dataset |
TITLE: Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset
ABSTRACT: The number of IoT devices in healthcare is expected to rise sharply due to
increased demand since the COVID-19 pandemic. Deep learning and IoT devices are
being employed to monitor body vitals... | {
"abstract": "The number of IoT devices in healthcare is expected to rise sharply due to\nincreased demand since the COVID-19 pandemic. Deep learning and IoT devices are\nbeing employed to monitor body vitals and automate anomaly detection in\nclinical and non-clinical settings. Most of the current technology requir... | null | null | no_new_dataset | admin | null | false | null | bf432be5-a787-4967-ad96-435304af3be2 | null | Validated | {
"text_length": 1132
} | 1no_new_dataset |
TITLE: Quantum Transfer Learning for Real-World, Small, and High-Dimensional Datasets
ABSTRACT: Quantum machine learning (QML) networks promise to have some computational
(or quantum) advantage for classifying supervised datasets (e.g., satellite
images) over some conventional deep learning (DL) techniques due to the... | {
"abstract": "Quantum machine learning (QML) networks promise to have some computational\n(or quantum) advantage for classifying supervised datasets (e.g., satellite\nimages) over some conventional deep learning (DL) techniques due to their\nexpressive power via their local effective dimension. There are, however, t... | null | null | no_new_dataset | admin | null | false | null | c751cb1f-cd90-46e8-8490-989b40bf0b76 | null | Validated | {
"text_length": 1964
} | 1no_new_dataset |
TITLE: Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
ABSTRACT: We analyze the growth of dataset sizes used in machine learning for natural
language processing and computer vision, and extrapolate these using two
methods; using the historical growth rate and estimating the ... | {
"abstract": "We analyze the growth of dataset sizes used in machine learning for natural\nlanguage processing and computer vision, and extrapolate these using two\nmethods; using the historical growth rate and estimating the compute-optimal\ndataset size for future predicted compute budgets. We investigate the grow... | null | null | no_new_dataset | admin | null | false | null | 8b03960a-0289-41a1-b6d6-0217646e99bc | null | Validated | {
"text_length": 1045
} | 1no_new_dataset |
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