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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