Learning 58 3D Reconstruction 57 Recommendation Systems 57 Common Sense Reasoning 56 Code Generation 53 Graph Classification 52 Word Embeddings 52 Abstractive Text Summarization 51 Optical Character Recognition (OCR) 50 3D Human Pose Estimation 49 Emotion Recognition 48 Video Understanding 48 Medical Image...
Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands. Dataset distillation, as a recent emerging strategy, aims to compress real-world datasets for efficient training. However, this line of research currently ...
Penn Machine Learning Benchmarks (PMLB) is a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms. Benchmarks Add a Result These leaderboards are used to track progress in Penn Machine Learning Benchmark TrendDatasetBest ModelPaperCode...
Now, with the semester being in full swing, I recently shared this set of dataset repositories with my deep learning class. However, beyond using this list to find inspiration for interesting student class projects, these are also good places to look for additional benchmark datasets for your m...
Another common use case is fine-tuning for downstream task, where it's useful to release a pretrained model so others can build on it for application to their own datasets. Lastly, some users might want to try out your model to see if it works on some example data. Providing pre-trained...
269 papers with code Explainable Artificial Intelligence (XAI) 1 benchmark 265 papers with code See all 67 tasks Playing Games Sentence 3694 papers with code Image Super-Resolution 76 benchmarks 724 papers with code Continuous Control 76 benchmarks ...
Seedatasetsnotebook for an example of how to load the datasets provided below. Theextractionnotebook shows how to useaxcellto extract text and tables from papers. Evaluation See theevaluationnotebook for the full example on how to evaluate AxCell on the PWCLeaderboards dataset. ...
The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previo
Toward learning a foundational representation of cells and genes Inspired by the success of large-scale machine learning models in natural language, several groups are adapting these models for cellular data using massive single-cell datasets.
combining these two methods, we now also implemented a computational model that can be used to study the evolution of learning. PDFAbstract Code AddRemoveMark official No code implementations yet. Submityour code now Datasets Add Datasetsintroduced or used in this paper...