The majority of ML work is actually data work. The training data is the “code” that determines the behavior of a model. Datasets can vary from a collection of text used to train a large language model (LLM) to a collection of driving scenarios (annotated videos) used to train a car’s collision avoidance system. However, the steps to develop an ML model typically follow the same iterative data-centric process: (1) find or collect data, (2) clean and refine the data, (3) train the model on the data, (4) test the model on more data, (5) discover the model does not work, (6) analyze the data to fi
Croissant: a metadata format for ML-ready datasets… While training on-device models directly from user data effectively improves the utility performance for applications such as NWP and smart text selection , protecting the privacy of user data for model training is important. …
… However, these formats were designed for data discovery rather than for the specific needs of ML data, such as the ability to extract and combine data from structured and unstructured sources, to include metadata that would enable responsible use of the data, or to describe ML usage characteristics… …
… We evaluated our approach on standard benchmark datasets known to contain spurious correlations Waterbirds , CelebA , CivilComments , MNLI . Each of these datasets contain groupings of data that share an attribute potentially correlated with the label in a spurious manner. …
… Pre-training data A powerful ViFM needs a very large collection of videos on which to train — similar to other foundation models FMs , such as those for large language models LLMs . Ideally, we would want the pre-training data to be a representative sample of all the videos in the world. …
… Training data are daily streamflow values from the Global Runoff Data Center over the time period 1980 - 2023. A single streamflow forecast model is trained using data from 5,680 diverse watershed streamflow gauges shown below to improve accuracy . …
… To fit a model like in the figure above, all it takes is the following 10 lines of code, using the scikit-learn –inspired estimator interface: import autobnn as ab model = ab.operators.Add bnns= ab.kernels.PeriodicBNN width=50 , ab.kernels.LinearBNN width=50 , ab.kernels.MaternBNN width=50 estimato… …
… This process involves creating a separate regression dataset specific to the downstream training data with the same data annotation process used to construct the pre-training data. …
… Like most neural networks, a GNN is trained on a dataset of many labeled examples ~millions , but each training step consists only of a much smaller batch of training examples say, hundreds . …
… This helps TimesFM understand the bigger picture and generalize better when provided with domain-specific contexts not seen during training. Zero-shot evaluation results We evaluate TimesFM zero-shot on data not seen during training using popular time-series benchmarks. …
… To evaluate this approach, we trained models on a downstream task using teledermatology data. Model training involved varying dataset sizes 12.5%, 25%, 50%, 100% to compare embedding-based linear classifiers against fine-tuning. …