A study proposes a hybrid Ret-DNN with XGBoost model to forecast customer behavior in e-commerce. Using 500,000 transaction records from a UK retailer, the model achieves a Mean Absolute Error of 0.2193, outperforming the existing Ret-DNN model.
Hybrid Ret-DNN with XGBoost for Customer Behavior Forecasting
Adaption Labs releases 'Invent a Dataset' to generate training data from task descriptions
Adaption Labs has introduced "Invent a Dataset," a feature that generates structured, training-ready datasets directly from natural language task descriptions without requiring a seed corpus, predefined schema, or labeling guide. The tool is available via the Adaption app, Python SDK, and REST API, allowing users to download generated rows in JSONL, JSON, CSV, or Parquet formats.
Anthropic releases Claude Fable 5.1 with improved coding and science benchmarks
Anthropic has released Claude Fable 5.1, a model update that sets a new standard for coding, knowledge work, and long-running problem-solving tasks. The release highlights significant performance gains on the new Terminal-Bench-Science 0.1 benchmark, where Fable 5.1 achieved a score of 52.6%, compared to 24.7% for Fable 5, 29.0% for Opus 5, and 22.4% for GPT-5.6 Sol.
Worlds Biggest Chat Title Dataset Released by SupraLabs
SupraLabs has released a curated chat title dataset with 115K samples, surpassing the previous record of 10K samples. The filtered dataset is available as `SupraLabs/chat-titles-filtered-115K`, while an unfiltered version with 150K samples is also provided, along with a legacy 12K dataset.
DataMagic Turns Tabular Data into Interactive Insight Videos
DataMagic transforms raw tabular data and natural language queries into narrative data-insight videos. It uses DVSpec to ensure data fidelity by linking visual elements to data fields via semantic references, and employs a multi-agent architecture to generate and orchestrate coherent video scenes. The system supports interactive exploration and provenance-based data Q&A, enabling users to engage with data beyond static views.
Topological Data Analysis for Real-Time Process Monitoring
A new method combines topological data analysis and machine learning to monitor high-dimensional dynamic processes. It represents time-series data as manifolds, uses topological descriptors to capture structure, and employs neural ordinary differential equations to model dynamic evolution. The approach effectively detects diverse events in industrial process data and outperforms reconstruction-based and trajectory-based alternatives.