Proof pending. Core topic summary fields are still materializing.
Topic-specific paper and score movement from the daily diff ledger.
Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains a fundamental challenge in robust machine learning. We address thi...
An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medi...
Many real-world applications provide a continuous stream of data that is subsequently used by machine learning models to solve regression tasks of interest. Hoeffding trees and their variants have a l...
Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of research, CLL methods remain competitive mainly o...
Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners often need group-leve...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID machine-learning | Route /topic/machine-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/machine-learningMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Machine Learning",
"cluster": "Machine Learning"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Machine Learning",
"normalized_query": "machine-learning",
"route": "/topic/machine-learning",
"paper_ref": null,
"topic_slug": "machine-learning",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.