Proof pending. This topic has not reached the minimum paper threshold yet.
The efficacy of deep neural networks is heavily reliant on the design of non-linear activation functions, yet existing approaches often struggle to balance optimization stability with computational ef...
Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity. Within historic deep architectures, while ReLU has been th...
The choice of activation function plays a crucial role in the optimization and performance of deep neural networks. While the Rectified Linear Unit (ReLU) remains the dominant choice due to its simpli...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID activation-functions | Route /topic/activation-functions
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/activation-functionsMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Activation Functions",
"cluster": "Activation Functions"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Activation Functions",
"normalized_query": "activation-functions",
"route": "/topic/activation-functions",
"paper_ref": null,
"topic_slug": "activation-functions",
"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.