Unverified50% confidenceFactExact time
In deep neural networks, the number of parameters can reach billions, and each parameter participates in the final prediction computation.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe decision-making process of neural networks is distributed across the weight matrices of billions of parameters, and there is currently no reliable method to attribute specific outputs to concrete decision logic.73% similarUnverifiedTaking reasoning models as an example, a single rollout may produce a chain of thought spanning thousands of tokens, and the total token generation volume of 100 million rollouts can exceed the scale of pretraining corpora by an order of magnitude.70% similarUnverified渐进式神经网络为每个新任务分配独立的网络列,代价是模型参数量随任务数线性增长69% similarUnverifiedThe universal approximation theorem states that as long as there are enough layers and neurons, a neural network can approximate any continuous function to arbitrary precision.69% similarUnverifiedLarge Language Models (LLMs) refer to neural network language models with parameter scales reaching tens of billions to hundreds of billions.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1015263API
curl https://kongchang.com/api/v1/knowledge/claims/1015263MCP
get_claim(id=1015263)