Unverified50% confidenceTradeoffExact time
The gradient correction method MGDA and its variants model multi-client objectives as multi-objective optimization to find Pareto-optimal solutions, but their common descent direction is not equivalent to the global aggregation direction, making global accuracy loss unavoidable.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAlgorithm selection must match the nature of the problem: gradient descent is suited for smooth, differentiable, continuous optimization, while gradient-free methods are suited for discrete, step-wise, or highly noisy objective functions.76% similarUnverifiedMulti-objective optimization theory indicates that decomposing a complex objective into multiple sub-objectives and optimizing them separately is generally more effective than directly optimizing a single mixed objective.74% similarUnverified传统梯度下降方法（如SGD、Adam）要求目标函数可微且连续，在面对离散、非凸、多模态问题时容易陷入局部最优74% similarUnverifiedTarget propagation assigns a local target activation value to each layer, allowing each layer to independently minimize the gap between its own output and the target, avoiding global gradient chain differentiation73% similarUnverifiedWhen a model contains a large number of scale-type parameters (such as covariance or rate parameters), especially in high-dimensional settings, it is advisable to consider optimization strategies that more closely follow the Fisher–Rao geodesic.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1001327API
curl https://kongchang.com/api/v1/knowledge/claims/1001327MCP
get_claim(id=1001327)