Unverified50% confidenceOpinionExact time
Frank Hutter believes tabular data is very dirty, which is one of the reasons it took deep learning a long time to truly handle it well.
1
Sources
50%
Confidence
Long-term
Relevance
10/3/2026
First Seen
Sources
Related Entities
Related Claims
Unverified初学者常见错误是在对底层数据处理和经典机器学习只有肤浅理解时就急于进入深度学习和大模型69% similarUnverifiedThe poor scalability of statistical modeling methods on high-dimensional data is the core reason for the gradual rise of deep learning methods.66% similarUnverifiedThe author acknowledges that the DeepSeek model is somewhat verbose and currently not as capable as some dedicated tools when it comes to looking up information in real time or repeatedly testing until success.66% similarUnverifiedThe information bottleneck hypothesis proposed by Shwartz-Ziv and Tishby attempts to explain why deep learning works from the perspective of information compression65% similarUnverified现代深度网络往往拥有数十亿乃至上万亿参数，远超训练样本数量，按经典统计学习理论应严重过拟合，实际上却展现出惊人的泛化能力63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/969521API
curl https://kongchang.com/api/v1/knowledge/claims/969521MCP
get_claim(id=969521)