Reading

In the field of Neural Architecture Search (NAS), traditional methods such as reinforcement learning or evolutionary algorithms typically treat the search space as a discrete and non-differentiable black-box problem, resulting in extremely high computational costs. For example, obtaining state-of-the-art architectures on CIFAR-10 and ImageNet previously required thousands of GPU days. DARTS (Differentiable Architecture Search) proposes a new paradigm based on continuous relaxation, transforming discrete architecture selection into continuous variables, thereby allowing efficient optimization using gradient descent.

The core of this method lies in constructing a mixed cell containing candidate operations. The edges between each node are no longer single operations but softmax-weighted combinations of all possible operations. Through bilevel optimization, the model simultaneously learns network weights and architecture parameters (i.e., operation mixing weights). This design not only simplifies the search process but also enables the algorithm to handle both convolutional and recurrent architecture types. Experiments show that DARTS achieves a 2.76% test error rate with 3.3M parameters on CIFAR-10, performing comparably to regularized evolutionary methods that use three orders of magnitude more computational resources, while also surpassing existing automatic search baselines on the Penn Treebank language modeling task.

For researchers and engineers seeking to deeply understand the underlying mechanisms of automated machine learning (AutoML), the paper "DARTS: Differentiable Architecture Search" (published in 2018) is a must-read classic. It demonstrates how to transform discrete combinatorial optimization problems into differentiable ones, laying the theoretical foundation for subsequent efficient architecture searches. It is recommended to start reading from Section 2 "Differentiable Architecture Search" of the paper, focusing on the derivation of the continuous relaxation formula and bilevel optimization.

In the 2022 letter to shareholders of Berkshire Hathaway Inc., Warren E. Buffett not only reviewed the company's operations but also focused on sharing the investment wisdom of his long-time partner, Charlie Munger. The core of this letter is not a listing of financial data, but profound insights into how to build a framework for rational decision-making.

First, the letter emphasizes the importance of "thinking in reverse" and "avoiding stupidity." Munger once said, "I just want to know where I'm going to die so I'll never go there." This method of avoiding risk by anticipating failure is the cornerstone of maintaining rationality in investing. If investors do not care about whether they are rational, they will not strive to correct themselves, ultimately leading to poor results. This concept suggests that success often comes from avoiding obvious mistakes rather than pursuing complex predictions.

Second, the letter explains the value logic of "holding high-quality assets for the long term." Quoting Benjamin Graham: "In the short run, the market is a voting machine; in the long run, it is a weighing machine." This means that as long as companies continue to create value, wise investors will eventually find and buy them. Munger further added, "A great company can continue to operate after you leave; an average company cannot." This reveals the necessity of choosing enterprises with durable competitive advantages and the role of patience as a significant advantage.

Finally, the letter mentions vigilance against leverage risks and the persistence of continuous learning. "There is nothing 100% certain in investing. Therefore, using leverage is dangerous. A string of beautiful numbers multiplied by zero always equals zero." This warning reminds investors that no matter how brilliant past performance has been, one catastrophic failure can reduce everything to zero. Meanwhile, "If you want to be a great investor, you must keep learning. When the world changes, you must change with it." This indicates that adapting to environmental changes is key to survival.

Suppose an investor, when facing market volatility, applies these principles: he does not panic-sell due to short-term stock price declines (the voting machine effect), but instead focuses on whether the company's fundamentals remain strong (the weighing machine effect); he avoids using high leverage to guard against the risk of "multiplying by zero"; and he improves his rationality by reading historical cases and reflecting on his own mistakes. This disciplined approach based on mental models brings more long-term compound returns than simply chasing hot topics.

For readers hoping to deeply understand the philosophy of value investing and improve their decision-making quality, it is recommended to re-read the "Berkshire Hathaway Inc. 2022 Letter to Shareholders." Author Warren Buffett released this letter in February 2023. It is suggested to start reading from the section on Charlie Munger's mental checklists in the letter, which condenses decades of investment wisdom and is suitable for individual investors seeking a stable investment framework.