萊斯利·瓦利安特
計(jì)算學(xué)習(xí)理論的先驅(qū)
算法的真正考驗(yàn)不僅在于其正確性，還在于其可擴(kuò)展性和魯棒性。
The essence of computational thinking is abstraction and automation.
計(jì)算思維的本質(zhì)是抽象和自動化。
The power of a computational model is not just in its ability to compute, but in its ability to generalize from specific instances.
計(jì)算模型的力量不僅在于其計(jì)算能力，還在于其從特定實(shí)例中概括的能力。
The future of artificial intelligence depends on our ability to create systems that can adapt to new information without explicit programming.
人工智能的未來取決于我們創(chuàng)造能夠適應(yīng)新信息而無需顯式編程的系統(tǒng)的能力。
In the realm of computational learning, the key is not just to learn, but to learn efficiently.
在計(jì)算學(xué)習(xí)領(lǐng)域，關(guān)鍵不僅在于學(xué)習(xí)，而在于高效地學(xué)習(xí)。
The beauty of algorithms lies in their ability to solve complex problems with simple steps.
算法的美在于它們能夠用簡單的步驟解決復(fù)雜的問題。
Understanding the limits of computation is as important as understanding its possibilities.
理解計(jì)算的極限與理解其可能性同樣重要。
The challenge in computer science is to bridge the gap between the theoretical models and practical applications.
計(jì)算機(jī)科學(xué)的挑戰(zhàn)在于彌合理論模型與實(shí)際應(yīng)用之間的差距。
The ultimate goal of machine learning is to make machines that can learn from experience and improve their performance over time.
The development of learning algorithms that can handle high-dimensional data is a major challenge in the field.
開發(fā)能夠處理高維數(shù)據(jù)的學(xué)習(xí)算法是該領(lǐng)域的一個(gè)主要挑戰(zhàn)。
The ability to learn from noisy data is a key aspect of robust machine learning systems.
從噪聲數(shù)據(jù)中學(xué)習(xí)的能力是魯棒機(jī)器學(xué)習(xí)系統(tǒng)的一個(gè)關(guān)鍵方面。