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CS

Carola Schönlieb

05/10/18

@ Y Combinator

Neural networks can struggle with images they haven't been trained on, highlighting the continued importance of handcrafted models in certain contexts.

Video

YC

Mathematical Approaches to Image Processing with Carola Schönlieb

@ Y Combinator

Mathematical Approaches to Image Processing with Carola Schönlieb

05/10/18

Related Takeaways

CS

Carola Schönlieb

05/10/18

@ Y Combinator

Understanding the properties of handcrafted models can help inform the development of neural networks, particularly in terms of stability and performance.

CS

Carola Schönlieb

05/10/18

@ Y Combinator

While handcrafted models for image denoising are still relevant, deep neural networks are increasingly outperforming them in various scenarios.

CS

Carola Schönlieb

05/10/18

@ Y Combinator

In using neural networks for problems like computer tomography, we are exploring how to combine handcrafted models with neural networks, particularly in how we feed them prior information.

CS

Carola Schönlieb

05/10/18

@ Y Combinator

The integration of handcrafted models with neural networks presents exciting opportunities for mathematicians to explore and improve image denoising techniques.

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

Models can struggle with tasks requiring expertise and taste, such as evaluating art, which presents a significant challenge for AI.

JD

Jeff Dean

05/12/25

@ Sequoia Capital

The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.

O

OpenAI Cast

04/11/25

@ OpenAI

Training large models is challenging because new issues arise that weren't anticipated at the project's inception, requiring ongoing adjustments and problem-solving.

JD

Jeff Dean

08/08/17

@ Y Combinator

The ability of neural networks to generate descriptive sentences about images has advanced more quickly than expected, showcasing their growing capabilities.

LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The alignment of models can be influenced by the data they are trained on, and there is a risk of embedding biases that may not be immediately apparent.