Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learning, all based on the fundamental learning-theoretic technique of uniform convergence. While it is well-known that many of the...
For example, since such models make specific assumptions about human behaviour and motivations, they may fall short if people’s behaviour is carried out in a different manner9,10. A number of studies have used high-capacity deep-network models to understand a given cognitive process. The black...
𝚊𝚖𝚢 𝚌𝚞𝚗@amytcun Every time I feel a bit intimidated about learning complex concepts I have to remember I have the help of @explain_paper, I’m going to dig into ML and feel more confident going deep into new territory ...
cognitive agingdeep learningSpecific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive ...
𝚊𝚖𝚢 𝚌𝚞𝚗@amytcun Every time I feel a bit intimidated about learning complex concepts I have to remember I have the help of @explain_paper, I’m going to dig into ML and feel more confident going deep into new territory ...
First things first, a little bit of theory. I won't go deep into quantum mechanics now (primarily because I don't understand it well enough to talk about it in public). What we really need to know about emulating a quantum computer, is that it's all about matrix multiplication. Quantum...
Llama3是一种革命性的新技术,它通过使用Unsloth进行微调,可以在保持相同的计算效率的同时,显著降低VRAM的使用量。最近的研究表明,使用Unsloth微调Llama3可以使上下文长度增长六倍,这比HF的flash attention技术要高得多。此外,由于Unsloth的优化算法,VRAM的使用量也大大减少。这意味着,对于那些需要处理大量数据的复杂任务...
While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works. In this paper, we develop a compression approach based on quantizing neural network parameters in a linear subspace, profoundly ...
Deep learning example with GradientExplainer (TensorFlow/Keras/PyTorch models) Expected gradients combines ideas from Integrated Gradients, SHAP, and SmoothGrad into a single expected value equation. This allows an entire dataset to be used as the background distribution (as opposed to a single referen...
内容提示: Uniform convergence may be unable to explaingeneralization in deep learningVaishnavh NagarajanDepartment of Computer ScienceCarnegie Mellon UniversityPittsburgh, PAvaishnavh@cs.cmu.eduJ. Zico KolterDepartment of Computer ScienceCarnegie Mellon University &Bosch Center for Artif icial Intelligence...