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...
Findings show that LbT fosters students' assessment literacy and deep learning. Results also reveal that by teaching other students, quasi-teachers promote a broader understanding of assessment and grade practices in comparison to other students. Unlike their counterparts, quasi-teachers de-emphasised ...
These reasons favour an approach that uses fixed length sequences of actions and outcomes to predict the next action. To this end, we trained the LSTM model to predict the participant’s action at time \(t\), given his/her \(K\) previous actions and the corresponding rewards (in times...
1. 使用Unsloth微调Llama3:显著减少内存使用且长上下文增6倍 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...
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...
machine learningneural networksartificial intelligencenatural language processingsentiment analysisconference callsWhen quantifying qualitative information from unstructured textual data, traditional bag-of-words approaches capture only semantic features of single words/phra...
In these situations, it is necessary for significant human oversight of the AI decision-making process. One might think that there should be sensible human oversight of any AI application that takes decisions that have direct human consequences. In fact, there are plenty of autom...
and coauthors[2], uses the gradient of the classification score with respect to the convolutional features determined by the network in order to understand which parts of the image are most important for classification. For more information, seeGrad-CAM Reveals the Why Behind Deep Learning ...
To get an overview of which features are most important for a model we can plot the SHAP values of every feature for every sample. The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature...
(PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature ...