Summary of Using Dynamic Loss Weighting to Boost Improvements in Forecast Stability, by Daan Caljon et al.
Using dynamic loss weighting to boost improvements in forecast stabilityby Daan Caljon, Jeff Vercauteren, Simon…
Summary of Efficient Bias Mitigation Without Privileged Information, by Mateo Espinosa Zarlenga et al.
Efficient Bias Mitigation Without Privileged Informationby Mateo Espinosa Zarlenga, Swami Sankaranarayanan, Jerone T. A. Andrews,…
Summary of Babyllama-2: Ensemble-distilled Models Consistently Outperform Teachers with Limited Data, by Jean-loup Tastet et al.
BabyLlama-2: Ensemble-Distilled Models Consistently Outperform Teachers With Limited Databy Jean-Loup Tastet, Inar TimiryasovFirst submitted to…
Summary of Assessing Simplification Levels in Neural Networks: the Impact Of Hyperparameter Configurations on Complexity and Sensitivity, by (joy) Huixin Guan
Assessing Simplification Levels in Neural Networks: The Impact of Hyperparameter Configurations on Complexity and Sensitivityby…
Summary of Archon: An Architecture Search Framework For Inference-time Techniques, by Jon Saad-falcon et al.
Archon: An Architecture Search Framework for Inference-Time Techniquesby Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan,…
Summary of Challenging the Performance-interpretability Trade-off: An Evaluation Of Interpretable Machine Learning Models, by Sven Kruschel et al.
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Modelsby Sven Kruschel, Nico Hambauer,…
Summary of Beta-sigma Vae: Separating Beta and Decoder Variance in Gaussian Variational Autoencoder, by Seunghwan Kim and Seungkyu Lee
Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoderby Seunghwan Kim, Seungkyu LeeFirst…
Summary of Cross-entropy Optimization For Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks, by Kevin Li et al.
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networksby Kevin…
Summary of Improve Machine Learning Carbon Footprint Using Nvidia Gpu and Mixed Precision Training For Classification Models — Part I, by Andrew Antonopoulos
Improve Machine Learning carbon footprint using Nvidia GPU and Mixed Precision training for classification models…
Summary of Reimagining Linear Probing: Kolmogorov-arnold Networks in Transfer Learning, by Sheng Shen and Rabih Younes
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learningby Sheng Shen, Rabih YounesFirst submitted to arxiv…