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Using Properties of Additive Noise Channels to Derive Generalization Bounds for Noisy Iterative Algorithms

by instadatahelp | Aug 28, 2023 | AI Blogs

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels Hao Wang, Rui Gao, Flavio P. Calmon; 24(26):1−43, 2023. Abstract This paper examines the generalization of models trained by noisy iterative algorithms and analyzes their...
Addressing Unconventional Hurdles with Machine Learning

Addressing Unconventional Hurdles with Machine Learning

by instadatahelp | Aug 28, 2023 | AI Blogs

[Submitted on 24 Aug 2023] Authors:Shan Guleria, Benjamin Schwartz, Yash Sharma, Philip Fernandes, James Jablonski, Sodiq Adewole, Sanjana Srivastava, Fisher Rhoads, Michael Porter, Michelle Yeghyayan, Dylan Hyatt, Andrew Copland, Lubaina Ehsan, Donald Brown, Sana...

Methodology and Asymptotics of the Convergence between Alpha-divergence Variational Inference and Importance Weighted Auto-Encoders

by instadatahelp | Aug 28, 2023 | AI Blogs

Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics Authors: Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet; 24(243):1−83, 2023. Abstract Many algorithms have been proposed that utilize the Variational...
Addressing Unconventional Hurdles with Machine Learning

Leveraging Fine-Tuned Llama 2 GPT Model for Analyzing Financial News (arXiv:2308.13032v1 [cs.CL])

by instadatahelp | Aug 28, 2023 | AI Blogs

The paper explores the potential of using the Llama 2 Large Language Model (LLM) for multitask analysis of financial news. The authors employed a fine-tuning approach based on PEFT/LoRA. During the study, the model was fine-tuned for several tasks, including analyzing...

Enhancing Single-Model Deep Uncertainty with Distance-Awareness: A Simplified Approach

by instadatahelp | Aug 28, 2023 | AI Blogs

A Simple Approach to Enhance Single-Model Deep Uncertainty via Distance-Awareness Authors: Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan; Published in 2023; Volume 24,...
Addressing Unconventional Hurdles with Machine Learning

Training Neural Networks using Universal Adiabatic Quantum Computing

by instadatahelp | Aug 28, 2023 | AI Blogs

arXivLabs is a platform where collaborators can create and share innovative features for the arXiv website. Both individuals and organizations that collaborate with arXivLabs are aligned with our core principles of openness, community, excellence, and user data...

Conformal p-values: A Predictive Approach to Selection

by instadatahelp | Aug 28, 2023 | AI Blogs

Selection by Prediction with Conformal p-values Authors: Ying Jin, Emmanuel J. Candes; Published in volume 24(244) on pages 1-41 in 2023. Abstract Decision making and scientific discovery pipelines often involve multiple stages, where initial screening is used to...
Addressing Unconventional Hurdles with Machine Learning

Extreme Value Theory for Mitigating Extreme Risks in Reinforcement Learning

by instadatahelp | Aug 28, 2023 | AI Blogs

The content can be rewritten as: arXivLabs is a platform that enables collaborators to create and share innovative features directly on our website. Both individuals and organizations that collaborate with arXivLabs have embraced and embraced our core principles of...

Solving Non-smooth, Non-convex Phase Retrieval with Arbitrary Initialization using Online Stochastic Gradient Descent

by instadatahelp | Aug 28, 2023 | AI Blogs

Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Authors: Yan Shuo Tan, Roman Vershynin; Published in 24(58):1−47, 2023. Abstract The problem of phase retrieval can be solved using a two-step procedure....
Addressing Unconventional Hurdles with Machine Learning

Comparing Performance of Design Optimization and Deep Learning-based Inverse Design: A Study (arXiv:2308.13000v1 [math.OC])

by instadatahelp | Aug 28, 2023 | AI Blogs

The use of surrogate model-based optimization in engineering design has been on the rise. This method involves creating a surrogate model using data from simulations or real-world experiments, and then using numerical optimization techniques to find the optimal...
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