Summary of What Does It Mean to Be a Transformer? Insights From a Theoretical Hessian Analysis, by Weronika Ormaniec et al.
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysisby Weronika…
Summary of Liger Kernel: Efficient Triton Kernels For Llm Training, by Pin-lun Hsu et al.
Liger Kernel: Efficient Triton Kernels for LLM Trainingby Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, Qingquan…
Summary of Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths, by Yew Ken Chia et al.
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Pathsby Yew Ken Chia, Guizhen…
Summary of Come: Test-time Adaption by Conservatively Minimizing Entropy, By Qingyang Zhang et al.
COME: Test-time adaption by Conservatively Minimizing Entropyby Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao,…
Summary of Aflow: Automating Agentic Workflow Generation, by Jiayi Zhang et al.
AFlow: Automating Agentic Workflow Generationby Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen,…
Summary of Hard-constrained Neural Networks with Universal Approximation Guarantees, by Youngjae Min et al.
Hard-Constrained Neural Networks with Universal Approximation Guaranteesby Youngjae Min, Anoopkumar Sonar, Navid AzizanFirst submitted to…
Summary of Burning Red: Unlocking Subtask-driven Reinforcement Learning and Risk-awareness in Average-reward Markov Decision Processes, by Juan Sebastian Rojas et al.
Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processesby Juan Sebastian…
Summary of Principled Bayesian Optimisation in Collaboration with Human Experts, by Wenjie Xu et al.
Principled Bayesian Optimisation in Collaboration with Human Expertsby Wenjie Xu, Masaki Adachi, Colin N. Jones,…
Summary of A Kernelizable Primal-dual Formulation Of the Multilinear Singular Value Decomposition, by Frederiek Wesel et al.
A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decompositionby Frederiek Wesel, Kim BatselierFirst submitted…
Summary of Non-convergence to Global Minimizers in Data Driven Supervised Deep Learning: Adam and Stochastic Gradient Descent Optimization Provably Fail to Converge to Global Minimizers in the Training Of Deep Neural Networks with Relu Activation, by Thang Do and Sonja Hannibal and Arnulf Jentzen
Non-convergence to global minimizers in data driven supervised deep learning: Adam and stochastic gradient descent…