Summary of The Road Less Scheduled, by Aaron Defazio et al.
The Road Less Scheduledby Aaron Defazio, Xingyu Alice Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled,…
Summary of Mcdfn: Supply Chain Demand Forecasting Via An Explainable Multi-channel Data Fusion Network Model, by Md Abrar Jahin et al.
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Modelby Md Abrar…
Summary of Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing, by Peter Lippmann et al.
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passingby Peter Lippmann, Gerrit Gerhartz, Roman Remme,…
Summary of Ftmixer: Frequency and Time Domain Representations Fusion For Time Series Modeling, by Zhengnan Li et al.
FTMixer: Frequency and Time Domain Representations Fusion for Time Series Modelingby Zhengnan Li, Yunxiao Qin,…
Summary of Scaling Law For Time Series Forecasting, by Jingzhe Shi et al.
Scaling Law for Time Series Forecastingby Jingzhe Shi, Qinwei Ma, Huan Ma, Lei LiFirst submitted…
Summary of Message-passing Monte Carlo: Generating Low-discrepancy Point Sets Via Graph Neural Networks, by T. Konstantin Rusch et al.
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networksby T. Konstantin Rusch, Nathan…
Summary of Elastogen: 4d Generative Elastodynamics, by Yutao Feng et al.
ElastoGen: 4D Generative Elastodynamicsby Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan, Shandian Zhe, Tianjia…
Summary of Efficiently Training Deep-learning Parametric Policies Using Lagrangian Duality, by Andrew Rosemberg et al.
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Dualityby Andrew Rosemberg, Alexandre Street, Davi M. Valladão,…
Summary of Scalable Optimization in the Modular Norm, by Tim Large and Yang Liu and Minyoung Huh and Hyojin Bahng and Phillip Isola and Jeremy Bernstein
Scalable Optimization in the Modular Normby Tim Large, Yang Liu, Minyoung Huh, Hyojin Bahng, Phillip…
Summary of Analysis Of Atom-level Pretraining with Quantum Mechanics (qm) Data For Graph Neural Networks Molecular Property Models, by Jose Arjona-medina and Ramil Nugmanov
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property…