Summary of Diffusion Spectral Representation For Reinforcement Learning, by Dmitry Shribak et al.
Diffusion Spectral Representation for Reinforcement Learningby Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo…
Summary of Hardware-aware Neural Dropout Search For Reliable Uncertainty Prediction on Fpga, by Zehuan Zhang et al.
Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGAby Zehuan Zhang, Hongxiang Fan, Hao…
Summary of Automatic Ai Model Selection For Wireless Systems: Online Learning Via Digital Twinning, by Qiushuo Hou et al.
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinningby Qiushuo Hou, Matteo…
Summary of Injectivity Of Relu-layers: Tools From Frame Theory, by Daniel Haider and Martin Ehler and Peter Balazs
Injectivity of ReLU-layers: Tools from Frame Theoryby Daniel Haider, Martin Ehler, Peter BalazsFirst submitted to…
Summary of Fair Clustering: Critique, Caveats, and Future Directions, by John Dickerson et al.
Fair Clustering: Critique, Caveats, and Future Directionsby John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire…
Summary of The Perils Of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret, by Lukas Fluri et al.
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regretby…
Summary of Teach Better or Show Smarter? on Instructions and Exemplars in Automatic Prompt Optimization, by Xingchen Wan et al.
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimizationby Xingchen Wan,…
Summary of Robust Reinforcement Learning From Corrupted Human Feedback, by Alexander Bukharin et al.
Robust Reinforcement Learning from Corrupted Human Feedbackby Alexander Bukharin, Ilgee Hong, Haoming Jiang, Zichong Li,…
Summary of Sail: Self-improving Efficient Online Alignment Of Large Language Models, by Mucong Ding et al.
SAIL: Self-Improving Efficient Online Alignment of Large Language Modelsby Mucong Ding, Souradip Chakraborty, Vibhu Agrawal,…
Summary of Dem: Distribution Edited Model For Training with Mixed Data Distributions, by Dhananjay Ram et al.
DEM: Distribution Edited Model for Training with Mixed Data Distributionsby Dhananjay Ram, Aditya Rawal, Momchil…