Summary of Investigating Symbolic Capabilities Of Large Language Models, by Neisarg Dave et al.
Investigating Symbolic Capabilities of Large Language Modelsby Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur…
Summary of Paired Autoencoders For Likelihood-free Estimation in Inverse Problems, by Matthias Chung and Emma Hart and Julianne Chung and Bas Peters and Eldad Haber
Paired Autoencoders for Likelihood-free Estimation in Inverse Problemsby Matthias Chung, Emma Hart, Julianne Chung, Bas…
Summary of Reducing Transformer Key-value Cache Size with Cross-layer Attention, by William Brandon et al.
Reducing Transformer Key-Value Cache Size with Cross-Layer Attentionby William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar…
Summary of Learning Causal Dynamics Models in Object-oriented Environments, by Zhongwei Yu et al.
Learning Causal Dynamics Models in Object-Oriented Environmentsby Zhongwei Yu, Jingqing Ruan, Dengpeng XingFirst submitted to…
Summary of Progress Measures For Grokking on Real-world Tasks, by Satvik Golechha
Progress Measures for Grokking on Real-world Tasksby Satvik GolechhaFirst submitted to arxiv on: 21 May…
Summary of Prompt-based Spatio-temporal Graph Transfer Learning, by Junfeng Hu et al.
Prompt-Based Spatio-Temporal Graph Transfer Learningby Junfeng Hu, Xu Liu, Zhencheng Fan, Yifang Yin, Shili Xiang,…
Summary of Prompt Learning For Generalized Vehicle Routing, by Fei Liu et al.
Prompt Learning for Generalized Vehicle Routingby Fei Liu, Xi Lin, Weiduo Liao, Zhenkun Wang, Qingfu…
Summary of Perturbing the Gradient For Alleviating Meta Overfitting, by Manas Gogoi et al.
Perturbing the Gradient for Alleviating Meta Overfittingby Manas Gogoi, Sambhavi Tiwari, Shekhar VermaFirst submitted to…
Summary of Scrutinize What We Ignore: Reining in Task Representation Shift Of Context-based Offline Meta Reinforcement Learning, by Hai Zhang et al.
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learningby…
Summary of Adversarially Diversified Rehearsal Memory (adrm): Mitigating Memory Overfitting Challenge in Continual Learning, by Hikmat Khan et al.
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual Learningby Hikmat Khan, Ghulam…