Summary of Transformers Provably Solve Parity Efficiently with Chain Of Thought, by Juno Kim and Taiji Suzuki
Transformers Provably Solve Parity Efficiently with Chain of Thoughtby Juno Kim, Taiji SuzukiFirst submitted to…
Summary of Qeft: Quantization For Efficient Fine-tuning Of Llms, by Changhun Lee et al.
QEFT: Quantization for Efficient Fine-Tuning of LLMsby Changhun Lee, Jun-gyu Jin, Younghyun Cho, Eunhyeok ParkFirst…
Summary of Why Pre-training Is Beneficial For Downstream Classification Tasks?, by Xin Jiang et al.
Why pre-training is beneficial for downstream classification tasks?by Xin Jiang, Xu Cheng, Zechao LiFirst submitted…
Summary of Muso: Achieving Exact Machine Unlearning in Over-parameterized Regimes, by Ruikai Yang et al.
MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimesby Ruikai Yang, Mingzhen He, Zhengbao He, Youmei…
Summary of Metalic: Meta-learning In-context with Protein Language Models, by Jacob Beck et al.
Metalic: Meta-Learning In-Context with Protein Language Modelsby Jacob Beck, Shikha Surana, Manus McAuliffe, Oliver Bent,…
Summary of Merging in a Bottle: Differentiable Adaptive Merging (dam) and the Path From Averaging to Automation, by Thomas Gauthier-caron et al.
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automationby…
Summary of Can Looped Transformers Learn to Implement Multi-step Gradient Descent For In-context Learning?, by Khashayar Gatmiry et al.
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?by Khashayar Gatmiry, Nikunj…
Summary of Randomized Asymmetric Chain Of Lora: the First Meaningful Theoretical Framework For Low-rank Adaptation, by Grigory Malinovsky et al.
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptationby Grigory Malinovsky,…
Summary of Hyperdpo: Conditioned One-shot Multi-objective Fine-tuning Framework, by Yinuo Ren et al.
HyperDPO: Conditioned One-Shot Multi-Objective Fine-Tuning Frameworkby Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou…
Summary of Think Beyond Size: Adaptive Prompting For More Effective Reasoning, by Kamesh R
Think Beyond Size: Adaptive Prompting for More Effective Reasoningby Kamesh RFirst submitted to arxiv on:…