Summary of Learning How Hard to Think: Input-adaptive Allocation Of Lm Computation, by Mehul Damani et al.
Learning How Hard to Think: Input-Adaptive Allocation of LM Computationby Mehul Damani, Idan Shenfeld, Andi…
Learning How Hard to Think: Input-Adaptive Allocation of LM Computationby Mehul Damani, Idan Shenfeld, Andi…
Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysisby Minjung Kim, Yusuke Hioka, Michael…
Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networksby Ankur Mali, Tommaso Salvatori, Alexander…
Rule-based Data Selection for Large Language Modelsby Xiaomin Li, Mingye Gao, Zhiwei Zhang, Chang Yue,…
ACDC: Autoregressive Coherent Multimodal Generation using Diffusion Correctionby Hyungjin Chung, Dohun Lee, Jong Chul YeFirst…
A Strategy for Label Alignment in Deep Neural Networksby Xuanrui ZengFirst submitted to arxiv on:…
TLDR: Token-Level Detective Reward Model for Large Vision Language Modelsby Deqing Fu, Tong Xiao, Rui…
ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learningby Guangzhi Xiong, Sanchit Sinha, Aidong…
TableRAG: Million-Token Table Understanding with Language Modelsby Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng…
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenarioby Hongjun Wang, Jiyuan Chen, Tong…