Papers
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression
Peijie Dong, Zhenheng Tang, Xiang Liu et al.
Can DBNNs Robust to Environmental Noise for Resource-constrained Scenarios?
Wendong Zheng, Junyang Chen, Husheng Guo et al.
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
Yujin Han, Andi Han, Wei Huang et al.
Can Large Language Models Understand Intermediate Representations in Compilers?
Hailong Jiang, Jianfeng Zhu, Yao Wan et al.
CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning
Xuankun Rong, Jianshu Zhang, Kun He et al.
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang et al.
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
Haoming Yang, Ke Ma, Xiaojun Jia et al.
Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers
Lokesh Veeramacheneni, Moritz Wolter, Hilde Kuehne et al.
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Jiawei Huang, Bingcong Li, Christoph Dann et al.
Can Transformers Learn Full Bayesian Inference in Context?
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin et al.
Can Transformers Reason Logically? A Study in SAT Solving
Leyan Pan, Vijay Ganesh, Jacob Abernethy et al.
Can We Predict Performance of Large Models across Vision-Language Tasks?
Qinyu Zhao, Ming Xu, Kartik Gupta et al.
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
Haoqi Wu, Wei Dai, Wang Li et al.
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Jan Pauls, Max Zimmer, Berkant Turan et al.
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Guangzhi Sun, Xiao Zhan, Shutong Feng et al.
Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation
Haozhe Ma, Fangling Li, Jing Yu Lim et al.
Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models
Yiyang Fang, Jian Liang, Wenke Huang et al.
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
Sen Peng, Mingyue Wang, Jianfei He et al.
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
Tyler Kastner, Mark Rowland, Yunhao Tang et al.
Categorical Schrödinger Bridge Matching
Grigoriy Ksenofontov, Alexander Korotin
CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration
Haoyun Jiang, Haolin Li, Jianwei Zhang et al.
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Wenju Sun, Qingyong Li, Yangliao Geng et al.
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
Chenlu Ye, Yujia Jin, Alekh Agarwal et al.
Causal Abstraction Inference under Lossy Representations
Kevin Muyuan Xia, Elias Bareinboim
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D’Acunto, Fabio Massimo Zennaro, Yorgos Felekis et al.