Most Cited 2024 Highlight by J Kolter Papers
9 papers found
Conference
#1
A Simple and Effective Pruning Approach for Large Language Models
Mingjie Sun, Zhuang Liu, Anna Bair et al.
ICLR 2024arXiv:2306.11695
700
citations
#2
Patches Are All You Need?
Asher Trockman, J Kolter
ICLR 2024arXiv:2201.09792
494
citations
#3
Manifold Preserving Guided Diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai et al.
ICLR 2024arXiv:2311.16424
129
citations
#4
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
Pratyush Maini, Sachin Goyal, Zachary Lipton et al.
ICLR 2024arXiv:2307.03132
42
citations
#5
Why is SAM Robust to Label Noise?
Christina Baek, J Kolter, Aditi Raghunathan
ICLR 2024arXiv:2405.03676
22
citations
#6
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Runtian Zhai, Bingbin Liu, Andrej Risteski et al.
ICLR 2024spotlightarXiv:2306.00788
18
citations
#7
Understanding prompt engineering may not require rethinking generalization
Victor Akinwande, Yiding Jiang, Dylan Sam et al.
ICLR 2024arXiv:2310.03957
10
citations
#8
The Update-Equivalence Framework for Decision-Time Planning
Samuel Sokota, Gabriele Farina, David Wu et al.
ICLR 2024arXiv:2304.13138
5
citations
#9
On the Joint Interaction of Models, Data, and Features
Yiding Jiang, Christina Baek, J Kolter
ICLR 2024arXiv:2306.04793
4
citations