Papers
In-Context Fine-Tuning for Time-Series Foundation Models
Matthew Faw, Rajat Sen, Yichen Zhou et al.
In-Context Learning and Occam’s Razor
Eric Elmoznino, Tom Marty, Tejas Kasetty et al.
In-Context Learning as Conditioned Associative Memory Retrieval
Weimin Wu, Teng-Yun Hsiao, Jerry Yao-Chieh Hu et al.
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
Jianliang He, Xintian Pan, Siyu Chen et al.
In-Context Reinforcement Learning From Suboptimal Historical Data
Juncheng Dong, Moyang Guo, Ethan X Fang et al.
Incorporating Arbitrary Matrix Group Equivariance into KANs
Lexiang Hu, Yisen Wang, Zhouchen Lin
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
Yujun Kim, Jaeyoung Cha, Chulhee Yun
Independence Tests for Language Models
Sally Zhu, Ahmed M Ahmed, Rohith Kuditipudi et al.
Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning
Anna Soligo, Pietro Ferraro, David Boyle
Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
Kexuan Shi, Hai Chen, Leheng Zhang et al.
Inductive Moment Matching
Linqi Zhou, Stefano Ermon, Jiaming Song
InfAlign: Inference-aware language model alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant et al.
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang et al.
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
Patrick Leask, Neel Nanda, Noura Al Moubayed
Info-Coevolution: An Efficient Framework for Data Model Coevolution
Ziheng Qin, Hailun Xu, Wei Chee Yew et al.
InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory
Feifei Li, Mi Zhang, Zhaoxiang Wang et al.
Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting
Min Chen, Guansong Pang, Wenjun Wang et al.
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
Yuanhong Zhang, Muyao Yuan, Weizhan Zhang et al.
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
Tianyu Cui, Song-Jun Xu, Artem Moskalev et al.
INRFlow: Flow Matching for INRs in Ambient Space
Yuyang Wang, Anurag Ranjan, Joshua M. Susskind et al.
Instance Correlation Graph-based Naive Bayes
Chengyuan Li, Liangxiao Jiang, Wenjun Zhang et al.
Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms
Sho Takemori, Yuhei Umeda, Aditya Gopalan
Instruct2See: Learning to Remove Any Obstructions Across Distributions
Junhang Li, Yu Guo, Chuhua Xian et al.
Instruction-Following Pruning for Large Language Models
Bairu Hou, Qibin Chen, Jianyu Wang et al.
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Rogers Brennan, Sebastien Lahaie, Adel Javanmard et al.