ICLR Papers
6,124 papers found • Page 106 of 123
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta et al.
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
Yibing Liu, Chris Xing TIAN, Haoliang Li et al.
Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice
Jicong Fan, Rui Chen, Zhao Zhang et al.
Neurosymbolic Grounding for Compositional World Models
Atharva Sehgal, Arya Grayeli, Jennifer Sun et al.
NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization
Gen Li, Lu Yin, Jie Ji et al.
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Ido Amos, Jonathan Berant, Ankit Gupta
Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley et al.
New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
Xinzhe Yuan, William de Vazelhes, Bin Gu et al.
NfgTransformer: Equivariant Representation Learning for Normal-form Games
SIQI LIU, Luke Marris, Georgios Piliouras et al.
Node2ket: Efficient High-Dimensional Network Embedding in Quantum Hilbert Space
Hao Xiong, Yehui Tang, Yunlin He et al.
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation
Pengfei Zheng, Yonggang Zhang, Zhen Fang et al.
Noise-free Score Distillation
Oren Katzir, Or Patashnik, Daniel Cohen-Or et al.
Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
Hansam Cho, Jonghyun Lee, Seoung Bum Kim et al.
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
Nirmit Joshi, Gal Vardi, Nathan Srebro
NOLA: Compressing LoRA using Linear Combination of Random Basis
Soroush Abbasi Koohpayegani, K L Navaneet, Parsa Nooralinejad et al.
Non-Exchangeable Conformal Risk Control
António Farinhas, Chrysoula Zerva, Dennis Ulmer et al.
Non-negative Contrastive Learning
Yifei Wang, Qi Zhang, Yaoyu Guo et al.
Nougat: Neural Optical Understanding for Academic Documents
Lukas Blecher, Guillem Cucurull Preixens, Thomas Scialom et al.
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Patricia Pauli, Aaron Havens, Alexandre Araujo et al.
Numerical Accounting in the Shuffle Model of Differential Privacy
Antti Koskela, Antti Honkela, Mikko Heikkilä
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Kun Wang, Hao Wu, Yifan Duan et al.
Object-Aware Inversion and Reassembly for Image Editing
Zhen Yang, Ganggui Ding, Wen Wang et al.
Object centric architectures enable efficient causal representation learning
Amin Mansouri, Jason Hartford, Yan Zhang et al.
Object-Centric Learning with Slot Mixture Module
Daniil Kirilenko, Vitaliy Vorobyov, Aleksey Kovalev et al.
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
Zeren Chen, ziqin wang, zhen wang et al.
OctoPack: Instruction Tuning Code Large Language Models
Niklas Muennighoff, Qian Liu, Armel Zebaze et al.
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets et al.
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Stéphane d'Ascoli, Sören Becker, Philippe Schwaller et al.
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
Liyuan Mao, Haoran Xu, Weinan Zhang et al.
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
Yifei Zhou, Ayush Sekhari, Yuda Song et al.
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Joey Hong, Anca Dragan, Sergey Levine
Off-Policy Primal-Dual Safe Reinforcement Learning
Zifan Wu, Bo Tang, Qian Lin et al.
OmniControl: Control Any Joint at Any Time for Human Motion Generation
Yiming Xie, Varun Jampani, Lei Zhong et al.
OMNI: Open-endedness via Models of human Notions of Interestingness
Jenny Zhang, Joel Lehman, Kenneth Stanley et al.
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang et al.
On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
On Adversarial Training without Perturbing all Examples
Max Losch, Mohamed Omran, David Stutz et al.
On Bias-Variance Alignment in Deep Models
Lin Chen, Michal Lukasik, Wittawat Jitkrittum et al.
On Characterizing the Trade-off in Invariant Representation Learning
Vishnu Boddeti, Sepehr Dehdashtian, Bashir Sadeghi
On Differentially Private Federated Linear Contextual Bandits
Xingyu Zhou, Sayak Ray Chowdhury
On Diffusion Modeling for Anomaly Detection
Victor Livernoche, Vineet Jain, Yashar Hezaveh et al.
On Double Descent in Reinforcement Learning with LSTD and Random Features
David Brellmann, Eloïse Berthier, David Filliat et al.
One For All: Towards Training One Graph Model For All Classification Tasks
Hao Liu, Jiarui Feng, Lecheng Kong et al.
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
Jinyang Jiang, Zeliang Zhang, Chenliang Xu et al.
One-hot Generalized Linear Model for Switching Brain State Discovery
Chengrui Li, Soon Ho Kim, Chris Rodgers et al.
On Error Propagation of Diffusion Models
Yangming Li, Mihaela van der Schaar
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Sheng-Jun Huang, Yi Li, Yiming Sun et al.
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh et al.
One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention
Arvind Mahankali, Tatsunori Hashimoto, Tengyu Ma
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
Christian Horvat, Jean-Pascal Pfister