Papers
11,015 papers found
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, Alexey 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 Randy 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 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.