Papers
11,951 papers found
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Ali Farshchian, Juan A. Gallego, Joseph P. Cohen et al.
Adversarial Imitation via Variational Inverse Reinforcement Learning
Ahmed H. Qureshi, Byron Boots, Michael C. Yip
Adversarial Reprogramming of Neural Networks
Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein
Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks
Jing Li, Deheng Ye, Shuo Shang
A Generative Model For Electron Paths
John Bradshaw, Matt J. Kusner, Brooks Paige et al.
Aggregated Momentum: Stability Through Passive Damping
James Lucas, Shengyang Sun, Richard Zemel et al.
A Kernel Random Matrix-Based Approach for Sparse PCA
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li et al.
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Sulaiman Alghunaim, Kun Yuan, Ali H. Sayed
ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
Jialin Liu, Xiaohan Chen, Zhangyang Wang et al.
A Max-Affine Spline Perspective of Recurrent Neural Networks
Zichao Wang, Randall Balestriero, Richard Baraniuk
A Mean Field Theory of Batch Normalization
Greg Yang, Jeffrey Pennington, Vinay Rao et al.
Amortized Bayesian Meta-Learning
Sachin Ravi, Alex Beatson
An Acoustic Study of Vowel Undershoot in a System with Several Degrees of Prominence
Janina Mołczanow, Beata Łukaszewicz, Anna Łukaszewicz
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang et al.
Analysing Mathematical Reasoning Abilities of Neural Models
David Saxton, Edward Grefenstette, Felix Hill et al.
Analysis of Quantized Models
Lu Hou, Ruiliang Zhang, James T. Kwok
Analyzing Inverse Problems with Invertible Neural Networks
Lynton Ardizzone, Jakob Kruse, Carsten Rother et al.
An analytic theory of generalization dynamics and transfer learning in deep linear networks
Andrew K. Lampinen, Surya Ganguli
An Empirical study of Binary Neural Networks' Optimisation
Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane et al.
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva*, Alessandro Sordoni*, Remi Tachet des Combes* et al.
A new dog learns old tricks: RL finds classic optimization algorithms
Weiwei Kong, Christopher Liaw, Aranyak Mehta et al.
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu et al.
ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
Amir Gholaminejad, Kurt Keutzer, George Biros
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber et al.